@Preamble{"\input bibnames.sty"}
@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
FAX: +1 801 581 4148,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|https://www.math.utah.edu/~beebe/|"}
@String{j-TIST = "ACM Transactions on Intelligent Systems and
Technology (TIST)"}
@Article{Yang:2010:IAT,
author = "Qiang Yang",
title = "Introduction to {ACM TIST}",
journal = j-TIST,
volume = "1",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858949",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2010:IAT,
author = "Huan Liu and Dana Nau",
title = "Introduction to the {ACM TIST} special issue {AI} in
social computing and cultural modeling",
journal = j-TIST,
volume = "1",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858950",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bainbridge:2010:VWC,
author = "William Sims Bainbridge",
title = "Virtual worlds as cultural models",
journal = j-TIST,
volume = "1",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858951",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Feldman:2010:SCR,
author = "Michal Feldman and Moshe Tennenholtz",
title = "Structured coalitions in resource selection games",
journal = j-TIST,
volume = "1",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858952",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2010:OFU,
author = "Fang Wu and Bernardo A. Huberman",
title = "Opinion formation under costly expression",
journal = j-TIST,
volume = "1",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858953",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Roos:2010:ESD,
author = "Patrick Roos and J. Ryan Carr and Dana S. Nau",
title = "Evolution of state-dependent risk preferences",
journal = j-TIST,
volume = "1",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858954",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Goolsby:2010:SMC,
author = "Rebecca Goolsby",
title = "Social media as crisis platform: The future of
community maps\slash crisis maps",
journal = j-TIST,
volume = "1",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858955",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2010:AIS,
author = "Meng Wang and Bo Liu and Xian-Sheng Hua",
title = "Accessible image search for colorblindness",
journal = j-TIST,
volume = "1",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858956",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2010:PSI,
author = "Yixin Chen",
title = "Preface to special issue on applications of automated
planning",
journal = j-TIST,
volume = "1",
number = "2",
pages = "9:1--9:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869398",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Porteous:2010:API,
author = "Julie Porteous and Marc Cavazza and Fred Charles",
title = "Applying planning to interactive storytelling:
Narrative control using state constraints",
journal = j-TIST,
volume = "1",
number = "2",
pages = "10:1--10:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869399",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bryce:2010:PIB,
author = "Daniel Bryce and Michael Verdicchio and Seungchan
Kim",
title = "Planning interventions in biological networks",
journal = j-TIST,
volume = "1",
number = "2",
pages = "11:1--11:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869400",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Refanidis:2010:CBA,
author = "Ioannis Refanidis and Neil Yorke-Smith",
title = "A constraint-based approach to scheduling an
individual's activities",
journal = j-TIST,
volume = "1",
number = "2",
pages = "12:1--12:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869401",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Benaskeur:2010:CRT,
author = "Abder Rezak Benaskeur and Froduald Kabanza and Eric
Beaudry",
title = "{CORALS}: a real-time planner for anti-air defense
operations",
journal = j-TIST,
volume = "1",
number = "2",
pages = "13:1--13:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869402",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Talamadupula:2010:PHR,
author = "Kartik Talamadupula and J. Benton and Subbarao
Kambhampati and Paul Schermerhorn and Matthias
Scheutz",
title = "Planning for human-robot teaming in open worlds",
journal = j-TIST,
volume = "1",
number = "2",
pages = "14:1--14:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869403",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cirillo:2010:HAT,
author = "Marcello Cirillo and Lars Karlsson and Alessandro
Saffiotti",
title = "Human-aware task planning: An application to mobile
robots",
journal = j-TIST,
volume = "1",
number = "2",
pages = "15:1--15:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869404",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2011:ISI,
author = "Daqing Zhang and Matthai Philipose and Qiang Yang",
title = "Introduction to the special issue on intelligent
systems for activity recognition",
journal = j-TIST,
volume = "2",
number = "1",
pages = "1:1--1:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889682",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2011:LTR,
author = "Yu Zheng and Xing Xie",
title = "Learning travel recommendations from user-generated
{GPS} traces",
journal = j-TIST,
volume = "2",
number = "1",
pages = "2:1--2:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889683",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Farrahi:2011:DRL,
author = "Katayoun Farrahi and Daniel Gatica-Perez",
title = "Discovering routines from large-scale human locations
using probabilistic topic models",
journal = j-TIST,
volume = "2",
number = "1",
pages = "3:1--3:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889684",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hsu:2011:PMC,
author = "Jane Yung-Jen Hsu and Chia-Chun Lian and Wan-Rong
Jih",
title = "Probabilistic models for concurrent chatting activity
recognition",
journal = j-TIST,
volume = "2",
number = "1",
pages = "4:1--4:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889685",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhou:2011:RPA,
author = "Yue Zhou and Bingbing Ni and Shuicheng Yan and Thomas
S. Huang",
title = "Recognizing pair-activities by causality analysis",
journal = j-TIST,
volume = "2",
number = "1",
pages = "5:1--5:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889686",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ward:2011:PMA,
author = "Jamie A. Ward and Paul Lukowicz and Hans W.
Gellersen",
title = "Performance metrics for activity recognition",
journal = j-TIST,
volume = "2",
number = "1",
pages = "6:1--6:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889687",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wyatt:2011:ICC,
author = "Danny Wyatt and Tanzeem Choudhury and Jeff Bilmes and
James A. Kitts",
title = "Inferring colocation and conversation networks from
privacy-sensitive audio with implications for
computational social science",
journal = j-TIST,
volume = "2",
number = "1",
pages = "7:1--7:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889688",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bao:2011:FRC,
author = "Xinlong Bao and Thomas G. Dietterich",
title = "{FolderPredictor}: Reducing the cost of reaching the
right folder",
journal = j-TIST,
volume = "2",
number = "1",
pages = "8:1--8:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889689",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hua:2011:ISI,
author = "Xian-Sheng Hua and Qi Tian and Alberto {Del Bimbo} and
Ramesh Jain",
title = "Introduction to the special issue on intelligent
multimedia systems and technology",
journal = j-TIST,
volume = "2",
number = "2",
pages = "9:1--9:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899413",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2011:ALM,
author = "Meng Wang and Xian-Sheng Hua",
title = "Active learning in multimedia annotation and
retrieval: a survey",
journal = j-TIST,
volume = "2",
number = "2",
pages = "10:1--10:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899414",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Active learning is a machine learning technique that
selects the most informative samples for labeling and
uses them as training data. It has been widely explored
in multimedia research community for its capability of
reducing human annotation effort. In this article, we
provide a survey on the efforts of leveraging active
learning in multimedia annotation and retrieval. We
mainly focus on two application domains: image/video
annotation and content-based image retrieval. We first
briefly introduce the principle of active learning and
then we analyze the sample selection criteria. We
categorize the existing sample selection strategies
used in multimedia annotation and retrieval into five
criteria: risk reduction, uncertainty, diversity,
density and relevance. We then introduce several
classification models used in active learning-based
multimedia annotation and retrieval, including
semi-supervised learning, multilabel learning and
multiple instance learning. We also provide a
discussion on several future trends in this research
direction. In particular, we discuss cost analysis of
human annotation and large-scale interactive multimedia
annotation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shao:2011:VIG,
author = "Yuanlong Shao and Yuan Zhou and Deng Cai",
title = "Variational inference with graph regularization for
image annotation",
journal = j-TIST,
volume = "2",
number = "2",
pages = "11:1--11:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899415",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Image annotation is a typical area where there are
multiple types of attributes associated with each
individual image. In order to achieve better
performance, it is important to develop effective
modeling by utilizing prior knowledge. In this article,
we extend the graph regularization approaches to a more
general case where the regularization is imposed on the
factorized variational distributions, instead of
posterior distributions implicitly involved in EM-like
algorithms. In this way, the problem modeling can be
more flexible, and we can choose any factor in the
problem domain to impose graph regularization wherever
there are similarity constraints among the instances.
We formulate the problem formally and show its
geometrical background in manifold learning. We also
design two practically effective algorithms and analyze
their properties such as the convergence. Finally, we
apply our approach to image annotation and show the
performance improvement of our algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yu:2011:CBS,
author = "Jie Yu and Xin Jin and Jiawei Han and Jiebo Luo",
title = "Collection-based sparse label propagation and its
application on social group suggestion from photos",
journal = j-TIST,
volume = "2",
number = "2",
pages = "12:1--12:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899416",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online social network services pose great
opportunities and challenges for many research areas.
In multimedia content analysis, automatic social group
recommendation for images holds the promise to expand
one's social network through media sharing. However,
most existing techniques cannot generate satisfactory
social group suggestions when the images are classified
independently. In this article, we present novel
methods to produce accurate suggestions of suitable
social groups from a user's personal photo collection.
First, an automatic clustering process is designed to
estimate the group similarities, select the optimal
number of clusters and categorize the social groups.
Both visual content and textual annotations are
integrated to generate initial predictions of the group
categories for the images. Next, the relationship among
images in a user's collection is modeled as a sparse
graph. A collection-based sparse label propagation
method is proposed to improve the group suggestions.
Furthermore, the sparse graph-based collection model
can be readily exploited to select the most influential
and informative samples for active relevance feedback,
which can be integrated with the label propagation
process without the need for classifier retraining. The
proposed methods have been tested on group suggestion
tasks for real user collections and demonstrated
superior performance over the state-of-the-art
techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2011:DML,
author = "Lei Wu and Steven C. H. Hoi and Rong Jin and Jianke
Zhu and Nenghai Yu",
title = "Distance metric learning from uncertain side
information for automated photo tagging",
journal = j-TIST,
volume = "2",
number = "2",
pages = "13:1--13:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899417",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Automated photo tagging is an important technique for
many intelligent multimedia information systems, for
example, smart photo management system and intelligent
digital media library. To attack the challenge, several
machine learning techniques have been developed and
applied for automated photo tagging. For example,
supervised learning techniques have been applied to
automated photo tagging by training statistical
classifiers from a collection of manually labeled
examples. Although the existing approaches work well
for small testbeds with relatively small number of
annotation words, due to the long-standing challenge of
object recognition, they often perform poorly in
large-scale problems. Another limitation of the
existing approaches is that they require a set of
high-quality labeled data, which is not only expensive
to collect but also time consuming. In this article, we
investigate a social image based annotation scheme by
exploiting implicit side information that is available
for a large number of social photos from the social web
sites. The key challenge of our intelligent annotation
scheme is how to learn an effective distance metric
based on implicit side information (visual or textual)
of social photos. To this end, we present a novel
``Probabilistic Distance Metric Learning'' (PDML)
framework, which can learn optimized metrics by
effectively exploiting the implicit side information
vastly available on the social web. We apply the
proposed technique to photo annotation tasks based on a
large social image testbed with over 1 million tagged
photos crawled from a social photo sharing portal.
Encouraging results show that the proposed technique is
effective and promising for social photo based
annotation tasks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2011:IAK,
author = "Jinhui Tang and Richang Hong and Shuicheng Yan and
Tat-Seng Chua and Guo-Jun Qi and Ramesh Jain",
title = "Image annotation by {$k$NN}-sparse graph-based label
propagation over noisily tagged web images",
journal = j-TIST,
volume = "2",
number = "2",
pages = "14:1--14:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899418",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we exploit the problem of annotating
a large-scale image corpus by label propagation over
noisily tagged web images. To annotate the images more
accurately, we propose a novel k NN-sparse graph-based
semi-supervised learning approach for harnessing the
labeled and unlabeled data simultaneously. The sparse
graph constructed by datum-wise one-vs- k NN sparse
reconstructions of all samples can remove most of the
semantically unrelated links among the data, and thus
it is more robust and discriminative than the
conventional graphs. Meanwhile, we apply the
approximate k nearest neighbors to accelerate the
sparse graph construction without loosing its
effectiveness. More importantly, we propose an
effective training label refinement strategy within
this graph-based learning framework to handle the noise
in the training labels, by bringing in a dual
regularization for both the quantity and sparsity of
the noise. We conduct extensive experiments on a
real-world image database consisting of 55,615 Flickr
images and noisily tagged training labels. The results
demonstrate both the effectiveness and efficiency of
the proposed approach and its capability to deal with
the noise in the training labels.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tong:2011:APL,
author = "Xiaofeng Tong and Jia Liu and Tao Wang and Yimin
Zhang",
title = "Automatic player labeling, tracking and field
registration and trajectory mapping in broadcast soccer
video",
journal = j-TIST,
volume = "2",
number = "2",
pages = "15:1--15:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899419",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we present a method to perform
automatic player trajectories mapping based on player
detection, unsupervised labeling, efficient
multi-object tracking, and playfield registration in
broadcast soccer videos. Player detector determines the
players' positions and scales by combining the ability
of dominant color based background subtraction and a
boosting detector with Haar features. We first learn
the dominant color with accumulate color histogram at
the beginning of processing, then use the player
detector to collect hundreds of player samples, and
learn player appearance codebook by unsupervised
clustering. In a soccer game, a player can be labeled
as one of four categories: two teams, referee or
outlier. The learning capability enables the method to
be generalized well to different videos without any
manual initialization. With the dominant color and
player appearance model, we can locate and label each
player. After that, we perform multi-object tracking by
using Markov Chain Monte Carlo (MCMC) data association
to generate player trajectories. Some data driven
dynamics are proposed to improve the Markov chain's
efficiency, such as label consistency, motion
consistency, and track length, etc. Finally, we extract
key-points and find the mapping from an image plane to
the standard field model, and then map players'
position and trajectories to the field. A large
quantity of experimental results on FIFA World Cup 2006
videos demonstrate that this method can reach high
detection and labeling precision, reliably tracking in
scenes of player occlusion, moderate camera motion and
pose variation, and yield promising field registration
results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:NJD,
author = "Qingzhong Liu and Andrew H. Sung and Mengyu Qiao",
title = "Neighboring joint density-based {JPEG} steganalysis",
journal = j-TIST,
volume = "2",
number = "2",
pages = "16:1--16:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899420",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib/",
abstract = "The threat posed by hackers, spies, terrorists, and
criminals, etc. using steganography for stealthy
communications and other illegal purposes is a serious
concern of cyber security. Several steganographic
systems that have been developed and made readily
available utilize JPEG images as carriers. Due to the
popularity of JPEG images on the Internet, effective
steganalysis techniques are called for to counter the
threat of JPEG steganography. In this article, we
propose a new approach based on feature mining on the
discrete cosine transform (DCT) domain and machine
learning for steganalysis of JPEG images. First,
neighboring joint density features on both intra-block
and inter-block are extracted from the DCT coefficient
array and the absolute array, respectively; then a
support vector machine (SVM) is applied to the features
for detection. An evolving neural-fuzzy inference
system is employed to predict the hiding amount in JPEG
steganograms. We also adopt a feature selection method
of support vector machine recursive feature elimination
to reduce the number of features. Experimental results
show that, in detecting several JPEG-based
steganographic systems, our method prominently
outperforms the well-known Markov-process based
approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bhatt:2011:PTM,
author = "Chidansh Bhatt and Mohan Kankanhalli",
title = "Probabilistic temporal multimedia data mining",
journal = j-TIST,
volume = "2",
number = "2",
pages = "17:1--17:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899421",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Existing sequence pattern mining techniques assume
that the obtained events from event detectors are
accurate. However, in reality, event detectors label
the events from different modalities with a certain
probability over a time-interval. In this article, we
consider for the first time Probabilistic Temporal
Multimedia (PTM) Event data to discover accurate
sequence patterns. PTM event data considers the start
time, end time, event label and associated probability
for the sequence pattern discovery. As the existing
sequence pattern mining techniques cannot work on such
realistic data, we have developed a novel framework for
performing sequence pattern mining on probabilistic
temporal multimedia event data. We perform probability
fusion to resolve the redundancy among detected events
from different modalities, considering their
cross-modal correlation. We propose a novel sequence
pattern mining algorithm called Probabilistic Interval
based Event Miner (PIE-Miner) for discovering frequent
sequence patterns from interval based events. PIE-Miner
has a new support counting mechanism developed for PTM
data. Existing sequence pattern mining algorithms have
event label level support counting mechanism, whereas
we have developed event cluster level support counting
mechanism. We discover the complete set of all possible
temporal relationships based on Allen's interval
algebra. The experimental results showed that the
discovered sequence patterns are more useful than the
patterns discovered with state-of-the-art sequence
pattern mining algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ling:2011:ISI,
author = "Charles X. Ling",
title = "Introduction to special issue on machine learning for
business applications",
journal = j-TIST,
volume = "2",
number = "3",
pages = "18:1--18:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961190",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Dhar:2011:PFM,
author = "Vasant Dhar",
title = "Prediction in financial markets: The case for small
disjuncts",
journal = j-TIST,
volume = "2",
number = "3",
pages = "19:1--19:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961191",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2011:LBC,
author = "Szu-Hao Huang and Shang-Hong Lai and Shih-Hsien Tai",
title = "A learning-based contrarian trading strategy via a
dual-classifier model",
journal = j-TIST,
volume = "2",
number = "3",
pages = "20:1--20:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961192",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2011:CCD,
author = "Bin Li and Steven C. H. Hoi and Vivekanand
Gopalkrishnan",
title = "{CORN}: Correlation-driven nonparametric learning
approach for portfolio selection",
journal = j-TIST,
volume = "2",
number = "3",
pages = "21:1--21:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961193",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bonchi:2011:SNA,
author = "Francesco Bonchi and Carlos Castillo and Aristides
Gionis and Alejandro Jaimes",
title = "Social Network Analysis and Mining for Business
Applications",
journal = j-TIST,
volume = "2",
number = "3",
pages = "22:1--22:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961194",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2011:HMF,
author = "Richong Zhang and Thomas Tran",
title = "A helpfulness modeling framework for electronic
word-of-mouth on consumer opinion platforms",
journal = j-TIST,
volume = "2",
number = "3",
pages = "23:1--23:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961195",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ge:2011:MLC,
author = "Yong Ge and Hui Xiong and Wenjun Zhou and Siming Li
and Ramendra Sahoo",
title = "Multifocal learning for customer problem analysis",
journal = j-TIST,
volume = "2",
number = "3",
pages = "24:1--24:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961196",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hsu:2011:ISI,
author = "Chun-Nan Hsu",
title = "Introduction to special issue on large-scale machine
learning",
journal = j-TIST,
volume = "2",
number = "3",
pages = "25:1--25:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961197",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:PPL,
author = "Zhiyuan Liu and Yuzhou Zhang and Edward Y. Chang and
Maosong Sun",
title = "{PLDA+}: Parallel latent {Dirichlet} allocation with
data placement and pipeline processing",
journal = j-TIST,
volume = "2",
number = "3",
pages = "26:1--26:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961198",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chang:2011:LLS,
author = "Chih-Chung Chang and Chih-Jen Lin",
title = "{LIBSVM}: a library for support vector machines",
journal = j-TIST,
volume = "2",
number = "3",
pages = "27:1--27:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961199",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "LIBSVM is a library for Support Vector Machines
(SVMs). We have been actively developing this package
since the year 2000. The goal is to help users to
easily apply SVM to their applications. LIBSVM has
gained wide popularity in machine learning and many
other areas. In this article, we present all
implementation details of LIBSVM. Issues such as
solving SVM optimization problems theoretical
convergence multiclass classification probability
estimates and parameter selection are discussed in
detail.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gasso:2011:BOL,
author = "Gilles Gasso and Aristidis Pappaioannou and Marina
Spivak and L{\'e}on Bottou",
title = "Batch and online learning algorithms for nonconvex
{Neyman--Pearson} classification",
journal = j-TIST,
volume = "2",
number = "3",
pages = "28:1--28:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961200",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ma:2011:LRE,
author = "Hao Ma and Irwin King and Michael R. Lyu",
title = "Learning to recommend with explicit and implicit
social relations",
journal = j-TIST,
volume = "2",
number = "3",
pages = "29:1--29:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961201",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ma:2011:LDM,
author = "Justin Ma and Lawrence K. Saul and Stefan Savage and
Geoffrey M. Voelker",
title = "Learning to detect malicious {URLs}",
journal = j-TIST,
volume = "2",
number = "3",
pages = "30:1--30:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961202",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Malicious Web sites are a cornerstone of Internet
criminal activities. The dangers of these sites have
created a demand for safeguards that protect end-users
from visiting them. This article explores how to detect
malicious Web sites from the lexical and host-based
features of their URLs. We show that this problem lends
itself naturally to modern algorithms for online
learning. Online algorithms not only process large
numbers of URLs more efficiently than batch algorithms,
they also adapt more quickly to new features in the
continuously evolving distribution of malicious URLs.
We develop a real-time system for gathering URL
features and pair it with a real-time feed of labeled
URLs from a large Web mail provider. From these
features and labels, we are able to train an online
classifier that detects malicious Web sites with 99\%
accuracy over a balanced dataset.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gomes:2011:ISI,
author = "Carla Gomes and Qiang Yang",
title = "Introduction to special issue on computational
sustainability",
journal = j-TIST,
volume = "2",
number = "4",
pages = "31:1--31:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989735",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Krause:2011:SAO,
author = "Andreas Krause and Carlos Guestrin",
title = "Submodularity and its applications in optimized
information gathering",
journal = j-TIST,
volume = "2",
number = "4",
pages = "32:1--32:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989736",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cattafi:2011:SBP,
author = "Massimiliano Cattafi and Marco Gavanelli and Michela
Milano and Paolo Cagnoli",
title = "Sustainable biomass power plant location in the
{Italian Emilia-Romagna} region",
journal = j-TIST,
volume = "2",
number = "4",
pages = "33:1--33:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989737",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Patnaik:2011:TDM,
author = "Debprakash Patnaik and Manish Marwah and Ratnesh K.
Sharma and Naren Ramakrishnan",
title = "Temporal data mining approaches for sustainable
chiller management in data centers",
journal = j-TIST,
volume = "2",
number = "4",
pages = "34:1--34:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989738",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ramchurn:2011:ABH,
author = "Sarvapali D. Ramchurn and Perukrishnen Vytelingum and
Alex Rogers and Nicholas R. Jennings",
title = "Agent-based homeostatic control for green energy in
the smart grid",
journal = j-TIST,
volume = "2",
number = "4",
pages = "35:1--35:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989739",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mithal:2011:MGF,
author = "Varun Mithal and Ashish Garg and Shyam Boriah and
Michael Steinbach and Vipin Kumar and Christopher
Potter and Steven Klooster and Juan Carlos
Castilla-Rubio",
title = "Monitoring global forest cover using data mining",
journal = j-TIST,
volume = "2",
number = "4",
pages = "36:1--36:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989740",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2011:MMM,
author = "Zhenhui Li and Jiawei Han and Ming Ji and Lu-An Tang
and Yintao Yu and Bolin Ding and Jae-Gil Lee and Roland
Kays",
title = "{MoveMine}: Mining moving object data for discovery of
animal movement patterns",
journal = j-TIST,
volume = "2",
number = "4",
pages = "37:1--37:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989741",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Toole:2011:SCC,
author = "Jameson L. Toole and Nathan Eagle and Joshua B.
Plotkin",
title = "Spatiotemporal correlations in criminal offense
records",
journal = j-TIST,
volume = "2",
number = "4",
pages = "38:1--38:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989742",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ding:2011:SCD,
author = "Wei Ding and Tomasz F. Stepinski and Yang Mu and
Lourenco Bandeira and Ricardo Ricardo and Youxi Wu and
Zhenyu Lu and Tianyu Cao and Xindong Wu",
title = "Subkilometer crater discovery with boosting and
transfer learning",
journal = j-TIST,
volume = "2",
number = "4",
pages = "39:1--39:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989743",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Berry:2011:PPA,
author = "Pauline M. Berry and Melinda Gervasio and Bart
Peintner and Neil Yorke-Smith",
title = "{PTIME}: Personalized assistance for calendaring",
journal = j-TIST,
volume = "2",
number = "4",
pages = "40:1--40:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989744",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reddy:2011:PSA,
author = "Sudhakar Y. Reddy and Jeremy D. Frank and Michael J.
Iatauro and Matthew E. Boyce and Elif K{\"u}rkl{\"u}
and Mitchell Ai-Chang and Ari K. J{\'o}nsson",
title = "Planning solar array operations on the {International
Space Station}",
journal = j-TIST,
volume = "2",
number = "4",
pages = "41:1--41:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989745",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Haigh:2011:RLL,
author = "Karen Zita Haigh and Fusun Yaman",
title = "{RECYCLE}: Learning looping workflows from annotated
traces",
journal = j-TIST,
volume = "2",
number = "4",
pages = "42:1--42:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989746",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guy:2011:I,
author = "Ido Guy and Li Chen and Michelle X. Zhou",
title = "Introduction",
journal = j-TIST,
volume = "3",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036265",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lipczak:2011:ETR,
author = "Marek Lipczak and Evangelos Milios",
title = "Efficient Tag Recommendation for Real-Life Data",
journal = j-TIST,
volume = "3",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036266",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Vasuki:2011:SAR,
author = "Vishvas Vasuki and Nagarajan Natarajan and Zhengdong
Lu and Berkant Savas and Inderjit Dhillon",
title = "Scalable Affiliation Recommendation using Auxiliary
Networks",
journal = j-TIST,
volume = "3",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036267",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{McNally:2011:CSC,
author = "Kevin McNally and Michael P. O'Mahony and Maurice
Coyle and Peter Briggs and Barry Smyth",
title = "A Case Study of Collaboration and Reputation in Social
{Web} Search",
journal = j-TIST,
volume = "3",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036268",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhao:2011:WDW,
author = "Shiwan Zhao and Michelle X. Zhou and Xiatian Zhang and
Quan Yuan and Wentao Zheng and Rongyao Fu",
title = "Who is Doing What and When: Social Map-Based
Recommendation for Content-Centric Social {Web} Sites",
journal = j-TIST,
volume = "3",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036269",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:I,
author = "Huan Liu and Dana Nau",
title = "Introduction",
journal = j-TIST,
volume = "3",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036270",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shakarian:2011:GGA,
author = "Paulo Shakarian and V. S. Subrahmanian and Maria Luisa
Sapino",
title = "{GAPs}: Geospatial Abduction Problems",
journal = j-TIST,
volume = "3",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036271",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gal:2011:AAN,
author = "Ya'akov Gal and Sarit Kraus and Michele Gelfand and
Hilal Khashan and Elizabeth Salmon",
title = "An Adaptive Agent for Negotiating with People in
Different Cultures",
journal = j-TIST,
volume = "3",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036272",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Vu:2011:FSK,
author = "Thuc Vu and Yoav Shoham",
title = "Fair Seeding in Knockout Tournaments",
journal = j-TIST,
volume = "3",
number = "1",
pages = "9:1--9:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036273",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cioffi-Revilla:2011:GIS,
author = "Claudio Cioffi-Revilla and J. Daniel Rogers and
Atesmachew Hailegiorgis",
title = "Geographic Information Systems and Spatial Agent-Based
Model Simulations for Sustainable Development",
journal = j-TIST,
volume = "3",
number = "1",
pages = "10:1--10:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036274",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jiang:2011:UMS,
author = "Yingying Jiang and Feng Tian and Xiaolong (Luke) Zhang
and Guozhong Dai and Hongan Wang",
title = "Understanding, Manipulating and Searching Hand-Drawn
Concept Maps",
journal = j-TIST,
volume = "3",
number = "1",
pages = "11:1--11:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036275",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2011:IIS,
author = "Jingdong Wang and Xian-Sheng Hua",
title = "Interactive Image Search by Color Map",
journal = j-TIST,
volume = "3",
number = "1",
pages = "12:1--12:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036276",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Prettenhofer:2011:CLA,
author = "Peter Prettenhofer and Benno Stein",
title = "Cross-Lingual Adaptation Using Structural
Correspondence Learning",
journal = j-TIST,
volume = "3",
number = "1",
pages = "13:1--13:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036277",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Anagnostopoulos:2011:WPS,
author = "Aris Anagnostopoulos and Andrei Z. Broder and Evgeniy
Gabrilovich and Vanja Josifovski and Lance Riedel",
title = "{Web} Page Summarization for Just-in-Time Contextual
Advertising",
journal = j-TIST,
volume = "3",
number = "1",
pages = "14:1--14:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036278",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2011:GPU,
author = "Lei Tang and Xufei Wang and Huan Liu",
title = "Group Profiling for Understanding Social Structures",
journal = j-TIST,
volume = "3",
number = "1",
pages = "15:1--15:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036279",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:TWC,
author = "Zhanyi Liu and Haifeng Wang and Hua Wu and Sheng Li",
title = "Two-Word Collocation Extraction Using Monolingual Word
Alignment Method",
journal = j-TIST,
volume = "3",
number = "1",
pages = "16:1--16:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036280",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liao:2011:MCS,
author = "Zhen Liao and Daxin Jiang and Enhong Chen and Jian Pei
and Huanhuan Cao and Hang Li",
title = "Mining Concept Sequences from Large-Scale Search Logs
for Context-Aware Query Suggestion",
journal = j-TIST,
volume = "3",
number = "1",
pages = "17:1--17:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036281",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sukthankar:2011:ARD,
author = "Gita Sukthankar and Katia Sycara",
title = "Activity Recognition for Dynamic Multi-Agent Teams",
journal = j-TIST,
volume = "3",
number = "1",
pages = "18:1--18:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036282",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2012:ISS,
author = "Shixia Liu and Michelle X. Zhou and Giuseppe Carenini
and Huamin Qu",
title = "Introduction to the Special Section on Intelligent
Visual Interfaces for Text Analysis",
journal = j-TIST,
volume = "3",
number = "2",
pages = "19:1--19:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089095",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cui:2012:WSU,
author = "Weiwei Cui and Huamin Qu and Hong Zhou and Wenbin
Zhang and Steve Skiena",
title = "Watch the Story Unfold with {TextWheel}: Visualization
of Large-Scale News Streams",
journal = j-TIST,
volume = "3",
number = "2",
pages = "20:1--20:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089096",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Keyword-based searching and clustering of news
articles have been widely used for news analysis.
However, news articles usually have other attributes
such as source, author, date and time, length, and
sentiment which should be taken into account. In
addition, news articles and keywords have complicated
macro/micro relations, which include relations between
news articles (i.e., macro relation), relations between
keywords (i.e., micro relation), and relations between
news articles and keywords (i.e., macro-micro
relation). These macro/micro relations are time varying
and pose special challenges for news analysis. In this
article we present a visual analytics system for news
streams which can bring multiple attributes of the news
articles and the macro/micro relations between news
streams and keywords into one coherent analytical
context, all the while conveying the dynamic natures of
news streams. We introduce a new visualization
primitive called TextWheel which consists of one or
multiple keyword wheels, a document transportation
belt, and a dynamic system which connects the wheels
and belt. By observing the TextWheel and its content
changes, some interesting patterns can be detected. We
use our system to analyze several news corpora related
to some major companies and the results demonstrate the
high potential of our method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Thai:2012:VAO,
author = "Vinhtuan Thai and Pierre-Yves Rouille and Siegfried
Handschuh",
title = "Visual Abstraction and Ordering in Faceted Browsing of
Text Collections",
journal = j-TIST,
volume = "3",
number = "2",
pages = "21:1--21:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089097",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Faceted navigation is a technique for the exploration
and discovery of a collection of resources, which can
be of various types including text documents. While
being information-rich resources, documents are usually
not treated as content-bearing items in faceted
browsing interfaces, and yet the required clean
metadata is not always available or matches users'
interest. In addition, the existing linear listing
paradigm for representing result items from the faceted
filtering process makes it difficult for users to
traverse or compare across facet values in different
orders of importance to them. In this context, we
report in this article a visual support toward faceted
browsing of a collection of documents based on a set of
entities of interest to users. Our proposed approach
involves using a multi-dimensional visualization as an
alternative to the linear listing of focus items. In
this visualization, visual abstraction based on a
combination of a conceptual structure and the
structural equivalence of documents can be
simultaneously used to deal with a large number of
items. Furthermore, the approach also enables visual
ordering based on the importance of facet values to
support prioritized, cross-facet comparisons of focus
items. A user study was conducted and the results
suggest that interfaces using the proposed approach can
support users better in exploratory tasks and were also
well-liked by the participants of the study, with the
hybrid interface combining the multi-dimensional
visualization with the linear listing receiving the
most favorable ratings.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Candan:2012:PMV,
author = "K. Sel{\c{c}}uk Candan and Luigi {Di Caro} and Maria
Luisa Sapino",
title = "{PhC}: Multiresolution Visualization and Exploration
of Text Corpora with Parallel Hierarchical
Coordinates",
journal = j-TIST,
volume = "3",
number = "2",
pages = "22:1--22:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089098",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The high-dimensional nature of the textual data
complicates the design of visualization tools to
support exploration of large document corpora. In this
article, we first argue that the Parallel Coordinates
(PC) technique, which can map multidimensional vectors
onto a 2D space in such a way that elements with
similar values are represented as similar poly-lines or
curves in the visualization space, can be used to help
users discern patterns in document collections. The
inherent reduction in dimensionality during the mapping
from multidimensional points to 2D lines, however, may
result in visual complications. For instance, the lines
that correspond to clusters of objects that are
separate in the multidimensional space may overlap each
other in the 2D space; the resulting increase in the
number of crossings would make it hard to distinguish
the individual document clusters. Such crossings of
lines and overly dense regions are significant sources
of visual clutter, thus avoiding them may help
interpret the visualization. In this article, we note
that visual clutter can be significantly reduced by
adjusting the resolution of the individual term
coordinates by clustering the corresponding values.
Such reductions in the resolution of the individual
term-coordinates, however, will lead to a certain
degree of information loss and thus the appropriate
resolution for the term-coordinates has to be selected
carefully. Thus, in this article we propose a
controlled clutter reduction approach, called Parallel
hierarchical Coordinates (or PhC ), for reducing the
visual clutter in PC-based visualizations of text
corpora. We define visual clutter and information loss
measures and provide extensive evaluations that show
that the proposed PhC provides significant visual gains
(i.e., multiple orders of reductions in visual clutter)
with small information loss during visualization and
exploration of document collections.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gretarsson:2012:TVA,
author = "Brynjar Gretarsson and John O'Donovan and Svetlin
Bostandjiev and Tobias H{\"o}llerer and Arthur Asuncion
and David Newman and Padhraic Smyth",
title = "{TopicNets}: Visual Analysis of Large Text Corpora
with Topic Modeling",
journal = j-TIST,
volume = "3",
number = "2",
pages = "23:1--23:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089099",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present TopicNets, a Web-based system for visual
and interactive analysis of large sets of documents
using statistical topic models. A range of
visualization types and control mechanisms to support
knowledge discovery are presented. These include
corpus- and document-specific views, iterative topic
modeling, search, and visual filtering. Drill-down
functionality is provided to allow analysts to
visualize individual document sections and their
relations within the global topic space. Analysts can
search across a dataset through a set of expansion
techniques on selected document and topic nodes.
Furthermore, analysts can select relevant subsets of
documents and perform real-time topic modeling on these
subsets to interactively visualize topics at various
levels of granularity, allowing for a better
understanding of the documents. A discussion of the
design and implementation choices for each visual
analysis technique is presented. This is followed by a
discussion of three diverse use cases in which
TopicNets enables fast discovery of information that is
otherwise hard to find. These include a corpus of
50,000 successful NSF grant proposals, 10,000
publications from a large research center, and single
documents including a grant proposal and a PhD
thesis.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:DFE,
author = "Yi Zhang and Tao Li",
title = "{DClusterE}: a Framework for Evaluating and
Understanding Document Clustering Using Visualization",
journal = j-TIST,
volume = "3",
number = "2",
pages = "24:1--24:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089100",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Over the last decade, document clustering, as one of
the key tasks in information organization and
navigation, has been widely studied. Many algorithms
have been developed for addressing various challenges
in document clustering and for improving clustering
performance. However, relatively few research efforts
have been reported on evaluating and understanding
document clustering results. In this article, we
present DClusterE, a comprehensive and effective
framework for document clustering evaluation and
understanding using information visualization.
DClusterE integrates cluster validation with user
interactions and offers rich visualization tools for
users to examine document clustering results from
multiple perspectives. In particular, through
informative views including force-directed layout view,
matrix view, and cluster view, DClusterE provides not
only different aspects of document
inter/intra-clustering structures, but also the
corresponding relationship between clustering results
and the ground truth. Additionally, DClusterE supports
general user interactions such as zoom in/out,
browsing, and interactive access of the documents at
different levels. Two new techniques are proposed to
implement DClusterE: (1) A novel multiplicative update
algorithm (MUA) for matrix reordering to generate
narrow-banded (or clustered) nonzero patterns from
documents. Combined with coarse seriation, MUA is able
to provide better visualization of the cluster
structures. (2) A Mallows-distance-based algorithm for
establishing the relationship between the clustering
results and the ground truth, which serves as the basis
for coloring schemes. Experiments and user studies are
conducted to demonstrate the effectiveness and
efficiency of DClusterE.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2012:TIT,
author = "Shixia Liu and Michelle X. Zhou and Shimei Pan and
Yangqiu Song and Weihong Qian and Weijia Cai and
Xiaoxiao Lian",
title = "{TIARA}: Interactive, Topic-Based Visual Text
Summarization and Analysis",
journal = j-TIST,
volume = "3",
number = "2",
pages = "25:1--25:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089101",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We are building an interactive visual text analysis
tool that aids users in analyzing large collections of
text. Unlike existing work in visual text analytics,
which focuses either on developing sophisticated text
analytic techniques or inventing novel text
visualization metaphors, ours tightly integrates
state-of-the-art text analytics with interactive
visualization to maximize the value of both. In this
article, we present our work from two aspects. We first
introduce an enhanced, LDA-based topic analysis
technique that automatically derives a set of topics to
summarize a collection of documents and their content
evolution over time. To help users understand the
complex summarization results produced by our topic
analysis technique, we then present the design and
development of a time-based visualization of the
results. Furthermore, we provide users with a set of
rich interaction tools that help them further interpret
the visualized results in context and examine the text
collection from multiple perspectives. As a result, our
work offers three unique contributions. First, we
present an enhanced topic modeling technique to provide
users with a time-sensitive and more meaningful text
summary. Second, we develop an effective visual
metaphor to transform abstract and often complex text
summarization results into a comprehensible visual
representation. Third, we offer users flexible visual
interaction tools as alternatives to compensate for the
deficiencies of current text summarization techniques.
We have applied our work to a number of text corpora
and our evaluation shows promise, especially in support
of complex text analyses.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rohrdantz:2012:FBV,
author = "Christian Rohrdantz and Ming C. Hao and Umeshwar Dayal
and Lars-Erik Haug and Daniel A. Keim",
title = "Feature-Based Visual Sentiment Analysis of Text
Document Streams",
journal = j-TIST,
volume = "3",
number = "2",
pages = "26:1--26:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089102",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article describes automatic methods and
interactive visualizations that are tightly coupled
with the goal to enable users to detect interesting
portions of text document streams. In this scenario the
interestingness is derived from the sentiment, temporal
density, and context coherence that comments about
features for different targets (e.g., persons,
institutions, product attributes, topics, etc.) have.
Contributions are made at different stages of the
visual analytics pipeline, including novel ways to
visualize salient temporal accumulations for further
exploration. Moreover, based on the visualization, an
automatic algorithm aims to detect and preselect
interesting time interval patterns for different
features in order to guide analysts. The main target
group for the suggested methods are business analysts
who want to explore time-stamped customer feedback to
detect critical issues. Finally, application case
studies on two different datasets and scenarios are
conducted and an extensive evaluation is provided for
the presented intelligent visual interface for
feature-based sentiment exploration over time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sugiyama:2012:ISS,
author = "Masashi Sugiyama and Qiang Yang",
title = "Introduction to the Special Section on the {2nd Asia
Conference on Machine Learning (ACML 2010)}",
journal = j-TIST,
volume = "3",
number = "2",
pages = "27:1--27:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089103",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hajimirsadeghi:2012:CIL,
author = "Hossein Hajimirsadeghi and Majid Nili Ahmadabadi and
Babak Nadjar Araabi and Hadi Moradi",
title = "Conceptual Imitation Learning in a Human-Robot
Interaction Paradigm",
journal = j-TIST,
volume = "3",
number = "2",
pages = "28:1--28:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089104",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In general, imitation is imprecisely used to address
different levels of social learning from high-level
knowledge transfer to low-level regeneration of motor
commands. However, true imitation is based on
abstraction and conceptualization. This article
presents a model for conceptual imitation through
interaction with the teacher to abstract
spatio-temporal demonstrations based on their
functional meaning. Abstraction, concept acquisition,
and self-organization of proto-symbols are performed
through an incremental and gradual learning algorithm.
In this algorithm, Hidden Markov Models (HMMs) are used
to abstract perceptually similar demonstrations.
However, abstract (relational) concepts emerge as a
collection of HMMs irregularly scattered in the
perceptual space but showing the same functionality.
Performance of the proposed algorithm is evaluated in
two experimental scenarios. The first one is a
human-robot interaction task of imitating signs
produced by hand movements. The second one is a
simulated interactive task of imitating whole body
motion patterns of a humanoid model. Experimental
results show efficiency of our model for concept
extraction, proto-symbol emergence, motion pattern
recognition, prediction, and generation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2012:MRC,
author = "Peipei Li and Xindong Wu and Xuegang Hu",
title = "Mining Recurring Concept Drifts with Limited Labeled
Streaming Data",
journal = j-TIST,
volume = "3",
number = "2",
pages = "29:1--29:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089105",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Tracking recurring concept drifts is a significant
issue for machine learning and data mining that
frequently appears in real-world stream classification
problems. It is a challenge for many streaming
classification algorithms to learn recurring concepts
in a data stream environment with unlabeled data, and
this challenge has received little attention from the
research community. Motivated by this challenge, this
article focuses on the problem of recurring contexts in
streaming environments with limited labeled data. We
propose a semi-supervised classification algorithm for
data streams with REcurring concept Drifts and Limited
LAbeled data, called REDLLA, in which a decision tree
is adopted as the classification model. When growing a
tree, a clustering algorithm based on k -means is
installed to produce concept clusters and unlabeled
data are labeled in the method of majority-class at
leaves. In view of deviations between history and new
concept clusters, potential concept drifts are
distinguished and recurring concepts are maintained.
Extensive studies on both synthetic and real-world data
confirm the advantages of our REDLLA algorithm over
three state-of-the-art online classification algorithms
of CVFDT, DWCDS, and CDRDT and several known online
semi-supervised algorithms, even in the case with more
than 90\% unlabeled data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bifet:2012:ERH,
author = "Albert Bifet and Eibe Frank and Geoff Holmes and
Bernhard Pfahringer",
title = "Ensembles of Restricted {Hoeffding} Trees",
journal = j-TIST,
volume = "3",
number = "2",
pages = "30:1--30:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089106",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The success of simple methods for classification shows
that it is often not necessary to model complex
attribute interactions to obtain good classification
accuracy on practical problems. In this article, we
propose to exploit this phenomenon in the data stream
context by building an ensemble of Hoeffding trees that
are each limited to a small subset of attributes. In
this way, each tree is restricted to model interactions
between attributes in its corresponding subset. Because
it is not known a priori which attribute subsets are
relevant for prediction, we build exhaustive ensembles
that consider all possible attribute subsets of a given
size. As the resulting Hoeffding trees are not all
equally important, we weigh them in a suitable manner
to obtain accurate classifications. This is done by
combining the log-odds of their probability estimates
using sigmoid perceptrons, with one perceptron per
class. We propose a mechanism for setting the
perceptrons' learning rate using the change detection
method for data streams, and also use to reset ensemble
members (i.e., Hoeffding trees) when they no longer
perform well. Our experiments show that the resulting
ensemble classifier outperforms bagging for data
streams in terms of accuracy when both are used in
conjunction with adaptive naive Bayes Hoeffding trees,
at the expense of runtime and memory consumption. We
also show that our stacking method can improve the
performance of a bagged ensemble.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ma:2012:RPC,
author = "Huadong Ma and Chengbin Zeng and Charles X. Ling",
title = "A Reliable People Counting System via Multiple
Cameras",
journal = j-TIST,
volume = "3",
number = "2",
pages = "31:1--31:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089107",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Reliable and real-time people counting is crucial in
many applications. Most previous works can only count
moving people from a single camera, which cannot count
still people or can fail badly when there is a crowd
(i.e., heavy occlusion occurs). In this article, we
build a system for robust and fast people counting
under occlusion through multiple cameras. To improve
the reliability of human detection from a single
camera, we use a dimensionality reduction method on the
multilevel edge and texture features to handle the
large variations in human appearance and poses. To
accelerate the detection speed, we propose a novel
two-stage cascade-of-rejectors method. To handle the
heavy occlusion in crowded scenes, we present a fusion
method with error tolerance to combine human detection
from multiple cameras. To improve the speed and
accuracy of moving people counting, we combine our
multiview fusion detection method with particle
tracking to count the number of people moving in/out
the camera view (`border control'). Extensive
experiments and analyses show that our method
outperforms state-of-the-art techniques in single- and
multicamera datasets for both speed and reliability. We
also design a deployed system for fast and reliable
people (still or moving) counting by using multiple
cameras.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kolomvatsos:2012:FLS,
author = "Kostas Kolomvatsos and Christos Anagnostopoulos and
Stathes Hadjiefthymiades",
title = "A Fuzzy Logic System for Bargaining in Information
Markets",
journal = j-TIST,
volume = "3",
number = "2",
pages = "32:1--32:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089108",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Future Web business models involve virtual
environments where entities interact in order to sell
or buy information goods. Such environments are known
as Information Markets (IMs). Intelligent agents are
used in IMs for representing buyers or information
providers (sellers). We focus on the decisions taken by
the buyer in the purchase negotiation process with
sellers. We propose a reasoning mechanism on the offers
(prices of information goods) issued by sellers based
on fuzzy logic. The buyer's knowledge on the
negotiation process is modeled through fuzzy sets. We
propose a fuzzy inference engine dealing with the
decisions that the buyer takes on each stage of the
negotiation process. The outcome of the proposed
reasoning method indicates whether the buyer should
accept or reject the sellers' offers. Our findings are
very promising for the efficiency of automated
transactions undertaken by intelligent agents.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2012:BMA,
author = "Lixin Shi and Yuhang Zhao and Jie Tang",
title = "Batch Mode Active Learning for Networked Data",
journal = j-TIST,
volume = "3",
number = "2",
pages = "33:1--33:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089109",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We study a novel problem of batch mode active learning
for networked data. In this problem, data instances are
connected with links and their labels are correlated
with each other, and the goal of batch mode active
learning is to exploit the link-based dependencies and
node-specific content information to actively select a
batch of instances to query the user for learning an
accurate model to label unknown instances in the
network. We present three criteria (i.e., minimum
redundancy, maximum uncertainty, and maximum impact) to
quantify the informativeness of a set of instances, and
formalize the batch mode active learning problem as
selecting a set of instances by maximizing an objective
function which combines both link and content
information. As solving the objective function is
NP-hard, we present an efficient algorithm to optimize
the objective function with a bounded approximation
rate. To scale to real large networks, we develop a
parallel implementation of the algorithm. Experimental
results on both synthetic datasets and real-world
datasets demonstrate the effectiveness and efficiency
of our approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shakarian:2012:AGA,
author = "Paulo Shakarian and John P. Dickerson and V. S.
Subrahmanian",
title = "Adversarial Geospatial Abduction Problems",
journal = j-TIST,
volume = "3",
number = "2",
pages = "34:1--34:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089110",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Geospatial Abduction Problems (GAPs) involve the
inference of a set of locations that `best explain' a
given set of locations of observations. For example,
the observations might include locations where a serial
killer committed murders or where insurgents carried
out Improvised Explosive Device (IED) attacks. In both
these cases, we would like to infer a set of locations
that explain the observations, for example, the set of
locations where the serial killer lives/works, and the
set of locations where insurgents locate weapons
caches. However, unlike all past work on abduction,
there is a strong adversarial component to this; an
adversary actively attempts to prevent us from
discovering such locations. We formalize such abduction
problems as a two-player game where both players (an
`agent' and an `adversary') use a probabilistic model
of their opponent (i.e., a mixed strategy). There is
asymmetry as the adversary can choose both the
locations of the observations and the locations of the
explanation, while the agent (i.e., us) tries to
discover these. In this article, we study the problem
from the point of view of both players. We define
reward functions axiomatically to capture the
similarity between two sets of explanations (one
corresponding to the locations chosen by the adversary,
one guessed by the agent). Many different reward
functions can satisfy our axioms. We then formalize the
Optimal Adversary Strategy (OAS) problem and the
Maximal Counter-Adversary strategy (MCA) and show that
both are NP-hard, that their associated counting
complexity problems are \#P-hard, and that MCA has no
fully polynomial approximation scheme unless P=NP. We
show that approximation guarantees are possible for MCA
when the reward function satisfies two simple
properties (zero-starting and monotonicity) which many
natural reward functions satisfy. We develop a mixed
integer linear programming algorithm to solve OAS and
two algorithms to (approximately) compute MCA; the
algorithms yield different approximation guarantees and
one algorithm assumes a monotonic reward function. Our
experiments use real data about IED attacks over a
21-month period in Baghdad. We are able to show that
both the MCA algorithms work well in practice; while
MCA-GREEDY-MONO is both highly accurate and slightly
faster than MCA-LS, MCA-LS (to our surprise) always
completely and correctly maximized the expected benefit
to the agent while running in an acceptable time
period.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2012:LIS,
author = "Xueying Li and Huanhuan Cao and Enhong Chen and Jilei
Tian",
title = "Learning to Infer the Status of Heavy-Duty Sensors for
Energy-Efficient Context-Sensing",
journal = j-TIST,
volume = "3",
number = "2",
pages = "35:1--35:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089111",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the prevalence of smart mobile devices with
multiple sensors, the commercial application of
intelligent context-aware services becomes more and
more attractive. However, limited by the battery
capacity, the energy efficiency of context-sensing is
the bottleneck for the success of context-aware
applications. Though several previous studies for
energy-efficient context-sensing have been reported,
none of them can be applied to multiple types of
high-energy-consuming sensors. Moreover, applying
machine learning technologies to energy-efficient
context-sensing is underexplored too. In this article,
we propose to leverage machine learning technologies
for improving the energy efficiency of multiple
high-energy-consuming context sensors by trading off
the sensing accuracy. To be specific, we try to infer
the status of high-energy-consuming sensors according
to the outputs of software-based sensors and the
physical sensors that are necessary to work all the
time for supporting the basic functions of mobile
devices. If the inference indicates the
high-energy-consuming sensor is in a stable status, we
avoid the unnecessary invocation and instead use the
latest invoked value as the estimation. The
experimental results on real datasets show that the
energy efficiency of GPS sensing and audio-level
sensing are significantly improved by the proposed
approach while the sensing accuracy is over 90\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:AKR,
author = "Weinan Zhang and Dingquan Wang and Gui-Rong Xue and
Hongyuan Zha",
title = "Advertising Keywords Recommendation for Short-Text
{Web} Pages Using {Wikipedia}",
journal = j-TIST,
volume = "3",
number = "2",
pages = "36:1--36:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089112",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Advertising keywords recommendation is an
indispensable component for online advertising with the
keywords selected from the target Web pages used for
contextual advertising or sponsored search. Several
ranking-based algorithms have been proposed for
recommending advertising keywords. However, for most of
them performance is still lacking, especially when
dealing with short-text target Web pages, that is,
those containing insufficient textual information for
ranking. In some cases, short-text Web pages may not
even contain enough keywords for selection. A natural
alternative is then to recommend relevant keywords not
present in the target Web pages. In this article, we
propose a novel algorithm for advertising keywords
recommendation for short-text Web pages by leveraging
the contents of Wikipedia, a user-contributed online
encyclopedia. Wikipedia contains numerous entities with
related entities on a topic linked to each other. Given
a target Web page, we propose to use a content-biased
PageRank on the Wikipedia graph to rank the related
entities. Furthermore, in order to recommend
high-quality advertising keywords, we also add an
advertisement-biased factor into our model. With these
two biases, advertising keywords that are both relevant
to a target Web page and valuable for advertising are
recommended. In our experiments, several
state-of-the-art approaches for keyword recommendation
are compared. The experimental results demonstrate that
our proposed approach produces substantial improvement
in the precision of the top 20 recommended keywords on
short-text Web pages over existing approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhou:2012:LAD,
author = "Ke Zhou and Jing Bai and Hongyuan Zha and Gui-Rong
Xue",
title = "Leveraging Auxiliary Data for Learning to Rank",
journal = j-TIST,
volume = "3",
number = "2",
pages = "37:1--37:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089113",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In learning to rank, both the quality and quantity of
the training data have significant impacts on the
performance of the learned ranking functions. However,
in many applications, there are usually not sufficient
labeled training data for the construction of an
accurate ranking model. It is therefore desirable to
leverage existing training data from other tasks when
learning the ranking function for a particular task, an
important problem which we tackle in this article
utilizing a boosting framework with transfer learning.
In particular, we propose to adaptively learn
transferable representations called super-features from
the training data of both the target task and the
auxiliary task. Those super-features and the
coefficients for combining them are learned in an
iterative stage-wise fashion. Unlike previous transfer
learning methods, the super-features can be adaptively
learned by weak learners from the data. Therefore, the
proposed framework is sufficiently flexible to deal
with complicated common structures among different
learning tasks. We evaluate the performance of the
proposed transfer learning method for two datasets from
the Letor collection and one dataset collected from a
commercial search engine, and we also compare our
methods with several existing transfer learning
methods. Our results demonstrate that the proposed
method can enhance the ranking functions of the target
tasks utilizing the training data from the auxiliary
tasks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Peng:2012:MVC,
author = "Wei Peng and Tong Sun and Shriram Revankar and Tao
Li",
title = "Mining the {``Voice} of the Customer'' for Business
Prioritization",
journal = j-TIST,
volume = "3",
number = "2",
pages = "38:1--38:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089114",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "To gain competitiveness and sustained growth in the
21st century, most businesses are on a mission to
become more customer-centric. In order to succeed in
this endeavor, it is crucial not only to synthesize and
analyze the VOC (the VOice of the Customer) data (i.e.,
the feedbacks or requirements raised by customers), but
also to quickly turn these data into actionable
knowledge. Although there are many technologies being
developed in this complex problem space, most existing
approaches in analyzing customer requests are ad hoc,
time-consuming, error-prone, people-based processes
which hardly scale well as the quantity of customer
information explodes. This often results in the slow
response to customer requests. In this article, in
order to mine VOC to extract useful knowledge for the
best product or service quality, we develop a hybrid
framework that integrates domain knowledge with
data-driven approaches to analyze the semi-structured
customer requests. The framework consists of capturing
functional features, discovering the overlap or
correlation among the features, and identifying the
evolving feature trend by using the knowledge
transformation model. In addition, since understanding
the relative importance of the individual customer
request is very critical and has a direct impact on the
effective prioritization in the development process, we
develop a novel semantic enhanced link-based ranking
(SELRank) algorithm for relatively rating/ranking both
customer requests and products. The framework has been
successfully applied on Xerox Office Group Feature
Enhancement Requirements (XOG FER) datasets to analyze
customer requests.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hua:2012:ISS,
author = "Xian-Sheng Hua and Qi Tian and Alberto {Del Bimbo} and
Ramesh Jain",
title = "Introduction to the {Special Section on Intelligent
Multimedia Systems and Technology Part II}",
journal = j-TIST,
volume = "3",
number = "3",
pages = "39:1--39:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168753",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2012:MRM,
author = "Yi-Hsuan Yang and Homer H. Chen",
title = "Machine Recognition of Music Emotion: a Review",
journal = j-TIST,
volume = "3",
number = "3",
pages = "40:1--40:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168754",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The proliferation of MP3 players and the exploding
amount of digital music content call for novel ways of
music organization and retrieval to meet the
ever-increasing demand for easy and effective
information access. As almost every music piece is
created to convey emotion, music organization and
retrieval by emotion is a reasonable way of accessing
music information. A good deal of effort has been made
in the music information retrieval community to train a
machine to automatically recognize the emotion of a
music signal. A central issue of machine recognition of
music emotion is the conceptualization of emotion and
the associated emotion taxonomy. Different viewpoints
on this issue have led to the proposal of different
ways of emotion annotation, model training, and result
visualization. This article provides a comprehensive
review of the methods that have been proposed for music
emotion recognition. Moreover, as music emotion
recognition is still in its infancy, there are many
open issues. We review the solutions that have been
proposed to address these issues and conclude with
suggestions for further research.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ewerth:2012:RVC,
author = "Ralph Ewerth and Markus M{\"u}hling and Bernd
Freisleben",
title = "Robust Video Content Analysis via Transductive
Learning",
journal = j-TIST,
volume = "3",
number = "3",
pages = "41:1--41:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168755",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Reliable video content analysis is an essential
prerequisite for effective video search. An important
current research question is how to develop robust
video content analysis methods that produce
satisfactory results for a large variety of video
sources, distribution platforms, genres, and content.
The work presented in this article exploits the
observation that the appearance of objects and events
is often related to a particular video sequence,
episode, program, or broadcast. This motivates our idea
of considering the content analysis task for a single
video or episode as a transductive setting: the final
classification model must be optimal for the given
video only, and not in general, as expected for
inductive learning. For this purpose, the unlabeled
video test data have to be used in the learning
process. In this article, a transductive learning
framework for robust video content analysis based on
feature selection and ensemble classification is
presented. In contrast to related transductive
approaches for video analysis (e.g., for concept
detection), the framework is designed in a general
manner and not only for a single task. The proposed
framework is applied to the following video analysis
tasks: shot boundary detection, face recognition,
semantic video retrieval, and semantic indexing of
computer game sequences. Experimental results for
diverse video analysis tasks and large test sets
demonstrate that the proposed transductive framework
improves the robustness of the underlying
state-of-the-art approaches, whereas transductive
support vector machines do not solve particular tasks
in a satisfactory manner.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Suk:2012:VHM,
author = "Myunghoon Suk and Ashok Ramadass and Yohan Jin and B.
Prabhakaran",
title = "Video Human Motion Recognition Using a Knowledge-Based
Hybrid Method Based on a Hidden {Markov} Model",
journal = j-TIST,
volume = "3",
number = "3",
pages = "42:1--42:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168756",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Human motion recognition in video data has several
interesting applications in fields such as gaming,
senior/assisted-living environments, and surveillance.
In these scenarios, we may have to consider adding new
motion classes (i.e., new types of human motions to be
recognized), as well as new training data (e.g., for
handling different type of subjects). Hence, both the
accuracy of classification and training time for the
machine learning algorithms become important
performance parameters in these cases. In this article,
we propose a knowledge-based hybrid (KBH) method that
can compute the probabilities for hidden Markov models
(HMMs) associated with different human motion classes.
This computation is facilitated by appropriately mixing
features from two different media types (3D motion
capture and 2D video). We conducted a variety of
experiments comparing the proposed KBH for HMMs and the
traditional Baum-Welch algorithms. With the advantage
of computing the HMM parameter in a noniterative
manner, the KBH method outperforms the Baum-Welch
algorithm both in terms of accuracy as well as in
reduced training time. Moreover, we show in additional
experiments that the KBH method also outperforms the
linear support vector machine (SVM).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:RVT,
author = "Shengping Zhang and Hongxun Yao and Xin Sun and
Shaohui Liu",
title = "Robust Visual Tracking Using an Effective Appearance
Model Based on Sparse Coding",
journal = j-TIST,
volume = "3",
number = "3",
pages = "43:1--43:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168757",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Intelligent video surveillance is currently one of the
most active research topics in computer vision,
especially when facing the explosion of video data
captured by a large number of surveillance cameras. As
a key step of an intelligent surveillance system,
robust visual tracking is very challenging for computer
vision. However, it is a basic functionality of the
human visual system (HVS). Psychophysical findings have
shown that the receptive fields of simple cells in the
visual cortex can be characterized as being spatially
localized, oriented, and bandpass, and it forms a
sparse, distributed representation of natural images.
In this article, motivated by these findings, we
propose an effective appearance model based on sparse
coding and apply it in visual tracking. Specifically,
we consider the responses of general basis functions
extracted by independent component analysis on a large
set of natural image patches as features and model the
appearance of the tracked target as the probability
distribution of these features. In order to make the
tracker more robust to partial occlusion, camouflage
environments, pose changes, and illumination changes,
we further select features that are related to the
target based on an entropy-gain criterion and ignore
those that are not. The target is finally represented
by the probability distribution of those related
features. The target search is performed by minimizing
the Matusita distance between the distributions of the
target model and a candidate using Newton-style
iterations. The experimental results validate that the
proposed method is more robust and effective than three
state-of-the-art methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ji:2012:CAS,
author = "Rongrong Ji and Hongxun Yao and Qi Tian and Pengfei Xu
and Xiaoshuai Sun and Xianming Liu",
title = "Context-Aware Semi-Local Feature Detector",
journal = j-TIST,
volume = "3",
number = "3",
pages = "44:1--44:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168758",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "How can interest point detectors benefit from
contextual cues? In this articles, we introduce a
context-aware semi-local detector (CASL) framework to
give a systematic answer with three contributions: (1)
We integrate the context of interest points to
recurrently refine their detections. (2) This
integration boosts interest point detectors from the
traditionally local scale to a semi-local scale to
discover more discriminative salient regions. (3) Such
context-aware structure further enables us to bring
forward category learning (usually in the subsequent
recognition phase) into interest point detection to
locate category-aware, meaningful salient regions. Our
CASL detector consists of two phases. The first phase
accumulates multiscale spatial correlations of local
features into a difference of contextual Gaussians
(DoCG) field. DoCG quantizes detector context to
highlight contextually salient regions at a semi-local
scale, which also reveals visual attentions to a
certain extent. The second phase locates contextual
peaks by mean shift search over the DoCG field, which
subsequently integrates contextual cues into feature
description. This phase enables us to integrate
category learning into mean shift search kernels. This
learning-based CASL mechanism produces more
category-aware features, which substantially benefits
the subsequent visual categorization process. We
conducted experiments in image search, object
characterization, and feature detector repeatability
evaluations, which reported superior discriminability
and comparable repeatability to state-of-the-art
works.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Berretti:2012:DFF,
author = "Stefano Berretti and Alberto {Del Bimbo} and Pietro
Pala",
title = "Distinguishing Facial Features for Ethnicity-Based
{$3$D} Face Recognition",
journal = j-TIST,
volume = "3",
number = "3",
pages = "45:1--45:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168759",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Among different approaches for 3D face recognition,
solutions based on local facial characteristics are
very promising, mainly because they can manage facial
expression variations by assigning different weights to
different parts of the face. However, so far, a few
works have investigated the individual relevance that
local features play in 3D face recognition with very
simple solutions applied in the practice. In this
article, a local approach to 3D face recognition is
combined with a feature selection model to study the
relative relevance of different regions of the face for
the purpose of discriminating between different
subjects. The proposed solution is experimented using
facial scans of the Face Recognition Grand Challenge
dataset. Results of the experimentation are two-fold:
they quantitatively demonstrate the assumption that
different regions of the face have different relevance
for face discrimination and also show that the
relevance of facial regions changes for different
ethnic groups.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:GAS,
author = "Ning Zhang and Ling-Yu Duan and Lingfang Li and
Qingming Huang and Jun Du and Wen Gao and Ling Guan",
title = "A Generic Approach for Systematic Analysis of Sports
Videos",
journal = j-TIST,
volume = "3",
number = "3",
pages = "46:1--46:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168760",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Various innovative and original works have been
applied and proposed in the field of sports video
analysis. However, individual works have focused on
sophisticated methodologies with particular sport types
and there has been a lack of scalable and holistic
frameworks in this field. This article proposes a
solution and presents a systematic and generic approach
which is experimented on a relatively large-scale
sports consortia. The system aims at the event
detection scenario of an input video with an orderly
sequential process. Initially, domain
knowledge-independent local descriptors are extracted
homogeneously from the input video sequence. Then the
video representation is created by adopting a
bag-of-visual-words (BoW) model. The video's genre is
first identified by applying the k-nearest neighbor
(k-NN) classifiers on the initially obtained video
representation, and various dissimilarity measures are
assessed and evaluated analytically. Subsequently, an
unsupervised probabilistic latent semantic analysis
(PLSA)-based approach is employed at the same
histogram-based video representation, characterizing
each frame of video sequence into one of four view
groups, namely closed-up-view, mid-view, long-view, and
outer-field-view. Finally, a hidden conditional random
field (HCRF) structured prediction model is utilized
for interesting event detection. From experimental
results, k-NN classifier using KL-divergence
measurement demonstrates the best accuracy at 82.16\%
for genre categorization. Supervised SVM and
unsupervised PLSA have average classification
accuracies at 82.86\% and 68.13\%, respectively. The
HCRF model achieves 92.31\% accuracy using the
unsupervised PLSA based label input, which is
comparable with the supervised SVM based input at an
accuracy of 93.08\%. In general, such a systematic
approach can be widely applied in processing massive
videos generically.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Leung:2012:ISM,
author = "Clement H. C. Leung and Alice W. S. Chan and Alfredo
Milani and Jiming Liu and Yuanxi Li",
title = "Intelligent Social Media Indexing and Sharing Using an
Adaptive Indexing Search Engine",
journal = j-TIST,
volume = "3",
number = "3",
pages = "47:1--47:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168761",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Effective sharing of diverse social media is often
inhibited by limitations in their search and discovery
mechanisms, which are particularly restrictive for
media that do not lend themselves to automatic
processing or indexing. Here, we present the structure
and mechanism of an adaptive search engine which is
designed to overcome such limitations. The basic
framework of the adaptive search engine is to capture
human judgment in the course of normal usage from user
queries in order to develop semantic indexes which link
search terms to media objects semantics. This approach
is particularly effective for the retrieval of
multimedia objects, such as images, sounds, and videos,
where a direct analysis of the object features does not
allow them to be linked to search terms, for example,
nontextual/icon-based search, deep semantic search, or
when search terms are unknown at the time the media
repository is built. An adaptive search architecture is
presented to enable the index to evolve with respect to
user feedback, while a randomized query-processing
technique guarantees avoiding local minima and allows
the meaningful indexing of new media objects and new
terms. The present adaptive search engine allows for
the efficient community creation and updating of social
media indexes, which is able to instill and propagate
deep knowledge into social media concerning the
advanced search and usage of media resources.
Experiments with various relevance distribution
settings have shown efficient convergence of such
indexes, which enable intelligent search and sharing of
social media resources that are otherwise hard to
discover.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chien:2012:ISS,
author = "Steve Chien and Amedeo Cesta",
title = "Introduction to the Special Section on Artificial
Intelligence in Space",
journal = j-TIST,
volume = "3",
number = "3",
pages = "48:1--48:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168762",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wagstaff:2012:DLS,
author = "Kiri L. Wagstaff and Julian Panetta and Adnan Ansar
and Ronald Greeley and Mary Pendleton Hoffer and
Melissa Bunte and Norbert Sch{\"o}rghofer",
title = "Dynamic Landmarking for Surface Feature Identification
and Change Detection",
journal = j-TIST,
volume = "3",
number = "3",
pages = "49:1--49:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168763",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Given the large volume of images being sent back from
remote spacecraft, there is a need for automated
analysis techniques that can quickly identify
interesting features in those images. Feature
identification in individual images and automated
change detection in multiple images of the same target
are valuable for scientific studies and can inform
subsequent target selection. We introduce a new
approach to orbital image analysis called dynamic
landmarking. It focuses on the identification and
comparison of visually salient features in images. We
have evaluated this approach on images collected by
five Mars orbiters. These evaluations were motivated by
three scientific goals: to study fresh impact craters,
dust devil tracks, and dark slope streaks on Mars. In
the process we also detected a different kind of
surface change that may indicate seasonally exposed
bedforms. These experiences also point the way to how
this approach could be used in an onboard setting to
analyze and prioritize data as it is collected.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Estlin:2012:AAS,
author = "Tara A. Estlin and Benjamin J. Bornstein and Daniel M.
Gaines and Robert C. Anderson and David R. Thompson and
Michael Burl and Rebecca Casta{\~n}o and Michele Judd",
title = "{AEGIS} Automated Science Targeting for the {MER
Opportunity Rover}",
journal = j-TIST,
volume = "3",
number = "3",
pages = "50:1--50:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168764",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The Autonomous Exploration for Gathering Increased
Science (AEGIS) system enables automated data
collection by planetary rovers. AEGIS software was
uploaded to the Mars Exploration Rover (MER) mission's
Opportunity rover in December 2009 and has successfully
demonstrated automated onboard targeting based on
scientist-specified objectives. Prior to AEGIS, images
were transmitted from the rover to the operations team
on Earth; scientists manually analyzed the images,
selected geological targets for the rover's
remote-sensing instruments, and then generated a
command sequence to execute the new measurements. AEGIS
represents a significant paradigm shift---by using
onboard data analysis techniques, the AEGIS software
uses scientist input to select high-quality science
targets with no human in the loop. This approach allows
the rover to autonomously select and sequence targeted
observations in an opportunistic fashion, which is
particularly applicable for narrow field-of-view
instruments (such as the MER Mini-TES spectrometer, the
MER Panoramic camera, and the 2011 Mars Science
Laboratory (MSL) ChemCam spectrometer). This article
provides an overview of the AEGIS automated targeting
capability and describes how it is currently being used
onboard the MER mission Opportunity rover.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hayden:2012:UCM,
author = "David S. Hayden and Steve Chien and David R. Thompson
and Rebecca Casta{\~n}o",
title = "Using Clustering and Metric Learning to Improve
Science Return of Remote Sensed Imagery",
journal = j-TIST,
volume = "3",
number = "3",
pages = "51:1--51:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168765",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Current and proposed remote space missions, such as
the proposed aerial exploration of Titan by an aerobot,
often can collect more data than can be communicated
back to Earth. Autonomous selective downlink algorithms
can choose informative subsets of data to improve the
science value of these bandwidth-limited transmissions.
This requires statistical descriptors of the data that
reflect very abstract and subtle distinctions in
science content. We propose a metric learning strategy
that teaches algorithms how best to cluster new data
based on training examples supplied by domain
scientists. We demonstrate that clustering informed by
metric learning produces results that more closely
match multiple scientists' labelings of aerial data
than do clusterings based on random or periodic
sampling. A new metric-learning strategy accommodates
training sets produced by multiple scientists with
different and potentially inconsistent mission
objectives. Our methods are fit for current spacecraft
processors (e.g., RAD750) and would further benefit
from more advanced spacecraft processor architectures,
such as OPERA.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hoi:2012:ISS,
author = "Steven C. H. Hoi and Rong Jin and Jinhui Tang and
Zhi-Hua Zhou",
title = "Introduction to the Special Section on Distance Metric
Learning in Intelligent Systems",
journal = j-TIST,
volume = "3",
number = "3",
pages = "52:1--52:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168766",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhai:2012:MML,
author = "Deming Zhai and Hong Chang and Shiguang Shan and Xilin
Chen and Wen Gao",
title = "Multiview Metric Learning with Global Consistency and
Local Smoothness",
journal = j-TIST,
volume = "3",
number = "3",
pages = "53:1--53:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168767",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In many real-world applications, the same object may
have different observations (or descriptions) from
multiview observation spaces, which are highly related
but sometimes look different from each other.
Conventional metric-learning methods achieve
satisfactory performance on distance metric computation
of data in a single-view observation space, but fail to
handle well data sampled from multiview observation
spaces, especially those with highly nonlinear
structure. To tackle this problem, we propose a new
method called Multiview Metric Learning with Global
consistency and Local smoothness (MVML-GL) under a
semisupervised learning setting, which jointly
considers global consistency and local smoothness. The
basic idea is to reveal the shared latent feature space
of the multiview observations by embodying global
consistency constraints and preserving local geometric
structures. Specifically, this framework is composed of
two main steps. In the first step, we seek a global
consistent shared latent feature space, which not only
preserves the local geometric structure in each space
but also makes those labeled corresponding instances as
close as possible. In the second step, the explicit
mapping functions between the input spaces and the
shared latent space are learned via regularized locally
linear regression. Furthermore, these two steps both
can be solved by convex optimizations in closed form.
Experimental results with application to manifold
alignment on real-world datasets of pose and facial
expression demonstrate the effectiveness of the
proposed method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:TML,
author = "Yu Zhang and Dit-Yan Yeung",
title = "Transfer Metric Learning with Semi-Supervised
Extension",
journal = j-TIST,
volume = "3",
number = "3",
pages = "54:1--54:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168768",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Distance metric learning plays a very crucial role in
many data mining algorithms because the performance of
an algorithm relies heavily on choosing a good metric.
However, the labeled data available in many
applications is scarce, and hence the metrics learned
are often unsatisfactory. In this article, we consider
a transfer-learning setting in which some related
source tasks with labeled data are available to help
the learning of the target task. We first propose a
convex formulation for multitask metric learning by
modeling the task relationships in the form of a task
covariance matrix. Then we regard transfer learning as
a special case of multitask learning and adapt the
formulation of multitask metric learning to the
transfer-learning setting for our method, called
transfer metric learning (TML). In TML, we learn the
metric and the task covariances between the source
tasks and the target task under a unified convex
formulation. To solve the convex optimization problem,
we use an alternating method in which each subproblem
has an efficient solution. Moreover, in many
applications, some unlabeled data is also available in
the target task, and so we propose a semi-supervised
extension of TML called STML to further improve the
generalization performance by exploiting the unlabeled
data based on the manifold assumption. Experimental
results on some commonly used transfer-learning
applications demonstrate the effectiveness of our
method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xu:2012:MLE,
author = "Jun-Ming Xu and Xiaojin Zhu and Timothy T. Rogers",
title = "Metric Learning for Estimating Psychological
Similarities",
journal = j-TIST,
volume = "3",
number = "3",
pages = "55:1--55:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168769",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An important problem in cognitive psychology is to
quantify the perceived similarities between stimuli.
Previous work attempted to address this problem with
multidimensional scaling (MDS) and its variants.
However, there are several shortcomings of the MDS
approaches. We propose Yada, a novel general
metric-learning procedure based on two-alternative
forced-choice behavioral experiments. Our method learns
forward and backward nonlinear mappings between an
objective space in which the stimuli are defined by the
standard feature vector representation and a subjective
space in which the distance between a pair of stimuli
corresponds to their perceived similarity. We conduct
experiments on both synthetic and real human behavioral
datasets to assess the effectiveness of Yada. The
results show that Yada outperforms several standard
embedding and metric-learning algorithms, both in terms
of likelihood and recovery error.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2012:MTP,
author = "Yan-Tao Zheng and Zheng-Jun Zha and Tat-Seng Chua",
title = "Mining Travel Patterns from Geotagged Photos",
journal = j-TIST,
volume = "3",
number = "3",
pages = "56:1--56:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168770",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recently, the phenomenal advent of photo-sharing
services, such as Flickr and Panoramio, have led to
voluminous community-contributed photos with text tags,
timestamps, and geographic references on the Internet.
The photos, together with their time- and
geo-references, become the digital footprints of photo
takers and implicitly document their spatiotemporal
movements. This study aims to leverage the wealth of
these enriched online photos to analyze people's travel
patterns at the local level of a tour destination.
Specifically, we focus our analysis on two aspects: (1)
tourist movement patterns in relation to the regions of
attractions (RoA), and (2) topological characteristics
of travel routes by different tourists. To do so, we
first build a statistically reliable database of travel
paths from a noisy pool of community-contributed
geotagged photos on the Internet. We then investigate
the tourist traffic flow among different RoAs by
exploiting the Markov chain model. Finally, the
topological characteristics of travel routes are
analyzed by performing a sequence clustering on tour
routes. Testings on four major cities demonstrate
promising results of the proposed system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rendle:2012:FML,
author = "Steffen Rendle",
title = "Factorization Machines with {libFM}",
journal = j-TIST,
volume = "3",
number = "3",
pages = "57:1--57:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168771",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Factorization approaches provide high accuracy in
several important prediction problems, for example,
recommender systems. However, applying factorization
approaches to a new prediction problem is a nontrivial
task and requires a lot of expert knowledge. Typically,
a new model is developed, a learning algorithm is
derived, and the approach has to be implemented.
Factorization machines (FM) are a generic approach
since they can mimic most factorization models just by
feature engineering. This way, factorization machines
combine the generality of feature engineering with the
superiority of factorization models in estimating
interactions between categorical variables of large
domain. libFM is a software implementation for
factorization machines that features stochastic
gradient descent (SGD) and alternating least-squares
(ALS) optimization, as well as Bayesian inference using
Markov Chain Monto Carlo (MCMC). This article
summarizes the recent research on factorization
machines both in terms of modeling and learning,
provides extensions for the ALS and MCMC algorithms,
and describes the software tool libFM.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gabrilovich:2012:ISS,
author = "Evgeniy Gabrilovich and Zhong Su and Jie Tang",
title = "Introduction to the {Special Section on Computational
Models of Collective Intelligence in the Social Web}",
journal = j-TIST,
volume = "3",
number = "4",
pages = "58:1--58:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337543",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Herdagdelen:2012:BGP,
author = "Ama{\c{c}} Herdagdelen and Marco Baroni",
title = "Bootstrapping a Game with a Purpose for Commonsense
Collection",
journal = j-TIST,
volume = "3",
number = "4",
pages = "59:1--59:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337544",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Text mining has been very successful in extracting
huge amounts of commonsense knowledge from data, but
the extracted knowledge tends to be extremely noisy.
Manual construction of knowledge repositories, on the
other hand, tends to produce high-quality data in very
small amounts. We propose an architecture to combine
the best of both worlds: A game with a purpose that
induces humans to clean up data automatically extracted
by text mining. First, a text miner trained on a set of
known commonsense facts harvests many more candidate
facts from corpora. Then, a simple
slot-machine-with-a-purpose game presents these
candidate facts to the players for verification by
playing. As a result, a new dataset of high precision
commonsense knowledge is created. This combined
architecture is able to produce significantly better
commonsense facts than the state-of-the-art text miner
alone. Furthermore, we report that bootstrapping (i.e.,
training the text miner on the output of the game)
improves the subsequent performance of the text
miner.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Carmel:2012:FBT,
author = "David Carmel and Erel Uziel and Ido Guy and Yosi Mass
and Haggai Roitman",
title = "Folksonomy-Based Term Extraction for Word Cloud
Generation",
journal = j-TIST,
volume = "3",
number = "4",
pages = "60:1--60:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337545",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this work we study the task of term extraction for
word cloud generation in sparsely tagged domains, in
which manual tags are scarce. We present a
folksonomy-based term extraction method, called
tag-boost, which boosts terms that are frequently used
by the public to tag content. Our experiments with
tag-boost based term extraction over different domains
demonstrate tremendous improvement in word cloud
quality, as reflected by the agreement between manual
tags of the testing items and the cloud's terms
extracted from the items' content. Moreover, our
results demonstrate the high robustness of this
approach, as compared to alternative cloud generation
methods that exhibit a high sensitivity to data
sparseness. Additionally, we show that tag-boost can be
effectively applied even in nontagged domains, by using
an external rich folksonomy borrowed from a well-tagged
domain.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2012:IOS,
author = "Guan Wang and Sihong Xie and Bing Liu and Philip S.
Yu",
title = "Identify Online Store Review Spammers via Social
Review Graph",
journal = j-TIST,
volume = "3",
number = "4",
pages = "61:1--61:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337546",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online shopping reviews provide valuable information
for customers to compare the quality of products, store
services, and many other aspects of future purchases.
However, spammers are joining this community trying to
mislead consumers by writing fake or unfair reviews to
confuse the consumers. Previous attempts have used
reviewers' behaviors such as text similarity and rating
patterns, to detect spammers. These studies are able to
identify certain types of spammers, for instance, those
who post many similar reviews about one target.
However, in reality, there are other kinds of spammers
who can manipulate their behaviors to act just like
normal reviewers, and thus cannot be detected by the
available techniques. In this article, we propose a
novel concept of review graph to capture the
relationships among all reviewers, reviews and stores
that the reviewers have reviewed as a heterogeneous
graph. We explore how interactions between nodes in
this graph could reveal the cause of spam and propose
an iterative computation model to identify suspicious
reviewers. In the review graph, we have three kinds of
nodes, namely, reviewer, review, and store. We capture
their relationships by introducing three fundamental
concepts, the trustiness of reviewers, the honesty of
reviews, and the reliability of stores, and identifying
their interrelationships: a reviewer is more
trustworthy if the person has written more honesty
reviews; a store is more reliable if it has more
positive reviews from trustworthy reviewers; and a
review is more honest if many other honest reviews
support it. This is the first time such intricate
relationships have been identified for spam detection
and captured in a graph model. We further develop an
effective computation method based on the proposed
graph model. Different from any existing approaches, we
do not use an review text information. Our model is
thus complementary to existing approaches and able to
find more difficult and subtle spamming activities,
which are agreed upon by human judges after they
evaluate our results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lerman:2012:USM,
author = "Kristina Lerman and Tad Hogg",
title = "Using Stochastic Models to Describe and Predict Social
Dynamics of {Web} Users",
journal = j-TIST,
volume = "3",
number = "4",
pages = "62:1--62:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337547",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The popularity of content in social media is unequally
distributed, with some items receiving a
disproportionate share of attention from users.
Predicting which newly-submitted items will become
popular is critically important for both the hosts of
social media content and its consumers. Accurate and
timely prediction would enable hosts to maximize
revenue through differential pricing for access to
content or ad placement. Prediction would also give
consumers an important tool for filtering the content.
Predicting the popularity of content in social media is
challenging due to the complex interactions between
content quality and how the social media site
highlights its content. Moreover, most social media
sites selectively present content that has been highly
rated by similar users, whose similarity is indicated
implicitly by their behavior or explicitly by links in
a social network. While these factors make it difficult
to predict popularity a priori, stochastic models of
user behavior on these sites can allow predicting
popularity based on early user reactions to new
content. By incorporating the various mechanisms
through which web sites display content, such models
improve on predictions that are based on simply
extrapolating from the early votes. Specifically, for
one such site, the news aggregator Digg, we show how a
stochastic model distinguishes the effect of the
increased visibility due to the network from how
interested users are in the content. We find a wide
range of interest, distinguishing stories primarily of
interest to users in the network (``niche interests'')
from those of more general interest to the user
community. This distinction is useful for predicting a
story's eventual popularity from users' early reactions
to the story.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yin:2012:LCT,
author = "Zhijun Yin and Liangliang Cao and Quanquan Gu and
Jiawei Han",
title = "Latent Community Topic Analysis: Integration of
Community Discovery with Topic Modeling",
journal = j-TIST,
volume = "3",
number = "4",
pages = "63:1--63:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337548",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article studies the problem of latent community
topic analysis in text-associated graphs. With the
development of social media, a lot of user-generated
content is available with user networks. Along with
rich information in networks, user graphs can be
extended with text information associated with nodes.
Topic modeling is a classic problem in text mining and
it is interesting to discover the latent topics in
text-associated graphs. Different from traditional
topic modeling methods considering links, we
incorporate community discovery into topic analysis in
text-associated graphs to guarantee the topical
coherence in the communities so that users in the same
community are closely linked to each other and share
common latent topics. We handle topic modeling and
community discovery in the same framework. In our model
we separate the concepts of community and topic, so one
community can correspond to multiple topics and
multiple communities can share the same topic. We
compare different methods and perform extensive
experiments on two real datasets. The results confirm
our hypothesis that topics could help understand
community structure, while community structure could
help model topics.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sizov:2012:LGS,
author = "Sergej Sizov",
title = "Latent Geospatial Semantics of Social Media",
journal = j-TIST,
volume = "3",
number = "4",
pages = "64:1--64:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337549",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multimodal understanding of shared content is an
important success factor for many Web 2.0 applications
and platforms. This article addresses the fundamental
question of geo-spatial awareness in social media
applications. In this context, we introduce an approach
for improved characterization of social media by
combining text features (e.g., tags as a prominent
example of short, unstructured text labels) with
spatial knowledge (e.g., geotags, coordinates of
images, and videos). Our model-based framework GeoFolk
combines these two aspects in order to construct better
algorithms for content management, retrieval, and
sharing. We demonstrate in systematic studies the
benefits of this combination for a broad spectrum of
scenarios related to social media: recommender systems,
automatic content organization and filtering, and event
detection. Furthermore, we establish a simple and
technically sound model that can be seen as a reference
baseline for future research in the field of geotagged
social media.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cortizo:2012:ISS,
author = "Jos{\'e} Carlos Cortizo and Francisco Carrero and
Iv{\'a}n Cantador and Jos{\'e} Antonio Troyano and
Paolo Rosso",
title = "Introduction to the Special Section on Search and
Mining User-Generated Content",
journal = j-TIST,
volume = "3",
number = "4",
pages = "65:1--65:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337550",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The primary goal of this special section of ACM
Transactions on Intelligent Systems and Technology is
to foster research in the interplay between Social
Media, Data/Opinion Mining and Search, aiming to
reflect the actual developments in technologies that
exploit user-generated content.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Paltoglou:2012:TMD,
author = "Georgios Paltoglou and Mike Thelwall",
title = "{Twitter}, {MySpace}, {Digg}: Unsupervised Sentiment
Analysis in Social Media",
journal = j-TIST,
volume = "3",
number = "4",
pages = "66:1--66:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337551",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Sentiment analysis is a growing area of research with
significant applications in both industry and academia.
Most of the proposed solutions are centered around
supervised, machine learning approaches and
review-oriented datasets. In this article, we focus on
the more common informal textual communication on the
Web, such as online discussions, tweets and social
network comments and propose an intuitive, less
domain-specific, unsupervised, lexicon-based approach
that estimates the level of emotional intensity
contained in text in order to make a prediction. Our
approach can be applied to, and is tested in, two
different but complementary contexts: subjectivity
detection and polarity classification. Extensive
experiments were carried on three real-world datasets,
extracted from online social Web sites and annotated by
human evaluators, against state-of-the-art supervised
approaches. The results demonstrate that the proposed
algorithm, even though unsupervised, outperforms
machine learning solutions in the majority of cases,
overall presenting a very robust and reliable solution
for sentiment analysis of informal communication on the
Web.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Trivedi:2012:LSB,
author = "Anusua Trivedi and Piyush Rai and Hal {Daum{\'e} III}
and Scott L. Duvall",
title = "Leveraging Social Bookmarks from Partially Tagged
Corpus for Improved {Web} Page Clustering",
journal = j-TIST,
volume = "3",
number = "4",
pages = "67:1--67:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337552",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Automatic clustering of Web pages helps a number of
information retrieval tasks, such as improving user
interfaces, collection clustering, introducing
diversity in search results, etc. Typically, Web page
clustering algorithms use only features extracted from
the page-text. However, the advent of
social-bookmarking Web sites, such as StumbleUpon.com
and Delicious.com, has led to a huge amount of
user-generated content such as the social tag
information that is associated with the Web pages. In
this article, we present a subspace based feature
extraction approach that leverages the social tag
information to complement the page-contents of a Web
page for extracting beter features, with the goal of
improved clustering performance. In our approach, we
consider page-text and tags as two separate views of
the data, and learn a shared subspace that maximizes
the correlation between the two views. Any clustering
algorithm can then be applied in this subspace. We then
present an extension that allows our approach to be
applicable even if the Web page corpus is only
partially tagged, that is, when the social tags are
present for not all, but only for a small number of Web
pages. We compare our subspace based approach with a
number of baselines that use tag information in various
other ways, and show that the subspace based approach
leads to improved performance on the Web page
clustering task. We also discuss some possible future
work including an active learning extension that can
help in choosing which Web pages to get tags for, if we
only can get the social tags for only a small number of
Web pages.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Potthast:2012:IRC,
author = "Martin Potthast and Benno Stein and Fabian Loose and
Steffen Becker",
title = "Information Retrieval in the {Commentsphere}",
journal = j-TIST,
volume = "3",
number = "4",
pages = "68:1--68:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337553",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article studies information retrieval tasks
related to Web comments. Prerequisite of such a study
and a main contribution of the article is a unifying
survey of the research field. We identify the most
important retrieval tasks related to comments, namely
filtering, ranking, and summarization. Within these
tasks, we distinguish two paradigms according to which
comments are utilized and which we designate as
comment-targeting and comment-exploiting. Within the
first paradigm, the comments themselves form the
retrieval targets. Within the second paradigm, the
commented items form the retrieval targets (i.e.,
comments are used as an additional information source
to improve the retrieval performance for the commented
items). We report on four case studies to demonstrate
the exploration of the commentsphere under information
retrieval aspects: comment filtering, comment ranking,
comment summarization and cross-media retrieval. The
first three studies deal primarily with
comment-targeting retrieval, while the last one deals
with comment-exploiting retrieval. Throughout the
article, connections to information retrieval research
are pointed out.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Carmel:2012:RBN,
author = "David Carmel and Haggai Roitman and Elad Yom-Tov",
title = "On the Relationship between Novelty and Popularity of
User-Generated Content",
journal = j-TIST,
volume = "3",
number = "4",
pages = "69:1--69:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337554",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This work deals with the task of predicting the
popularity of user-generated content. We demonstrate
how the novelty of newly published content plays an
important role in affecting its popularity. More
specifically, we study three dimensions of novelty. The
first one, termed contemporaneous novelty, models the
relative novelty embedded in a new post with respect to
contemporary content that was generated by others. The
second type of novelty, termed self novelty, models the
relative novelty with respect to the user's own
contribution history. The third type of novelty, termed
discussion novelty, relates to the novelty of the
comments associated by readers with respect to the post
content. We demonstrate the contribution of the new
novelty measures to estimating blog-post popularity by
predicting the number of comments expected for a fresh
post. We further demonstrate how novelty based measures
can be utilized for predicting the citation volume of
academic papers.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2012:ERQ,
author = "Xiaonan Li and Chengkai Li and Cong Yu",
title = "Entity-Relationship Queries over {Wikipedia}",
journal = j-TIST,
volume = "3",
number = "4",
pages = "70:1--70:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337555",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Wikipedia is the largest user-generated knowledge
base. We propose a structured query mechanism,
entity-relationship query, for searching entities in
the Wikipedia corpus by their properties and
interrelationships. An entity-relationship query
consists of multiple predicates on desired entities.
The semantics of each predicate is specified with
keywords. Entity-relationship query searches entities
directly over text instead of preextracted structured
data stores. This characteristic brings two benefits:
(1) Query semantics can be intuitively expressed by
keywords; (2) It only requires rudimentary entity
annotation, which is simpler than explicitly extracting
and reasoning about complex semantic information before
query-time. We present a ranking framework for general
entity-relationship queries and a position-based
Bounded Cumulative Model (BCM) for accurate ranking of
query answers. We also explore various weighting
schemes for further improving the accuracy of BCM. We
test our ideas on a 2008 version of Wikipedia using a
collection of 45 queries pooled from INEX entity
ranking track and our own crafted queries. Experiments
show that the ranking and weighting schemes are both
effective, particularly on multipredicate queries.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2012:EFW,
author = "Haofen Wang and Linyun Fu and Wei Jin and Yong Yu",
title = "{EachWiki}: Facilitating Wiki Authoring by Annotation
Suggestion",
journal = j-TIST,
volume = "3",
number = "4",
pages = "71:1--71:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337556",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Wikipedia, one of the best-known wikis and the world's
largest free online encyclopedia, has embraced the
power of collaborative editing to harness collective
intelligence. However, using such a wiki to create
high-quality articles is not as easy as people imagine,
given for instance the difficulty of reusing knowledge
already available in Wikipedia. As a result, the heavy
burden of upbuilding and maintaining the ever-growing
online encyclopedia still rests on a small group of
people. In this article, we aim at facilitating wiki
authoring by providing annotation recommendations, thus
lightening the burden of both contributors and
administrators. We leverage the collective wisdom of
the users by exploiting Semantic Web technologies with
Wikipedia data and adopt a unified algorithm to support
link, category, and semantic relation recommendation. A
prototype system named EachWiki is proposed and
evaluated. The experimental results show that it has
achieved considerable improvements in terms of
effectiveness, efficiency and usability. The proposed
approach can also be applied to other wiki-based
collaborative editing systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lampos:2012:NES,
author = "Vasileios Lampos and Nello Cristianini",
title = "Nowcasting Events from the Social {Web} with
Statistical Learning",
journal = j-TIST,
volume = "3",
number = "4",
pages = "72:1--72:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337557",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present a general methodology for inferring the
occurrence and magnitude of an event or phenomenon by
exploring the rich amount of unstructured textual
information on the social part of the Web. Having
geo-tagged user posts on the microblogging service of
Twitter as our input data, we investigate two case
studies. The first consists of a benchmark problem,
where actual levels of rainfall in a given location and
time are inferred from the content of tweets. The
second one is a real-life task, where we infer regional
Influenza-like Illness rates in the effort of detecting
timely an emerging epidemic disease. Our analysis
builds on a statistical learning framework, which
performs sparse learning via the bootstrapped version
of LASSO to select a consistent subset of textual
features from a large amount of candidates. In both
case studies, selected features indicate close semantic
correlation with the target topics and inference,
conducted by regression, has a significant performance,
especially given the short length --approximately one
year-- of Twitter's data time series.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2012:RUI,
author = "Xuning Tang and Christopher C. Yang",
title = "Ranking User Influence in Healthcare Social Media",
journal = j-TIST,
volume = "3",
number = "4",
pages = "73:1--73:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337558",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Due to the revolutionary development of Web 2.0
technology, individual users have become major
contributors of Web content in online social media. In
light of the growing activities, how to measure a
user's influence to other users in online social media
becomes increasingly important. This research need is
urgent especially in the online healthcare community
since positive influence can be beneficial while
negative influence may cause-negative impact on other
users of the same community. In this article, a
research framework was proposed to study user influence
within the online healthcare community. We proposed a
new approach to incorporate users' reply relationship,
conversation content and response immediacy which
capture both explicit and implicit interaction between
users to identify influential users of online
healthcare community. A weighted social network is
developed to represent the influence between users. We
tested our proposed techniques thoroughly on two
medical support forums. Two algorithms UserRank and
Weighted in-degree are benchmarked with PageRank and
in-degree. Experiment results demonstrated the validity
and effectiveness of our proposed approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Strohmaier:2012:EFI,
author = "Markus Strohmaier and Denis Helic and Dominik Benz and
Christian K{\"o}rner and Roman Kern",
title = "Evaluation of Folksonomy Induction Algorithms",
journal = j-TIST,
volume = "3",
number = "4",
pages = "74:1--74:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337559",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Algorithms for constructing hierarchical structures
from user-generated metadata have caught the interest
of the academic community in recent years. In social
tagging systems, the output of these algorithms is
usually referred to as folksonomies (from
folk-generated taxonomies). Evaluation of folksonomies
and folksonomy induction algorithms is a challenging
issue complicated by the lack of golden standards, lack
of comprehensive methods and tools as well as a lack of
research and empirical/simulation studies applying
these methods. In this article, we report results from
a broad comparative study of state-of-the-art
folksonomy induction algorithms that we have applied
and evaluated in the context of five social tagging
systems. In addition to adopting semantic evaluation
techniques, we present and adopt a new technique that
can be used to evaluate the usefulness of folksonomies
for navigation. Our work sheds new light on the
properties and characteristics of state-of-the-art
folksonomy induction algorithms and introduces a new
pragmatic approach to folksonomy evaluation, while at
the same time identifying some important limitations
and challenges of folksonomy evaluation. Our results
show that folksonomy induction algorithms specifically
developed to capture intuitions of social tagging
systems outperform traditional hierarchical clustering
techniques. To the best of our knowledge, this work
represents the largest and most comprehensive
evaluation study of state-of-the-art folksonomy
induction algorithms to date.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:EAL,
author = "Xiaoqin Shelley Zhang and Bhavesh Shrestha and
Sungwook Yoon and Subbarao Kambhampati and Phillip
DiBona and Jinhong K. Guo and Daniel McFarlane and
Martin O. Hofmann and Kenneth Whitebread and Darren
Scott Appling and Elizabeth T. Whitaker and Ethan B.
Trewhitt and Li Ding and James R. Michaelis and Deborah
L. McGuinness and James A. Hendler and Janardhan Rao
Doppa and Charles Parker and Thomas G. Dietterich and
Prasad Tadepalli and Weng-Keen Wong and Derek Green and
Anton Rebguns and Diana Spears and Ugur Kuter and Geoff
Levine and Gerald DeJong and Reid L. MacTavish and
Santiago Onta{\~n}{\'o}n and Jainarayan Radhakrishnan
and Ashwin Ram and Hala Mostafa and Huzaifa Zafar and
Chongjie Zhang and Daniel Corkill and Victor Lesser and
Zhexuan Song",
title = "An Ensemble Architecture for Learning Complex
Problem-Solving Techniques from Demonstration",
journal = j-TIST,
volume = "3",
number = "4",
pages = "75:1--75:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337560",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present a novel ensemble architecture for learning
problem-solving techniques from a very small number of
expert solutions and demonstrate its effectiveness in a
complex real-world domain. The key feature of our
``Generalized Integrated Learning Architecture'' (GILA)
is a set of heterogeneous independent learning and
reasoning (ILR) components, coordinated by a central
meta-reasoning executive (MRE). The ILRs are weakly
coupled in the sense that all coordination during
learning and performance happens through the MRE. Each
ILR learns independently from a small number of expert
demonstrations of a complex task. During performance,
each ILR proposes partial solutions to subproblems
posed by the MRE, which are then selected from and
pieced together by the MRE to produce a complete
solution. The heterogeneity of the learner-reasoners
allows both learning and problem solving to be more
effective because their abilities and biases are
complementary and synergistic. We describe the
application of this novel learning and problem solving
architecture to the domain of airspace management,
where multiple requests for the use of airspaces need
to be deconflicted, reconciled, and managed
automatically. Formal evaluations show that our system
performs as well as or better than humans after
learning from the same training data. Furthermore, GILA
outperforms any individual ILR run in isolation, thus
demonstrating the power of the ensemble architecture
for learning and problem solving.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "75",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2012:LCR,
author = "Zhenxing Wang and Laiwan Chan",
title = "Learning Causal Relations in Multivariate Time Series
Data",
journal = j-TIST,
volume = "3",
number = "4",
pages = "76:1--76:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337561",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many applications naturally involve time series data
and the vector autoregression (VAR), and the structural
VAR (SVAR) are dominant tools to investigate relations
between variables in time series. In the first part of
this work, we show that the SVAR method is incapable of
identifying contemporaneous causal relations for
Gaussian process. In addition, least squares estimators
become unreliable when the scales of the problems are
large and observations are limited. In the remaining
part, we propose an approach to apply Bayesian network
learning algorithms to identify SVARs from time series
data in order to capture both temporal and
contemporaneous causal relations, and avoid high-order
statistical tests. The difficulty of applying Bayesian
network learning algorithms to time series is that the
sizes of the networks corresponding to time series tend
to be large, and high-order statistical tests are
required by Bayesian network learning algorithms in
this case. To overcome the difficulty, we show that the
search space of conditioning sets d-separating two
vertices should be a subset of the Markov blankets.
Based on this fact, we propose an algorithm enabling us
to learn Bayesian networks locally, and make the
largest order of statistical tests independent of the
scales of the problems. Empirical results show that our
algorithm outperforms existing methods in terms of both
efficiency and accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "76",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mandrake:2012:SSD,
author = "Lukas Mandrake and Umaa Rebbapragada and Kiri L.
Wagstaff and David Thompson and Steve Chien and Daniel
Tran and Robert T. Pappalardo and Damhnait Gleeson and
Rebecca Casta{\~n}o",
title = "Surface Sulfur Detection via Remote Sensing and
Onboard Classification",
journal = j-TIST,
volume = "3",
number = "4",
pages = "77:1--77:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337562",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Orbital remote sensing provides a powerful way to
efficiently survey targets such as the Earth and other
planets and moons for features of interest. One such
feature of astrobiological relevance is the presence of
surface sulfur deposits. These deposits have been
observed to be associated with microbial activity at
the Borup Fiord glacial springs in Canada, a location
that may provide an analogue to other icy environments
such as Europa. This article evaluates automated
classifiers for detecting sulfur in remote sensing
observations by the hyperion spectrometer on the EO-1
spacecraft. We determined that a data-driven machine
learning solution was needed because the sulfur could
not be detected by simply matching observations to
sulfur lab spectra. We also evaluated several methods
(manual and automated) for identifying the most
relevant attributes (spectral wavelengths) needed for
successful sulfur detection. Our findings include (1)
the Borup Fiord sulfur deposits were best modeled as
containing two sub-populations: sulfur on ice and
sulfur on rock; (2) as expected, classifiers using
Gaussian kernels outperformed those based on linear
kernels, and should be adopted when onboard
computational constraints permit; and (3) Recursive
Feature Elimination selected sensible and effective
features for use in the computationally constrained
environment onboard EO-1. This study helped guide the
selection of algorithm parameters and configuration for
the classification system currently operational on
EO-1. Finally, we discuss implications for a similar
onboard classification system for a future Europa
orbiter.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "77",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{King:2013:ISS,
author = "Irwin King and Wolfgang Nejdl",
title = "Introduction to the special section on {Twitter} and
microblogging services",
journal = j-TIST,
volume = "4",
number = "1",
pages = "1:1--1:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414426",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cheng:2013:CDF,
author = "Zhiyuan Cheng and James Caverlee and Kyumin Lee",
title = "A content-driven framework for geolocating microblog
users",
journal = j-TIST,
volume = "4",
number = "1",
pages = "2:1--2:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414427",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Highly dynamic real-time microblog systems have
already published petabytes of real-time human sensor
data in the form of status updates. However, the lack
of user adoption of geo-based features per user or per
post signals that the promise of microblog services as
location-based sensing systems may have only limited
reach and impact. Thus, in this article, we propose and
evaluate a probabilistic framework for estimating a
microblog user's location based purely on the content
of the user's posts. Our framework can overcome the
sparsity of geo-enabled features in these services and
bring augmented scope and breadth to emerging
location-based personalized information services. Three
of the key features of the proposed approach are: (i)
its reliance purely on publicly available content; (ii)
a classification component for automatically
identifying words in posts with a strong local
geo-scope; and (iii) a lattice-based neighborhood
smoothing model for refining a user's location
estimate. On average we find that the location
estimates converge quickly, placing 51\% of users
within 100 miles of their actual location.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2013:NER,
author = "Xiaohua Liu and Furu Wei and Shaodian Zhang and Ming
Zhou",
title = "Named entity recognition for tweets",
journal = j-TIST,
volume = "4",
number = "1",
pages = "3:1--3:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414428",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Two main challenges of Named Entity Recognition (NER)
for tweets are the insufficient information in a tweet
and the lack of training data. We propose a novel
method consisting of three core elements: (1)
normalization of tweets; (2) combination of a K-Nearest
Neighbors (KNN) classifier with a linear Conditional
Random Fields (CRF) model; and (3) semisupervised
learning framework. The tweet normalization
preprocessing corrects common ill-formed words using a
global linear model. The KNN-based classifier conducts
prelabeling to collect global coarse evidence across
tweets while the CRF model conducts sequential labeling
to capture fine-grained information encoded in a tweet.
The semisupervised learning plus the gazetteers
alleviate the lack of training data. Extensive
experiments show the advantages of our method over the
baselines as well as the effectiveness of
normalization, KNN, and semisupervised learning.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chang:2013:IRR,
author = "Yi Chang and Anlei Dong and Pranam Kolari and Ruiqiang
Zhang and Yoshiyuki Inagaki and Fernanodo Diaz and
Hongyuan Zha and Yan Liu",
title = "Improving recency ranking using {Twitter} data",
journal = j-TIST,
volume = "4",
number = "1",
pages = "4:1--4:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414429",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In Web search and vertical search, recency ranking
refers to retrieving and ranking documents by both
relevance and freshness. As impoverished in-links and
click information is the biggest challenge for recency
ranking, we advocate the use of Twitter data to address
the challenge in this article. We propose a method to
utilize Twitter TinyURL to detect fresh and
high-quality documents, and leverage Twitter data to
generate novel and effective features for ranking. The
empirical experiments demonstrate that the proposed
approach effectively improves a commercial search
engine for both Web search ranking and tweet vertical
ranking.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Han:2013:LNS,
author = "Bo Han and Paul Cook and Timothy Baldwin",
title = "Lexical normalization for social media text",
journal = j-TIST,
volume = "4",
number = "1",
pages = "5:1--5:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414430",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Twitter provides access to large volumes of data in
real time, but is notoriously noisy, hampering its
utility for NLP. In this article, we target
out-of-vocabulary words in short text messages and
propose a method for identifying and normalizing
lexical variants. Our method uses a classifier to
detect lexical variants, and generates correction
candidates based on morphophonemic similarity. Both
word similarity and context are then exploited to
select the most probable correction candidate for the
word. The proposed method doesn't require any
annotations, and achieves state-of-the-art performance
over an SMS corpus and a novel dataset based on
Twitter.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shen:2013:RUT,
author = "Keyi Shen and Jianmin Wu and Ya Zhang and Yiping Han
and Xiaokang Yang and Li Song and Xiao Gu",
title = "Reorder user's tweets",
journal = j-TIST,
volume = "4",
number = "1",
pages = "6:1--6:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414431",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Twitter displays the tweets a user received in a
reversed chronological order, which is not always the
best choice. As Twitter is full of messages of very
different qualities, many informative or relevant
tweets might be flooded or displayed at the bottom
while some nonsense buzzes might be ranked higher. In
this work, we present a supervised learning method for
personalized tweets reordering based on user interests.
User activities on Twitter, in terms of tweeting,
retweeting, and replying, are leveraged to obtain the
training data for reordering models. Through exploring
a rich set of social and personalized features, we
model the relevance of tweets by minimizing the
pairwise loss of relevant and irrelevant tweets. The
tweets are then reordered according to the predicted
relevance scores. Experimental results with real
Twitter user activities demonstrated the effectiveness
of our method. The new method achieved above 30\%
accuracy gain compared with the default ordering in
Twitter based on time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guy:2013:ISS,
author = "Ido Guy and Li Chen and Michelle X. Zhou",
title = "Introduction to the special section on social
recommender systems",
journal = j-TIST,
volume = "4",
number = "1",
pages = "7:1--7:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414432",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Quijano-Sanchez:2013:SFG,
author = "Lara Quijano-Sanchez and Juan A. Recio-Garcia and
Belen Diaz-Agudo and Guillermo Jimenez-Diaz",
title = "Social factors in group recommender systems",
journal = j-TIST,
volume = "4",
number = "1",
pages = "8:1--8:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414433",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article we review the existing techniques in
group recommender systems and we propose some
improvement based on the study of the different
individual behaviors when carrying out a
decision-making process. Our method includes an
analysis of group personality composition and trust
between each group member to improve the accuracy of
group recommenders. This way we simulate the
argumentation process followed by groups of people when
agreeing on a common activity in a more realistic way.
Moreover, we reflect how they expect the system to
behave in a long term recommendation process. This is
achieved by including a memory of past recommendations
that increases the satisfaction of users whose
preferences have not been taken into account in
previous recommendations.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2013:GVR,
author = "Weishi Zhang and Guiguang Ding and Li Chen and
Chunping Li and Chengbo Zhang",
title = "Generating virtual ratings from {Chinese} reviews to
augment online recommendations",
journal = j-TIST,
volume = "4",
number = "1",
pages = "9:1--9:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414434",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative filtering (CF) recommenders based on
User-Item rating matrix as explicitly obtained from end
users have recently appeared promising in recommender
systems. However, User-Item rating matrix is not always
available or very sparse in some web applications,
which has critical impact to the application of CF
recommenders. In this article we aim to enhance the
online recommender system by fusing virtual ratings as
derived from user reviews. Specifically, taking into
account of Chinese reviews' characteristics, we propose
to fuse the self-supervised emotion-integrated
sentiment classification results into CF recommenders,
by which the User-Item Rating Matrix can be inferred by
decomposing item reviews that users gave to the items.
The main advantage of this approach is that it can
extend CF recommenders to some web applications without
user rating information. In the experiments, we have
first identified the self-supervised sentiment
classification's higher precision and recall by
comparing it with traditional classification methods.
Furthermore, the classification results, as behaving as
virtual ratings, were incorporated into both user-based
and item-based CF algorithms. We have also conducted an
experiment to evaluate the proximity between the
virtual and real ratings and clarified the
effectiveness of the virtual ratings. The experimental
results demonstrated the significant impact of virtual
ratings on increasing system's recommendation accuracy
in different data conditions (i.e., conditions with
real ratings and without).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Biancalana:2013:ASR,
author = "Claudio Biancalana and Fabio Gasparetti and Alessandro
Micarelli and Giuseppe Sansonetti",
title = "An approach to social recommendation for context-aware
mobile services",
journal = j-TIST,
volume = "4",
number = "1",
pages = "10:1--10:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414435",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Nowadays, several location-based services (LBSs) allow
their users to take advantage of information from the
Web about points of interest (POIs) such as cultural
events or restaurants. To the best of our knowledge,
however, none of these provides information taking into
account user preferences, or other elements, in
addition to location, that contribute to define the
context of use. The provided suggestions do not
consider, for example, time, day of week, weather, user
activity or means of transport. This article describes
a social recommender system able to identify user
preferences and information needs, thus suggesting
personalized recommendations related to POIs in the
surroundings of the user's current location. The
proposed approach achieves the following goals: (i) to
supply, unlike the current LBSs, a methodology for
identifying user preferences and needs to be used in
the information filtering process; (ii) to exploit the
ever-growing amount of information from social
networking, user reviews, and local search Web sites;
(iii) to establish procedures for defining the context
of use to be employed in the recommendation of POIs
with low effort. The flexibility of the architecture is
such that our approach can be easily extended to any
category of POI. Experimental tests carried out on real
users enabled us to quantify the benefits of the
proposed approach in terms of performance
improvement.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gedikli:2013:IRA,
author = "Fatih Gedikli and Dietmar Jannach",
title = "Improving recommendation accuracy based on
item-specific tag preferences",
journal = j-TIST,
volume = "4",
number = "1",
pages = "11:1--11:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414436",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, different proposals have been made to
exploit Social Web tagging information to build more
effective recommender systems. The tagging data, for
example, were used to identify similar users or were
viewed as additional information about the
recommendable items. Recent research has indicated that
``attaching feelings to tags'' is experienced by users
as a valuable means to express which features of an
item they particularly like or dislike. When following
such an approach, users would therefore not only add
tags to an item as in usual Web 2.0 applications, but
also attach a preference ( affect ) to the tag itself,
expressing, for example, whether or not they liked a
certain actor in a given movie. In this work, we show
how this additional preference data can be exploited by
a recommender system to make more accurate predictions.
In contrast to previous work, which also relied on
so-called tag preferences to enhance the predictive
accuracy of recommender systems, we argue that tag
preferences should be considered in the context of an
item. We therefore propose new schemes to infer and
exploit context-specific tag preferences in the
recommendation process. An evaluation on two different
datasets reveals that our approach is capable of
providing more accurate recommendations than previous
tag-based recommender algorithms and recent
tag-agnostic matrix factorization techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:MRW,
author = "Yu-Chih Chen and Yu-Shi Lin and Yu-Chun Shen and
Shou-De Lin",
title = "A modified random walk framework for handling negative
ratings and generating explanations",
journal = j-TIST,
volume = "4",
number = "1",
pages = "12:1--12:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414437",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The concept of random walk (RW) has been widely
applied in the design of recommendation systems.
RW-based approaches are effective in handling locality
problem and taking extra information, such as the
relationships between items or users, into
consideration. However, the traditional RW-based
approach has a serious limitation in handling
bidirectional opinions. The propagation of positive and
negative information simultaneously in a graph is
nontrivial using random walk. To address the problem,
this article presents a novel and efficient RW-based
model that can handle both positive and negative
comments with the guarantee of convergence.
Furthermore, we argue that a good recommendation system
should provide users not only a list of recommended
items but also reasonable explanations for the
decisions. Therefore, we propose a technique that
generates explanations by backtracking the influential
paths and subgraphs. The results of experiments on the
MovieLens and Netflix datasets show that our model
significantly outperforms state-of-the-art RW-based
algorithms, and is capable of improving the overall
performance in the ensemble with other models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Said:2013:MRC,
author = "Alan Said and Shlomo Berkovsky and Ernesto W. {De
Luca}",
title = "Movie recommendation in context",
journal = j-TIST,
volume = "4",
number = "1",
pages = "13:1--13:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414438",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The challenge and workshop on Context-Aware Movie
Recommendation (CAMRa2010) were conducted jointly in
2010 with the Recommender Systems conference. The
challenge focused on three context-aware recommendation
scenarios: time-based, mood-based, and social
recommendation. The participants were provided with
anonymized datasets from two real-world online movie
recommendation communities and competed against each
other for obtaining the highest accuracy of
recommendations. The datasets contained contextual
features, such as tags, annotation, social
relationsips, and comments, normally not available in
public recommendation datasets. More than 40 teams from
21 countries participated in the challenge. Their
participation was summarized by 10 papers published by
the workshop, which have been extended and revised for
this special section. In this preface we overview the
challenge datasets, tasks, evaluation metrics, and the
obtained outcomes.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bellogin:2013:ECS,
author = "Alejandro Bellog{\'\i}n and Iv{\'a}n Cantador and
Fernando D{\'\i}ez and Pablo Castells and Enrique
Chavarriaga",
title = "An empirical comparison of social, collaborative
filtering, and hybrid recommenders",
journal = j-TIST,
volume = "4",
number = "1",
pages = "14:1--14:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414439",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In the Social Web, a number of diverse recommendation
approaches have been proposed to exploit the user
generated contents available in the Web, such as
rating, tagging, and social networking information. In
general, these approaches naturally require the
availability of a wide amount of these user
preferences. This may represent an important limitation
for real applications, and may be somewhat unnoticed in
studies focusing on overall precision, in which a
failure to produce recommendations gets blurred when
averaging the obtained results or, even worse, is just
not accounted for, as users with no recommendations are
typically excluded from the performance calculations.
In this article, we propose a coverage metric that
uncovers and compensates for the incompleteness of
performance evaluations based only on precision. We use
this metric together with precision metrics in an
empirical comparison of several social, collaborative
filtering, and hybrid recommenders. The obtained
results show that a better balance between precision
and coverage can be achieved by combining social-based
filtering (high accuracy, low coverage) and
collaborative filtering (low accuracy, high coverage)
recommendation techniques. We thus explore several
hybrid recommendation approaches to balance this
trade-off. In particular, we compare, on the one hand,
techniques integrating collaborative and social
information into a single model, and on the other,
linear combinations of recommenders. For the last
approach, we also propose a novel strategy to
dynamically adjust the weight of each recommender on a
user-basis, utilizing graph measures as indicators of
the target user's connectedness and relevance in a
social network.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2013:STC,
author = "Nathan N. Liu and Luheng He and Min Zhao",
title = "Social temporal collaborative ranking for context
aware movie recommendation",
journal = j-TIST,
volume = "4",
number = "1",
pages = "15:1--15:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414440",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Most existing collaborative filtering models only
consider the use of user feedback (e.g., ratings) and
meta data (e.g., content, demographics). However, in
most real world recommender systems, context
information, such as time and social networks, are also
very important factors that could be considered in
order to produce more accurate recommendations. In this
work, we address several challenges for the context
aware movie recommendation tasks in CAMRa 2010: (1) how
to combine multiple heterogeneous forms of user
feedback? (2) how to cope with dynamic user and item
characteristics? (3) how to capture and utilize social
connections among users? For the first challenge, we
propose a novel ranking based matrix factorization
model to aggregate explicit and implicit user feedback.
For the second challenge, we extend this model to a
sequential matrix factorization model to enable
time-aware parametrization. Finally, we introduce a
network regularization function to constrain user
parameters based on social connections. To the best of
our knowledge, this is the first study that
investigates the collective modeling of social and
temporal dynamics. Experiments on the CAMRa 2010
dataset demonstrated clear improvements over many
baselines.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2013:MCM,
author = "Yue Shi and Martha Larson and Alan Hanjalic",
title = "Mining contextual movie similarity with matrix
factorization for context-aware recommendation",
journal = j-TIST,
volume = "4",
number = "1",
pages = "16:1--16:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414441",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Context-aware recommendation seeks to improve
recommendation performance by exploiting various
information sources in addition to the conventional
user-item matrix used by recommender systems. We
propose a novel context-aware movie recommendation
algorithm based on joint matrix factorization (JMF). We
jointly factorize the user-item matrix containing
general movie ratings and other contextual movie
similarity matrices to integrate contextual information
into the recommendation process. The algorithm was
developed within the scope of the mood-aware
recommendation task that was offered by the Moviepilot
mood track of the 2010 context-aware movie
recommendation (CAMRa) challenge. Although the
algorithm could generalize to other types of contextual
information, in this work, we focus on two: movie mood
tags and movie plot keywords. Since the objective in
this challenge track is to recommend movies for a user
given a specified mood, we devise a novel mood-specific
movie similarity measure for this purpose. We enhance
the recommendation based on this measure by also
deploying the second movie similarity measure proposed
in this article that takes into account the movie plot
keywords. We validate the effectiveness of the proposed
JMF algorithm with respect to the recommendation
performance by carrying out experiments on the
Moviepilot challenge dataset. We demonstrate that
exploiting contextual information in JMF leads to
significant improvement over several state-of-the-art
approaches that generate movie recommendations without
using contextual information. We also demonstrate that
our proposed mood-specific movie similarity is better
suited for the task than the conventional mood-based
movie similarity measures. Finally, we show that the
enhancement provided by the movie similarity capturing
the plot keywords is particularly helpful in improving
the recommendation to those users who are significantly
more active in rating the movies than other users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Okada:2013:MDA,
author = "Isamu Okada and Hitoshi Yamamoto",
title = "Mathematical description and analysis of adaptive risk
choice behavior",
journal = j-TIST,
volume = "4",
number = "1",
pages = "17:1--17:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414442",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Which risk should one choose when facing alternatives
with different levels of risk? We discuss here adaptive
processes in such risk choice behavior by generalizing
the study of Roos et al. [2010]. We deal with an n
-choice game in which every player sequentially chooses
n times of lotteries of which there are two types: a
safe lottery and a risky lottery. We analyze this model
in more detail by elaborating the game. Based on the
results of mathematical analysis, replicator dynamics
analysis, and numerical simulations, we derived some
salient features of risk choice behavior. We show that
all the risk strategies can be divided into two groups:
persistence and nonpersistence. We also proved that the
dynamics with perturbation in which a mutation is
installed is globally asymptotically stable to a unique
equilibrium point for any initial population. The
numerical simulations clarify that the number of
persistent strategies seldom increases regardless of
the increase in n, and suggest that a rarity of
dominant choice strategies is widely observed in many
social contexts. These facts not only go hand-in-hand
with some well-known insights from prospect theory, but
may also provide some theoretical hypotheses for
various fields such as behavioral economics, ecology,
sociology, and consumer behavioral theory.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Song:2013:OSM,
author = "Xuan Song and Huijing Zhao and Jinshi Cui and Xiaowei
Shao and Ryosuke Shibasaki and Hongbin Zha",
title = "An online system for multiple interacting targets
tracking: Fusion of laser and vision, tracking and
learning",
journal = j-TIST,
volume = "4",
number = "1",
pages = "18:1--18:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414443",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multitarget tracking becomes significantly more
challenging when the targets are in close proximity or
frequently interact with each other. This article
presents a promising online system to deal with these
problems. The novelty of this system is that laser and
vision are integrated with tracking and online learning
to complement each other in one framework: when the
targets do not interact with each other, the
laser-based independent trackers are employed and the
visual information is extracted simultaneously to train
some classifiers online for ``possible interacting
targets''. When the targets are in close proximity, the
classifiers learned online are used alongside visual
information to assist in tracking. Therefore, this mode
of cooperation not only deals with various tough
problems encountered in tracking, but also ensures that
the entire process can be completely online and
automatic. Experimental results demonstrate that laser
and vision fully display their respective advantages in
our system, and it is easy for us to obtain a good
trade-off between tracking accuracy and the time-cost
factor.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chopra:2013:ISS,
author = "Amit K. Chopra and Alexander Artikis and Jamal
Bentahar and Frank Dignum",
title = "Introduction to the special section on agent
communication",
journal = j-TIST,
volume = "4",
number = "2",
pages = "19:1--19:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chopra:2013:RDA,
author = "Amit K. Chopra and Alexander Artikis and Jamal
Bentahar and Marco Colombetti and Frank Dignum and
Nicoletta Fornara and Andrew J. I. Jones and Munindar
P. Singh and Pinar Yolum",
title = "Research directions in agent communication",
journal = j-TIST,
volume = "4",
number = "2",
pages = "20:1--20:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Increasingly, software engineering involves open
systems consisting of autonomous and heterogeneous
participants or agents who carry out loosely coupled
interactions. Accordingly, understanding and specifying
communications among agents is a key concern. A focus
on ways to formalize meaning distinguishes agent
communication from traditional distributed computing:
meaning provides a basis for flexible interactions and
compliance checking. Over the years, a number of
approaches have emerged with some essential and some
irrelevant distinctions drawn among them. As agent
abstractions gain increasing traction in the software
engineering of open systems, it is important to resolve
the irrelevant and highlight the essential
distinctions, so that future research can be focused in
the most productive directions. This article is an
outcome of extensive discussions among agent
communication researchers, aimed at taking stock of the
field and at developing, criticizing, and refining
their positions on specific approaches and future
challenges. This article serves some important
purposes, including identifying (1) points of broad
consensus; (2) points where substantive differences
remain; and (3) interesting directions of future
work.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gerard:2013:FVP,
author = "Scott N. Gerard and Munindar P. Singh",
title = "Formalizing and verifying protocol refinements",
journal = j-TIST,
volume = "4",
number = "2",
pages = "21:1--21:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A (business) protocol describes, in high-level terms,
a pattern of communication between two or more
participants, specifically via the creation and
manipulation of the commitments between them. In this
manner, a protocol offers both flexibility and rigor: a
participant may communicate in any way it chooses as
long as it discharges all of its activated commitments.
Protocols thus promise benefits in engineering
cross-organizational business processes. However,
software engineering using protocols presupposes a
formalization of protocols and a notion of the
refinement of one protocol by another. Refinement for
protocols is both intuitively obvious (e.g.,
PayViaCheck is clearly a kind of Pay ) and technically
nontrivial (e.g., compared to Pay, PayViaCheck involves
different participants exchanging different messages).
This article formalizes protocols and their refinement.
It develops Proton, an analysis tool for protocol
specifications that overlays a model checker to compute
whether one protocol refines another with respect to a
stated mapping. Proton and its underlying theory are
evaluated by formalizing several protocols from the
literature and verifying all and only the expected
refinements.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Baldoni:2013:CRS,
author = "Matteo Baldoni and Cristina Baroglio and Elisa Marengo
and Viviana Patti",
title = "Constitutive and regulative specifications of
commitment protocols: a decoupled approach",
journal = j-TIST,
volume = "4",
number = "2",
pages = "22:1--22:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Interaction protocols play a fundamental role in
multiagent systems. In this work, after analyzing the
trends that are emerging not only from research on
multiagent interaction protocols but also from
neighboring fields, like research on workflows and
business processes, we propose a novel definition of
commitment-based interaction protocols, that is
characterized by the decoupling of the constitutive and
the regulative specifications and that explicitly
foresees a representation of the latter based on
constraints among commitments. A clear distinction
between the two representations has many advantages,
mainly residing in a greater openness of multiagent
systems, and an easier reuse of protocols and of action
definitions. A language, named 2CL, for writing
regulative specifications is also given together with a
designer-oriented graphical notation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Falcone:2013:ISS,
author = "Rino Falcone and Munindar P. Singh",
title = "Introduction to special section on trust in multiagent
systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "23:1--23:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2013:FTM,
author = "Jie Zhang and Robin Cohen",
title = "A framework for trust modeling in multiagent
electronic marketplaces with buying advisors to
consider varying seller behavior and the limiting of
seller bids",
journal = j-TIST,
volume = "4",
number = "2",
pages = "24:1--24:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we present a framework of use in
electronic marketplaces that allows buying agents to
model the trustworthiness of selling agents in an
effective way, making use of seller ratings provided by
other buying agents known as advisors. The
trustworthiness of the advisors is also modeled, using
an approach that combines both personal and public
knowledge and allows the relative weighting to be
adjusted over time. Through a series of experiments
that simulate e-marketplaces, including ones where
sellers may vary their behavior over time, we are able
to demonstrate that our proposed framework delivers
effective seller recommendations to buyers, resulting
in important buyer profit. We also propose limiting
seller bids as a method for promoting seller honesty,
thus facilitating successful selection of sellers by
buyers, and demonstrate the value of this approach
through experimental results. Overall, this research is
focused on the technological aspects of electronic
commerce and specifically on technology that would be
used to manage trust.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Erriquez:2013:BUS,
author = "Elisabetta Erriquez and Wiebe van der Hoek and Michael
Wooldridge",
title = "Building and using social structures: a case study
using the agent {ART} testbed",
journal = j-TIST,
volume = "4",
number = "2",
pages = "25:1--25:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article investigates the conjecture that agents
who make decisions in scenarios where trust is
important can benefit from the use of a social
structure, representing the social relationships that
exist between agents. We propose techniques that can be
used by agents to initially build and then
progressively update such a structure in the light of
experience. We describe an implementation of our
techniques in the domain of the Agent ART testbed: we
take two existing agents for this domain (``Simplet''
and ``Connected'') and compare their performance with
versions that use our social structure
(``SocialSimplet'' and ``SocialConnected''). We show
that SocialSimplet and SocialConnected outperform their
counterparts with respect to the quality of the
interactions, the number of rounds won in a
competition, and the total utility gained.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Burnett:2013:STB,
author = "Chris Burnett and Timothy J. Norman and Katia Sycara",
title = "Stereotypical trust and bias in dynamic multiagent
systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "26:1--26:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Large-scale multiagent systems have the potential to
be highly dynamic. Trust and reputation are crucial
concepts in these environments, as it may be necessary
for agents to rely on their peers to perform as
expected, and learn to avoid untrustworthy partners.
However, aspects of highly dynamic systems introduce
issues which make the formation of trust relationships
difficult. For example, they may be short-lived,
precluding agents from gaining the necessary
experiences to make an accurate trust evaluation. This
article describes a new approach, inspired by theories
of human organizational behavior, whereby agents
generalize their experiences with previously
encountered partners as stereotypes, based on the
observable features of those partners and their
behaviors. Subsequently, these stereotypes are applied
when evaluating new and unknown partners. Furthermore,
these stereotypical opinions can be communicated within
the society, resulting in the notion of stereotypical
reputation. We show how this approach can complement
existing state-of-the-art trust models, and enhance the
confidence in the evaluations that can be made about
trustees when direct and reputational information is
lacking or limited. Furthermore, we show how a
stereotyping approach can help agents detect unwanted
biases in the reputational opinions they receive from
others in the society.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Falcone:2013:MKR,
author = "Rino Falcone and Michele Piunti and Matteo Venanzi and
Cristiano Castelfranchi",
title = "From manifesta to krypta: The relevance of categories
for trusting others",
journal = j-TIST,
volume = "4",
number = "2",
pages = "27:1--27:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article we consider the special abilities
needed by agents for assessing trust based on inference
and reasoning. We analyze the case in which it is
possible to infer trust towards unknown counterparts by
reasoning on abstract classes or categories of agents
shaped in a concrete application domain. We present a
scenario of interacting agents providing a
computational model implementing different strategies
to assess trust. Assuming a medical domain, categories,
including both competencies and dispositions of
possible trustees, are exploited to infer trust towards
possibly unknown counterparts. The proposed approach
for the cognitive assessment of trust relies on agents'
abilities to analyze heterogeneous information sources
along different dimensions. Trust is inferred based on
specific observable properties (manifesta), namely
explicitly readable signals indicating internal
features (krypta) regulating agents' behavior and
effectiveness on specific tasks. Simulative experiments
evaluate the performance of trusting agents adopting
different strategies to delegate tasks to possibly
unknown trustees, while experimental results show the
relevance of this kind of cognitive ability in the case
of open multiagent systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2013:ISS,
author = "Qing Li and Xiangfeng Luo and Liu Wenyin and Cristina
Conati",
title = "Introduction to the special section on intelligent
tutoring and coaching systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "28:1--28:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Folsom-Kovarik:2013:TPR,
author = "Jeremiah T. Folsom-Kovarik and Gita Sukthankar and Sae
Schatz",
title = "Tractable {POMDP} representations for intelligent
tutoring systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "29:1--29:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With Partially Observable Markov Decision Processes
(POMDPs), Intelligent Tutoring Systems (ITSs) can model
individual learners from limited evidence and plan
ahead despite uncertainty. However, POMDPs need
appropriate representations to become tractable in ITSs
that model many learner features, such as mastery of
individual skills or the presence of specific
misconceptions. This article describes two POMDP
representations- state queues and observation chains
-that take advantage of ITS task properties and let
POMDPs scale to represent over 100 independent learner
features. A real-world military training problem is
given as one example. A human study ( n = 14) provides
initial validation for the model construction. Finally,
evaluating the experimental representations with
simulated students helps predict their impact on ITS
performance. The compressed representations can model a
wide range of simulated problems with instructional
efficacy equal to lossless representations. With
improved tractability, POMDP ITSs can accommodate more
numerous or more detailed learner states and inputs.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yen:2013:LIS,
author = "Neil Y. Yen and Timothy K. Shih and Qun Jin",
title = "{LONET}: an interactive search network for intelligent
lecture path generation",
journal = j-TIST,
volume = "4",
number = "2",
pages = "30:1--30:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Sharing resources and information on the Internet has
become an important activity for education. In distance
learning, instructors can benefit from resources, also
known as Learning Objects (LOs), to create plenteous
materials for specific learning purposes. Our
repository (called the MINE Registry) has been
developed for storing and sharing learning objects,
around 22,000 in total, in the past few years. To
enhance reusability, one significant concept named
Reusability Tree was implemented to trace the process
of changes. Also, weighting and ranking metrics have
been proposed to enhance the searchability in the
repository. Following the successful implementation,
this study goes further to investigate the
relationships between LOs from a perspective of social
networks. The LONET (Learning Object Network), as an
extension of Reusability Tree, is newly proposed and
constructed to clarify the vague reuse scenario in the
past, and to summarize collaborative intelligence
through past interactive usage experiences. We define a
social structure in our repository based on past usage
experiences from instructors, by proposing a set of
metrics to evaluate the interdependency such as
prerequisites and references. The structure identifies
usage experiences and can be graphed in terms of
implicit and explicit relations among learning objects.
As a practical contribution, an adaptive algorithm is
proposed to mine the social structure in our
repository. The algorithm generates adaptive routes,
based on past usage experiences, by computing possible
interactive input, such as search criteria and feedback
from instructors, and assists them in generating
specific lectures.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ehara:2013:PRS,
author = "Yo Ehara and Nobuyuki Shimizu and Takashi Ninomiya and
Hiroshi Nakagawa",
title = "Personalized reading support for second-language {Web}
documents",
journal = j-TIST,
volume = "4",
number = "2",
pages = "31:1--31:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A novel intelligent interface eases the browsing of
Web documents written in the second languages of users.
It automatically predicts words unfamiliar to the user
by a collective intelligence method and glosses them
with their meaning in advance. If the prediction
succeeds, the user does not need to consult a
dictionary; even if it fails, the user can correct the
prediction. The correction data are collected and used
to improve the accuracy of further predictions. The
prediction is personalized in that every user's
language ability is estimated by a state-of-the-art
language testing model, which is trained in a practical
response time with only a small sacrifice of prediction
accuracy. The system was evaluated in terms of
prediction accuracy and reading simulation. The reading
simulation results show that this system can reduce the
number of clicks for most readers with insufficient
vocabulary to read documents and can significantly
reduce the remaining number of unfamiliar words after
the prediction and glossing for all users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2013:RCI,
author = "Fei-Yue Wang and Pak Kin Wong",
title = "Research commentary: Intelligent systems and
technology for integrative and predictive medicine: an
{ACP} approach",
journal = j-TIST,
volume = "4",
number = "2",
pages = "32:1--32:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One of the principal goals in medicine is to determine
and implement the best treatment for patients through
fastidious estimation of the effects and benefits of
therapeutic procedures. The inherent complexities of
physiological and pathological networks that span
across orders of magnitude in time and length scales,
however, represent fundamental hurdles in determining
effective treatments for patients. Here we argue for a
new approach, called the ACP-based approach, that
combines artificial (societies), computational
(experiments), and parallel (execution) methods in
intelligent systems and technology for integrative and
predictive medicine, or more generally, precision
medicine and smart health management. The advent of
artificial societies that collect the clinically
relevant information in prognostics and therapeutics
provides a promising platform for organizing and
experimenting complex physiological systems toward
integrative medicine. The ability of computational
experiments to analyze distinct, interactive systems
such as the host mechanisms, pathological pathways, and
therapeutic strategies, as well as other factors using
the artificial systems, will enable control and
management through parallel execution of real and
artificial systems concurrently within the integrative
medicine context. The development of this framework in
integrative medicine, fueled by close collaborations
between physicians, engineers, and scientists, will
result in preventive and predictive practices of a
personal, proactive, and precise nature, including
rational combinatorial treatments, adaptive
therapeutics, and patient-oriented disease
management.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tabia:2013:PBA,
author = "Hedi Tabia and Mohamed Daoudi and Jean-Philippe
Vandeborre and Olivier Colot",
title = "A parts-based approach for automatic {$3$D} shape
categorization using belief functions",
journal = j-TIST,
volume = "4",
number = "2",
pages = "33:1--33:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Grouping 3D objects into (semantically) meaningful
categories is a challenging and important problem in 3D
mining and shape processing. Here, we present a novel
approach to categorize 3D objects. The method described
in this article, is a belief-function-based approach
and consists of two stages: the training stage, where
3D objects in the same category are processed and a set
of representative parts is constructed, and the
labeling stage, where unknown objects are categorized.
The experimental results obtained on the Tosca-Sumner
and the Shrec07 datasets show that the system
efficiently performs in categorizing 3D models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2013:LIC,
author = "Zhengxiang Wang and Yiqun Hu and Liang-Tien Chia",
title = "Learning image-to-class distance metric for image
classification",
journal = j-TIST,
volume = "4",
number = "2",
pages = "34:1--34:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Image-To-Class (I2C) distance is a novel distance used
for image classification and has successfully handled
datasets with large intra-class variances. However, it
uses Euclidean distance for measuring the distance
between local features in different classes, which may
not be the optimal distance metric in real image
classification problems. In this article, we propose a
distance metric learning method to improve the
performance of I2C distance by learning per-class
Mahalanobis metrics in a large margin framework. Our
I2C distance is adaptive to different classes by
combining with the learned metric for each class. These
multiple per-class metrics are learned simultaneously
by forming a convex optimization problem with the
constraints that the I2C distance from each training
image to its belonging class should be less than the
distances to other classes by a large margin. A
subgradient descent method is applied to efficiently
solve this optimization problem. For efficiency and
scalability to large-scale problems, we also show how
to simplify the method to learn a diagonal matrix for
each class. We show in experiments that our learned
Mahalanobis I2C distance can significantly outperform
the original Euclidean I2C distance as well as other
distance metric learning methods in several prevalent
image datasets, and our simplified diagonal matrices
can preserve the performance but significantly speed up
the metric learning procedure for large-scale datasets.
We also show in experiment that our method is able to
correct the class imbalance problem, which usually
leads the NN-based methods toward classes containing
more training images.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Song:2013:FOU,
author = "Xuan Song and Xiaowei Shao and Quanshi Zhang and
Ryosuke Shibasaki and Huijing Zhao and Jinshi Cui and
Hongbin Zha",
title = "A fully online and unsupervised system for large and
high-density area surveillance: Tracking, semantic
scene learning and abnormality detection",
journal = j-TIST,
volume = "4",
number = "2",
pages = "35:1--35:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "For reasons of public security, an intelligent
surveillance system that can cover a large, crowded
public area has become an urgent need. In this article,
we propose a novel laser-based system that can
simultaneously perform tracking, semantic scene
learning, and abnormality detection in a fully online
and unsupervised way. Furthermore, these three tasks
cooperate with each other in one framework to improve
their respective performances. The proposed system has
the following key advantages over previous ones: (1) It
can cover quite a large area (more than 60$ \times
$35m), and simultaneously perform robust tracking,
semantic scene learning, and abnormality detection in a
high-density situation. (2) The overall system can vary
with time, incrementally learn the structure of the
scene, and perform fully online abnormal activity
detection and tracking. This feature makes our system
suitable for real-time applications. (3) The
surveillance tasks are carried out in a fully
unsupervised manner, so that there is no need for
manual labeling and the construction of huge training
datasets. We successfully apply the proposed system to
the JR subway station in Tokyo, and demonstrate that it
can cover an area of 60$ \times $35m, robustly track
more than 150 targets at the same time, and
simultaneously perform online semantic scene learning
and abnormality detection with no human intervention.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tran:2013:CPB,
author = "Vien Tran and Khoi Nguyen and Tran Cao Son and Enrico
Pontelli",
title = "A conformant planner based on approximation:
{CpA(H)}",
journal = j-TIST,
volume = "4",
number = "2",
pages = "36:1--36:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article describes the planner C pA( H ), the
recipient of the Best Nonobservable Nondeterministic
Planner Award in the ``Uncertainty Track'' of the 6
$^{th}$ International Planning Competition (IPC), 2008.
The article presents the various techniques that help
CpA( H ) to achieve the level of performance and
scalability exhibited in the competition. The article
also presents experimental results comparing CpA( H )
with state-of-the-art conformant planners.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2013:ISS,
author = "Haifeng Wang and Bill Dolan and Idan Szpektor and
Shiqi Zhao",
title = "Introduction to special section on paraphrasing",
journal = j-TIST,
volume = "4",
number = "3",
pages = "37:1--37:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483670",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Resnik:2013:UTP,
author = "Philip Resnik and Olivia Buzek and Yakov Kronrod and
Chang Hu and Alexander J. Quinn and Benjamin B.
Bederson",
title = "Using targeted paraphrasing and monolingual
crowdsourcing to improve translation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "38:1--38:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483671",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Targeted paraphrasing is a new approach to the problem
of obtaining cost-effective, reasonable quality
translation, which makes use of simple and inexpensive
human computations by monolingual speakers in
combination with machine translation. The key insight
behind the process is that it is possible to spot
likely translation errors with only monolingual
knowledge of the target language, and it is possible to
generate alternative ways to say the same thing (i.e.,
paraphrases) with only monolingual knowledge of the
source language. Formal evaluation demonstrates that
this approach can yield substantial improvements in
translation quality, and the idea has been integrated
into a broader framework for monolingual collaborative
translation that produces fully accurate, fully fluent
translations for a majority of sentences in a
real-world translation task, with no involvement of
human bilingual speakers.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Marton:2013:DPP,
author = "Yuval Marton",
title = "Distributional phrasal paraphrase generation for
statistical machine translation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "39:1--39:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483672",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Paraphrase generation has been shown useful for
various natural language processing tasks, including
statistical machine translation. A commonly used method
for paraphrase generation is pivoting [Callison-Burch
et al. 2006], which benefits from linguistic knowledge
implicit in the sentence alignment of parallel texts,
but has limited applicability due to its reliance on
parallel texts. Distributional paraphrasing [Marton et
al. 2009a] has wider applicability, is more
language-independent, but doesn't benefit from any
linguistic knowledge. Nevertheless, we show that using
distributional paraphrasing can yield greater gains in
translation tasks. We report method improvements
leading to higher gains than previously published, of
almost 2 B leu points, and provide implementation
details, complexity analysis, and further insight into
this method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Madnani:2013:GTP,
author = "Nitin Madnani and Bonnie J. Dorr",
title = "Generating targeted paraphrases for improved
translation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "40:1--40:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483673",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Today's Statistical Machine Translation (SMT) systems
require high-quality human translations for parameter
tuning, in addition to large bitexts for learning the
translation units. This parameter tuning usually
involves generating translations at different points in
the parameter space and obtaining feedback against
human-authored reference translations as to how good
the translations. This feedback then dictates what
point in the parameter space should be explored next.
To measure this feedback, it is generally considered
wise to have multiple (usually 4) reference
translations to avoid unfair penalization of
translation hypotheses which could easily happen given
the large number of ways in which a sentence can be
translated from one language to another. However, this
reliance on multiple reference translations creates a
problem since they are labor intensive and expensive to
obtain. Therefore, most current MT datasets only
contain a single reference. This leads to the problem
of reference sparsity. In our previously published
research, we had proposed the first paraphrase-based
solution to this problem and evaluated its effect on
Chinese--English translation. In this article, we first
present extended results for that solution on
additional source languages. More importantly, we
present a novel way to generate ``targeted''
paraphrases that yields substantially larger gains (up
to 2.7 BLEU points) in translation quality when
compared to our previous solution (up to 1.6 BLEU
points). In addition, we further validate these
improvements by supplementing with human preference
judgments obtained via Amazon Mechanical Turk.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cohn:2013:AAS,
author = "Trevor Cohn and Mirella Lapata",
title = "An abstractive approach to sentence compression",
journal = j-TIST,
volume = "4",
number = "3",
pages = "41:1--41:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483674",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article we generalize the sentence compression
task. Rather than simply shorten a sentence by deleting
words or constituents, as in previous work, we rewrite
it using additional operations such as substitution,
reordering, and insertion. We present an experimental
study showing that humans can naturally create
abstractive sentences using a variety of rewrite
operations, not just deletion. We next create a new
corpus that is suited to the abstractive compression
task and formulate a discriminative tree-to-tree
transduction model that can account for structural and
lexical mismatches. The model incorporates a grammar
extraction method, uses a language model for coherent
output, and can be easily tuned to a wide range of
compression-specific loss functions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Moon:2013:IBM,
author = "Taesun Moon and Katrin Erk",
title = "An inference-based model of word meaning in context as
a paraphrase distribution",
journal = j-TIST,
volume = "4",
number = "3",
pages = "42:1--42:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483675",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Graded models of word meaning in context characterize
the meaning of individual usages (occurrences) without
reference to dictionary senses. We introduce a novel
approach that frames the task of computing word meaning
in context as a probabilistic inference problem. The
model represents the meaning of a word as a probability
distribution over potential paraphrases, inferred using
an undirected graphical model. Evaluated on
paraphrasing tasks, the model achieves state-of-the-art
performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Burrows:2013:PAC,
author = "Steven Burrows and Martin Potthast and Benno Stein",
title = "Paraphrase acquisition via crowdsourcing and machine
learning",
journal = j-TIST,
volume = "4",
number = "3",
pages = "43:1--43:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483676",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "To paraphrase means to rewrite content while
preserving the original meaning. Paraphrasing is
important in fields such as text reuse in journalism,
anonymizing work, and improving the quality of
customer-written reviews. This article contributes to
paraphrase acquisition and focuses on two aspects that
are not addressed by current research: (1) acquisition
via crowdsourcing, and (2) acquisition of passage-level
samples. The challenge of the first aspect is automatic
quality assurance; without such a means the
crowdsourcing paradigm is not effective, and without
crowdsourcing the creation of test corpora is
unacceptably expensive for realistic order of
magnitudes. The second aspect addresses the deficit
that most of the previous work in generating and
evaluating paraphrases has been conducted using
sentence-level paraphrases or shorter; these
short-sample analyses are limited in terms of
application to plagiarism detection, for example. We
present the Webis Crowd Paraphrase Corpus 2011
(Webis-CPC-11), which recently formed part of the PAN
2010 international plagiarism detection competition.
This corpus comprises passage-level paraphrases with
4067 positive samples and 3792 negative samples that
failed our criteria, using Amazon's Mechanical Turk for
crowdsourcing. In this article, we review the lessons
learned at PAN 2010, and explain in detail the method
used to construct the corpus. The empirical
contributions include machine learning experiments to
explore if passage-level paraphrases can be identified
in a two-class classification problem using paraphrase
similarity features, and we find that a
k-nearest-neighbor classifier can correctly distinguish
between paraphrased and nonparaphrased samples with
0.980 precision at 0.523 recall. This result implies
that just under half of our samples must be discarded
(remaining 0.477 fraction), but our cost analysis shows
that the automation we introduce results in a 18\%
financial saving and over 100 hours of time returned to
the researchers when repeating a similar corpus design.
On the other hand, when building an unrelated corpus
requiring, say, 25\% training data for the automated
component, we show that the financial outcome is cost
neutral, while still returning over 70 hours of time to
the researchers. The work presented here is the first
to join the paraphrasing and plagiarism communities.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bouamor:2013:MPA,
author = "Houda Bouamor and Aur{\'e}elien Max and Anne Vilnat",
title = "Multitechnique paraphrase alignment: a contribution to
pinpointing sub-sentential paraphrases",
journal = j-TIST,
volume = "4",
number = "3",
pages = "44:1--44:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483677",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This work uses parallel monolingual corpora for a
detailed study of the task of sub-sentential paraphrase
acquisition. We argue that the scarcity of this type of
resource is compensated by the fact that it is the most
suited type for studies on paraphrasing. We propose a
large exploration of this task with experiments on two
languages with five different acquisition techniques,
selected for their complementarity, their combinations,
as well as four monolingual corpus types of varying
comparability. We report, under all conditions, a
significant improvement over all techniques by
validating candidate paraphrases using a maximum
entropy classifier. An important result of our study is
the identification of difficult-to-acquire paraphrase
pairs, which are classified and quantified in a
bilingual typology.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yu:2013:ISS,
author = "Zhiwen Yu and Daqing Zhang and Nathan Eagle and Diane
Cook",
title = "Introduction to the special section on intelligent
systems for socially aware computing",
journal = j-TIST,
volume = "4",
number = "3",
pages = "45:1--45:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483678",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Schuster:2013:PSC,
author = "Daniel Schuster and Alberto Rosi and Marco Mamei and
Thomas Springer and Markus Endler and Franco
Zambonelli",
title = "Pervasive social context: Taxonomy and survey",
journal = j-TIST,
volume = "4",
number = "3",
pages = "46:1--46:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483679",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "As pervasive computing meets social networks, there is
a fast growing research field called pervasive social
computing. Applications in this area exploit the
richness of information arising out of people using
sensor-equipped pervasive devices in their everyday
life combined with intense use of different social
networking services. We call this set of information
pervasive social context. We provide a taxonomy to
classify pervasive social context along the dimensions
space, time, people, and information source (STiPI) as
well as commenting on the type and reason for creating
such context. A survey of recent research shows the
applicability and usefulness of the taxonomy in
classifying and assessing applications and systems in
the area of pervasive social computing. Finally, we
present some research challenges in this area and
illustrate how they affect the systems being
surveyed.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2013:NLR,
author = "Yue Shi and Pavel Serdyukov and Alan Hanjalic and
Martha Larson",
title = "Nontrivial landmark recommendation using geotagged
photos",
journal = j-TIST,
volume = "4",
number = "3",
pages = "47:1--47:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483680",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online photo-sharing sites provide a wealth of
information about user behavior and their potential is
increasing as it becomes ever-more common for images to
be associated with location information in the form of
geotags. In this article, we propose a novel approach
that exploits geotagged images from an online community
for the purpose of personalized landmark
recommendation. Under our formulation of the task,
recommended landmarks should be relevant to user
interests and additionally they should constitute
nontrivial recommendations. In other words,
recommendations of landmarks that are highly popular
and frequently visited and can be easily discovered
through other information sources such as travel guides
should be avoided in favor of recommendations that
relate to users' personal interests. We propose a
collaborative filtering approach to the personalized
landmark recommendation task within a matrix
factorization framework. Our approach, WMF-CR, combines
weighted matrix factorization and category-based
regularization. The integrated weights emphasize the
contribution of nontrivial landmarks in order to focus
the recommendation model specifically on the generation
of nontrivial recommendations. They support the
judicious elimination of trivial landmarks from
consideration without also discarding information
valuable for recommendation. Category-based
regularization addresses the sparse data problem, which
is arguably even greater in the case of our landmark
recommendation task than in other recommendation
scenarios due to the limited amount of travel
experience recorded in the online image set of any
given user. We use category information extracted from
Wikipedia in order to provide the system with a method
to generalize the semantics of landmarks and allow the
model to relate them not only on the basis of identity,
but also on the basis of topical commonality. The
proposed approach is computational scalable, that is,
its complexity is linear with the number of observed
preferences in the user-landmark preference matrix and
the number of nonzero similarities in the
category-based landmark similarity matrix. We evaluate
the approach on a large collection of geotagged photos
gathered from Flickr. Our experimental results
demonstrate that WMF-CR outperforms several
state-of-the-art baseline approaches in recommending
nontrivial landmarks. Additionally, they demonstrate
that the approach is well suited for addressing data
sparseness and provides particular performance
improvement in the case of users who have limited
travel experience, that is, have visited only few
cities or few landmarks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wei:2013:EPA,
author = "Ling-Yin Wei and Wen-Chih Peng and Wang-Chien Lee",
title = "Exploring pattern-aware travel routes for trajectory
search",
journal = j-TIST,
volume = "4",
number = "3",
pages = "48:1--48:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483681",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the popularity of positioning devices, Web 2.0
technology, and trip sharing services, many users are
willing to log and share their trips on the Web. Thus,
trip planning Web sites are able to provide some new
services by inferring Regions-Of-Interest (ROIs) and
recommending popular travel routes from trip
trajectories. We argue that simply providing some
travel routes consisting of popular ROIs to users is
not sufficient. To tour around a wide geographical
area, for example, a city, some users may prefer a trip
to visit as many ROIs as possible, while others may
like to stop by only a few ROIs for an in-depth visit.
We refer to a trip fitting the former user group as an
in-breadth trip and a trip suitable for the latter user
group as an in-depth trip. Prior studies on trip
planning have focused on mining ROIs and travel routes
without considering these different preferences. In
this article, given a spatial range and a user
preference of depth/breadth specified by a user, we
develop a Pattern-Aware Trajectory Search (PATS)
framework to retrieve the top K trajectories passing
through popular ROIs. PATS is novel because the
returned travel trajectories, discovered from travel
patterns hidden in trip trajectories, may represent the
most valuable travel experiences of other travelers
fitting the user's trip preference in terms of depth or
breadth. The PATS framework comprises two components:
travel behavior exploration and trajectory search. The
travel behavior exploration component determines a set
of ROIs along with their attractive scores by
considering not only the popularity of the ROIs but
also the travel sequential relationships among the
ROIs. To capture the travel sequential relationships
among ROIs and to derive their attractive scores, a
user movement graph is constructed. For the trajectory
search component of PATS, we formulate two trajectory
score functions, the depth-trip score function and the
breadth-trip score function, by taking into account the
number of ROIs in a trajectory and their attractive
scores. Accordingly, we propose an algorithm, namely,
Bounded Trajectory Search (BTS), to efficiently
retrieve the top K trajectories based on the two
trajectory scores. The PATS framework is evaluated by
experiments and user studies using a real dataset. The
experimental results demonstrate the effectiveness and
the efficiency of the proposed PATS framework.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yan:2013:STM,
author = "Zhixian Yan and Dipanjan Chakraborty and Christine
Parent and Stefano Spaccapietra and Karl Aberer",
title = "Semantic trajectories: Mobility data computation and
annotation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "49:1--49:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483682",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the large-scale adoption of GPS equipped mobile
sensing devices, positional data generated by moving
objects (e.g., vehicles, people, animals) are being
easily collected. Such data are typically modeled as
streams of spatio-temporal (x,y,t) points, called
trajectories. In recent years trajectory management
research has progressed significantly towards efficient
storage and indexing techniques, as well as suitable
knowledge discovery. These works focused on the
geometric aspect of the raw mobility data. We are now
witnessing a growing demand in several application
sectors (e.g., from shipment tracking to geo-social
networks) on understanding the semantic behavior of
moving objects. Semantic behavior refers to the use of
semantic abstractions of the raw mobility data,
including not only geometric patterns but also
knowledge extracted jointly from the mobility data and
the underlying geographic and application domains
information. The core contribution of this article lies
in a semantic model and a computation and annotation
platform for developing a semantic approach that
progressively transforms the raw mobility data into
semantic trajectories enriched with segmentations and
annotations. We also analyze a number of experiments we
did with semantic trajectories in different domains.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chin:2013:CPT,
author = "Alvin Chin and Bin Xu and Hao Wang and Lele Chang and
Hao Wang and Lijun Zhu",
title = "Connecting people through physical proximity and
physical resources at a conference",
journal = j-TIST,
volume = "4",
number = "3",
pages = "50:1--50:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483683",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This work investigates how to bridge the gap between
offline and online behaviors at a conference and how
the physical resources in the conference (the physical
objects used in the conference for gathering attendees
together in engaging an activity such as rooms,
sessions, and papers) can be used to help facilitate
social networking. We build Find and Connect, a system
that integrates offline activities and interactions
captured in real time with online connections in a
conference environment, to provide a list of potential
people one should connect to for forming an ephemeral
social network. We investigate how social connections
can be established and integrated with physical
resources through positioning technology, and the
relationship between physical proximity encounters and
online social connections. Results from our two
datasets of two trials, one at the UIC/ATC 2010
conference and GCJK internal marketing event, show that
social connections that are reciprocal in relationship,
such as friendship and exchanged contacts, have
tighter, denser, and highly clustered networks compared
to unidirectional relationships such as follow. We
discover that there is a positive relationship between
physical proximity encounters and online social
connections before the social connection is made for
friends, but a negative relationship for after the
social connection is made. The first indicates social
selection is strong, and the second indicates social
influence is weak. Even though our dataset is sparse,
nonetheless we believe our work is promising and novel
which is worthy of future research.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2013:ISS,
author = "Shanchieh Jay Yang and Dana Nau and John Salerno",
title = "Introduction to the special section on social
computing, behavioral-cultural modeling, and
prediction",
journal = j-TIST,
volume = "4",
number = "3",
pages = "51:1--51:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483684",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hung:2013:OBI,
author = "Benjamin W. K. Hung and Stephan E. Kolitz and Asuman
Ozdaglar",
title = "Optimization-based influencing of village social
networks in a counterinsurgency",
journal = j-TIST,
volume = "4",
number = "3",
pages = "52:1--52:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483685",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article considers the nonlethal targeting
assignment problem in the counterinsurgency in
Afghanistan, the problem of deciding on the people whom
U.S. forces should engage through outreach,
negotiations, meetings, and other interactions in order
to ultimately win the support of the population in
their area of operations. We propose two models: (1)
the Afghan counterinsurgency (COIN) social influence
model, to represent how attitudes of local leaders are
affected by repeated interactions with other local
leaders, insurgents, and counterinsurgents, and (2) the
nonlethal targeting model, a NonLinear Programming
(NLP) optimization formulation that identifies a
strategy for assigning k U.S. agents to produce the
greatest arithmetic mean of the expected long-term
attitude of the population. We demonstrate in an
experiment the merits of the optimization model in
nonlethal targeting, which performs significantly
better than both doctrine-based and random methods of
assignment in a large network.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gintis:2013:MMS,
author = "Herbert Gintis",
title = "{Markov} models of social dynamics: Theory and
applications",
journal = j-TIST,
volume = "4",
number = "3",
pages = "53:1--53:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483686",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article shows how agent-based models of social
dynamics can be treated rigorously and analytically as
finite Markov processes, and their long-run properties
are then given by an expanded version of the ergodic
theorem for Markov processes. A Markov process model of
a simplified market economy shows the fruitfulness of
this approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fridman:2013:UQR,
author = "Natalie Fridman and Gal A. Kaminka",
title = "Using qualitative reasoning for social simulation of
crowds",
journal = j-TIST,
volume = "4",
number = "3",
pages = "54:1--54:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483687",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The ability to model and reason about the potential
violence level of a demonstration is important to the
police decision making process. Unfortunately, existing
knowledge regarding demonstrations is composed of
partial qualitative descriptions without complete and
precise numerical information. In this article we
describe a first attempt to use qualitative reasoning
techniques to model demonstrations. To our knowledge,
such techniques have never been applied to modeling and
reasoning regarding crowd behaviors, nor in particular
demonstrations. We develop qualitative models
consistent with the partial, qualitative social science
literature, allowing us to model the interactions
between different factors that influence violence in
demonstrations. We then utilize qualitative simulation
to predict the potential eruption of violence, at
various levels, based on a description of the
demographics, environmental settings, and police
responses. We incrementally present and compare three
such qualitative models. The results show that while
two of these models fail to predict the outcomes of
real-world events reported and analyzed in the
literature, one model provides good results. We also
examine whether a popular machine learning algorithm
(decision tree learning) can be used. While the results
show that the decision trees provide improved
predictions, we show that the QR models can be more
sensitive to changes, and can account for what-if
scenarios, in contrast to decision trees. Moreover, we
introduce a novel analysis algorithm that analyzes the
QR simulations, to automatically determine the factors
that are most important in influencing the outcome in
specific real-world demonstrations. We show that the
algorithm identifies factors that correspond to
experts' analysis of these events.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Saito:2013:DCI,
author = "Kazumi Saito and Masahiro Kimura and Kouzou Ohara and
Hiroshi Motoda",
title = "Detecting changes in information diffusion patterns
over social networks",
journal = j-TIST,
volume = "4",
number = "3",
pages = "55:1--55:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483688",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We addressed the problem of detecting the change in
behavior of information diffusion over a social network
which is caused by an unknown external situation change
using a small amount of observation data in a
retrospective setting. The unknown change is assumed
effectively reflected in changes in the parameter
values in the probabilistic information diffusion
model, and the problem is reduced to detecting where in
time and how long this change persisted and how big
this change is. We solved this problem by searching the
change pattern that maximizes the likelihood of
generating the observed information diffusion
sequences, and in doing so we devised a very efficient
general iterative search algorithm using the derivative
of the likelihood which avoids parameter value
optimization during each search step. This is in
contrast to the naive learning algorithm in that it has
to iteratively update the pattern boundaries, each
requiring the parameter value optimization and thus is
very inefficient. We tested this algorithm for two
instances of the probabilistic information diffusion
model which has different characteristics. One is of
information push style and the other is of information
pull style. We chose Asynchronous Independent Cascade
(AsIC) model as the former and Value-weighted Voter
(VwV) model as the latter. The AsIC is the model for
general information diffusion with binary states and
the parameter to detect its change is diffusion
probability and the VwV is the model for opinion
formation with multiple states and the parameter to
detect its change is opinion value. The results tested
on these two models using four real-world network
structures confirm that the algorithm is robust enough
and can efficiently identify the correct change pattern
of the parameter values. Comparison with the naive
method that finds the best combination of change
boundaries by an exhaustive search through a set of
randomly selected boundary candidates shows that the
proposed algorithm far outperforms the native method
both in terms of accuracy and computation time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Marathe:2013:AFN,
author = "Achla Marathe and Zhengzheng Pan and Andrea Apolloni",
title = "Analysis of friendship network and its role in
explaining obesity",
journal = j-TIST,
volume = "4",
number = "3",
pages = "56:1--56:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483689",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We employ Add Health data to show that friendship
networks, constructed from mutual friendship
nominations, are important in building weight
perception, setting weight goals, and measuring social
marginalization among adolescents and young adults. We
study the relationship between individuals' perceived
weight status, actual weight status, weight status
relative to friends' weight status, and weight goals.
This analysis helps us understand how individual weight
perceptions might be formed, what these perceptions do
to the weight goals, and how friends' relative weight
affects weight perception and weight goals. Combining
this information with individuals' friendship network
helps determine the influence of social relationships
on weight-related variables. Multinomial logistic
regression results indicate that relative status is
indeed a significant predictor of perceived status, and
perceived status is a significant predictor of weight
goals. We also address the issue of causality between
actual weight status and social marginalization (as
measured by the number of friends) and show that
obesity precedes social marginalization in time rather
than the other way around. This lends credence to the
hypothesis that obesity leads to social marginalization
not vice versa. Attributes of the friendship network
can provide new insights into effective interventions
for combating obesity since adolescent friendships
provide an important social context for weight-related
behaviors.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jiang:2013:MSB,
author = "Daxin Jiang and Jian Pei and Hang Li",
title = "Mining search and browse logs for {Web} search: a
survey",
journal = j-TIST,
volume = "4",
number = "4",
pages = "57:1--57:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508038",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Huge amounts of search log data have been accumulated
at Web search engines. Currently, a popular Web search
engine may receive billions of queries and collect
terabytes of records about user search behavior daily.
Beside search log data, huge amounts of browse log data
have also been collected through client-side browser
plugins. Such massive amounts of search and browse log
data provide great opportunities for mining the wisdom
of crowds and improving Web search. At the same time,
designing effective and efficient methods to clean,
process, and model log data also presents great
challenges. In this survey, we focus on mining search
and browse log data for Web search. We start with an
introduction to search and browse log data and an
overview of frequently-used data summarizations in log
mining. We then elaborate how log mining applications
enhance the five major components of a search engine,
namely, query understanding, document understanding,
document ranking, user understanding, and monitoring
and feedback. For each aspect, we survey the major
tasks, fundamental principles, and state-of-the-art
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2013:SAM,
author = "Xi Li and Weiming Hu and Chunhua Shen and Zhongfei
Zhang and Anthony Dick and Anton {Van Den Hengel}",
title = "A survey of appearance models in visual object
tracking",
journal = j-TIST,
volume = "4",
number = "4",
pages = "58:1--58:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508039",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Visual object tracking is a significant computer
vision task which can be applied to many domains, such
as visual surveillance, human computer interaction, and
video compression. Despite extensive research on this
topic, it still suffers from difficulties in handling
complex object appearance changes caused by factors
such as illumination variation, partial occlusion,
shape deformation, and camera motion. Therefore,
effective modeling of the 2D appearance of tracked
objects is a key issue for the success of a visual
tracker. In the literature, researchers have proposed a
variety of 2D appearance models. To help readers
swiftly learn the recent advances in 2D appearance
models for visual object tracking, we contribute this
survey, which provides a detailed review of the
existing 2D appearance models. In particular, this
survey takes a module-based architecture that enables
readers to easily grasp the key points of visual object
tracking. In this survey, we first decompose the
problem of appearance modeling into two different
processing stages: visual representation and
statistical modeling. Then, different 2D appearance
models are categorized and discussed with respect to
their composition modules. Finally, we address several
issues of interest as well as the remaining challenges
for future research on this topic. The contributions of
this survey are fourfold. First, we review the
literature of visual representations according to their
feature-construction mechanisms (i.e., local and
global). Second, the existing statistical modeling
schemes for tracking-by-detection are reviewed
according to their model-construction mechanisms:
generative, discriminative, and hybrid
generative-discriminative. Third, each type of visual
representations or statistical modeling techniques is
analyzed and discussed from a theoretical or practical
viewpoint. Fourth, the existing benchmark resources
(e.g., source codes and video datasets) are examined in
this survey.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cena:2013:PSA,
author = "Federica Cena and Antonina Dattolo and Pasquale Lops
and Julita Vassileva",
title = "Perspectives in {Semantic Adaptive Social Web}",
journal = j-TIST,
volume = "4",
number = "4",
pages = "59:1--59:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2501603",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The Social Web is now a successful reality with its
quickly growing number of users and applications. Also
the Semantic Web, which started with the objective of
describing Web resources in a machine-processable way,
is now outgrowing the research labs and is being
massively exploited in many websites, incorporating
high-quality user-generated content and semantic
annotations. The primary goal of this special section
is to showcase some recent research at the intersection
of the Social Web and the Semantic Web that explores
the benefits that adaptation and personalization have
to offer in the Web of the future, the so-called Social
Adaptive Semantic Web. We have selected two articles
out of fourteen submissions based on the quality of the
articles and we present the main lessons learned from
the overall analysis of these submissions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Biancalana:2013:SSQ,
author = "Claudio Biancalana and Fabio Gasparetti and Alessandro
Micarelli and Giuseppe Sansonetti",
title = "Social semantic query expansion",
journal = j-TIST,
volume = "4",
number = "4",
pages = "60:1--60:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508041",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Weak semantic techniques rely on the integration of
Semantic Web techniques with social annotations and aim
to embrace the strengths of both. In this article, we
propose a novel weak semantic technique for query
expansion. Traditional query expansion techniques are
based on the computation of two-dimensional
co-occurrence matrices. Our approach proposes the use
of three-dimensional matrices, where the added
dimension is represented by semantic classes (i.e.,
categories comprising all the terms that share a
semantic property) related to the folksonomy extracted
from social bookmarking services, such as delicious and
StumbleUpon. The results of an indepth experimental
evaluation performed on both artificial datasets and
real users show that our approach outperforms
traditional techniques, such as relevance feedback and
personalized PageRank, so confirming the validity and
usefulness of the categorization of the user needs and
preferences in semantic classes. We also present the
results of a questionnaire aimed to know the users
opinion regarding the system. As one drawback of
several query expansion techniques is their high
computational costs, we also provide a complexity
analysis of our system, in order to show its capability
of operating in real time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:WMS,
author = "Chao Chen and Qiusha Zhu and Lin Lin and Mei-Ling
Shyu",
title = "{Web} media semantic concept retrieval via tag removal
and model fusion",
journal = j-TIST,
volume = "4",
number = "4",
pages = "61:1--61:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508042",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multimedia data on social websites contain rich
semantics and are often accompanied with user-defined
tags. To enhance Web media semantic concept retrieval,
the fusion of tag-based and content-based models can be
used, though it is very challenging. In this article, a
novel semantic concept retrieval framework that
incorporates tag removal and model fusion is proposed
to tackle such a challenge. Tags with useful
information can facilitate media search, but they are
often imprecise, which makes it important to apply
noisy tag removal (by deleting uncorrelated tags) to
improve the performance of semantic concept retrieval.
Therefore, a multiple correspondence analysis
(MCA)-based tag removal algorithm is proposed, which
utilizes MCA's ability to capture the relationships
among nominal features and identify representative and
discriminative tags holding strong correlations with
the target semantic concepts. To further improve the
retrieval performance, a novel model fusion method is
also proposed to combine ranking scores from both
tag-based and content-based models, where the
adjustment of ranking scores, the reliability of
models, and the correlations between the intervals
divided on the ranking scores and the semantic concepts
are all considered. Comparative results with extensive
experiments on the NUS-WIDE-LITE as well as the
NUS-WIDE-270K benchmark datasets with 81 semantic
concepts show that the proposed framework outperforms
baseline results and the other comparison methods with
each component being evaluated separately.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reddy:2013:ISS,
author = "Chandan K. Reddy and Cristopher C. Yang",
title = "Introduction to the special section on intelligent
systems for health informatics",
journal = j-TIST,
volume = "4",
number = "4",
pages = "62:1--62:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508043",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Batal:2013:TPM,
author = "Iyad Batal and Hamed Valizadegan and Gregory F. Cooper
and Milos Hauskrecht",
title = "A temporal pattern mining approach for classifying
electronic health record data",
journal = j-TIST,
volume = "4",
number = "4",
pages = "63:1--63:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508044",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We study the problem of learning classification models
from complex multivariate temporal data encountered in
electronic health record systems. The challenge is to
define a good set of features that are able to
represent well the temporal aspect of the data. Our
method relies on temporal abstractions and temporal
pattern mining to extract the classification features.
Temporal pattern mining usually returns a large number
of temporal patterns, most of which may be irrelevant
to the classification task. To address this problem, we
present the Minimal Predictive Temporal Patterns
framework to generate a small set of predictive and
nonspurious patterns. We apply our approach to the
real-world clinical task of predicting patients who are
at risk of developing heparin-induced thrombocytopenia.
The results demonstrate the benefit of our approach in
efficiently learning accurate classifiers, which is a
key step for developing intelligent clinical monitoring
systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rashidi:2013:CMM,
author = "Parisa Rashidi and Diane J. Cook",
title = "{COM}: a method for mining and monitoring human
activity patterns in home-based health monitoring
systems",
journal = j-TIST,
volume = "4",
number = "4",
pages = "64:1--64:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508045",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The increasing aging population in the coming decades
will result in many complications for society and in
particular for the healthcare system due to the
shortage of healthcare professionals and healthcare
facilities. To remedy this problem, researchers have
pursued developing remote monitoring systems and
assisted living technologies by utilizing recent
advances in sensor and networking technology, as well
as in the data mining and machine learning fields. In
this article, we report on our fully automated approach
for discovering and monitoring patterns of daily
activities. Discovering and tracking patterns of daily
activities can provide unprecedented opportunities for
health monitoring and assisted living applications,
especially for older adults and individuals with mental
disabilities. Previous approaches usually rely on
preselected activities or labeled data to track and
monitor daily activities. In this article, we present a
fully automated approach by discovering natural
activity patterns and their variations in real-life
data. We will show how our activity discovery component
can be integrated with an activity recognition
component to track and monitor various daily activity
patterns. We also provide an activity visualization
component to allow caregivers to visually observe and
examine the activity patterns using a user-friendly
interface. We validate our algorithms using real-life
data obtained from two apartments during a three-month
period.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wolf:2013:DUR,
author = "Hannes Wolf and Klaus Herrmann and Kurt Rothermel",
title = "Dealing with uncertainty: Robust workflow navigation
in the healthcare domain",
journal = j-TIST,
volume = "4",
number = "4",
pages = "65:1--65:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508046",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Processes in the healthcare domain are characterized
by coarsely predefined recurring procedures that are
flexibly adapted by the personnel to suite-specific
situations. In this setting, a workflow management
system that gives guidance and documents the
personnel's actions can lead to a higher quality of
care, fewer mistakes, and higher efficiency. However,
most existing workflow management systems enforce rigid
inflexible workflows and rely on direct manual input.
Both are inadequate for healthcare processes. In
particular, direct manual input is not possible in most
cases since (1) it would distract the personnel even in
critical situations and (2) it would violate
fundamental hygiene principles by requiring disinfected
doctors and nurses to touch input devices. The solution
could be activity recognition systems that use sensor
data (e.g., audio and acceleration data) to infer the
current activities by the personnel and provide input
to a workflow (e.g., informing it that a certain
activity is finished now). However, state-of-the-art
activity recognition technologies have difficulties in
providing reliable information. We describe a
comprehensive framework tailored for flexible
human-centric healthcare processes that improves the
reliability of activity recognition data. We present a
set of mechanisms that exploit the application
knowledge encoded in workflows in order to reduce the
uncertainty of this data, thus enabling unobtrusive
robust healthcare workflows. We evaluate our work based
on a real-world case study and show that the robustness
of unobtrusive healthcare workflows can be increased to
an absolute value of up to 91\% (compared to only 12\%
with a classical workflow system). This is a major
breakthrough that paves the way towards future
IT-enabled healthcare systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Park:2013:CPC,
author = "Yubin Park and Joydeep Ghosh",
title = "{CUDIA}: Probabilistic cross-level imputation using
individual auxiliary information",
journal = j-TIST,
volume = "4",
number = "4",
pages = "66:1--66:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508047",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In healthcare-related studies, individual patient or
hospital data are not often publicly available due to
privacy restrictions, legal issues, or reporting norms.
However, such measures may be provided at a higher or
more aggregated level, such as state-level,
county-level summaries or averages over health zones,
such as hospital referral regions (HRR) or hospital
service areas (HSA). Such levels constitute partitions
over the underlying individual level data, which may
not match the groupings that would have been obtained
if one clustered the data based on individual-level
attributes. Moreover, treating aggregated values as
representatives for the individuals can result in the
ecological fallacy. How can one run data mining
procedures on such data where different variables are
available at different levels of aggregation or
granularity? In this article, we seek a better
utilization of variably aggregated datasets, which are
possibly assembled from different sources. We propose a
novel cross-level imputation technique that models the
generative process of such datasets using a Bayesian
directed graphical model. The imputation is based on
the underlying data distribution and is shown to be
unbiased. This imputation can be further utilized in a
subsequent predictive modeling, yielding improved
accuracies. The experimental results using a simulated
dataset and the Behavioral Risk Factor Surveillance
System (BRFSS) dataset are provided to illustrate the
generality and capabilities of the proposed
framework.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hoens:2013:RMR,
author = "T. Ryan Hoens and Marina Blanton and Aaron Steele and
Nitesh V. Chawla",
title = "Reliable medical recommendation systems with patient
privacy",
journal = j-TIST,
volume = "4",
number = "4",
pages = "67:1--67:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508048",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One of the concerns patients have when confronted with
a medical condition is which physician to trust. Any
recommendation system that seeks to answer this
question must ensure that any sensitive medical
information collected by the system is properly
secured. In this article, we codify these privacy
concerns in a privacy-friendly framework and present
two architectures that realize it: the Secure
Processing Architecture (SPA) and the Anonymous
Contributions Architecture (ACA). In SPA, patients
submit their ratings in a protected form without
revealing any information about their data and the
computation of recommendations proceeds over the
protected data using secure multiparty computation
techniques. In ACA, patients submit their ratings in
the clear, but no link between a submission and patient
data can be made. We discuss various aspects of both
architectures, including techniques for ensuring
reliability of computed recommendations and system
performance, and provide their comparison.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Khan:2013:VOM,
author = "Atif Khan and John A. Doucette and Robin Cohen",
title = "Validation of an ontological medical decision support
system for patient treatment using a repository of
patient data: Insights into the value of machine
learning",
journal = j-TIST,
volume = "4",
number = "4",
pages = "68:1--68:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508049",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we begin by presenting OMeD, a
medical decision support system, and argue for its
value over purely probabilistic approaches that reason
about patients for time-critical decision scenarios. We
then progress to present Holmes, a Hybrid Ontological
and Learning MEdical System which supports decision
making about patient treatment. This system is
introduced in order to cope with the case of missing
data. We demonstrate its effectiveness by operating on
an extensive set of real-world patient health data from
the CDC, applied to the decision-making scenario of
administering sleeping pills. In particular, we clarify
how the combination of semantic, ontological
representations, and probabilistic reasoning together
enable the proposal of effective patient treatments.
Our focus is thus on presenting an approach for
interpreting medical data in the context of real-time
decision making. This constitutes a comprehensive
framework for the design of medical recommendation
systems for potential use by medical professionals and
patients both, with the end result being personalized
patient treatment. We conclude with a discussion of the
value of our particular approach for such diverse
considerations as coping with misinformation provided
by patients, performing effectively in time-critical
environments where real-time decisions are necessary,
and potential applications facilitating patient
information gathering.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2013:CPR,
author = "Suk Jin Lee and Yuichi Motai and Elisabeth Weiss and
Shumei S. Sun",
title = "Customized prediction of respiratory motion with
clustering from multiple patient interaction",
journal = j-TIST,
volume = "4",
number = "4",
pages = "69:1--69:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508050",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Information processing of radiotherapy systems has
become an important research area for sophisticated
radiation treatment methodology. Geometrically precise
delivery of radiotherapy in the thorax and upper
abdomen is compromised by respiratory motion during
treatment. Accurate prediction of the respiratory
motion would be beneficial for improving tumor
targeting. However, a wide variety of breathing
patterns can make it difficult to predict the breathing
motion with explicit models. We proposed a respiratory
motion predictor, that is, customized prediction with
multiple patient interactions using neural network
(CNN). For the preprocedure of prediction for
individual patient, we construct the clustering based
on breathing patterns of multiple patients using the
feature selection metrics that are composed of a
variety of breathing features. In the intraprocedure,
the proposed CNN used neural networks (NN) for a part
of the prediction and the extended Kalman filter (EKF)
for a part of the correction. The prediction accuracy
of the proposed method was investigated with a variety
of prediction time horizons using normalized root mean
squared error (NRMSE) values in comparison with the
alternate recurrent neural network (RNN). We have also
evaluated the prediction accuracy using the marginal
value that can be used as the reference value to judge
how many signals lie outside the confidence level. The
experimental results showed that the proposed CNN can
outperform RNN with respect to the prediction accuracy
with an improvement of 50\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Baralis:2013:EPH,
author = "Elena Baralis and Tania Cerquitelli and Silvia
Chiusano and Vincenzo D'Elia and Riccardo Molinari and
Davide Susta",
title = "Early prediction of the highest workload in
incremental cardiopulmonary tests",
journal = j-TIST,
volume = "4",
number = "4",
pages = "70:1--70:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508051",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Incremental tests are widely used in cardiopulmonary
exercise testing, both in the clinical domain and in
sport sciences. The highest workload (denoted
W$_{peak}$ ) reached in the test is key information for
assessing the individual body response to the test and
for analyzing possible cardiac failures and planning
rehabilitation, and training sessions. Being physically
very demanding, incremental tests can significantly
increase the body stress on monitored individuals and
may cause cardiopulmonary overload. This article
presents a new approach to cardiopulmonary testing that
addresses these drawbacks. During the test, our
approach analyzes the individual body response to the
exercise and predicts the W$_{peak}$ value that will be
reached in the test and an evaluation of its accuracy.
When the accuracy of the prediction becomes
satisfactory, the test can be prematurely stopped, thus
avoiding its entire execution. To predict W$_{peak}$,
we introduce a new index, the CardioPulmonary
Efficiency Index (CPE), summarizing the cardiopulmonary
response of the individual to the test. Our approach
analyzes the CPE trend during the test, together with
the characteristics of the individual, and predicts
W$_{peak}$. A K-nearest-neighbor-based classifier and
an ANN-based classier are exploited for the prediction.
The experimental evaluation showed that the W$_{peak}$
value can be predicted with a limited error from the
first steps of the test.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2013:SFI,
author = "Yugyung Lee and Saranya Krishnamoorthy and Deendayal
Dinakarpandian",
title = "A semantic framework for intelligent matchmaking for
clinical trial eligibility criteria",
journal = j-TIST,
volume = "4",
number = "4",
pages = "71:1--71:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508052",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An integral step in the discovery of new treatments
for medical conditions is the matching of potential
subjects with appropriate clinical trials. Eligibility
criteria for clinical trials are typically specified as
inclusion and exclusion criteria for each study in
freetext form. While this is sufficient for a human to
guide a recruitment interview, it cannot be reliably
and computationally construed to identify potential
subjects. Standardization of the representation of
eligibility criteria can enhance the efficiency and
accuracy of this process. This article presents a
semantic framework that facilitates intelligent
matchmaking by identifying a minimal set of eligibility
criteria with maximal coverage of clinical trials. In
contrast to existing top-down manual standardization
efforts, a bottom-up data driven approach is presented
to find a canonical nonredundant representation of an
arbitrary collection of clinical trial criteria. The
methodology has been validated with a corpus of 709
clinical trials related to Generalized Anxiety Disorder
containing 2,760 inclusion and 4,871 exclusion
eligibility criteria. This corpus is well represented
by a relatively small number of 126 inclusion clusters
and 175 exclusion clusters, each of which corresponds
to a semantically distinct criterion. Internal and
external validation measures provide an objective
evaluation of the method. An eligibility criteria
ontology has been constructed based on the clustering.
The resulting model has been incorporated into the
development of the MindTrial clinical trial recruiting
system. The prototype for clinical trial recruitment
illustrates the effectiveness of the methodology in
characterizing clinical trials and subjects and
accurate matching between them.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bi:2013:MLA,
author = "Jinbo Bi and Jiangwen Sun and Yu Wu and Howard Tennen
and Stephen Armeli",
title = "A machine learning approach to college drinking
prediction and risk factor identification",
journal = j-TIST,
volume = "4",
number = "4",
pages = "72:1--72:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508053",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Alcohol misuse is one of the most serious public
health problems facing adolescents and young adults in
the United States. National statistics shows that
nearly 90\% of alcohol consumed by youth under 21 years
of age involves binge drinking and 44\% of college
students engage in high-risk drinking activities.
Conventional alcohol intervention programs, which aim
at installing either an alcohol reduction norm or
prohibition against underage drinking, have yielded
little progress in controlling college binge drinking
over the years. Existing alcohol studies are deductive
where data are collected to investigate a
psychological/behavioral hypothesis, and statistical
analysis is applied to the data to confirm the
hypothesis. Due to this confirmatory manner of
analysis, the resulting statistical models are
cohort-specific and typically fail to replicate on a
different sample. This article presents two machine
learning approaches for a secondary analysis of
longitudinal data collected in college alcohol studies
sponsored by the National Institute on Alcohol Abuse
and Alcoholism. Our approach aims to discover
knowledge, from multiwave cohort-sequential daily data,
which may or may not align with the original hypothesis
but quantifies predictive models with higher likelihood
to generalize to new samples. We first propose a
so-called temporally-correlated support vector machine
to construct a classifier as a function of daily moods,
stress, and drinking expectancies to distinguish days
with nighttime binge drinking from days without for
individual students. We then propose a combination of
cluster analysis and feature selection, where cluster
analysis is used to identify drinking patterns based on
averaged daily drinking behavior and feature selection
is used to identify risk factors associated with each
pattern. We evaluate our methods on two cohorts of 530
total college students recruited during the Spring and
Fall semesters, respectively. Cross validation on these
two cohorts and further on 100 random partitions of the
total students demonstrate that our methods improve the
model generalizability in comparison with traditional
multilevel logistic regression. The discovered risk
factors and the interaction of these factors delineated
in our models can set a potential basis and offer
insights to a new design of more effective college
alcohol interventions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Subbu:2013:LMF,
author = "Kalyan Pathapati Subbu and Brandon Gozick and Ram
Dantu",
title = "{LocateMe}: Magnetic-fields-based indoor localization
using smartphones",
journal = j-TIST,
volume = "4",
number = "4",
pages = "73:1--73:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508054",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Fine-grained localization is extremely important to
accurately locate a user indoors. Although innovative
solutions have already been proposed, there is no
solution that is universally accepted, easily
implemented, user centric, and, most importantly, works
in the absence of GSM coverage or WiFi availability.
The advent of sensor rich smartphones has paved a way
to develop a solution that can cater to these
requirements. By employing a smartphone's built-in
magnetic field sensor, magnetic signatures were
collected inside buildings. These signatures displayed
a uniqueness in their patterns due to the presence of
different kinds of pillars, doors, elevators, etc.,
that consist of ferromagnetic materials like steel or
iron. We theoretically analyze the cause of this
uniqueness and then present an indoor localization
solution by classifying signatures based on their
patterns. However, to account for user walking speed
variations so as to provide an application usable to a
variety of users, we follow a dynamic
time-warping-based approach that is known to work on
similar signals irrespective of their variations in the
time axis. Our approach resulted in localization
distances of approximately 2m--6m with accuracies
between 80--100\% implying that it is sufficient to
walk short distances across hallways to be located by
the smartphone. The implementation of the application
on different smartphones yielded response times of less
than five secs, thereby validating the feasibility of
our approach and making it a viable solution.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:RWM,
author = "Bin Chen and Jian Su and Chew Lim Tan",
title = "Random walks down the mention graphs for event
coreference resolution",
journal = j-TIST,
volume = "4",
number = "4",
pages = "74:1--74:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508055",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Event coreference is an important task in event
extraction and other natural language processing tasks.
Despite its importance, it was merely discussed in
previous studies. In this article, we present a global
coreference resolution system dedicated to various
sophisticated event coreference phenomena. First, seven
resolvers are utilized to resolve different event and
object coreference mention pairs with a new instance
selection strategy and new linguistic features. Second,
a global solution-a modified random walk
partitioning-is employed for the chain formation. Being
the first attempt to apply the random walk model for
coreference resolution, the revised model utilizes a
sampling method, termination criterion, and stopping
probability to greatly improve the effectiveness of
random walk model for event coreference resolution.
Last but not least, the new model facilitates a
convenient way to incorporate sophisticated linguistic
constraints and preferences, the related object mention
graph, as well as pronoun coreference information not
used in previous studies for effective chain formation.
In total, these techniques impose more than 20\%
F-score improvement over the baseline system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Editors:2013:ISS,
author = "Editors",
title = "Introduction to special section on intelligent mobile
knowledge discovery and management systems",
journal = j-TIST,
volume = "5",
number = "1",
pages = "1:1--1:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542183",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ying:2013:MGT,
author = "Josh Jia-Ching Ying and Wang-Chien Lee and Vincent S.
Tseng",
title = "Mining geographic-temporal-semantic patterns in
trajectories for location prediction",
journal = j-TIST,
volume = "5",
number = "1",
pages = "2:1--2:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542184",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, research on location predictions by
mining trajectories of users has attracted a lot of
attention. Existing studies on this topic mostly treat
such predictions as just a type of location
recommendation, that is, they predict the next location
of a user using location recommenders. However, an user
usually visits somewhere for reasons other than
interestingness. In this article, we propose a novel
mining-based location prediction approach called
Geographic-Temporal-Semantic-based Location Prediction
(GTS-LP), which takes into account a user's
geographic-triggered intentions, temporal-triggered
intentions, and semantic-triggered intentions, to
estimate the probability of the user in visiting a
location. The core idea underlying our proposal is the
discovery of trajectory patterns of users, namely GTS
patterns, to capture frequent movements triggered by
the three kinds of intentions. To achieve this goal, we
define a new trajectory pattern to capture the key
properties of the behaviors that are motivated by the
three kinds of intentions from trajectories of users.
In our GTS-LP approach, we propose a series of novel
matching strategies to calculate the similarity between
the current movement of a user and discovered GTS
patterns based on various moving intentions. On the
basis of similitude, we make an online prediction as to
the location the user intends to visit. To the best of
our knowledge, this is the first work on location
prediction based on trajectory pattern mining that
explores the geographic, temporal, and semantic
properties simultaneously. By means of a comprehensive
evaluation using various real trajectory datasets, we
show that our proposed GTS-LP approach delivers
excellent performance and significantly outperforms
existing state-of-the-art location prediction
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2013:FTC,
author = "Lu-An Tang and Yu Zheng and Jing Yuan and Jiawei Han
and Alice Leung and Wen-Chih Peng and Thomas {La
Porta}",
title = "A framework of traveling companion discovery on
trajectory data streams",
journal = j-TIST,
volume = "5",
number = "1",
pages = "3:1--3:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542185",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The advance of mobile technologies leads to huge
volumes of spatio-temporal data collected in the form
of trajectory data streams. In this study, we
investigate the problem of discovering object groups
that travel together (i.e., traveling companions ) from
trajectory data streams. Such technique has broad
applications in the areas of scientific study,
transportation management, and military surveillance.
To discover traveling companions, the monitoring system
should cluster the objects of each snapshot and
intersect the clustering results to retrieve
moving-together objects. Since both clustering and
intersection steps involve high computational overhead,
the key issue of companion discovery is to improve the
efficiency of algorithms. We propose the models of
closed companion candidates and smart intersection to
accelerate data processing. A data structure termed
traveling buddy is designed to facilitate scalable and
flexible companion discovery from trajectory streams.
The traveling buddies are microgroups of objects that
are tightly bound together. By only storing the object
relationships rather than their spatial coordinates,
the buddies can be dynamically maintained along the
trajectory stream with low cost. Based on traveling
buddies, the system can discover companions without
accessing the object details. In addition, we extend
the proposed framework to discover companions on more
complicated scenarios with spatial and temporal
constraints, such as on the road network and
battlefield. The proposed methods are evaluated with
extensive experiments on both real and synthetic
datasets. Experimental results show that our proposed
buddy-based approach is an order of magnitude faster
than the baselines and achieves higher accuracy in
companion discovery.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Doo:2013:MTF,
author = "Myungcheol Doo and Ling Liu",
title = "{Mondrian} tree: a fast index for spatial alarm
processing",
journal = j-TIST,
volume = "5",
number = "1",
pages = "4:1--4:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542186",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With ubiquitous wireless connectivity and
technological advances in mobile devices, we witness
the growing demands and increasing market shares of
mobile intelligent systems and technologies for
real-time decision making and location-based knowledge
discovery. Spatial alarms are considered as one of the
fundamental capabilities for intelligent mobile
location-based systems. Like time-based alarms that
remind us the arrival of a future time point, spatial
alarms remind us the arrival of a future spatial point.
Existing approaches for scaling spatial alarm
processing are focused on computing Alarm-Free Regions
(A fr) and Alarm-Free Period (Afp) such that mobile
objects traveling within an Afr can safely hibernate
the alarm evaluation process for the computed Afp, to
save battery power, until approaching the nearest alarm
of interest. A key technical challenge in scaling
spatial alarm processing is to efficiently compute Afr
and Afp such that mobile objects traveling within an
Afr can safely hibernate the alarm evaluation process
during the computed Afp, while maintaining high
accuracy. In this article we argue that on-demand
computation of Afr is expensive and may not scale well
for dense populations of mobile objects. Instead, we
propose to maintain an index for both spatial alarms
and empty regions (Afr) such that for a given mobile
user's location, we can find relevant spatial alarms
and whether it is in an alarm-free region more
efficiently. We also show that conventional spatial
indexing methods, such as R-tree family, k -d tree,
Quadtree, and Grid, are by design not well suited to
index empty regions. We present Mondrian Tree --- a
region partitioning tree for indexing both spatial
alarms and alarm-free regions. We first introduce the
Mondrian Tree indexing algorithms, including index
construction, search, and maintenance. Then we describe
a suite of Mondrian Tree optimizations to further
enhance the performance of spatial alarm processing.
Our experimental evaluation shows that the Mondrian
Tree index, as an intelligent technology for mobile
systems, outperforms traditional index methods, such as
R-tree, Quadtree, and k -d tree, for spatial alarm
processing.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bonchi:2013:ISI,
author = "Francesco Bonchi and Wray Buntine and Ricard
Gavald{\'a} and Shengbo Guo",
title = "Introduction to the special issue on {Social Web}
mining",
journal = j-TIST,
volume = "5",
number = "1",
pages = "5:1--5:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542187",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{He:2013:DJS,
author = "Yulan He and Chenghua Lin and Wei Gao and Kam-Fai
Wong",
title = "Dynamic joint sentiment-topic model",
journal = j-TIST,
volume = "5",
number = "1",
pages = "6:1--6:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542188",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Social media data are produced continuously by a large
and uncontrolled number of users. The dynamic nature of
such data requires the sentiment and topic analysis
model to be also dynamically updated, capturing the
most recent language use of sentiments and topics in
text. We propose a dynamic Joint Sentiment-Topic model
(dJST) which allows the detection and tracking of views
of current and recurrent interests and shifts in topic
and sentiment. Both topic and sentiment dynamics are
captured by assuming that the current
sentiment-topic-specific word distributions are
generated according to the word distributions at
previous epochs. We study three different ways of
accounting for such dependency information: (1) sliding
window where the current sentiment-topic word
distributions are dependent on the previous
sentiment-topic-specific word distributions in the last
S epochs; (2) skip model where history sentiment topic
word distributions are considered by skipping some
epochs in between; and (3) multiscale model where
previous long- and short- timescale distributions are
taken into consideration. We derive efficient online
inference procedures to sequentially update the model
with newly arrived data and show the effectiveness of
our proposed model on the Mozilla add-on reviews
crawled between 2007 and 2011.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cataldi:2013:PET,
author = "Mario Cataldi and Luigi {Di Caro} and Claudio
Schifanella",
title = "Personalized emerging topic detection based on a term
aging model",
journal = j-TIST,
volume = "5",
number = "1",
pages = "7:1--7:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542189",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Twitter is a popular microblogging service that acts
as a ground-level information news flashes portal where
people with different background, age, and social
condition provide information about what is happening
in front of their eyes. This characteristic makes
Twitter probably the fastest information service in the
world. In this article, we recognize this role of
Twitter and propose a novel, user-aware topic detection
technique that permits to retrieve, in real time, the
most emerging topics of discussion expressed by the
community within the interests of specific users.
First, we analyze the topology of Twitter looking at
how the information spreads over the network, taking
into account the authority/influence of each active
user. Then, we make use of a novel term aging model to
compute the burstiness of each term, and provide a
graph-based method to retrieve the minimal set of terms
that can represent the corresponding topic. Finally,
since any user can have topic preferences inferable
from the shared content, we leverage such knowledge to
highlight the most emerging topics within her foci of
interest. As evaluation we then provide several
experiments together with a user study proving the
validity and reliability of the proposed approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Arias:2013:FTD,
author = "Marta Arias and Argimiro Arratia and Ramon Xuriguera",
title = "Forecasting with {Twitter} data",
journal = j-TIST,
volume = "5",
number = "1",
pages = "8:1--8:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542190",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The dramatic rise in the use of social network
platforms such as Facebook or Twitter has resulted in
the availability of vast and growing user-contributed
repositories of data. Exploiting this data by
extracting useful information from it has become a
great challenge in data mining and knowledge discovery.
A recently popular way of extracting useful information
from social network platforms is to build indicators,
often in the form of a time series, of general public
mood by means of sentiment analysis. Such indicators
have been shown to correlate with a diverse variety of
phenomena. In this article we follow this line of work
and set out to assess, in a rigorous manner, whether a
public sentiment indicator extracted from daily Twitter
messages can indeed improve the forecasting of social,
economic, or commercial indicators. To this end we have
collected and processed a large amount of Twitter posts
from March 2011 to the present date for two very
different domains: stock market and movie box office
revenue. For each of these domains, we build and
evaluate forecasting models for several target time
series both using and ignoring the Twitter-related
data. If Twitter does help, then this should be
reflected in the fact that the predictions of models
that use Twitter-related data are better than the
models that do not use this data. By systematically
varying the models that we use and their parameters,
together with other tuning factors such as lag or the
way in which we build our Twitter sentiment index, we
obtain a large dataset that allows us to test our
hypothesis under different experimental conditions.
Using a novel decision-tree-based technique that we
call summary tree we are able to mine this large
dataset and obtain automatically those configurations
that lead to an improvement in the prediction power of
our forecasting models. As a general result, we have
seen that nonlinear models do take advantage of Twitter
data when forecasting trends in volatility indices,
while linear ones fail systematically when forecasting
any kind of financial time series. In the case of
predicting box office revenue trend, it is support
vector machines that make best use of Twitter data. In
addition, we conduct statistical tests to determine the
relation between our Twitter time series and the
different target time series.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2013:CES,
author = "Kyumin Lee and James Caverlee and Zhiyuan Cheng and
Daniel Z. Sui",
title = "Campaign extraction from social media",
journal = j-TIST,
volume = "5",
number = "1",
pages = "9:1--9:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542191",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this manuscript, we study the problem of detecting
coordinated free text campaigns in large-scale social
media. These campaigns-ranging from coordinated spam
messages to promotional and advertising campaigns to
political astro-turfing-are growing in significance and
reach with the commensurate rise in massive-scale
social systems. Specifically, we propose and evaluate a
content-driven framework for effectively linking free
text posts with common ``talking points'' and
extracting campaigns from large-scale social media.
Three of the salient features of the campaign
extraction framework are: (i) first, we investigate
graph mining techniques for isolating coherent
campaigns from large message-based graphs; (ii) second,
we conduct a comprehensive comparative study of
text-based message correlation in message and user
levels; and (iii) finally, we analyze temporal
behaviors of various campaign types. Through an
experimental study over millions of Twitter messages we
identify five major types of campaigns-namely Spam,
Promotion, Template, News, and Celebrity campaigns-and
we show how these campaigns may be extracted with high
precision and recall.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fire:2013:CEL,
author = "Michael Fire and Lena Tenenboim-Chekina and Rami Puzis
and Ofrit Lesser and Lior Rokach and Yuval Elovici",
title = "Computationally efficient link prediction in a variety
of social networks",
journal = j-TIST,
volume = "5",
number = "1",
pages = "10:1--10:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542192",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online social networking sites have become
increasingly popular over the last few years. As a
result, new interdisciplinary research directions have
emerged in which social network analysis methods are
applied to networks containing hundreds of millions of
users. Unfortunately, links between individuals may be
missing either due to an imperfect acquirement process
or because they are not yet reflected in the online
network (i.e., friends in the real world did not form a
virtual connection). The primary bottleneck in link
prediction techniques is extracting the structural
features required for classifying links. In this
article, we propose a set of simple, easy-to-compute
structural features that can be analyzed to identify
missing links. We show that by using simple structural
features, a machine learning classifier can
successfully identify missing links, even when applied
to a predicament of classifying links between
individuals with at least one common friend. We also
present a method for calculating the amount of data
needed in order to build more accurate classifiers. The
new Friends measure and Same community features we
developed are shown to be good predictors for missing
links. An evaluation experiment was performed on ten
large social networks datasets: Academia.edu, DBLP,
Facebook, Flickr, Flixster, Google+, Gowalla,
TheMarker, Twitter, and YouTube. Our methods can
provide social network site operators with the
capability of helping users to find known, offline
contacts and to discover new friends online. They may
also be used for exposing hidden links in online social
networks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cruz:2013:CDV,
author = "Juan David Cruz and C{\'e}cile Bothorel and
Fran{\c{c}}ois Poulet",
title = "Community detection and visualization in social
networks: Integrating structural and semantic
information",
journal = j-TIST,
volume = "5",
number = "1",
pages = "11:1--11:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542193",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Due to the explosion of social networking and the
information sharing among their users, the interest in
analyzing social networks has increased over the recent
years. Two general interests in this kind of studies
are community detection and visualization. In the first
case, most of the classic algorithms for community
detection use only the structural information to
identify groups, that is, how clusters are formed
according to the topology of the relationships.
However, these methods do not take into account any
semantic information which could guide the clustering
process, and which may add elements to conduct further
analyses. In the second case most of the layout
algorithms for clustered graphs have been designed to
differentiate the groups within the graph, but they are
not designed to analyze the interactions between such
groups. Identifying these interactions gives an insight
into the way different communities exchange messages or
information, and allows the social network researcher
to identify key actors, roles, and paths from one
community to another. This article presents a novel
model to use, in a conjoint way, the semantic
information from the social network and its structural
information to, first, find structurally and
semantically related groups of nodes, and second, a
layout algorithm for clustered graphs which divides the
nodes into two types, one for nodes with edges
connecting other communities and another with nodes
connecting nodes only within their own community. With
this division the visualization tool focuses on the
connections between groups facilitating deep studies of
augmented social networks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cagliero:2013:PTR,
author = "Luca Cagliero and Alessandro Fiori and Luigi
Grimaudo",
title = "Personalized tag recommendation based on generalized
rules",
journal = j-TIST,
volume = "5",
number = "1",
pages = "12:1--12:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542194",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Tag recommendation is focused on recommending useful
tags to a user who is annotating a Web resource. A
relevant research issue is the recommendation of
additional tags to partially annotated resources, which
may be based on either personalized or collective
knowledge. However, since the annotation process is
usually not driven by any controlled vocabulary, the
collections of user-specific and collective annotations
are often very sparse. Indeed, the discovery of the
most significant associations among tags becomes a
challenging task. This article presents a novel
personalized tag recommendation system that discovers
and exploits generalized association rules, that is,
tag correlations holding at different abstraction
levels, to identify additional pertinent tags to
suggest. The use of generalized rules relevantly
improves the effectiveness of traditional rule-based
systems in coping with sparse tag collections, because:
(i) correlations hidden at the level of individual tags
may be anyhow figured out at higher abstraction levels
and (ii) low-level tag associations discovered from
collective data may be exploited to specialize
high-level associations discovered in the user-specific
context. The effectiveness of the proposed system has
been validated against other personalized approaches on
real-life and benchmark collections retrieved from the
popular photo-sharing system Flickr.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Elahi:2013:ALS,
author = "Mehdi Elahi and Francesco Ricci and Neil Rubens",
title = "Active learning strategies for rating elicitation in
collaborative filtering: a system-wide perspective",
journal = j-TIST,
volume = "5",
number = "1",
pages = "13:1--13:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542195",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The accuracy of collaborative-filtering recommender
systems largely depends on three factors: the quality
of the rating prediction algorithm, and the quantity
and quality of available ratings. While research in the
field of recommender systems often concentrates on
improving prediction algorithms, even the best
algorithms will fail if they are fed poor-quality data
during training, that is, garbage in, garbage out.
Active learning aims to remedy this problem by focusing
on obtaining better-quality data that more aptly
reflects a user's preferences. However, traditional
evaluation of active learning strategies has two major
flaws, which have significant negative ramifications on
accurately evaluating the system's performance
(prediction error, precision, and quantity of elicited
ratings). (1) Performance has been evaluated for each
user independently (ignoring system-wide improvements).
(2) Active learning strategies have been evaluated in
isolation from unsolicited user ratings (natural
acquisition). In this article we show that an elicited
rating has effects across the system, so a typical
user-centric evaluation which ignores any changes of
rating prediction of other users also ignores these
cumulative effects, which may be more influential on
the performance of the system as a whole (system
centric). We propose a new evaluation methodology and
use it to evaluate some novel and state-of-the-art
rating elicitation strategies. We found that the
system-wide effectiveness of a rating elicitation
strategy depends on the stage of the rating elicitation
process, and on the evaluation measures (MAE, NDCG, and
Precision). In particular, we show that using some
common user-centric strategies may actually degrade the
overall performance of a system. Finally, we show that
the performance of many common active learning
strategies changes significantly when evaluated
concurrently with the natural acquisition of ratings in
recommender systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{deMeo:2013:AUB,
author = "Pasquale de Meo and Emilio Ferrara and Fabian Abel and
Lora Aroyo and Geert-Jan Houben",
title = "Analyzing user behavior across social sharing
environments",
journal = j-TIST,
volume = "5",
number = "1",
pages = "14:1--14:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2535526",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this work we present an in-depth analysis of the
user behaviors on different Social Sharing systems. We
consider three popular platforms, Flickr, Delicious and
StumbleUpon, and, by combining techniques from social
network analysis with techniques from semantic
analysis, we characterize the tagging behavior as well
as the tendency to create friendship relationships of
the users of these platforms. The aim of our
investigation is to see if (and how) the features and
goals of a given Social Sharing system reflect on the
behavior of its users and, moreover, if there exists a
correlation between the social and tagging behavior of
the users. We report our findings in terms of the
characteristics of user profiles according to three
different dimensions: (i) intensity of user activities,
(ii) tag-based characteristics of user profiles, and
(iii) semantic characteristics of user profiles.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2013:ACL,
author = "Ziqiang Shi and Jiqing Han and Tieran Zheng",
title = "Audio classification with low-rank matrix
representation features",
journal = j-TIST,
volume = "5",
number = "1",
pages = "15:1--15:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542197",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, a novel framework based on trace norm
minimization for audio classification is proposed. In
this framework, both the feature extraction and
classification are obtained by solving corresponding
convex optimization problem with trace norm
regularization. For feature extraction, robust
principle component analysis (robust PCA) via
minimization a combination of the nuclear norm and the
l$_1$ -norm is used to extract low-rank matrix features
which are robust to white noise and gross corruption
for audio signal. These low-rank matrix features are
fed to a linear classifier where the weight and bias
are learned by solving similar trace norm constrained
problems. For this linear classifier, most methods find
the parameters, that is the weight matrix and bias in
batch-mode, which makes it inefficient for large scale
problems. In this article, we propose a parallel online
framework using accelerated proximal gradient method.
This framework has advantages in processing speed and
memory cost. In addition, as a result of the
regularization formulation of matrix classification,
the Lipschitz constant was given explicitly, and hence
the step size estimation of the general proximal
gradient method was omitted, and this part of computing
burden is saved in our approach. Extensive experiments
on real data sets for laugh/non-laugh and
applause/non-applause classification indicate that this
novel framework is effective and noise robust.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Osman:2013:TMA,
author = "Nardine Osman and Carles Sierra and Fiona Mcneill and
Juan Pane and John Debenham",
title = "Trust and matching algorithms for selecting suitable
agents",
journal = j-TIST,
volume = "5",
number = "1",
pages = "16:1--16:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542198",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article addresses the problem of finding suitable
agents to collaborate with for a given interaction in
distributed open systems, such as multiagent and P2P
systems. The agent in question is given the chance to
describe its confidence in its own capabilities.
However, since agents may be malicious, misinformed,
suffer from miscommunication, and so on, one also needs
to calculate how much trusted is that agent. This
article proposes a novel trust model that calculates
the expectation about an agent's future performance in
a given context by assessing both the agent's
willingness and capability through the semantic
comparison of the current context in question with the
agent's performance in past similar experiences. The
proposed mechanism for assessing trust may be applied
to any real world application where past commitments
are recorded and observations are made that assess
these commitments, and the model can then calculate
one's trust in another with respect to a future
commitment by assessing the other's past performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Montali:2013:MBC,
author = "Marco Montali and Fabrizio M. Maggi and Federico
Chesani and Paola Mello and Wil M. P. van der Aalst",
title = "Monitoring business constraints with the event
calculus",
journal = j-TIST,
volume = "5",
number = "1",
pages = "17:1--17:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542199",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Today, large business processes are composed of
smaller, autonomous, interconnected subsystems,
achieving modularity and robustness. Quite often, these
large processes comprise software components as well as
human actors, they face highly dynamic environments and
their subsystems are updated and evolve independently
of each other. Due to their dynamic nature and
complexity, it might be difficult, if not impossible,
to ensure at design-time that such systems will always
exhibit the desired/expected behaviors. This, in turn,
triggers the need for runtime verification and
monitoring facilities. These are needed to check
whether the actual behavior complies with expected
business constraints, internal/external regulations and
desired best practices. In this work, we present
Mobucon EC, a novel monitoring framework that tracks
streams of events and continuously determines the state
of business constraints. In Mobucon EC, business
constraints are defined using the declarative language
Declare. For the purpose of this work, Declare has been
suitably extended to support quantitative time
constraints and non-atomic, durative activities. The
logic-based language Event Calculus (EC) has been
adopted to provide a formal specification and semantics
to Declare constraints, while a light-weight, logic
programming-based EC tool supports dynamically
reasoning about partial, evolving execution traces. To
demonstrate the applicability of our approach, we
describe a case study about maritime safety and
security and provide a synthetic benchmark to evaluate
its scalability.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lu:2013:SBA,
author = "Qiang Lu and Ruoyun Huang and Yixin Chen and You Xu
and Weixiong Zhang and Guoliang Chen",
title = "A {SAT-based} approach to cost-sensitive temporally
expressive planning",
journal = j-TIST,
volume = "5",
number = "1",
pages = "18:1--18:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542200",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Complex features, such as temporal dependencies and
numerical cost constraints, are hallmarks of real-world
planning problems. In this article, we consider the
challenging problem of cost-sensitive temporally
expressive (CSTE) planning, which requires concurrency
of durative actions and optimization of action costs.
We first propose a scheme to translate a CSTE planning
problem to a minimum cost (MinCost) satisfiability
(SAT) problem and to integrate with a relaxed parallel
planning semantics for handling true temporal
expressiveness. Our scheme finds solution plans that
optimize temporal makespan, and also minimize total
action costs at the optimal makespan. We propose two
approaches for solving MinCost SAT. The first is based
on a transformation of a MinCost SAT problem to a
weighted partial Max-SAT (WPMax-SAT), and the second,
called BB-CDCL, is an integration of the
branch-and-bound technique and the conflict driven
clause learning (CDCL) method. We also develop a CSTE
customized variable branching scheme for BB-CDCL which
can significantly improve the search efficiency. Our
experiments on the existing CSTE benchmark domains show
that our planner compares favorably to the
state-of-the-art temporally expressive planners in both
efficiency and quality.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shieh:2013:RTS,
author = "Jyh-Ren Shieh and Ching-Yung Lin and Shun-Xuan Wang
and Ja-Ling Wu",
title = "Relational term-suggestion graphs incorporating
multipartite concept and expertise networks",
journal = j-TIST,
volume = "5",
number = "1",
pages = "19:1--19:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542201",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Term suggestions recommend query terms to a user based
on his initial query. Suggesting adequate terms is a
challenging issue. Most existing commercial search
engines suggest search terms based on the frequency of
prior used terms that match the leading alphabets the
user types. In this article, we present a novel
mechanism to construct semantic term-relation graphs to
suggest relevant search terms in the semantic level. We
built term-relation graphs based on multipartite
networks of existing social media, especially from
Wikipedia. The multipartite linkage networks of
contributor-term, term-category, and term-term are
extracted from Wikipedia to eventually form term
relation graphs. For fusing these multipartite linkage
networks, we propose to incorporate the
contributor-category networks to model the expertise of
the contributors. Based on our experiments, this step
has demonstrated clear enhancement on the accuracy of
the inferred relatedness of the term-semantic graphs.
Experiments on keyword-expanded search based on 200
TREC-5 ad-hoc topics showed obvious advantage of our
algorithms over existing approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:EEM,
author = "Tianshi Chen and Yunji Chen and Qi Guo and Zhi-Hua
Zhou and Ling Li and Zhiwei Xu",
title = "Effective and efficient microprocessor design space
exploration using unlabeled design configurations",
journal = j-TIST,
volume = "5",
number = "1",
pages = "20:1--20:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542202",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Ever-increasing design complexity and advances of
technology impose great challenges on the design of
modern microprocessors. One such challenge is to
determine promising microprocessor configurations to
meet specific design constraints, which is called
Design Space Exploration (DSE). In the computer
architecture community, supervised learning techniques
have been applied to DSE to build regression models for
predicting the qualities of design configurations. For
supervised learning, however, considerable simulation
costs are required for attaining the labeled design
configurations. Given limited resources, it is
difficult to achieve high accuracy. In this article,
inspired by recent advances in semisupervised learning
and active learning, we propose the COAL approach which
can exploit unlabeled design configurations to
significantly improve the models. Empirical study
demonstrates that COAL significantly outperforms a
state-of-the-art DSE technique by reducing mean squared
error by 35\% to 95\%, and thus, promising
architectures can be attained more efficiently.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Singh:2013:NBG,
author = "Munindar P. Singh",
title = "Norms as a basis for governing sociotechnical
systems",
journal = j-TIST,
volume = "5",
number = "1",
pages = "21:1--21:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542203",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We understand a sociotechnical system as a
multistakeholder cyber-physical system. We introduce
governance as the administration of such a system by
the stakeholders themselves. In this regard, governance
is a peer-to-peer notion and contrasts with traditional
management, which is a top-down hierarchical notion.
Traditionally, there is no computational support for
governance and it is achieved through out-of-band
interactions among system administrators. Not
surprisingly, traditional approaches simply do not
scale up to large sociotechnical systems. We develop an
approach for governance based on a computational
representation of norms in organizations. Our approach
is motivated by the Ocean Observatory Initiative, a
thirty-year \$400 million project, which supports a
variety of resources dealing with monitoring and
studying the world's oceans. These resources include
autonomous underwater vehicles, ocean gliders, buoys,
and other instrumentation as well as more traditional
computational resources. Our approach has the benefit
of directly reflecting stakeholder needs and assuring
stakeholders of the correctness of the resulting
governance decisions while yielding adaptive resource
allocation in the face of changes in both stakeholder
needs and physical circumstances.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{He:2014:ISI,
author = "Qi He and Juanzi Li and Rong Yan and John Yen and
Haizheng Zhang",
title = "Introduction to the {Special Issue on Linking Social
Granularity and Functions}",
journal = j-TIST,
volume = "5",
number = "2",
pages = "22:1--22:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2594452",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2014:IUI,
author = "Jinpeng Wang and Wayne Xin Zhao and Yulan He and
Xiaoming Li",
title = "Infer User Interests via Link Structure
Regularization",
journal = j-TIST,
volume = "5",
number = "2",
pages = "23:1--23:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2499380",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Learning user interests from online social networks
helps to better understand user behaviors and provides
useful guidance to design user-centric applications.
Apart from analyzing users' online content, it is also
important to consider users' social connections in the
social Web. Graph regularization methods have been
widely used in various text mining tasks, which can
leverage the graph structure information extracted from
data. Previously, graph regularization methods operate
under the cluster assumption that nearby nodes are more
similar and nodes on the same structure (typically
referred to as a cluster or a manifold) are likely to
be similar. We argue that learning user interests from
complex, sparse, and dynamic social networks should be
based on the link structure assumption under which node
similarities are evaluated based on the local link
structures instead of explicit links between two nodes.
We propose a regularization framework based on the
relation bipartite graph, which can be constructed from
any type of relations. Using Twitter as our case study,
we evaluate our proposed framework from social networks
built from retweet relations. Both quantitative and
qualitative experiments show that our proposed method
outperforms a few competitive baselines in learning
user interests over a set of predefined topics. It also
gives superior results compared to the baselines on
retweet prediction and topical authority
identification.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Javari:2014:CBC,
author = "Amin Javari and Mahdi Jalili",
title = "Cluster-Based Collaborative Filtering for Sign
Prediction in Social Networks with Positive and
Negative Links",
journal = j-TIST,
volume = "5",
number = "2",
pages = "24:1--24:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2501977",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Social network analysis and mining get
ever-increasingly important in recent years, which is
mainly due to the availability of large datasets and
advances in computing systems. A class of social
networks is those with positive and negative links. In
such networks, a positive link indicates friendship (or
trust), whereas links with a negative sign correspond
to enmity (or distrust). Predicting the sign of the
links in these networks is an important issue and has
many applications, such as friendship recommendation
and identifying malicious nodes in the network. In this
manuscript, we proposed a new method for sign
prediction in networks with positive and negative
links. Our algorithm is based first on clustering the
network into a number of clusters and then applying a
collaborative filtering algorithm. The clusters are
such that the number of intra-cluster negative links
and inter-cluster positive links are minimal, that is,
the clusters are socially balanced as much as possible
(a signed graph is socially balanced if it can be
divided into clusters with all positive links inside
the clusters and all negative links between them). We
then used similarity between the clusters (based on the
links between them) in a collaborative filtering
algorithm. Our experiments on a number of real datasets
showed that the proposed method outperformed previous
methods, including those based on social balance and
status theories and one based on a machine learning
framework (logistic regression in this work).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2014:CCB,
author = "Yi-Cheng Chen and Wen-Yuan Zhu and Wen-Chih Peng and
Wang-Chien Lee and Suh-Yin Lee",
title = "{CIM}: Community-Based Influence Maximization in
Social Networks",
journal = j-TIST,
volume = "5",
number = "2",
pages = "25:1--25:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532549",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Given a social graph, the problem of influence
maximization is to determine a set of nodes that
maximizes the spread of influences. While some recent
research has studied the problem of influence
maximization, these works are generally too time
consuming for practical use in a large-scale social
network. In this article, we develop a new framework,
community-based influence maximization (CIM), to tackle
the influence maximization problem with an emphasis on
the time efficiency issue. Our proposed framework, CIM,
comprises three phases: (i) community detection, (ii)
candidate generation, and (iii) seed selection.
Specifically, phase (i) discovers the community
structure of the network; phase (ii) uses the
information of communities to narrow down the possible
seed candidates; and phase (iii) finalizes the seed
nodes from the candidate set. By exploiting the
properties of the community structures, we are able to
avoid overlapped information and thus efficiently
select the number of seeds to maximize information
spreads. The experimental results on both synthetic and
real datasets show that the proposed CIM algorithm
significantly outperforms the state-of-the-art
algorithms in terms of efficiency and scalability, with
almost no compromise of effectiveness.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2014:SOG,
author = "Jaewon Yang and Jure Leskovec",
title = "Structure and Overlaps of Ground-Truth Communities in
Networks",
journal = j-TIST,
volume = "5",
number = "2",
pages = "26:1--26:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2594454",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One of the main organizing principles in real-world
networks is that of network communities, where sets of
nodes organize into densely linked clusters. Even
though detection of such communities is of great
interest, understanding the structure communities in
large networks remains relatively limited. In
particular, due to the unavailability of labeled
ground-truth data, it was traditionally very hard to
develop accurate models of network community structure.
Here we use six large social, collaboration, and
information networks where nodes explicitly state their
ground-truth community memberships. For example, nodes
in social networks join into explicitly defined
interest based groups, and we use such groups as
explicitly labeled ground-truth communities. We use
such ground-truth communities to study their structural
signatures by analyzing how ground-truth communities
emerge in networks and how they overlap. We observe
some surprising phenomena. First, ground-truth
communities contain high-degree hub nodes that reside
in community overlaps and link to most of the members
of the community. Second, the overlaps of communities
are more densely connected than the non-overlapping
parts of communities. We show that this in contrast to
the conventional wisdom that community overlaps are
more sparsely connected than the non-overlapping parts
themselves. We then show that many existing models of
network communities do not capture dense community
overlaps. This in turn means that most present models
and community detection methods confuse overlaps as
separate communities. In contrast, we present the
community-affiliation graph model (AGM), a conceptual
model of network community structure. We demonstrate
that AGM reliably captures the overall structure of
networks as well as the overlapping and hierarchical
nature of network communities.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gong:2014:JLP,
author = "Neil Zhenqiang Gong and Ameet Talwalkar and Lester
Mackey and Ling Huang and Eui Chul Richard Shin and
Emil Stefanov and Elaine (Runting) Shi and Dawn Song",
title = "Joint Link Prediction and Attribute Inference Using a
Social-Attribute Network",
journal = j-TIST,
volume = "5",
number = "2",
pages = "27:1--27:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2594455",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The effects of social influence and homophily suggest
that both network structure and node-attribute
information should inform the tasks of link prediction
and node-attribute inference. Recently, Yin et al.
[2010a, 2010b] proposed an attribute-augmented social
network model, which we call Social-Attribute Network
(SAN), to integrate network structure and node
attributes to perform both link prediction and
attribute inference. They focused on generalizing the
random walk with a restart algorithm to the SAN
framework and showed improved performance. In this
article, we extend the SAN framework with several
leading supervised and unsupervised link-prediction
algorithms and demonstrate performance improvement for
each algorithm on both link prediction and attribute
inference. Moreover, we make the novel observation that
attribute inference can help inform link prediction,
that is, link-prediction accuracy is further improved
by first inferring missing attributes. We
comprehensively evaluate these algorithms and compare
them with other existing algorithms using a novel,
large-scale Google+ dataset, which we make publicly
available
(http://www.cs.berkeley.edu/~stevgong/gplus.html).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Pool:2014:DDC,
author = "Simon Pool and Francesco Bonchi and Matthijs van
Leeuwen",
title = "Description-Driven Community Detection",
journal = j-TIST,
volume = "5",
number = "2",
pages = "28:1--28:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2517088",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Traditional approaches to community detection, as
studied by physicists, sociologists, and more recently
computer scientists, aim at simply partitioning the
social network graph. However, with the advent of
online social networking sites, richer data has become
available: beyond the link information, each user in
the network is annotated with additional information,
for example, demographics, shopping behavior, or
interests. In this context, it is therefore important
to develop mining methods which can take advantage of
all available information. In the case of community
detection, this means finding good communities (a set
of nodes cohesive in the social graph) which are
associated with good descriptions in terms of user
information (node attributes). Having good descriptions
associated to our models make them understandable by
domain experts and thus more useful in real-world
applications. Another requirement dictated by
real-world applications, is to develop methods that can
use, when available, any domain-specific background
knowledge. In the case of community detection the
background knowledge could be a vague description of
the communities sought in a specific application, or
some prototypical nodes (e.g., good customers in the
past), that represent what the analyst is looking for
(a community of similar users). Towards this goal, in
this article, we define and study the problem of
finding a diverse set of cohesive communities with
concise descriptions. We propose an effective algorithm
that alternates between two phases: a hill-climbing
phase producing (possibly overlapping) communities, and
a description induction phase which uses techniques
from supervised pattern set mining. Our framework has
the nice feature of being able to build well-described
cohesive communities starting from any given
description or seed set of nodes, which makes it very
flexible and easily applicable in real-world
applications. Our experimental evaluation confirms that
the proposed method discovers cohesive communities with
concise descriptions in realistic and large online
social networks such as Delicious, Flickr, and
LastFM.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2014:LPH,
author = "Nan Li and William Cushing and Subbarao Kambhampati
and Sungwook Yoon",
title = "Learning Probabilistic Hierarchical Task Networks as
Probabilistic Context-Free Grammars to Capture User
Preferences",
journal = j-TIST,
volume = "5",
number = "2",
pages = "29:1--29:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2589481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We introduce an algorithm to automatically learn
probabilistic hierarchical task networks (pHTNs) that
capture a user's preferences on plans by observing only
the user's behavior. HTNs are a common choice of
representation for a variety of purposes in planning,
including work on learning in planning. Our
contributions are twofold. First, in contrast with
prior work, which employs HTNs to represent domain
physics or search control knowledge, we use HTNs to
model user preferences. Second, while most prior work
on HTN learning requires additional information (e.g.,
annotated traces or tasks) to assist the learning
process, our system only takes plan traces as input.
Initially, we will assume that users carry out
preferred plans more frequently, and thus the observed
distribution of plans is an accurate representation of
user preference. We then generalize to the situation
where feasibility constraints frequently prevent the
execution of preferred plans. Taking the prevalent
perspective of viewing HTNs as grammars over primitive
actions, we adapt an expectation-maximization (EM)
technique from the discipline of probabilistic grammar
induction to acquire probabilistic context-free
grammars (pCFG) that capture the distribution on plans.
To account for the difference between the distributions
of possible and preferred plans, we subsequently modify
this core EM technique by rescaling its input. We
empirically demonstrate that the proposed approaches
are able to learn HTNs representing user preferences
better than the inside-outside algorithm. Furthermore,
when feasibility constraints are obfuscated, the
algorithm with rescaled input performs better than the
algorithm with the original input.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reches:2014:FEC,
author = "Shulamit Reches and Meir Kalech and Philip Hendrix",
title = "A Framework for Effectively Choosing between
Alternative Candidate Partners",
journal = j-TIST,
volume = "5",
number = "2",
pages = "30:1--30:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2589482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many multi-agent settings require that agents identify
appropriate partners or teammates with whom to work on
tasks. When selecting potential partners, agents may
benefit from obtaining information about the
alternatives, for instance, through gossip (i.e., by
consulting others) or reputation systems. When
information is uncertain and associated with cost,
deciding on the amount of information needed is a hard
optimization problem. This article defines a
statistical model, the Information-Acquisition Source
Utility model (IASU), by which agents, operating in an
uncertain world, can determine (1) which information
sources they should request for information, and (2)
the amount of information to collect about potential
partners from each source. To maximize the expected
gain from the choice, IASU computes the utility of
choosing a partner by estimating the benefit of
additional information. The article presents empirical
studies through a simulation domain as well as a
real-world domain of restaurants. We compare the IASU
model to other relevant models and show that the use of
the IASU model significantly increases agents' overall
utility.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Heath:2014:CST,
author = "Derrall Heath and David Norton and Dan Ventura",
title = "Conveying Semantics through Visual Metaphor",
journal = j-TIST,
volume = "5",
number = "2",
pages = "31:1--31:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2589483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In the field of visual art, metaphor is a way to
communicate meaning to the viewer. We present a
computational system for communicating visual metaphor
that can identify adjectives for describing an image
based on a low-level visual feature representation of
the image. We show that the system can use this
visual-linguistic association to render source images
that convey the meaning of adjectives in a way
consistent with human understanding. Our conclusions
are based on a detailed analysis of how the system's
artifacts cluster, how these clusters correspond to the
semantic relationships of adjectives as documented in
WordNet, and how these clusters correspond to human
opinion.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lian:2014:MCH,
author = "Defu Lian and Xing Xie",
title = "Mining Check-In History for Personalized Location
Naming",
journal = j-TIST,
volume = "5",
number = "2",
pages = "32:1--32:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2490890",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many innovative location-based services have been
established to offer users greater convenience in their
everyday lives. These services usually cannot map
user's physical locations into semantic names
automatically. The semantic names of locations provide
important context for mobile recommendations and
advertisements. In this article, we proposed a novel
location naming approach which can automatically
provide semantic names for users given their locations
and time. In particular, when a user opens a GPS device
and submits a query with her physical location and
time, she will be returned the most appropriate
semantic name. In our approach, we drew an analogy
between location naming and local search, and designed
a local search framework to propose a spatiotemporal
and user preference (STUP) model for location naming.
STUP combined three components, user preference (UP),
spatial preference (SP), and temporal preference (TP),
by leveraging learning-to-rank techniques. We evaluated
STUP on 466,190 check-ins of 5,805 users from Shanghai
and 135,052 check-ins of 1,361 users from Beijing. The
results showed that SP was most effective among three
components and that UP can provide personalized
semantic names, and thus it was a necessity for
location naming. Although TP was not as discriminative
as the others, it can still be beneficial when
integrated with SP and UP. Finally, according to the
experimental results, STUP outperformed the proposed
baselines and returned accurate semantic names for
23.6\% and 26.6\% of the testing queries from Beijing
and Shanghai, respectively.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bian:2014:EUP,
author = "Jiang Bian and Bo Long and Lihong Li and Taesup Moon
and Anlei Dong and Yi Chang",
title = "Exploiting User Preference for Online Learning in
{Web} Content Optimization Systems",
journal = j-TIST,
volume = "5",
number = "2",
pages = "33:1--33:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2493259",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Web portal services have become an important medium to
deliver digital content (e.g. news, advertisements,
etc.) to Web users in a timely fashion. To attract more
users to various content modules on the Web portal, it
is necessary to design a recommender system that can
effectively achieve Web portal content optimization by
automatically estimating content item attractiveness
and relevance to user interests. The state-of-the-art
online learning methodology adapts dedicated pointwise
models to independently estimate the attractiveness
score for each candidate content item. Although such
pointwise models can be easily adapted for online
recommendation, there still remain a few critical
problems. First, this pointwise methodology fails to
use invaluable user preferences between content items.
Moreover, the performance of pointwise models decreases
drastically when facing the problem of sparse learning
samples. To address these problems, we propose
exploring a new dynamic pairwise learning methodology
for Web portal content optimization in which we exploit
dynamic user preferences extracted based on users'
actions on portal services to compute the
attractiveness scores of content items. In this
article, we introduce two specific pairwise learning
algorithms, a straightforward graph-based algorithm and
a formalized Bayesian modeling one. Experiments on
large-scale data from a commercial Web portal
demonstrate the significant improvement of pairwise
methodologies over the baseline pointwise models.
Further analysis illustrates that our new pairwise
learning approaches can benefit personalized
recommendation more than pointwise models, since the
data sparsity is more critical for personalized content
optimization.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hossain:2014:AFS,
author = "M. Shahriar Hossain and Manish Marwah and Amip Shah
and Layne T. Watson and Naren Ramakrishnan",
title = "{AutoLCA}: a Framework for Sustainable Redesign and
Assessment of Products",
journal = j-TIST,
volume = "5",
number = "2",
pages = "34:1--34:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2505270",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With increasing public consciousness regarding
sustainability, companies are ever more eager to
introduce eco-friendly products and services. Assessing
environmental footprints and designing sustainable
products are challenging tasks since they require
analysis of each component of a product through their
life cycle. To achieve sustainable design of products,
companies need to evaluate the environmental impact of
their system, identify the major contributors to the
footprint, and select the design alternative with the
lowest environmental footprint. In this article, we
formulate sustainable design as a series of clustering
and classification problems, and propose a framework
called AutoLCA that simplifies the effort of estimating
the environmental footprint of a product bill of
materials by more than an order of magnitude over
current methods, which are mostly labor intensive. We
apply AutoLCA to real data from a large computer
manufacturer. We conduct a case study on bill of
materials of four different products, perform a
``hotspot'' assessment analysis to identify major
contributors to carbon footprint, and determine design
alternatives that can reduce the carbon footprint from
1\% to 36\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2014:MLC,
author = "Chuan Shi and Xiangnan Kong and Di Fu and Philip S. Yu
and Bin Wu",
title = "Multi-Label Classification Based on Multi-Objective
Optimization",
journal = j-TIST,
volume = "5",
number = "2",
pages = "35:1--35:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2505272",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multi-label classification refers to the task of
predicting potentially multiple labels for a given
instance. Conventional multi-label classification
approaches focus on single objective setting, where the
learning algorithm optimizes over a single performance
criterion (e.g., Ranking Loss ) or a heuristic
function. The basic assumption is that the optimization
over one single objective can improve the overall
performance of multi-label classification and meet the
requirements of various applications. However, in many
real applications, an optimal multi-label classifier
may need to consider the trade-offs among multiple
inconsistent objectives, such as minimizing Hamming
Loss while maximizing Micro F1. In this article, we
study the problem of multi-objective multi-label
classification and propose a novel solution (called
Moml) to optimize over multiple objectives
simultaneously. Note that optimization objectives may
be inconsistent, even conflicting, thus one cannot
identify a single solution that is optimal on all
objectives. Our Moml algorithm finds a set of
non-dominated solutions which are optimal according to
different trade-offs among multiple objectives. So
users can flexibly construct various predictive models
from the solution set, which provides more meaningful
classification results in different application
scenarios. Empirical studies on real-world tasks
demonstrate that the Moml can effectively boost the
overall performance of multi-label classification by
optimizing over multiple objectives simultaneously.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2014:DSM,
author = "Xuning Tang and Christopher C. Yang",
title = "Detecting Social Media Hidden Communities Using
Dynamic Stochastic Blockmodel with Temporal {Dirichlet}
Process",
journal = j-TIST,
volume = "5",
number = "2",
pages = "36:1--36:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2517085",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Detecting evolving hidden communities within dynamic
social networks has attracted significant attention
recently due to its broad applications in e-commerce,
online social media, security intelligence, public
health, and other areas. Many community network
detection techniques employ a two-stage approach to
identify and detect evolutionary relationships between
communities of two adjacent time epochs. These
techniques often identify communities with high
temporal variation, since the two-stage approach
detects communities of each epoch independently without
considering the continuity of communities across two
time epochs. Other techniques require identification of
a predefined number of hidden communities which is not
realistic in many applications. To overcome these
limitations, we propose the Dynamic Stochastic
Blockmodel with Temporal Dirichlet Process, which
enables the detection of hidden communities and tracks
their evolution simultaneously from a network stream.
The number of hidden communities is automatically
determined by a temporal Dirichlet process without
human intervention. We tested our proposed technique on
three different testbeds with results identifying a
high performance level when compared to the baseline
algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2014:ISS,
author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai
Yang",
title = "Introduction to the Special Section on Urban
Computing",
journal = j-TIST,
volume = "5",
number = "3",
pages = "37:1--37:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2642650",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2014:UCC,
author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai
Yang",
title = "Urban Computing: Concepts, Methodologies, and
Applications",
journal = j-TIST,
volume = "5",
number = "3",
pages = "38:1--38:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629592",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Urbanization's rapid progress has modernized many
people's lives but also engendered big issues, such as
traffic congestion, energy consumption, and pollution.
Urban computing aims to tackle these issues by using
the data that has been generated in cities (e.g.,
traffic flow, human mobility, and geographical data).
Urban computing connects urban sensing, data
management, data analytics, and service providing into
a recurrent process for an unobtrusive and continuous
improvement of people's lives, city operation systems,
and the environment. Urban computing is an
interdisciplinary field where computer sciences meet
conventional city-related fields, like transportation,
civil engineering, environment, economy, ecology, and
sociology in the context of urban spaces. This article
first introduces the concept of urban computing,
discussing its general framework and key challenges
from the perspective of computer sciences. Second, we
classify the applications of urban computing into seven
categories, consisting of urban planning,
transportation, the environment, energy, social,
economy, and public safety and security, presenting
representative scenarios in each category. Third, we
summarize the typical technologies that are needed in
urban computing into four folds, which are about urban
sensing, urban data management, knowledge fusion across
heterogeneous data, and urban data visualization.
Finally, we give an outlook on the future of urban
computing, suggesting a few research topics that are
somehow missing in the community.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Etienne:2014:MBC,
author = "C{\^o}me Etienne and Oukhellou Latifa",
title = "Model-Based Count Series Clustering for Bike Sharing
System Usage Mining: a Case Study with the {V{\'e}lib'}
System of {Paris}",
journal = j-TIST,
volume = "5",
number = "3",
pages = "39:1--39:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2560188",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Today, more and more bicycle sharing systems (BSSs)
are being introduced in big cities. These
transportation systems generate sizable transportation
data, the mining of which can reveal the underlying
urban phenomenon linked to city dynamics. This article
presents a statistical model to automatically analyze
the trip data of a bike sharing system. The proposed
solution partitions (i.e., clusters) the stations
according to their usage profiles. To do so, count
series describing the stations's usage through
departure/arrival counts per hour throughout the day
are built and analyzed. The model for processing these
count series is based on Poisson mixtures and
introduces a station scaling factor that handles the
differences between the stations's global usage.
Differences between weekday and weekend usage are also
taken into account. This model identifies the latent
factors that shape the geography of trips, and the
results may thus offer insights into the relationships
between station neighborhood type (its amenities, its
demographics, etc.) and the generated mobility
patterns. In other words, the proposed method brings to
light the different functions in different areas that
induce specific patterns in BSS data. These potentials
are demonstrated through an in-depth analysis of the
results obtained on the Paris V{\'e}lib' large-scale
bike sharing system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ying:2014:MUC,
author = "Josh Jia-Ching Ying and Wen-Ning Kuo and Vincent S.
Tseng and Eric Hsueh-Chan Lu",
title = "Mining User Check-In Behavior with a Random Walk for
Urban Point-of-Interest Recommendations",
journal = j-TIST,
volume = "5",
number = "3",
pages = "40:1--40:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2523068",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, research into the mining of user
check-in behavior for point-of-interest (POI)
recommendations has attracted a lot of attention.
Existing studies on this topic mainly treat such
recommendations in a traditional manner-that is, they
treat POIs as items and check-ins as ratings. However,
users usually visit a place for reasons other than to
simply say that they have visited. In this article, we
propose an approach referred to as Urban POI-Walk
(UPOI-Walk), which takes into account a user's
social-triggered intentions (SI), preference-triggered
intentions (PreI), and popularity-triggered intentions
(PopI), to estimate the probability of a user
checking-in to a POI. The core idea of UPOI-Walk
involves building a HITS-based random walk on the
normalized check-in network, thus supporting the
prediction of POI properties related to each user's
preferences. To achieve this goal, we define several
user--POI graphs to capture the key properties of the
check-in behavior motivated by user intentions. In our
UPOI-Walk approach, we propose a new kind of random
walk model-Dynamic HITS-based Random Walk-which
comprehensively considers the relevance between POIs
and users from different aspects. On the basis of
similitude, we make an online recommendation as to the
POI the user intends to visit. To the best of our
knowledge, this is the first work on urban POI
recommendations that considers user check-in behavior
motivated by SI, PreI, and PopI in location-based
social network data. Through comprehensive experimental
evaluations on two real datasets, the proposed
UPOI-Walk is shown to deliver excellent performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mcardle:2014:UDF,
author = "Gavin Mcardle and Eoghan Furey and Aonghus Lawlor and
Alexei Pozdnoukhov",
title = "Using Digital Footprints for a City-Scale Traffic
Simulation",
journal = j-TIST,
volume = "5",
number = "3",
pages = "41:1--41:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2517028",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article introduces a microsimulation of urban
traffic flows within a large-scale scenario implemented
for the Greater Dublin region in Ireland.
Traditionally, the data available for traffic
simulations come from a population census and dedicated
road surveys that only partly cover shopping, leisure,
or recreational trips. To account for the latter, the
presented traffic modeling framework exploits the
digital footprints of city inhabitants on services such
as Twitter and Foursquare. We enriched the model with
findings from our previous studies on geographical
layout of communities in a country-wide mobile phone
network to account for socially related journeys. These
datasets were used to calibrate a variant of a
radiation model of spatial choice, which we introduced
in order to drive individuals' decisions on trip
destinations within an assigned daily activity plan. We
observed that given the distribution of population, the
workplace locations, a comprehensive set of urban
facilities, and a list of typical activity sequences of
city dwellers collected within a national travel
survey, the developed microsimulation reproduces not
only the journey statistics such as peak travel periods
but also the traffic volumes at main road segments with
surprising accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Momtazpour:2014:CSI,
author = "Marjan Momtazpour and Patrick Butler and Naren
Ramakrishnan and M. Shahriar Hossain and Mohammad C.
Bozchalui and Ratnesh Sharma",
title = "Charging and Storage Infrastructure Design for
Electric Vehicles",
journal = j-TIST,
volume = "5",
number = "3",
pages = "42:1--42:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2513567",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Ushered by recent developments in various areas of
science and technology, modern energy systems are going
to be an inevitable part of our societies. Smart grids
are one of these modern systems that have attracted
many research activities in recent years. Before
utilizing the next generation of smart grids, we should
have a comprehensive understanding of the
interdependent energy networks and processes.
Next-generation energy systems networks cannot be
effectively designed, analyzed, and controlled in
isolation from the social, economic, sensing, and
control contexts in which they operate. In this
article, we present a novel framework to support
charging and storage infrastructure design for electric
vehicles. We develop coordinated clustering techniques
to work with network models of urban environments to
aid in placement of charging stations for an electrical
vehicle deployment scenario. Furthermore, we evaluate
the network before and after the deployment of charging
stations, to recommend the installation of appropriate
storage units to overcome the extra load imposed on the
network by the charging stations. We demonstrate the
multiple factors that can be simultaneously leveraged
in our framework to achieve practical urban deployment.
Our ultimate goal is to help realize sustainable energy
system management in urban electrical infrastructure by
modeling and analyzing networks of interactions between
electric systems and urban populations.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tan:2014:OOT,
author = "Chang Tan and Qi Liu and Enhong Chen and Hui Xiong and
Xiang Wu",
title = "Object-Oriented Travel Package Recommendation",
journal = j-TIST,
volume = "5",
number = "3",
pages = "43:1--43:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542665",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Providing better travel services for tourists is one
of the important applications in urban computing.
Though many recommender systems have been developed for
enhancing the quality of travel service, most of them
lack a systematic and open framework to dynamically
incorporate multiple types of additional context
information existing in the tourism domain, such as the
travel area, season, and price of travel packages. To
that end, in this article, we propose an open
framework, the Objected-Oriented Recommender System
(ORS), for the developers performing personalized
travel package recommendations to tourists. This
framework has the ability to import all the available
additional context information to the travel package
recommendation process in a cost-effective way.
Specifically, the different types of additional
information are extracted and uniformly represented as
feature--value pairs. Then, we define the Object, which
is the collection of the feature--value pairs. We
propose two models that can be used in the ORS
framework for extracting the implicit relationships
among Objects. The Objected-Oriented Topic Model (OTM)
can extract the topics conditioned on the intrinsic
feature--value pairs of the Objects. The
Objected-Oriented Bayesian Network (OBN) can
effectively infer the cotravel probability of two
tourists by calculating the co-occurrence time of
feature--value pairs belonging to different kinds of
Objects. Based on the relationships mined by OTM or
OBN, the recommendation list is generated by the
collaborative filtering method. Finally, we evaluate
these two models and the ORS framework on real-world
travel package data, and the experimental results show
that the ORS framework is more flexible in terms of
incorporating additional context information, and thus
leads to better performances for travel package
recommendations. Meanwhile, for feature selection in
ORS, we define the feature information entropy, and the
experimental results demonstrate that using features
with lower entropies usually leads to better
recommendation results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gurung:2014:TIP,
author = "Sashi Gurung and Dan Lin and Wei Jiang and Ali Hurson
and Rui Zhang",
title = "Traffic Information Publication with Privacy
Preservation",
journal = j-TIST,
volume = "5",
number = "3",
pages = "44:1--44:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542666",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We are experiencing the expanding use of
location-based services such as AT\&T's TeleNav GPS
Navigator and Intel's Thing Finder. Existing
location-based services have collected a large amount
of location data, which has great potential for
statistical usage in applications like traffic flow
analysis, infrastructure planning, and advertisement
dissemination. The key challenge is how to wisely use
the data without violating each user's location privacy
concerns. In this article, we first identify a new
privacy problem, namely, the inference-route problem,
and then present our anonymization algorithms for
privacy-preserving trajectory publishing. The
experimental results have demonstrated that our
approach outperforms the latest related work in terms
of both efficiency and effectiveness.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hsieh:2014:MRT,
author = "Hsun-Ping Hsieh and Cheng-Te Li and Shou-De Lin",
title = "Measuring and Recommending Time-Sensitive Routes from
Location-Based Data",
journal = j-TIST,
volume = "5",
number = "3",
pages = "45:1--45:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542668",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Location-based services allow users to perform
geospatial recording actions, which facilitates the
mining of the moving activities of human beings. This
article proposes to recommend time-sensitive trip
routes consisting of a sequence of locations with
associated timestamps based on knowledge extracted from
large-scale timestamped location sequence data (e.g.,
check-ins and GPS traces). We argue that a good route
should consider (a) the popularity of places, (b) the
visiting order of places, (c) the proper visiting time
of each place, and (d) the proper transit time from one
place to another. By devising a statistical model, we
integrate these four factors into a route goodness
function that aims to measure the quality of a route.
Equipped with the route goodness, we recommend
time-sensitive routes for two scenarios. The first is
about constructing the route based on the
user-specified source location with the starting time.
The second is about composing the route between the
specified source location and the destination location
given a starting time. To handle these queries, we
propose a search method, Guidance Search, which
consists of a novel heuristic satisfaction function
that guides the search toward the destination location
and a backward checking mechanism to boost the
effectiveness of the constructed route. Experiments on
the Gowalla check-in datasets demonstrate the
effectiveness of our model on detecting real routes and
performing cloze test of routes, comparing with other
baseline methods. We also develop a system TripRouter
as a real-time demo platform.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Joseph:2014:CIB,
author = "Kenneth Joseph and Kathleen M. Carley and Jason I.
Hong",
title = "Check-ins in {``Blau Space''}: Applying {Blau}'s
Macrosociological Theory to Foursquare Check-ins from
New {York} City",
journal = j-TIST,
volume = "5",
number = "3",
pages = "46:1--46:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2566617",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Peter Blau was one of the first to define a latent
social space and utilize it to provide concrete
hypotheses. Blau defines social structure via social
``parameters'' (constraints). Actors that are closer
together (more homogeneous) in this social parameter
space are more likely to interact. One of Blau's most
important hypotheses resulting from this work was that
the consolidation of parameters could lead to isolated
social groups. For example, the consolidation of race
and income might lead to segregation. In the present
work, we use Foursquare data from New York City to
explore evidence of homogeneity along certain social
parameters and consolidation that breeds social
isolation in communities of locations checked in to by
similar users. More specifically, we first test the
extent to which communities detected via Latent
Dirichlet Allocation are homogeneous across a set of
four social constraints-racial homophily, income
homophily, personal interest homophily and physical
space. Using a bootstrapping approach, we find that 14
(of 20) communities are statistically, and all but one
qualitatively, homogeneous along one of these social
constraints, showing the relevance of Blau's latent
space model in venue communities determined via user
check-in behavior. We then consider the extent to which
communities with consolidated parameters, those
homogeneous on more than one parameter, represent
socially isolated populations. We find communities
homogeneous on multiple parameters, including a
homosexual community and a ``hipster'' community, that
show support for Blau's hypothesis that consolidation
breeds social isolation. We consider these results in
the context of mediated communication, in particular in
the context of self-representation on social media.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mahmud:2014:HLI,
author = "Jalal Mahmud and Jeffrey Nichols and Clemens Drews",
title = "Home Location Identification of {Twitter} Users",
journal = j-TIST,
volume = "5",
number = "3",
pages = "47:1--47:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2528548",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present a new algorithm for inferring the home
location of Twitter users at different granularities,
including city, state, time zone, or geographic region,
using the content of users' tweets and their tweeting
behavior. Unlike existing approaches, our algorithm
uses an ensemble of statistical and heuristic
classifiers to predict locations and makes use of a
geographic gazetteer dictionary to identify place-name
entities. We find that a hierarchical classification
approach, where time zone, state, or geographic region
is predicted first and city is predicted next, can
improve prediction accuracy. We have also analyzed
movement variations of Twitter users, built a
classifier to predict whether a user was travelling in
a certain period of time, and use that to further
improve the location detection accuracy. Experimental
evidence suggests that our algorithm works well in
practice and outperforms the best existing algorithms
for predicting the home location of Twitter users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Neviarouskaya:2014:IIT,
author = "Alena Neviarouskaya and Masaki Aono and Helmut
Prendinger and Mitsuru Ishizuka",
title = "Intelligent Interface for Textual Attitude Analysis",
journal = j-TIST,
volume = "5",
number = "3",
pages = "48:1--48:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2535912",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article describes a novel intelligent interface
for attitude sensing in text driven by a robust
computational tool for the analysis of fine-grained
attitudes (emotions, judgments, and appreciations)
expressed in text. The module responsible for textual
attitude analysis was developed using a compositional
linguistic approach based on the attitude-conveying
lexicon, the analysis of syntactic and dependency
relations between words in a sentence, the
compositionality principle applied at various
grammatical levels, the rules elaborated for
semantically distinct verb classes, and a method
considering the hierarchy of concepts. The performance
of this module was evaluated on sentences from personal
stories about life experiences. The developed web-based
interface supports recognition of nine emotions,
positive and negative judgments, and positive and
negative appreciations conveyed in text. It allows
users to adjust parameters, to enable or disable
various functionality components of the algorithm, and
to select the format of text annotation and attitude
statistics visualization.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Song:2014:UGF,
author = "Yicheng Song and Yongdong Zhang and Juan Cao and
Jinhui Tang and Xingyu Gao and Jintao Li",
title = "A Unified Geolocation Framework for {Web} Videos",
journal = j-TIST,
volume = "5",
number = "3",
pages = "49:1--49:??",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2533989",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Jul 18 14:11:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we propose a unified geolocation
framework to automatically determine where on the earth
a web video was shot. We analyze different social,
visual, and textual relationships from a real-world
dataset and find four relationships with apparent
geography clues that can be used for web video
geolocation. Then, the geolocation process is
formulated as an optimization problem that
simultaneously takes the social, visual, and textual
relationships into consideration. The optimization
problem is solved by an iterative procedure, which can
be interpreted as a propagation of the geography
information among the web video social network.
Extensive experiments on a real-world dataset clearly
demonstrate the effectiveness of our proposed
framework, with the geolocation accuracy higher than
state-of-the-art approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhao:2014:PRL,
author = "Yi-Liang Zhao and Liqiang Nie and Xiangyu Wang and
Tat-Seng Chua",
title = "Personalized Recommendations of Locally Interesting
Venues to Tourists via Cross-Region Community
Matching",
journal = j-TIST,
volume = "5",
number = "3",
pages = "50:1--50:??",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532439",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Jul 18 14:11:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "You are in a new city. You are not familiar with the
places and neighborhoods. You want to know all about
the exciting sights, food outlets, and cultural venues
that the locals frequent, in particular those that suit
your personal interests. Even though there exist many
mapping, local search, and travel assistance sites,
they mostly provide popular and famous listings such as
Statue of Liberty and Eiffel Tower, which are
well-known places but may not suit your personal needs
or interests. Therefore, there is a gap between what
tourists want and what dominant tourism resources are
providing. In this work, we seek to provide a solution
to bridge this gap by exploiting the rich
user-generated location contents in location-based
social networks in order to offer tourists the most
relevant and personalized local venue recommendations.
In particular, we first propose a novel Bayesian
approach to extract the social dimensions of people at
different geographical regions to capture their latent
local interests. We next mine the local interest
communities in each geographical region. We then
represent each local community using aggregated
behaviors of community members. Finally, we correlate
communities across different regions and generate venue
recommendations to tourists via cross-region community
matching. We have sampled a representative subset of
check-ins from Foursquare and experimentally verified
the effectiveness of our proposed approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2014:VNF,
author = "Shuaiqiang Wang and Jiankai Sun and Byron J. Gao and
Jun Ma",
title = "{VSRank}: a Novel Framework for Ranking-Based
Collaborative Filtering",
journal = j-TIST,
volume = "5",
number = "3",
pages = "51:1--51:??",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542048",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Jul 18 14:11:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative filtering (CF) is an effective technique
addressing the information overload problem. CF
approaches generally fall into two categories: rating
based and ranking based. The former makes
recommendations based on historical rating scores of
items and the latter based on their rankings.
Ranking-based CF has demonstrated advantages in
recommendation accuracy, being able to capture the
preference similarity between users even if their
rating scores differ significantly. In this study, we
propose VSRank, a novel framework that seeks accuracy
improvement of ranking-based CF through adaptation of
the vector space model. In VSRank, we consider each
user as a document and his or her pairwise relative
preferences as terms. We then use a novel
degree-specialty weighting scheme resembling TF-IDF to
weight the terms. Extensive experiments on benchmarks
in comparison with the state-of-the-art approaches
demonstrate the promise of our approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Castells:2015:ISI,
author = "Pablo Castells and Jun Wang and Rub{\'e}n Lara and
Dell Zhang",
title = "Introduction to the Special Issue on Diversity and
Discovery in Recommender Systems",
journal = j-TIST,
volume = "5",
number = "4",
pages = "52:1--52:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668113",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ribeiro:2015:MPE,
author = "Marco Tulio Ribeiro and Nivio Ziviani and Edleno
{Silva De Moura} and Itamar Hata and Anisio Lacerda and
Adriano Veloso",
title = "Multiobjective {Pareto}-Efficient Approaches for
Recommender Systems",
journal = j-TIST,
volume = "5",
number = "4",
pages = "53:1--53:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629350",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommender systems are quickly becoming ubiquitous in
applications such as e-commerce, social media channels,
and content providers, among others, acting as an
enabling mechanism designed to overcome the information
overload problem by improving browsing and consumption
experience. A typical task in many recommender systems
is to output a ranked list of items, so that items
placed higher in the rank are more likely to be
interesting to the users. Interestingness measures
include how accurate, novel, and diverse are the
suggested items, and the objective is usually to
produce ranked lists optimizing one of these measures.
Suggesting items that are simultaneously accurate,
novel, and diverse is much more challenging, since this
may lead to a conflicting-objective problem, in which
the attempt to improve a measure further may result in
worsening other measures. In this article, we propose
new approaches for multiobjective recommender systems
based on the concept of Pareto efficiency-a state
achieved when the system is devised in the most
efficient manner in the sense that there is no way to
improve one of the objectives without making any other
objective worse off. Given that existing multiobjective
recommendation algorithms differ in their level of
accuracy, diversity, and novelty, we exploit the
Pareto-efficiency concept in two distinct manners: (i)
the aggregation of ranked lists produced by existing
algorithms into a single one, which we call
Pareto-efficient ranking, and (ii) the weighted
combination of existing algorithms resulting in a
hybrid one, which we call Pareto-efficient
hybridization. Our evaluation involves two real
application scenarios: music recommendation with
implicit feedback (i.e., Last.fm) and movie
recommendation with explicit feedback (i.e.,
MovieLens). We show that the proposed Pareto-efficient
approaches are effective in suggesting items that are
likely to be simultaneously accurate, diverse, and
novel. We discuss scenarios where the system achieves
high levels of diversity and novelty without
compromising its accuracy. Further, comparison against
multiobjective baselines reveals improvements in terms
of accuracy (from 10.4\% to 10.9\%), novelty (from
5.7\% to 7.5\%), and diversity (from 1.6\% to 4.2\%).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Adamopoulos:2015:URS,
author = "Panagiotis Adamopoulos and Alexander Tuzhilin",
title = "On Unexpectedness in Recommender Systems: Or How to
Better Expect the Unexpected",
journal = j-TIST,
volume = "5",
number = "4",
pages = "54:1--54:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2559952",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Although the broad social and business success of
recommender systems has been achieved across several
domains, there is still a long way to go in terms of
user satisfaction. One of the key dimensions for
significant improvement is the concept of
unexpectedness. In this article, we propose a method to
improve user satisfaction by generating unexpected
recommendations based on the utility theory of
economics. In particular, we propose a new concept of
unexpectedness as recommending to users those items
that depart from what they would expect from the system
--- the consideration set of each user. We define and
formalize the concept of unexpectedness and discuss how
it differs from the related notions of novelty,
serendipity, and diversity. In addition, we suggest
several mechanisms for specifying the users'
expectations and propose specific performance metrics
to measure the unexpectedness of recommendation lists.
We also take into consideration the quality of
recommendations using certain utility functions and
present an algorithm for providing users with
unexpected recommendations of high quality that are
hard to discover but fairly match their interests.
Finally, we conduct several experiments on
``real-world'' datasets and compare our recommendation
results with other methods. The proposed approach
outperforms these baseline methods in terms of
unexpectedness and other important metrics, such as
coverage, aggregate diversity and dispersion, while
avoiding any accuracy loss.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kucuktunc:2015:DCR,
author = "Onur K{\"u}{\c{c}}{\"u}ktun{\c{c}} and Erik Saule and
Kamer Kaya and {\"U}mit V. {\c{C}}ataly{\"u}rek",
title = "Diversifying Citation Recommendations",
journal = j-TIST,
volume = "5",
number = "4",
pages = "55:1--55:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668106",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Literature search is one of the most important steps
of academic research. With more than 100,000 papers
published each year just in computer science,
performing a complete literature search becomes a
Herculean task. Some of the existing approaches and
tools for literature search cannot compete with the
characteristics of today's literature, and they suffer
from ambiguity and homonymy. Techniques based on
citation information are more robust to the mentioned
issues. Thus, we recently built a Web service called
the advisor, which provides personalized
recommendations to researchers based on their papers of
interest. Since most recommendation methods may return
redundant results, diversifying the results of the
search process is necessary to increase the amount of
information that one can reach via an automated search.
This article targets the problem of result
diversification in citation-based bibliographic search,
assuming that the citation graph itself is the only
information available and no categories or intents are
known. The contribution of this work is threefold. We
survey various random walk--based diversification
methods and enhance them with the direction awareness
property to allow users to reach either old,
foundational (possibly well-cited and well-known)
research papers or recent (most likely less-known)
ones. Next, we propose a set of novel algorithms based
on vertex selection and query refinement. A set of
experiments with various evaluation criteria shows that
the proposed $ \gamma $-RLM algorithm performs better
than the existing approaches and is suitable for
real-time bibliographic search in practice.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Javari:2015:ANR,
author = "Amin Javari and Mahdi Jalili",
title = "Accurate and Novel Recommendations: an Algorithm Based
on Popularity Forecasting",
journal = j-TIST,
volume = "5",
number = "4",
pages = "56:1--56:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668107",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommender systems are in the center of network
science, and they are becoming increasingly important
in individual businesses for providing efficient,
personalized services and products to users. Previous
research in the field of recommendation systems focused
on improving the precision of the system through
designing more accurate recommendation lists. Recently,
the community has been paying attention to diversity
and novelty of recommendation lists as key
characteristics of modern recommender systems. In many
cases, novelty and precision do not go hand in hand,
and the accuracy--novelty dilemma is one of the
challenging problems in recommender systems, which
needs efforts in making a trade-off between them. In
this work, we propose an algorithm for providing novel
and accurate recommendation to users. We consider the
standard definition of accuracy and an effective
self-information--based measure to assess novelty of
the recommendation list. The proposed algorithm is
based on item popularity, which is defined as the
number of votes received in a certain time interval.
Wavelet transform is used for analyzing popularity time
series and forecasting their trend in future timesteps.
We introduce two filtering algorithms based on the
information extracted from analyzing popularity time
series of the items. The popularity-based filtering
algorithm gives a higher chance to items that are
predicted to be popular in future timesteps. The other
algorithm, denoted as a novelty and population-based
filtering algorithm, is to move toward items with low
popularity in past timesteps that are predicted to
become popular in the future. The introduced filters
can be applied as adds-on to any recommendation
algorithm. In this article, we use the proposed
algorithms to improve the performance of classic
recommenders, including item-based collaborative
filtering and Markov-based recommender systems. The
experiments show that the algorithms could
significantly improve both the accuracy and effective
novelty of the classic recommenders.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shen:2015:ISI,
author = "Dou Shen and Deepak Agarwal",
title = "Introduction to the Special Issue on Online
Advertising",
journal = j-TIST,
volume = "5",
number = "4",
pages = "57:1--57:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668123",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhu:2015:MMU,
author = "Hengshu Zhu and Enhong Chen and Hui Xiong and Kuifei
Yu and Huanhuan Cao and Jilei Tian",
title = "Mining Mobile User Preferences for Personalized
Context-Aware Recommendation",
journal = j-TIST,
volume = "5",
number = "4",
pages = "58:1--58:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532515",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recent advances in mobile devices and their sensing
capabilities have enabled the collection of rich
contextual information and mobile device usage records
through the device logs. These context-rich logs open a
venue for mining the personal preferences of mobile
users under varying contexts and thus enabling the
development of personalized context-aware
recommendation and other related services, such as
mobile online advertising. In this article, we
illustrate how to extract personal context-aware
preferences from the context-rich device logs, or
context logs for short, and exploit these identified
preferences for building personalized context-aware
recommender systems. A critical challenge along this
line is that the context log of each individual user
may not contain sufficient data for mining his or her
context-aware preferences. Therefore, we propose to
first learn common context-aware preferences from the
context logs of many users. Then, the preference of
each user can be represented as a distribution of these
common context-aware preferences. Specifically, we
develop two approaches for mining common context-aware
preferences based on two different assumptions, namely,
context-independent and context-dependent assumptions,
which can fit into different application scenarios.
Finally, extensive experiments on a real-world dataset
show that both approaches are effective and outperform
baselines with respect to mining personal context-aware
preferences for mobile users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ashkan:2015:LQA,
author = "Azin Ashkan and Charles L. A. Clarke",
title = "Location- and Query-Aware Modeling of Browsing and
Click Behavior in Sponsored Search",
journal = j-TIST,
volume = "5",
number = "4",
pages = "59:1--59:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2534398",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An online advertisement's clickthrough rate provides a
fundamental measure of its quality, which is widely
used in ad selection strategies. Unfortunately, ads
placed in contexts where they are rarely viewed-or
where users are unlikely to be interested in commercial
results-may receive few clicks regardless of their
quality. In this article, we model the variability of a
user's browsing behavior for the purpose of click
analysis and prediction in sponsored search. Our model
incorporates several important contextual factors that
influence ad clickthrough rates, including the user's
query and ad placement on search engine result pages.
We formally model these factors with respect to the
list of ads displayed on a result page, the probability
that the user will initiate browsing of this list, and
the persistence of the user in browsing the list. We
incorporate these factors into existing click models by
augmenting them with appropriate query and location
biases. Using expectation maximization, we learn the
parameters of these augmented models from click signals
recorded in the logs of a commercial search engine. To
evaluate the performance of the models and to compare
them with state-of-the-art performance, we apply
standard evaluation metrics, including log-likelihood
and perplexity. Our evaluation results indicate that,
through the incorporation of query and location biases,
significant improvements can be achieved in predicting
browsing and click behavior in sponsored search. In
addition, we explore the extent to which these biases
actually reflect varying behavioral patterns. Our
observations confirm that correlations exist between
the biases and user search behavior.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Qin:2015:SSA,
author = "Tao Qin and Wei Chen and Tie-Yan Liu",
title = "Sponsored Search Auctions: Recent Advances and Future
Directions",
journal = j-TIST,
volume = "5",
number = "4",
pages = "60:1--60:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668108",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Sponsored search has been proven to be a successful
business model, and sponsored search auctions have
become a hot research direction. There have been many
exciting advances in this field, especially in recent
years, while at the same time, there are also many open
problems waiting for us to resolve. In this article, we
provide a comprehensive review of sponsored search
auctions in hopes of helping both industry
practitioners and academic researchers to become
familiar with this field, to know the state of the art,
and to identify future research topics. Specifically,
we organize the article into two parts. In the first
part, we review research works on sponsored search
auctions with basic settings, where fully rational
advertisers without budget constraints, preknown
click-through rates (CTRs) without interdependence, and
exact match between queries and keywords are assumed.
Under these assumptions, we first introduce the
generalized second price (GSP) auction, which is the
most popularly used auction mechanism in the industry.
Then we give the definitions of several well-studied
equilibria and review the latest results on GSP's
efficiency and revenue in these equilibria. In the
second part, we introduce some advanced topics on
sponsored search auctions. In these advanced topics,
one or more assumptions made in the basic settings are
relaxed. For example, the CTR of an ad could be unknown
and dependent on other ads; keywords could be broadly
matched to queries before auctions are executed; and
advertisers are not necessarily fully rational, could
have budget constraints, and may prefer rich bidding
languages. Given that the research on these advanced
topics is still immature, in each section of the second
part, we provide our opinions on how to make further
advances, in addition to describing what has been done
by researchers in the corresponding direction.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chapelle:2015:SSR,
author = "Olivier Chapelle and Eren Manavoglu and Romer
Rosales",
title = "Simple and Scalable Response Prediction for Display
Advertising",
journal = j-TIST,
volume = "5",
number = "4",
pages = "61:1--61:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532128",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Clickthrough and conversation rates estimation are two
core predictions tasks in display advertising. We
present in this article a machine learning framework
based on logistic regression that is specifically
designed to tackle the specifics of display
advertising. The resulting system has the following
characteristics: It is easy to implement and deploy, it
is highly scalable (we have trained it on terabytes of
data), and it provides models with state-of-the-art
accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Balakrishnan:2015:RTB,
author = "Raju Balakrishnan and Rushi P. Bhatt",
title = "Real-Time Bid Optimization for Group-Buying Ads",
journal = j-TIST,
volume = "5",
number = "4",
pages = "62:1--62:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532441",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Group-buying ads seeking a minimum number of customers
before the deal expiry are increasingly used by
daily-deal providers. Unlike traditional web ads, the
advertiser's profits for group-buying ads depend on the
time to expiry and additional customers needed to
satisfy the minimum group size. Since both these
quantities are time-dependent, optimal bid amounts to
maximize profits change with every impression.
Consequently, traditional static bidding strategies are
far from optimal. Instead, bid values need to be
optimized in real-time to maximize expected bidder
profits. This online optimization of deal profits is
made possible by the advent of ad exchanges offering
real-time (spot) bidding. To this end, we propose a
real-time bidding strategy for group-buying deals based
on the online optimization of bid values. We derive the
expected bidder profit of deals as a function of the
bid amounts and dynamically vary the bids to maximize
profits. Furthermore, to satisfy time constraints of
the online bidding, we present methods of minimizing
computation timings. Subsequently, we derive the
real-time ad selection, admissibility, and real-time
bidding of the traditional ads as the special cases of
the proposed method. We evaluate the proposed bidding,
selection, and admission strategies on a multimillion
click stream of 935 ads. The proposed real-time
bidding, selection, and admissibility show significant
profit increases over the existing strategies. Further
experiments illustrate the robustness of the bidding
and acceptable computation timings.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2015:IAC,
author = "Qingzhong Liu and Zhongxue Chen",
title = "Improved Approaches with Calibrated Neighboring Joint
Density to Steganalysis and Seam-Carved Forgery
Detection in {JPEG} Images",
journal = j-TIST,
volume = "5",
number = "4",
pages = "63:1--63:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2560365",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Steganalysis and forgery detection in image forensics
are generally investigated separately. We have designed
a method targeting the detection of both steganography
and seam-carved forgery in JPEG images. We analyze the
neighboring joint density of the DCT coefficients and
reveal the difference between the untouched image and
the modified version. In realistic detection, the
untouched image and the modified version may not be
obtained at the same time, and different JPEG images
may have different neighboring joint density features.
By exploring the self-calibration under different shift
recompressions, we propose calibrated neighboring joint
density-based approaches with a simple feature set to
distinguish steganograms and tampered images from
untouched ones. Our study shows that this approach has
multiple promising applications in image forensics.
Compared to the state-of-the-art steganalysis
detectors, our approach delivers better or comparable
detection performances with a much smaller feature set
while detecting several JPEG-based steganographic
systems including DCT-embedding-based adaptive
steganography and Yet Another Steganographic Scheme
(YASS). Our approach is also effective in detecting
seam-carved forgery in JPEG images. By integrating
calibrated neighboring density with spatial domain rich
models that were originally designed for steganalysis,
the hybrid approach obtains the best detection accuracy
to discriminate seam-carved forgery from an untouched
image. Our study also offers a promising manner to
explore steganalysis and forgery detection together.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Azaria:2015:SID,
author = "Amos Azaria and Zinovi Rabinovich and Claudia V.
Goldman and Sarit Kraus",
title = "Strategic Information Disclosure to People with
Multiple Alternatives",
journal = j-TIST,
volume = "5",
number = "4",
pages = "64:1--64:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2558397",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we study automated agents that are
designed to encourage humans to take some actions over
others by strategically disclosing key pieces of
information. To this end, we utilize the framework of
persuasion games-a branch of game theory that deals
with asymmetric interactions where one player (Sender)
possesses more information about the world, but it is
only the other player (Receiver) who can take an
action. In particular, we use an extended persuasion
model, where the Sender's information is imperfect and
the Receiver has more than two alternative actions
available. We design a computational algorithm that,
from the Sender's standpoint, calculates the optimal
information disclosure rule. The algorithm is
parameterized by the Receiver's decision model (i.e.,
what choice he will make based on the information
disclosed by the Sender) and can be retuned
accordingly. We then provide an extensive experimental
study of the algorithm's performance in interactions
with human Receivers. First, we consider a fully
rational (in the Bayesian sense) Receiver decision
model and experimentally show the efficacy of the
resulting Sender's solution in a routing domain.
Despite the discrepancy in the Sender's and the
Receiver's utilities from each of the Receiver's
choices, our Sender agent successfully persuaded human
Receivers to select an option more beneficial for the
agent. Dropping the Receiver's rationality assumption,
we introduce a machine learning procedure that
generates a more realistic human Receiver model. We
then show its significant benefit to the Sender
solution by repeating our routing experiment. To
complete our study, we introduce a second
(supply--demand) experimental domain and, by
contrasting it with the routing domain, obtain general
guidelines for a Sender on how to construct a Receiver
model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2015:SPA,
author = "Si Liu and Qiang Chen and Shuicheng Yan and Changsheng
Xu and Hanqing Lu",
title = "{Snap \& Play}: Auto-Generated Personalized
Find-the-Difference Game",
journal = j-TIST,
volume = "5",
number = "4",
pages = "65:1--65:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668109",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, by taking a popular game, the
Find-the-Difference (FiDi) game, as a concrete example,
we explore how state-of-the-art image processing
techniques can assist in developing a personalized,
automatic, and dynamic game. Unlike the traditional
FiDi game, where image pairs (source image and target
image) with five different patches are manually
produced by professional game developers, the proposed
Personalized FiDi (P-FiDi) electronic game can be
played in a fully automatic Snap \& Play mode. Snap
means that players first take photos with their digital
cameras. The newly captured photos are used as source
images and fed into the P-FiDi system to autogenerate
the counterpart target images for users to play. Four
steps are adopted to autogenerate target images:
enhancing the visual quality of source images,
extracting some changeable patches from the source
image, selecting the most suitable combination of
changeable patches and difference styles for the image,
and generating the differences on the target image with
state-of-the-art image processing techniques. In
addition, the P-FiDi game can be easily redesigned for
the im-game advertising. Extensive experiments show
that the P-FiDi electronic game is satisfying in terms
of player experience, seamless advertisement, and
technical feasibility.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reches:2015:CCU,
author = "Shulamit Reches and Meir Kalech",
title = "Choosing a Candidate Using Efficient Allocation of
Biased Information",
journal = j-TIST,
volume = "5",
number = "4",
pages = "66:1--66:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2558327",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article deals with a decision-making problem
concerning an agent who wants to choose a partner from
multiple candidates for long-term collaboration. To
choose the best partner, the agent can rely on prior
information he knows about the candidates. However, to
improve his decision, he can request additional
information from information sources. Nonetheless,
acquiring information from external information sources
about candidates may be biased due to different
personalities of the agent searching for a partner and
the information source. In addition, information may be
costly. Considering the bias and the cost of the
information sources, the optimization problem addressed
in this article is threefold: (1) determining the
necessary amount of additional information, (2)
selecting information sources from which to request the
information, and (3) choosing the candidates on whom to
request the additional information. We propose a
heuristic to solve this optimization problem. The
results of experiments on simulated and real-world
domains demonstrate the efficiency of our algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhuang:2015:CDS,
author = "Jinfeng Zhuang and Tao Mei and Steven C. H. Hoi and
Xian-Sheng Hua and Yongdong Zhang",
title = "Community Discovery from Social Media by Low-Rank
Matrix Recovery",
journal = j-TIST,
volume = "5",
number = "4",
pages = "67:1--67:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668110",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The pervasive usage and reach of social media have
attracted a surge of attention in the multimedia
research community. Community discovery from social
media has therefore become an important yet challenging
issue. However, due to the subjective generating
process, the explicitly observed communities (e.g.,
group-user and user-user relationship) are often noisy
and incomplete in nature. This paper presents a novel
approach to discovering communities from social media,
including the group membership and user friend
structure, by exploring a low-rank matrix recovery
technique. In particular, we take Flickr as one
exemplary social media platform. We first model the
observed indicator matrix of the Flickr community as a
summation of a low-rank true matrix and a sparse error
matrix. We then formulate an optimization problem by
regularizing the true matrix to coincide with the
available rich context and content (i.e., photos and
their associated tags). An iterative algorithm is
developed to recover the true community indicator
matrix. The proposed approach leads to a variety of
social applications, including community visualization,
interest group refinement, friend suggestion, and
influential user identification. The evaluations on a
large-scale testbed, consisting of 4,919 Flickr users,
1,467 interest groups, and over five million photos,
show that our approach opens a new yet effective
perspective to solve social network problems with
sparse learning technique. Despite being focused on
Flickr, our technique can be applied in any other
social media community.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2015:IPI,
author = "Yiyang Yang and Zhiguo Gong and Leong Hou U.",
title = "Identifying Points of Interest Using Heterogeneous
Features",
journal = j-TIST,
volume = "5",
number = "4",
pages = "68:1--68:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668111",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Deducing trip-related information from web-scale
datasets has received large amounts of attention
recently. Identifying points of interest (POIs) in
geo-tagged photos is one of these problems. The problem
can be viewed as a standard clustering problem of
partitioning two-dimensional objects. In this work, we
study spectral clustering, which is the first attempt
for the identification of POIs. However, there is no
unified approach to assigning the subjective clustering
parameters, and these parameters vary immensely in
different metropolitans and locations. To address this
issue, we study a self-tuning technique that can
properly determine the parameters for the clustering
needed. Besides geographical information, web photos
inherently store other rich information. Such
heterogeneous information can be used to enhance the
identification accuracy. Thereby, we study a novel
refinement framework that is based on the tightness and
cohesion degree of the additional information. We
thoroughly demonstrate our findings by web-scale
datasets collected from Flickr.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ji:2015:WLM,
author = "Rongrong Ji and Yue Gao and Wei Liu and Xing Xie and
Qi Tian and Xuelong Li",
title = "When Location Meets Social Multimedia: a Survey on
Vision-Based Recognition and Mining for Geo-Social
Multimedia Analytics",
journal = j-TIST,
volume = "6",
number = "1",
pages = "1:1--1:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2597181",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Coming with the popularity of multimedia sharing
platforms such as Facebook and Flickr, recent years
have witnessed an explosive growth of geographical tags
on social multimedia content. This trend enables a wide
variety of emerging applications, for example, mobile
location search, landmark recognition, scene
reconstruction, and touristic recommendation, which
range from purely research prototype to commercial
systems. In this article, we give a comprehensive
survey on these applications, covering recent advances
in recognition and mining of geographical-aware social
multimedia. We review related work in the past decade
regarding to location recognition, scene summarization,
tourism suggestion, 3D building modeling, mobile visual
search and city navigation. At the end, we further
discuss potential challenges, future topics, as well as
open issues related to geo-social multimedia computing,
recognition, mining, and analytics.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chin:2015:FPS,
author = "Wei-Sheng Chin and Yong Zhuang and Yu-Chin Juan and
Chih-Jen Lin",
title = "A Fast Parallel Stochastic Gradient Method for Matrix
Factorization in Shared Memory Systems",
journal = j-TIST,
volume = "6",
number = "1",
pages = "2:1--2:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668133",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Matrix factorization is known to be an effective
method for recommender systems that are given only the
ratings from users to items. Currently, stochastic
gradient (SG) method is one of the most popular
algorithms for matrix factorization. However, as a
sequential approach, SG is difficult to be parallelized
for handling web-scale problems. In this article, we
develop a fast parallel SG method, FPSG, for shared
memory systems. By dramatically reducing the cache-miss
rate and carefully addressing the load balance of
threads, FPSG is more efficient than state-of-the-art
parallel algorithms for matrix factorization.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Feuz:2015:TLA,
author = "Kyle D. Feuz and Diane J. Cook",
title = "Transfer Learning across Feature-Rich Heterogeneous
Feature Spaces via {Feature-Space Remapping (FSR)}",
journal = j-TIST,
volume = "6",
number = "1",
pages = "3:1--3:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629528",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Transfer learning aims to improve performance on a
target task by utilizing previous knowledge learned
from source tasks. In this paper we introduce a novel
heterogeneous transfer learning technique,
Feature-Space Remapping (FSR), which transfers
knowledge between domains with different feature
spaces. This is accomplished without requiring typical
feature-feature, feature instance, or instance-instance
co-occurrence data. Instead we relate features in
different feature-spaces through the construction of
metafeatures. We show how these techniques can utilize
multiple source datasets to construct an ensemble
learner which further improves performance. We apply
FSR to an activity recognition problem and a document
classification problem. The ensemble technique is able
to outperform all other baselines and even performs
better than a classifier trained using a large amount
of labeled data in the target domain. These problems
are especially difficult because, in addition to having
different feature-spaces, the marginal probability
distributions and the class labels are also different.
This work extends the state of the art in transfer
learning by considering large transfer across
dramatically different spaces.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Patel:2015:DSI,
author = "Dhaval Patel",
title = "On Discovery of Spatiotemporal Influence-Based Moving
Clusters",
journal = j-TIST,
volume = "6",
number = "1",
pages = "4:1--4:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2631926",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A moving object cluster is a set of objects that move
close to each other for a long time interval. Existing
works have utilized object trajectories to discover
moving object clusters efficiently. In this article, we
define a spatiotemporal influence-based moving cluster
that captures spatiotemporal influence spread over a
set of spatial objects. A spatiotemporal
influence-based moving cluster is a sequence of spatial
clusters, where each cluster is a set of nearby
objects, such that each object in a cluster influences
at least one object in the next immediate cluster and
is also influenced by an object from the immediate
preceding cluster. Real-life examples of spatiotemporal
influence-based moving clusters include diffusion of
infectious diseases and spread of innovative ideas. We
study the discovery of spatiotemporal influence-based
moving clusters in a database of spatiotemporal events.
While the search space for discovering all
spatiotemporal influence-based moving clusters is
prohibitively huge, we design a method, STIMer, to
efficiently retrieve the maximal answer. The algorithm
STIMer adopts a top-down recursive refinement method to
generate the maximal spatiotemporal influence-based
moving clusters directly. Empirical studies on the real
data as well as large synthetic data demonstrate the
effectiveness and efficiency of our method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sepehri-Rad:2015:ICW,
author = "Hoda Sepehri-Rad and Denilson Barbosa",
title = "Identifying Controversial {Wikipedia} Articles Using
Editor Collaboration Networks",
journal = j-TIST,
volume = "6",
number = "1",
pages = "5:1--5:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2630075",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Wikipedia is probably the most commonly used knowledge
reference nowadays, and the high quality of its
articles is widely acknowledged. Nevertheless,
disagreement among editors often causes some articles
to become controversial over time. These articles span
thousands of popular topics, including religion,
history, and politics, to name a few, and are manually
tagged as controversial by the editors, which is
clearly suboptimal. Moreover, disagreement, bias, and
conflict are expressed quite differently in Wikipedia
compared to other social media, rendering previous
approaches ineffective. On the other hand, the social
process of editing Wikipedia is partially captured in
the edit history of the articles, opening the door for
novel approaches. This article describes a novel
controversy model that builds on the interaction
history of the editors and not only predicts
controversy but also sheds light on the process that
leads to controversy. The model considers the
collaboration history of pairs of editors to predict
their attitude toward one another. This is done in a
supervised way, where the votes of Wikipedia
administrator elections are used as labels indicating
agreement (i.e., support vote) or disagreement (i.e.,
oppose vote). From each article, a collaboration
network is built, capturing the pairwise attitude among
editors, allowing the accurate detection of
controversy. Extensive experimental results establish
the superiority of this approach compared to previous
work and very competitive baselines on a wide range of
settings.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Changuel:2015:RSU,
author = "Sahar Changuel and Nicolas Labroche and Bernadette
Bouchon-Meunier",
title = "Resources Sequencing Using Automatic
Prerequisite--Outcome Annotation",
journal = j-TIST,
volume = "6",
number = "1",
pages = "6:1--6:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2505349",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The objective of any tutoring system is to provide
resources to learners that are adapted to their current
state of knowledge. With the availability of a large
variety of online content and the disjunctive nature of
results provided by traditional search engines, it
becomes crucial to provide learners with adapted
learning paths that propose a sequence of resources
that match their learning objectives. In an ideal case,
the sequence of documents provided to the learner
should be such that each new document relies on
concepts that have been already defined in previous
documents. Thus, the problem of determining an
effective learning path from a corpus of web documents
depends on the accurate identification of outcome and
prerequisite concepts in these documents and on their
ordering according to this information. Until now, only
a few works have been proposed to distinguish between
prerequisite and outcome concepts, and to the best of
our knowledge, no method has been introduced so far to
benefit from this information to produce a meaningful
learning path. To this aim, this article first
describes a concept annotation method that relies on
machine-learning techniques to predict the class of
each concept-prerequisite or outcome-on the basis of
contextual and local features. Then, this
categorization is exploited to produce an automatic
resource sequencing on the basis of different
representations and scoring functions that transcribe
the precedence relation between learning resources.
Experiments conducted on a real dataset built from
online resources show that our concept annotation
approach outperforms the baseline method and that the
learning paths automatically generated are consistent
with the ground truth provided by the author of the
online content.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ghosh:2015:MTD,
author = "Siddhartha Ghosh and Steve Reece and Alex Rogers and
Stephen Roberts and Areej Malibari and Nicholas R.
Jennings",
title = "Modeling the Thermal Dynamics of Buildings: a
Latent-Force- Model-Based Approach",
journal = j-TIST,
volume = "6",
number = "1",
pages = "7:1--7:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629674",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Minimizing the energy consumed by heating,
ventilation, and air conditioning (HVAC) systems of
residential buildings without impacting occupants'
comfort has been highlighted as an important artificial
intelligence (AI) challenge. Typically, approaches that
seek to address this challenge use a model that
captures the thermal dynamics within a building, also
referred to as a thermal model. Among thermal models,
gray-box models are a popular choice for modeling the
thermal dynamics of buildings. They combine knowledge
of the physical structure of a building with various
data-driven inputs and are accurate estimators of the
state (internal temperature). However, existing
gray-box models require a detailed specification of all
the physical elements that can affect the thermal
dynamics of a building a priori. This limits their
applicability, particularly in residential buildings,
where additional dynamics can be induced by human
activities such as cooking, which contributes
additional heat, or opening of windows, which leads to
additional leakage of heat. Since the incidence of
these additional dynamics is rarely known, their
combined effects cannot readily be accommodated within
existing models. To overcome this limitation and
improve the general applicability of gray-box models,
we introduce a novel model, which we refer to as a
latent force thermal model of the thermal dynamics of a
building, or LFM-TM. Our model is derived from an
existing gray-box thermal model, which is augmented
with an extra term referred to as the learned residual.
This term is capable of modeling the effect of any a
priori unknown additional dynamic, which, if not
captured, appears as a structure in a thermal model's
residual (the error induced by the model). More
importantly, the learned residual can also capture the
effects of physical elements such as a building's
envelope or the lags in a heating system, leading to a
significant reduction in complexity compared to
existing models. To evaluate the performance of LFM-TM,
we apply it to two independent data sources. The first
is an established dataset, referred to as the FlexHouse
data, which was previously used for evaluating the
efficacy of existing gray-box models [Bacher and Madsen
2011]. The second dataset consists of heating data
logged within homes located on the University of
Southampton campus, which were specifically
instrumented to collect data for our thermal modeling
experiments. On both datasets, we show that LFM-TM
outperforms existing models in its ability to
accurately fit the observed data, generate accurate
day-ahead internal temperature predictions, and explain
a large amount of the variability in the future
observations. This, along with the fact that we also
use a corresponding efficient sequential inference
scheme for LFM-TM, makes it an ideal candidate for
model-based predictive control, where having accurate
online predictions of internal temperatures is
essential for high-quality solutions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:SPL,
author = "Zhao Zhang and Cheng-Lin Liu and Ming-Bo Zhao",
title = "A Sparse Projection and Low-Rank Recovery Framework
for Handwriting Representation and Salient Stroke
Feature Extraction",
journal = j-TIST,
volume = "6",
number = "1",
pages = "9:1--9:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2601408",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we consider the problem of
simultaneous low-rank recovery and sparse projection.
More specifically, a new Robust Principal Component
Analysis (RPCA)-based framework called Sparse
Projection and Low-Rank Recovery (SPLRR) is proposed
for handwriting representation and salient stroke
feature extraction. In addition to achieving a low-rank
component encoding principal features and identify
errors or missing values from a given data matrix as
RPCA, SPLRR also learns a similarity-preserving sparse
projection for extracting salient stroke features and
embedding new inputs for classification. These
properties make SPLRR applicable for handwriting
recognition and stroke correction and enable online
computation. A cosine-similarity-style regularization
term is incorporated into the SPLRR formulation for
encoding the similarities of local handwriting
features. The sparse projection and low-rank recovery
are calculated from a convex minimization problem that
can be efficiently solved in polynomial time. Besides,
the supervised extension of SPLRR is also elaborated.
The effectiveness of our SPLRR is examined by extensive
handwritten digital repairing, stroke correction, and
recognition based on benchmark problems. Compared with
other related techniques, SPLRR delivers strong
generalization capability and state-of-the-art
performance for handwriting representation and
recognition.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Stapleton:2015:CST,
author = "Gem Stapleton and Beryl Plimmer and Aidan Delaney and
Peter Rodgers",
title = "Combining Sketching and Traditional Diagram Editing
Tools",
journal = j-TIST,
volume = "6",
number = "1",
pages = "10:1--10:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2631925",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The least cognitively demanding way to create a
diagram is to draw it with a pen. Yet there is also a
need for more formal visualizations, that is, diagrams
created using both traditional keyboard and mouse
interaction. Our objective is to allow the creation of
diagrams using traditional and stylus-based input.
Having two diagram creation interfaces requires that
changes to a diagram should be automatically rendered
in the other visualization. Because sketches are
imprecise, there is always the possibility that
conversion between visualizations results in a lack of
syntactic consistency between the two visualizations.
We propose methods for converting diagrams between
forms, checking them for equivalence, and rectifying
inconsistencies. As a result of our theoretical
contributions, we present an intelligent software
system allowing users to create and edit diagrams in
sketch or formal mode. Our proof-of-concept tool
supports diagrams with connected and spatial syntactic
elements. Two user studies show that this approach is
viable and participants found the software easy to use.
We conclude that supporting such diagram creation is
now possible in practice.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hong:2015:VUR,
author = "Richang Hong and Shuicheng Yan and Zhengyou Zhang",
title = "Visual Understanding with {RGB-D} Sensors: an
Introduction to the Special Issue",
journal = j-TIST,
volume = "6",
number = "2",
pages = "11:1--11:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2732265",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:KDR,
author = "Chongyu Chen and Jianfei Cai and Jianmin Zheng and Tat
Jen Cham and Guangming Shi",
title = "{Kinect} Depth Recovery Using a Color-Guided,
Region-Adaptive, and Depth-Selective Framework",
journal = j-TIST,
volume = "6",
number = "2",
pages = "12:1--12:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700475",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Considering that the existing depth recovery
approaches have different limitations when applied to
Kinect depth data, in this article, we propose to
integrate their effective features including adaptive
support region selection, reliable depth selection, and
color guidance together under an optimization framework
for Kinect depth recovery. In particular, we formulate
our depth recovery as an energy minimization problem,
which solves the depth hole filling and denoising
simultaneously. The energy function consists of a
fidelity term and a regularization term, which are
designed according to the Kinect characteristics. Our
framework inherits and improves the idea of guided
filtering by incorporating structure information and
prior knowledge of the Kinect noise model. Through
analyzing the solution to the optimization framework,
we also derive a local filtering version that provides
an efficient and effective way of improving the
existing filtering techniques. Quantitative evaluations
on our developed synthesized dataset and experiments on
real Kinect data show that the proposed method achieves
superior performance in terms of recovery accuracy and
visual quality.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Figueroa:2015:CAT,
author = "Nadia Figueroa and Haiwei Dong and Abdulmotaleb {El
Saddik}",
title = "A Combined Approach Toward Consistent Reconstructions
of Indoor Spaces Based on {$6$D RGB-D} Odometry and
{KinectFusion}",
journal = j-TIST,
volume = "6",
number = "2",
pages = "14:1--14:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629673",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We propose a 6D RGB-D odometry approach that finds the
relative camera pose between consecutive RGB-D frames
by keypoint extraction and feature matching both on the
RGB and depth image planes. Furthermore, we feed the
estimated pose to the highly accurate KinectFusion
algorithm, which uses a fast ICP (Iterative Closest
Point) to fine-tune the frame-to-frame relative pose
and fuse the depth data into a global implicit surface.
We evaluate our method on a publicly available RGB-D
SLAM benchmark dataset by Sturm et al. The experimental
results show that our proposed reconstruction method
solely based on visual odometry and KinectFusion
outperforms the state-of-the-art RGB-D SLAM system
accuracy. Moreover, our algorithm outputs a
ready-to-use polygon mesh (highly suitable for creating
3D virtual worlds) without any postprocessing steps.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zha:2015:RMF,
author = "Zheng-Jun Zha and Yang Yang and Jinhui Tang and Meng
Wang and Tat-Seng Chua",
title = "Robust Multiview Feature Learning for {RGB-D} Image
Understanding",
journal = j-TIST,
volume = "6",
number = "2",
pages = "15:1--15:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2735521",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The availability of massive RGB-depth (RGB-D) images
poses a compelling need for effective RGB-D content
understanding techniques. RGB-D images provide
synchronized information from multiple views (e.g.,
color and depth) of real-world objects and scenes. This
work proposes learning compact and discriminative
features from the multiple views of RGB-D content
toward effective feature representation for RGB-D image
understanding. In particular, a robust multiview
feature learning approach is developed, which exploits
the intrinsic relations among multiple views. The
feature learning in multiple views is jointly optimized
in an integrated formulation. The joint optimization
essentially exploits the intrinsic relations among the
views, leading to effective features and making the
learning process robust to noises. The feature learning
function is formulated as a robust nonnegative graph
embedding function over multiple graphs in various
views. The graphs characterize the local geometric and
discriminating structure of the multiview data. The
joint sparsity in $ l_1$-norm graph embedding and $
l_{21}$-norm data factorization further enhances the
robustness of feature learning. We derive an efficient
computational solution for the proposed approach and
provide rigorous theoretical proof with regard to its
convergence. We apply the proposed approach to two
RGB-D image understanding tasks: RGB-D object
classification and RGB-D scene categorization. We
conduct extensive experiments on two real-world RGB-D
image datasets. The experimental results have
demonstrated the effectiveness of the proposed
approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:RDI,
author = "Quanshi Zhang and Xuan Song and Xiaowei Shao and
Huijing Zhao and Ryosuke Shibasaki",
title = "From {RGB-D} Images to {RGB} Images: Single Labeling
for Mining Visual Models",
journal = j-TIST,
volume = "6",
number = "2",
pages = "16:1--16:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629701",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Mining object-level knowledge, that is, building a
comprehensive category model base, from a large set of
cluttered scenes presents a considerable challenge to
the field of artificial intelligence. How to initiate
model learning with the least human supervision (i.e.,
manual labeling) and how to encode the structural
knowledge are two elements of this challenge, as they
largely determine the scalability and applicability of
any solution. In this article, we propose a
model-learning method that starts from a single-labeled
object for each category, and mines further model
knowledge from a number of informally captured,
cluttered scenes. However, in these scenes, target
objects are relatively small and have large variations
in texture, scale, and rotation. Thus, to reduce the
model bias normally associated with less supervised
learning methods, we use the robust 3D shape in RGB-D
images to guide our model learning, then apply the
properly trained category models to both object
detection and recognition in more conventional RGB
images. In addition to model training for their own
categories, the knowledge extracted from the RGB-D
images can also be transferred to guide model learning
for a new category, in which only RGB images without
depth information in the new category are provided for
training. Preliminary testing shows that the proposed
method performs as well as fully supervised learning
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2015:ARM,
author = "Meiyu Huang and Yiqiang Chen and Wen Ji and Chunyan
Miao",
title = "Accurate and Robust Moving-Object Segmentation for
Telepresence Systems",
journal = j-TIST,
volume = "6",
number = "2",
pages = "17:1--17:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629480",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Moving-object segmentation is the key issue of
Telepresence systems. With monocular camera--based
segmentation methods, desirable segmentation results
are hard to obtain in challenging scenes with ambiguous
color, illumination changes, and shadows. Approaches
based on depth sensors often cause holes inside the
object and missegmentations on the object boundary due
to inaccurate and unstable estimation of depth data.
This work proposes an adaptive multi-cue decision
fusion method based on Kinect (which integrates a depth
sensor with an RGB camera). First, the algorithm
obtains an initial foreground mask based on the depth
cue. Second, the algorithm introduces a postprocessing
framework to refine the segmentation results, which
consists of two main steps: (1) automatically adjusting
the weight of two weak decisions to identify foreground
holes based on the color and contrast cue separately;
and (2) refining the object boundary by integrating the
motion probability weighted temporal prior, color
likelihood, and smoothness constraint. The extensive
experiments we conducted demonstrate that our method
can segment moving objects accurately and robustly in
various situations in real time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhu:2015:FMF,
author = "Yu Zhu and Wenbin Chen and Guodong Guo",
title = "Fusing Multiple Features for Depth-Based Action
Recognition",
journal = j-TIST,
volume = "6",
number = "2",
pages = "18:1--18:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Human action recognition is a very active research
topic in computer vision and pattern recognition.
Recently, it has shown a great potential for human
action recognition using the three-dimensional (3D)
depth data captured by the emerging RGB-D sensors.
Several features and/or algorithms have been proposed
for depth-based action recognition. A question is
raised: Can we find some complementary features and
combine them to improve the accuracy significantly for
depth-based action recognition? To address the question
and have a better understanding of the problem, we
study the fusion of different features for depth-based
action recognition. Although data fusion has shown
great success in other areas, it has not been well
studied yet on 3D action recognition. Some issues need
to be addressed, for example, whether the fusion is
helpful or not for depth-based action recognition, and
how to do the fusion properly. In this article, we
study different fusion schemes comprehensively, using
diverse features for action characterization in depth
videos. Two different levels of fusion schemes are
investigated, that is, feature level and decision
level. Various methods are explored at each fusion
level. Four different features are considered to
characterize the depth action patterns from different
aspects. The experiments are conducted on four
challenging depth action databases, in order to
evaluate and find the best fusion methods generally.
Our experimental results show that the four different
features investigated in the article can complement
each other, and appropriate fusion methods can improve
the recognition accuracies significantly over each
individual feature. More importantly, our fusion-based
action recognition outperforms the state-of-the-art
approaches on these challenging databases.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Spurlock:2015:EGD,
author = "Scott Spurlock and Richard Souvenir",
title = "An Evaluation of Gamesourced Data for Human Pose
Estimation",
journal = j-TIST,
volume = "6",
number = "2",
pages = "19:1--19:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629465",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Gamesourcing has emerged as an approach for rapidly
acquiring labeled data for learning-based, computer
vision recognition algorithms. In this article, we
present an approach for using RGB-D sensors to acquire
annotated training data for human pose estimation from
2D images. Unlike other gamesourcing approaches, our
method does not require a specific game, but runs
alongside any gesture-based game using RGB-D sensors.
The automatically generated datasets resulting from
this approach contain joint estimates within a few
pixel units of manually labeled data, and a gamesourced
dataset created using a relatively small number of
players, games, and locations performs as well as
large-scale, manually annotated datasets when used as
training data with recent learning-based human pose
estimation methods for 2D images.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sun:2015:LSV,
author = "Chao Sun and Tianzhu Zhang and Changsheng Xu",
title = "Latent Support Vector Machine Modeling for Sign
Language Recognition with {Kinect}",
journal = j-TIST,
volume = "6",
number = "2",
pages = "20:1--20:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Vision-based sign language recognition has attracted
more and more interest from researchers in the computer
vision field. In this article, we propose a novel
algorithm to model and recognize sign language
performed in front of a Microsoft Kinect sensor. Under
the assumption that some frames are expected to be both
discriminative and representative in a sign language
video, we first assign a binary latent variable to each
frame in training videos for indicating its
discriminative capability, then develop a latent
support vector machine model to classify the signs, as
well as localize the discriminative and representative
frames in each video. In addition, we utilize the depth
map together with the color image captured by the
Kinect sensor to obtain a more effective and accurate
feature to enhance the recognition accuracy. To
evaluate our approach, we conducted experiments on both
word-level sign language and sentence-level sign
language. An American Sign Language dataset including
approximately 2,000 word-level sign language phrases
and 2,000 sentence-level sign language phrases was
collected using the Kinect sensor, and each phrase
contains color, depth, and skeleton information.
Experiments on our dataset demonstrate the
effectiveness of the proposed method for sign language
recognition.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2015:RTH,
author = "Ao Tang and Ke Lu and Yufei Wang and Jie Huang and
Houqiang Li",
title = "A Real-Time Hand Posture Recognition System Using Deep
Neural Networks",
journal = j-TIST,
volume = "6",
number = "2",
pages = "21:1--21:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2735952",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Hand posture recognition (HPR) is quite a challenging
task, due to both the difficulty in detecting and
tracking hands with normal cameras and the limitations
of traditional manually selected features. In this
article, we propose a two-stage HPR system for Sign
Language Recognition using a Kinect sensor. In the
first stage, we propose an effective algorithm to
implement hand detection and tracking. The algorithm
incorporates both color and depth information, without
specific requirements on uniform-colored or stable
background. It can handle the situations in which hands
are very close to other parts of the body or hands are
not the nearest objects to the camera and allows for
occlusion of hands caused by faces or other hands. In
the second stage, we apply deep neural networks (DNNs)
to automatically learn features from hand posture
images that are insensitive to movement, scaling, and
rotation. Experiments verify that the proposed system
works quickly and accurately and achieves a recognition
accuracy as high as 98.12\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:RTS,
author = "Liyan Zhang and Fan Liu and Jinhui Tang",
title = "Real-Time System for Driver Fatigue Detection by
{RGB-D} Camera",
journal = j-TIST,
volume = "6",
number = "2",
pages = "22:1--22:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Drowsy driving is one of the major causes of fatal
traffic accidents. In this article, we propose a
real-time system that utilizes RGB-D cameras to
automatically detect driver fatigue and generate alerts
to drivers. By introducing RGB-D cameras, the depth
data can be obtained, which provides extra evidence to
benefit the task of head detection and head pose
estimation. In this system, two important visual cues
(head pose and eye state) for driver fatigue detection
are extracted and leveraged simultaneously. We first
present a real-time 3D head pose estimation method by
leveraging RGB and depth data. Then we introduce a
novel method to predict eye states employing the WLBP
feature, which is a powerful local image descriptor
that is robust to noise and illumination variations.
Finally, we integrate the results from both head pose
and eye states to generate the overall conclusion. The
combination and collaboration of the two types of
visual cues can reduce the uncertainties and resolve
the ambiguity that a single cue may induce. The
experiments were performed using an inside-car
environment during the day and night, and they fully
demonstrate the effectiveness and robustness of our
system as well as the proposed methods of predicting
head pose and eye states.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kyan:2015:ABD,
author = "Matthew Kyan and Guoyu Sun and Haiyan Li and Ling
Zhong and Paisarn Muneesawang and Nan Dong and Bruce
Elder and Ling Guan",
title = "An Approach to Ballet Dance Training through {MS
Kinect} and Visualization in a {CAVE} Virtual Reality
Environment",
journal = j-TIST,
volume = "6",
number = "2",
pages = "23:1--23:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2735951",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article proposes a novel framework for the
real-time capture, assessment, and visualization of
ballet dance movements as performed by a student in an
instructional, virtual reality (VR) setting. The
acquisition of human movement data is facilitated by
skeletal joint tracking captured using the popular
Microsoft (MS) Kinect camera system, while instruction
and performance evaluation are provided in the form of
3D visualizations and feedback through a CAVE virtual
environment, in which the student is fully immersed.
The proposed framework is based on the unsupervised
parsing of ballet dance movement into a structured
posture space using the spherical self-organizing map
(SSOM). A unique feature descriptor is proposed to more
appropriately reflect the subtleties of ballet dance
movements, which are represented as gesture
trajectories through posture space on the SSOM. This
recognition subsystem is used to identify the category
of movement the student is attempting when prompted (by
a virtual instructor) to perform a particular dance
sequence. The dance sequence is then segmented and
cross-referenced against a library of gestural
components performed by the teacher. This facilitates
alignment and score-based assessment of individual
movements within the context of the dance sequence. An
immersive interface enables the student to review his
or her performance from a number of vantage points,
each providing a unique perspective and spatial context
suggestive of how the student might make improvements
in training. An evaluation of the recognition and
virtual feedback systems is presented.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2015:ESC,
author = "Miaojing Shi and Xinghai Sun and Dacheng Tao and Chao
Xu and George Baciu and Hong Liu",
title = "Exploring Spatial Correlation for Visual Object
Retrieval",
journal = j-TIST,
volume = "6",
number = "2",
pages = "24:1--24:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2641576",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Bag-of-visual-words (BOVW)-based image representation
has received intense attention in recent years and has
improved content-based image retrieval (CBIR)
significantly. BOVW does not consider the spatial
correlation between visual words in natural images and
thus biases the generated visual words toward noise
when the corresponding visual features are not stable.
This article outlines the construction of a visual word
co-occurrence matrix by exploring visual word
co-occurrence extracted from small affine-invariant
regions in a large collection of natural images. Based
on this co-occurrence matrix, we first present a novel
high-order predictor to accelerate the generation of
spatially correlated visual words and a penalty tree
(PTree) to continue generating the words after the
prediction. Subsequently, we propose two methods of
co-occurrence weighting similarity measure for image
ranking: Co-Cosine and Co-TFIDF. These two new schemes
down-weight the contributions of the words that are
less discriminative because of frequent co-occurrences
with other words. We conduct experiments on Oxford and
Paris Building datasets, in which the ImageNet dataset
is used to implement a large-scale evaluation.
Cross-dataset evaluations between the Oxford and Paris
datasets and Oxford and Holidays datasets are also
provided. Thorough experimental results suggest that
our method outperforms the state of the art without
adding much additional cost to the BOVW model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Doherty:2015:PMT,
author = "Jonathan Doherty and Kevin Curran and Paul McKevitt",
title = "Pattern Matching Techniques for Replacing Missing
Sections of Audio Streamed across Wireless Networks",
journal = j-TIST,
volume = "6",
number = "2",
pages = "25:1--25:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2663358",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Streaming media on the Internet can be unreliable.
Services such as audio-on-demand drastically increase
the loads on networks; therefore, new, robust, and
highly efficient coding algorithms are necessary. One
method overlooked to date, which can work alongside
existing audio compression schemes, is that which takes
into account the semantics and natural repetition of
music. Similarity detection within polyphonic audio has
presented problematic challenges within the field of
music information retrieval. One approach to deal with
bursty errors is to use self-similarity to replace
missing segments. Many existing systems exist based on
packet loss and replacement on a network level, but
none attempt repairs of large dropouts of 5 seconds or
more. Music exhibits standard structures that can be
used as a forward error correction (FEC) mechanism. FEC
is an area that addresses the issue of packet loss with
the onus of repair placed as much as possible on the
listener's device. We have developed a
server--client-based framework (SoFI) for automatic
detection and replacement of large packet losses on
wireless networks when receiving time-dependent
streamed audio. Whenever dropouts occur, SoFI swaps
audio presented to the listener between a live stream
and previous sections of the audio stored locally.
Objective and subjective evaluations of SoFI where
subjects were presented with other simulated approaches
to audio repair together with simulations of
replacements including varying lengths of time in the
repair give positive results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hai:2015:ABU,
author = "Zhen Hai and Kuiyu Chang and Gao Cong and Christopher
C. Yang",
title = "An Association-Based Unified Framework for Mining
Features and Opinion Words",
journal = j-TIST,
volume = "6",
number = "2",
pages = "26:1--26:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2663359",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Mining features and opinion words is essential for
fine-grained opinion analysis of customer reviews. It
is observed that semantic dependencies naturally exist
between features and opinion words, even among features
or opinion words themselves. In this article, we employ
a corpus statistics association measure to quantify the
pairwise word dependencies and propose a generalized
association-based unified framework to identify
features, including explicit and implicit features, and
opinion words from reviews. We first extract explicit
features and opinion words via an association-based
bootstrapping method (ABOOT). ABOOT starts with a small
list of annotated feature seeds and then iteratively
recognizes a large number of domain-specific features
and opinion words by discovering the corpus statistics
association between each pair of words on a given
review domain. Two instances of this ABOOT method are
evaluated based on two particular association models,
likelihood ratio tests (LRTs) and latent semantic
analysis (LSA). Next, we introduce a natural extension
to identify implicit features by employing the
recognized known semantic correlations between features
and opinion words. Experimental results illustrate the
benefits of the proposed association-based methods for
identifying features and opinion words versus benchmark
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2015:HMC,
author = "Shanshan Huang and Jun Ma and Peizhe Cheng and
Shuaiqiang Wang",
title = "A {Hybrid Multigroup CoClustering} Recommendation
Framework Based on Information Fusion",
journal = j-TIST,
volume = "6",
number = "2",
pages = "27:1--27:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700465",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative Filtering (CF) is one of the most
successful algorithms in recommender systems. However,
it suffers from data sparsity and scalability problems.
Although many clustering techniques have been
incorporated to alleviate these two problems, most of
them fail to achieve further significant improvement in
recommendation accuracy. First of all, most of them
assume each user or item belongs to a single cluster.
Since usually users can hold multiple interests and
items may belong to multiple categories, it is more
reasonable to assume that users and items can join
multiple clusters (groups), where each cluster is a
subset of like-minded users and items they prefer.
Furthermore, most of the clustering-based CF models
only utilize historical rating information in the
clustering procedure but ignore other data resources in
recommender systems such as the social connections of
users and the correlations between items. In this
article, we propose HMCoC, a Hybrid Multigroup
CoClustering recommendation framework, which can
cluster users and items into multiple groups
simultaneously with different information resources. In
our framework, we first integrate information of
user--item rating records, user social networks, and
item features extracted from the DBpedia knowledge
base. We then use an optimization method to mine
meaningful user--item groups with all the information.
Finally, we apply the conventional CF method in each
cluster to make predictions. By merging the predictions
from each cluster, we generate the top-n
recommendations to the target users for return.
Extensive experimental results demonstrate the superior
performance of our approach in top-n recommendation in
terms of MAP, NDCG, and F1 compared with other
clustering-based CF models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fire:2015:DMO,
author = "Michael Fire and Yuval Elovici",
title = "Data Mining of Online Genealogy Datasets for Revealing
Lifespan Patterns in Human Population",
journal = j-TIST,
volume = "6",
number = "2",
pages = "28:1--28:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700464",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online genealogy datasets contain extensive
information about millions of people and their past and
present family connections. This vast amount of data
can help identify various patterns in the human
population. In this study, we present methods and
algorithms that can assist in identifying variations in
lifespan distributions of the human population in the
past centuries, in detecting social and genetic
features that correlate with the human lifespan, and in
constructing predictive models of human lifespan based
on various features that can easily be extracted from
genealogy datasets. We have evaluated the presented
methods and algorithms on a large online genealogy
dataset with over a million profiles and over 9 million
connections, all of which were collected from the
WikiTree website. Our findings indicate that
significant but small positive correlations exist
between the parents' lifespan and their children's
lifespan. Additionally, we found slightly higher and
significant correlations between the lifespans of
spouses. We also discovered a very small positive and
significant correlation between longevity and
reproductive success in males, and a small and
significant negative correlation between longevity and
reproductive success in females. Moreover, our
predictive models presented results with a Mean
Absolute Error as low as 13.18 in predicting the
lifespans of individuals who outlived the age of 10,
and our classification models presented better than
random classification results in predicting which
people who outlive the age of 50 will also outlive the
age of 80. We believe that this study will be the first
of many studies to utilize the wealth of data on human
populations, existing in online genealogy datasets, to
better understand factors that influence the human
lifespan. Understanding these factors can assist
scientists in providing solutions for successful
aging.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2015:TDM,
author = "Yu Zheng",
title = "Trajectory Data Mining: an Overview",
journal = j-TIST,
volume = "6",
number = "3",
pages = "29:1--29:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2743025",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The advances in location-acquisition and mobile
computing techniques have generated massive spatial
trajectory data, which represent the mobility of a
diversity of moving objects, such as people, vehicles,
and animals. Many techniques have been proposed for
processing, managing, and mining trajectory data in the
past decade, fostering a broad range of applications.
In this article, we conduct a systematic survey on the
major research into trajectory data mining, providing a
panorama of the field as well as the scope of its
research topics. Following a road map from the
derivation of trajectory data, to trajectory data
preprocessing, to trajectory data management, and to a
variety of mining tasks (such as trajectory pattern
mining, outlier detection, and trajectory
classification), the survey explores the connections,
correlations, and differences among these existing
techniques. This survey also introduces the methods
that transform trajectories into other data formats,
such as graphs, matrices, and tensors, to which more
data mining and machine learning techniques can be
applied. Finally, some public trajectory datasets are
presented. This survey can help shape the field of
trajectory data mining, providing a quick understanding
of this field to the community.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bouguessa:2015:IAO,
author = "Mohamed Bouguessa and Lotfi Ben Romdhane",
title = "Identifying Authorities in Online Communities",
journal = j-TIST,
volume = "6",
number = "3",
pages = "30:1--30:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Several approaches have been proposed for the problem
of identifying authoritative actors in online
communities. However, the majority of existing methods
suffer from one or more of the following limitations:
(1) There is a lack of an automatic mechanism to
formally discriminate between authoritative and
nonauthoritative users. In fact, a common approach to
authoritative user identification is to provide a
ranked list of users expecting authorities to come
first. A major problem of such an approach is the
question of where to stop reading the ranked list of
users. How many users should be chosen as
authoritative? (2) Supervised learning approaches for
authoritative user identification suffer from their
dependency on the training data. The problem here is
that labeled samples are more difficult, expensive, and
time consuming to obtain than unlabeled ones. (3)
Several approaches rely on some user parameters to
estimate an authority score. Detection accuracy of
authoritative users can be seriously affected if
incorrect values are used. In this article, we propose
a parameterless mixture model-based approach that is
capable of addressing the three aforementioned issues
in a single framework. In our approach, we first
represent each user with a feature vector composed of
information related to its social behavior and activity
in an online community. Next, we propose a statistical
framework, based on the multivariate beta mixtures, in
order to model the estimated set of feature vectors.
The probability density function is therefore estimated
and the beta component that corresponds to the most
authoritative users is identified. The suitability of
the proposed approach is illustrated on real data
extracted from the Stack Exchange question-answering
network and Twitter.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2015:WWR,
author = "Kyumin Lee and Jalal Mahmud and Jilin Chen and
Michelle Zhou and Jeffrey Nichols",
title = "Who Will Retweet This? {Detecting} Strangers from
{Twitter} to Retweet Information",
journal = j-TIST,
volume = "6",
number = "3",
pages = "31:1--31:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700466",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "There has been much effort on studying how social
media sites, such as Twitter, help propagate
information in different situations, including
spreading alerts and SOS messages in an emergency.
However, existing work has not addressed how to
actively identify and engage the right strangers at the
right time on social media to help effectively
propagate intended information within a desired time
frame. To address this problem, we have developed three
models: (1) a feature-based model that leverages
people's exhibited social behavior, including the
content of their tweets and social interactions, to
characterize their willingness and readiness to
propagate information on Twitter via the act of
retweeting; (2) a wait-time model based on a user's
previous retweeting wait times to predict his or her
next retweeting time when asked; and (3) a subset
selection model that automatically selects a subset of
people from a set of available people using
probabilities predicted by the feature-based model and
maximizes retweeting rate. Based on these three models,
we build a recommender system that predicts the
likelihood of a stranger to retweet information when
asked, within a specific time window, and recommends
the top-N qualified strangers to engage with. Our
experiments, including live studies in the real world,
demonstrate the effectiveness of our work.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hirschprung:2015:SDD,
author = "Ron Hirschprung and Eran Toch and Oded Maimon",
title = "Simplifying Data Disclosure Configurations in a Cloud
Computing Environment",
journal = j-TIST,
volume = "6",
number = "3",
pages = "32:1--32:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700472",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Cloud computing offers a compelling vision of
computation, enabling an unprecedented level of data
distribution and sharing. Beyond improving the
computing infrastructure, cloud computing enables a
higher level of interoperability between information
systems, simplifying tasks such as sharing documents
between coworkers or enabling collaboration between an
organization and its suppliers. While these abilities
may result in significant benefits to users and
organizations, they also present privacy challenges due
to unwanted exposure of sensitive information. As
information-sharing processes in cloud computing are
complex and domain specific, configuring these
processes can be an overwhelming and burdensome task
for users. This article investigates the feasibility of
configuring sharing processes through a small and
representative set of canonical configuration options.
For this purpose, we present a generic method, named
SCON-UP (Simplified CON-figuration of User
Preferences). SCON-UP simplifies configuration
interfaces by using a clustering algorithm that
analyzes a massive set of sharing preferences and
condenses them into a small number of discrete
disclosure levels. Thus, the user is provided with a
usable configuration model while guaranteeing adequate
privacy control. We describe the algorithm and
empirically evaluate our model using data collected in
two user studies (n = 121 and n = 352). Our results
show that when provided with three canonical
configuration options, on average, 82\% of the
population can be covered by at least one option. We
exemplify the feasibility of discretizing sharing
levels and discuss the tradeoff between coverage and
simplicity in discrete configuration options.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Elbadrawy:2015:USF,
author = "Asmaa Elbadrawy and George Karypis",
title = "User-Specific Feature-Based Similarity Models for
Top-$n$ Recommendation of New Items",
journal = j-TIST,
volume = "6",
number = "3",
pages = "33:1--33:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700495",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommending new items for suitable users is an
important yet challenging problem due to the lack of
preference history for the new items. Noncollaborative
user modeling techniques that rely on the item features
can be used to recommend new items. However, they only
use the past preferences of each user to provide
recommendations for that user. They do not utilize
information from the past preferences of other users,
which can potentially be ignoring useful information.
More recent factor models transfer knowledge across
users using their preference information in order to
provide more accurate recommendations. These methods
learn a low-rank approximation for the preference
matrix, which can lead to loss of information.
Moreover, they might not be able to learn useful
patterns given very sparse datasets. In this work, we
present {{\sc UFSM}, a method for top-$n$
recommendation of new items given binary user
preferences. {\sc UFSM} learns {{\bf U}ser}-specific
{\bf F}eature}-based item-{\bf S}imilarity {\bf
M}odels, and its strength lies in combining two points:
(1) exploiting preference information across all users
to learn multiple global item similarity functions and
(2) learning user-specific weights that determine the
contribution of each global similarity function in
generating recommendations for each user. {\sc UFSM}
can be considered as a sparse high-dimensional factor
model where the previous preferences of each user are
incorporated within his or her latent representation.
This way, {\sc UFSM} combines the merits of item
similarity models that capture local relations among
items and factor models that learn global preference
patterns. A comprehensive set of experiments was
conduced to compare {\sc UFSM} against state-of-the-art
collaborative factor models and noncollaborative user
modeling techniques. Results show that {\sc UFSM}
outperforms other techniques in terms of recommendation
quality. {\sc UFSM} manages to yield better
recommendations even with very sparse datasets. Results
also show that {\sc UFSM} can efficiently handle
high-dimensional as well as low-dimensional item
feature spaces.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:TGO,
author = "Mingjin Zhang and Huibo Wang and Yun Lu and Tao Li and
Yudong Guang and Chang Liu and Erik Edrosa and Hongtai
Li and Naphtali Rishe",
title = "{TerraFly GeoCloud}: an Online Spatial Data Analysis
and Visualization System",
journal = j-TIST,
volume = "6",
number = "3",
pages = "34:1--34:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700494",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the exponential growth of the usage of web map
services, geo-data analysis has become more and more
popular. This article develops an online spatial data
analysis and visualization system, TerraFly GeoCloud,
which helps end-users visualize and analyze spatial
data and share the analysis results. Built on the
TerraFly Geo spatial database, TerraFly GeoCloud is an
extra layer running upon the TerraFly map and can
efficiently support many different visualization
functions and spatial data analysis models.
Furthermore, users can create unique URLs to visualize
and share the analysis results. TerraFly GeoCloud also
enables the MapQL technology to customize map
visualization using SQL-like statements. The system is
available at http://terrafly.fiu.edu/GeoCloud/.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:SCP,
author = "Yi-Cheng Chen and Wen-Chih Peng and Jiun-Long Huang
and Wang-Chien Lee",
title = "Significant Correlation Pattern Mining in Smart
Homes",
journal = j-TIST,
volume = "6",
number = "3",
pages = "35:1--35:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700484",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Owing to the great advent of sensor technology, the
usage data of appliances in a house can be logged and
collected easily today. However, it is a challenge for
the residents to visualize how these appliances are
used. Thus, mining algorithms are much needed to
discover appliance usage patterns. Most previous
studies on usage pattern discovery are mainly focused
on analyzing the patterns of single appliance rather
than mining the usage correlation among appliances. In
this article, a novel algorithm, namely Correlation
Pattern Miner (CoPMiner), is developed to capture the
usage patterns and correlations among appliances
probabilistically. CoPMiner also employs four pruning
techniques and a statistical model to reduce the search
space and filter out insignificant patterns,
respectively. Furthermore, the proposed algorithm is
applied on a real-world dataset to show the
practicability of correlation pattern mining.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guo:2015:ISI,
author = "Bin Guo and Alvin Chin and Zhiwen Yu and Runhe Huang
and Daqing Zhang",
title = "An Introduction to the Special Issue on Participatory
Sensing and Crowd Intelligence",
journal = j-TIST,
volume = "6",
number = "3",
pages = "36:1--36:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2745712",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:SPU,
author = "Fuzheng Zhang and Nicholas Jing Yuan and David Wilkie
and Yu Zheng and Xing Xie",
title = "Sensing the Pulse of Urban Refueling Behavior: a
Perspective from Taxi Mobility",
journal = j-TIST,
volume = "6",
number = "3",
pages = "37:1--37:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2644828",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Urban transportation is an important factor in energy
consumption and pollution, and is of increasing concern
due to its complexity and economic significance. Its
importance will only increase as urbanization continues
around the world. In this article, we explore drivers'
refueling behavior in urban areas. Compared to
questionnaire-based methods of the past, we propose a
complete data-driven system that pushes towards
real-time sensing of individual refueling behavior and
citywide petrol consumption. Our system provides the
following: detection of individual refueling events
(REs) from which refueling preference can be analyzed;
estimates of gas station wait times from which
recommendations can be made; an indication of overall
fuel demand from which macroscale economic decisions
can be made, and a spatial, temporal, and economic view
of urban refueling characteristics. For individual
behavior, we use reported trajectories from a fleet of
GPS-equipped taxicabs to detect gas station visits. For
time spent estimates, to solve the sparsity issue along
time and stations, we propose context-aware tensor
factorization (CATF), a factorization model that
considers a variety of contextual factors (e.g., price,
brand, and weather condition) that affect consumers'
refueling decision. For fuel demand estimates, we apply
a queue model to calculate the overall visits based on
the time spent inside the station. We evaluated our
system on large-scale and real-world datasets, which
contain 4-month trajectories of 32,476 taxicabs, 689
gas stations, and the self-reported refueling details
of 8,326 online users. The results show that our system
can determine REs with an accuracy of more than 90\%,
estimate time spent with less than 2 minutes of error,
and measure overall visits in the same order of
magnitude with the records in the field study.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tangmunarunkit:2015:OGE,
author = "H. Tangmunarunkit and C. K. Hsieh and B. Longstaff and
S. Nolen and J. Jenkins and C. Ketcham and J. Selsky
and F. Alquaddoomi and D. George and J. Kang and Z.
Khalapyan and J. Ooms and N. Ramanathan and D. Estrin",
title = "{Ohmage}: a General and Extensible End-to-End
Participatory Sensing Platform",
journal = j-TIST,
volume = "6",
number = "3",
pages = "38:1--38:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2717318",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Participatory sensing (PS) is a distributed data
collection and analysis approach where individuals,
acting alone or in groups, use their personal mobile
devices to systematically explore interesting aspects
of their lives and communities [Burke et al. 2006].
These mobile devices can be used to capture diverse
spatiotemporal data through both intermittent
self-report and continuous recording from on-board
sensors and applications. Ohmage (http://ohmage.org) is
a modular and extensible open-source, mobile to Web PS
platform that records, stores, analyzes, and visualizes
data from both prompted self-report and continuous data
streams. These data streams are authorable and can
dynamically be deployed in diverse settings. Feedback
from hundreds of behavioral and technology researchers,
focus group participants, and end users has been
integrated into ohmage through an iterative
participatory design process. Ohmage has been used as
an enabling platform in more than 20 independent
projects in many disciplines. We summarize the PS
requirements, challenges and key design objectives
learned through our design process, and ohmage system
architecture to achieve those objectives. The
flexibility, modularity, and extensibility of ohmage in
supporting diverse deployment settings are presented
through three distinct case studies in education,
health, and clinical research.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xiong:2015:EEE,
author = "Haoyi Xiong and Daqing Zhang and Leye Wang and J. Paul
Gibson and Jie Zhu",
title = "{EEMC}: Enabling Energy-Efficient Mobile Crowdsensing
with Anonymous Participants",
journal = j-TIST,
volume = "6",
number = "3",
pages = "39:1--39:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2644827",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Mobile Crowdsensing (MCS) requires users to be
motivated to participate. However, concerns regarding
energy consumption and privacy-among other things-may
compromise their willingness to join such a crowd. Our
preliminary observations and analysis of common MCS
applications have shown that the data transfer in MCS
applications may incur significant energy consumption
due to the 3G connection setup. However, if data are
transferred in parallel with a traditional phone call,
then such transfer can be done almost ``for free'':
with only an insignificant additional amount of energy
required to piggy-back the data-usually incoming task
assignments and outgoing sensor results-on top of the
call. Here, we present an {\em Energy-Efficient Mobile
Crowdsensing\/} (EEMC) framework where task assignments
and sensing results are transferred in parallel with
phone calls. The main objective, and the principal
contribution of this article, is an MCS task assignment
scheme that guarantees that a minimum number of
anonymous participants return sensor results within a
specified time frame, while also minimizing the waste
of energy due to redundant task assignments and
considering privacy concerns of participants.
Evaluations with a large-scale real-world phone call
dataset show that our proposed {EEMC} framework
outperforms the baseline approaches, and it can reduce
overall energy consumption in data transfer by 54--66\%
when compared to the 3G-based solution.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:CSS,
author = "Wangsheng Zhang and Guande Qi and Gang Pan and Hua Lu
and Shijian Li and Zhaohui Wu",
title = "City-Scale Social Event Detection and Evaluation with
Taxi Traces",
journal = j-TIST,
volume = "6",
number = "3",
pages = "40:1--40:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700478",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A social event is an occurrence that involves lots of
people and is accompanied by an obvious rise in human
flow. Analysis of social events has real-world
importance because events bring about impacts on many
aspects of city life. Traditionally, detection and
impact measurement of social events rely on social
investigation, which involves considerable human
effort. Recently, by analyzing messages in social
networks, researchers can also detect and evaluate
country-scale events. Nevertheless, the analysis of
city-scale events has not been explored. In this
article, we use human flow dynamics, which reflect the
social activeness of a region, to detect social events
and measure their impacts. We first extract human flow
dynamics from taxi traces. Second, we propose a method
that can not only discover the happening time and venue
of events from abnormal social activeness, but also
measure the scale of events through changes in such
activeness. Third, we extract traffic congestion
information from traces and use its change during
social events to measure their impact. The results of
experiments validate the effectiveness of both the
event detection and impact measurement methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sang:2015:ASC,
author = "Jitao Sang and Tao Mei and Changsheng Xu",
title = "Activity Sensor: Check-In Usage Mining for Local
Recommendation",
journal = j-TIST,
volume = "6",
number = "3",
pages = "41:1--41:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700468",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "While on the go, people are using their phones as a
personal concierge discovering what is around and
deciding what to do. Mobile phone has become a
recommendation terminal customized for
individuals-capable of recommending activities and
simplifying the accomplishment of related tasks. In
this article, we conduct usage mining on the check-in
data, with summarized statistics identifying the local
recommendation challenges of huge solution space,
sparse available data, and complicated user intent, and
discovered observations to motivate the hierarchical,
contextual, and sequential solution. We present a
point-of-interest (POI) category-transition--based
approach, with a goal of estimating the visiting
probability of a series of successive POIs conditioned
on current user context and sensor context. A mobile
local recommendation demo application is deployed. The
objective and subjective evaluations validate the
effectiveness in providing mobile users both accurate
recommendation and favorable user experience.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:EDQ,
author = "Bo Zhang and Zheng Song and Chi Harold Liu and Jian Ma
and Wendong Wang",
title = "An Event-Driven {QoI}-Aware Participatory Sensing
Framework with Energy and Budget Constraints",
journal = j-TIST,
volume = "6",
number = "3",
pages = "42:1--42:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2630074",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Participatory sensing systems can be used for
concurrent event monitoring applications, like noise
levels, fire, and pollutant concentrations. However,
they are facing new challenges as to how to accurately
detect the exact boundaries of these events, and
further, to select the most appropriate participants to
collect the sensing data. On the one hand,
participants' handheld smart devices are constrained
with different energy conditions and sensing
capabilities, and they move around with uncontrollable
mobility patterns in their daily life. On the other
hand, these sensing tasks are within time-varying
quality-of-information (QoI) requirements and budget to
afford the users' incentive expectations. Toward this
end, this article proposes an event-driven QoI-aware
participatory sensing framework with energy and budget
constraints. The main method of this framework is event
boundary detection. For the former, a two-step
heuristic solution is proposed where the coarse-grained
detection step finds its approximation and the
fine-grained detection step identifies the exact
location. Participants are selected by explicitly
considering their mobility pattern, required QoI of
multiple tasks, and users' incentive requirements,
under the constraint of an aggregated task budget.
Extensive experimental results, based on a real trace
in Beijing, show the effectiveness and robustness of
our approach, while comparing with existing schemes.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Anantharam:2015:ECT,
author = "Pramod Anantharam and Payam Barnaghi and Krishnaprasad
Thirunarayan and Amit Sheth",
title = "Extracting City Traffic Events from Social Streams",
journal = j-TIST,
volume = "6",
number = "4",
pages = "43:1--43:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2717317",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Cities are composed of complex systems with physical,
cyber, and social components. Current works on
extracting and understanding city events mainly rely on
technology-enabled infrastructure to observe and record
events. In this work, we propose an approach to
leverage citizen observations of various city systems
and services, such as traffic, public transport, water
supply, weather, sewage, and public safety, as a source
of city events. We investigate the feasibility of using
such textual streams for extracting city events from
annotated text. We formalize the problem of annotating
social streams such as microblogs as a sequence
labeling problem. We present a novel training data
creation process for training sequence labeling models.
Our automatic training data creation process utilizes
instance-level domain knowledge (e.g., locations in a
city, possible event terms). We compare this automated
annotation process to a state-of-the-art tool that
needs manually created training data and show that it
has comparable performance in annotation tasks. An
aggregation algorithm is then presented for event
extraction from annotated text. We carry out a
comprehensive evaluation of the event annotation and
event extraction on a real-world dataset consisting of
event reports and tweets collected over 4 months from
the San Francisco Bay Area. The evaluation results are
promising and provide insights into the utility of
social stream for extracting city events.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sawant:2015:AGC,
author = "Anshul Sawant and John P. Dickerson and Mohammad T.
Hajiaghayi and V. S. Subrahmanian",
title = "Automated Generation of Counterterrorism Policies
Using Multiexpert Input",
journal = j-TIST,
volume = "6",
number = "4",
pages = "44:1--44:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2716328",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The use of game theory to model conflict has been
studied by several researchers, spearheaded by
Schelling. Most of these efforts assume a single payoff
matrix that captures players' utilities under different
assumptions about what the players will do. Our
experience in counterterrorism applications is that
experts disagree on these payoffs. We leverage
Shapley's notion of vector equilibria, which formulates
games where there are multiple payoff matrices, but
note that they are very hard to compute in practice. To
effectively enumerate large numbers of equilibria with
payoffs provided by multiple experts, we propose a
novel combination of vector payoffs and well-supported
$ \epsilon $-approximate equilibria. We develop bounds
related to computation of these equilibria for some
special cases and give a quasipolynomial time
approximation scheme (QPTAS) for the general case when
the number of players is small (which is true in many
real-world applications). Leveraging this QPTAS, we
give efficient algorithms to find such equilibria and
experimental results showing that they work well on
simulated data. We then built a policy recommendation
engine based on vector equilibria, called PREVE. We use
PREVE to model the terrorist group Lashkar-e-Taiba
(LeT), responsible for the 2008 Mumbai attacks, as a
five-player game. Specifically, we apply it to three
payoff matrices provided by experts in India--Pakistan
relations, analyze the equilibria generated by PREVE,
and suggest counterterrorism policies that may reduce
attacks by LeT. We briefly discuss these results and
identify their strengths and weaknesses from a policy
point of view.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bai:2015:OPL,
author = "Aijun Bai and Feng Wu and Xiaoping Chen",
title = "Online Planning for Large {Markov} Decision Processes
with Hierarchical Decomposition",
journal = j-TIST,
volume = "6",
number = "4",
pages = "45:1--45:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2717316",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Markov decision processes (MDPs) provide a rich
framework for planning under uncertainty. However,
exactly solving a large MDP is usually intractable due
to the ``curse of dimensionality''- the state space
grows exponentially with the number of state variables.
Online algorithms tackle this problem by avoiding
computing a policy for the entire state space. On the
other hand, since online algorithm has to find a
near-optimal action online in almost real time, the
computation time is often very limited. In the context
of reinforcement learning, MAXQ is a value function
decomposition method that exploits the underlying
structure of the original MDP and decomposes it into a
combination of smaller subproblems arranged over a task
hierarchy. In this article, we present MAXQ-OP-a novel
online planning algorithm for large MDPs that utilizes
MAXQ hierarchical decomposition in online settings.
Compared to traditional online planning algorithms,
MAXQ-OP is able to reach much more deeper states in the
search tree with relatively less computation time by
exploiting MAXQ hierarchical decomposition online. We
empirically evaluate our algorithm in the standard Taxi
domain-a common benchmark for MDPs-to show the
effectiveness of our approach. We have also conducted a
long-term case study in a highly complex simulated
soccer domain and developed a team named WrightEagle
that has won five world champions and five runners-up
in the recent 10 years of RoboCup Soccer Simulation 2D
annual competitions. The results in the RoboCup domain
confirm the scalability of MAXQ-OP to very large
domains.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ye:2015:SSB,
author = "Yanfang Ye and Tao Li and Haiyin Shen",
title = "{Soter}: Smart Bracelets for Children's Safety",
journal = j-TIST,
volume = "6",
number = "4",
pages = "46:1--46:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, crimes against children and cases of
missing children have increased at a high rate.
Therefore, there is an urgent need for safety support
systems to prevent crimes against children or for
antiloss, especially when parents are not with their
children, such as to and from school. However, existing
children's tracking systems are not smart enough to
provide the safety supports, as they simply locate the
children's positions without offering any notification
to parents that their children may be in danger. In
addition, there is limited research on children's
tracking and their antiloss. In this article, based on
location histories, we introduce novel notions of
children's life patterns that capture their general
lifestyles and regularities, and develop an intelligent
data mining framework to learn the safe regions and
safe routes of children on the cloud side. When the
children may be in danger, their parents will receive
automatic notifications from the cloud. We also propose
an effective energy-efficient positioning scheme that
leverages the location tracking accuracy of the
children while keeping energy overhead low by using a
hybrid global positioning system and a global system
for mobile communications. To the best of our
knowledge, this is the first attempt in applying data
mining techniques to applications designed for
children's safety. Our proposed techniques have been
incorporated into Soter, a children's safeguard system
that is used to provide cloud service for smart
bracelets produced by Qihoo. The case studies on real
smart bracelet users of Qihoo demonstrate the
effectiveness of our proposed methods and Soter for
children's safety.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2015:PLL,
author = "Yi Wang and Xuemin Zhao and Zhenlong Sun and Hao Yan
and Lifeng Wang and Zhihui Jin and Liubin Wang and Yang
Gao and Ching Law and Jia Zeng",
title = "{Peacock}: Learning Long-Tail Topic Features for
Industrial Applications",
journal = j-TIST,
volume = "6",
number = "4",
pages = "47:1--47:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700497",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Latent Dirichlet allocation (LDA) is a popular topic
modeling technique in academia but less so in industry,
especially in large-scale applications involving search
engine and online advertising systems. A main
underlying reason is that the topic models used have
been too small in scale to be useful; for example, some
of the largest LDA models reported in literature have
up to 10$^3$ topics, which difficultly cover the
long-tail semantic word sets. In this article, we show
that the number of topics is a key factor that can
significantly boost the utility of topic-modeling
systems. In particular, we show that a ``big'' LDA
model with at least 10$^5$ topics inferred from 10$^9$
search queries can achieve a significant improvement on
industrial search engine and online advertising
systems, both of which serve hundreds of millions of
users. We develop a novel distributed system called
Peacock to learn big LDA models from big data. The main
features of Peacock include hierarchical distributed
architecture, real-time prediction, and topic
de-duplication. We empirically demonstrate that the
Peacock system is capable of providing significant
benefits via highly scalable LDA topic models for
several industrial applications.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jumadinova:2015:APM,
author = "Janyl Jumadinova and Prithviraj Dasgupta",
title = "Automated Pricing in a Multiagent Prediction Market
Using a Partially Observable Stochastic Game",
journal = j-TIST,
volume = "6",
number = "4",
pages = "48:1--48:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700488",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Prediction markets offer an efficient market-based
mechanism to aggregate large amounts of dispersed or
distributed information from different people to
predict the possible outcome of future events.
Recently, automated prediction markets where software
trading agents perform market operations such as
trading and updating beliefs on behalf of humans have
been proposed. A challenging aspect in automated
prediction markets is to develop suitable techniques
that can be used by automated trading agents to update
the price at which they should trade securities related
to an event so that they can increase their profit.
This problem is nontrivial, as the decision to trade
and the price at which trading should occur depends on
several dynamic factors, such as incoming information
related to the event for which the security is being
traded, the belief-update mechanism and risk attitude
of the trading agent, and the trading decision and
trading prices of other agents. To address this
problem, we have proposed a new behavior model for
trading agents based on a game-theoretic framework
called partially observable stochastic game with
information (POSGI). We propose a correlated
equilibrium (CE)-based solution strategy for this game
that allows each agent to dynamically choose an action
(to buy or sell or hold) in the prediction market. We
have also performed extensive simulation experiments
using the data obtained from the Intrade prediction
market for four different prediction markets. Our
results show that our POSGI model and CE strategy
produces prices that are strongly correlated with the
prices of the real prediction markets. Results
comparing our CE strategy with five other strategies
commonly used in similar market show that our CE
strategy improves price predictions and provides higher
utilities to the agents compared to other existing
strategies.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fu:2015:ESG,
author = "Hao Fu and Aston Zhang and Xing Xie",
title = "Effective Social Graph Deanonymization Based on Graph
Structure and Descriptive Information",
journal = j-TIST,
volume = "6",
number = "4",
pages = "49:1--49:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700836",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The study of online social networks has attracted
increasing interest. However, concerns are raised for
the privacy risks of user data since they have been
frequently shared among researchers, advertisers, and
application developers. To solve this problem, a number
of anonymization algorithms have been recently
developed for protecting the privacy of social graphs.
In this article, we proposed a graph node similarity
measurement in consideration with both graph structure
and descriptive information, and a deanonymization
algorithm based on the measurement. Using the proposed
algorithm, we evaluated the privacy risks of several
typical anonymization algorithms on social graphs with
thousands of nodes from Microsoft Academic Search,
LiveJournal, and the Enron email dataset, and a social
graph with millions of nodes from Tencent Weibo. Our
results showed that the proposed algorithm was
efficient and effective to deanonymize social graphs
without any initial seed mappings. Based on the
experiments, we also pointed out suggestions on how to
better maintain the data utility while preserving
privacy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:HIR,
author = "Bo-Hao Chen and Shih-Chia Huang and Jian Hui Ye",
title = "Hazy Image Restoration by Bi-Histogram Modification",
journal = j-TIST,
volume = "6",
number = "4",
pages = "50:1--50:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2710024",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Visibility restoration techniques are widely used for
information recovery of hazy images in many computer
vision applications. Estimation of haze density is an
essential task of visibility restoration techniques.
However, conventional visibility restoration techniques
often suffer from either the generation of serious
artifacts or the loss of object information in the
restored images due to uneven haze density, which
usually means that the images contain heavy haze
formation within their background regions and little
haze formation within their foreground regions. This
frequently occurs when the images feature real-world
scenes with a deep depth of field. How to effectively
and accurately estimate the haze density in the
transmission map for these images is the most
challenging aspect of the traditional state-of-the-art
techniques. In response to this problem, this work
proposes a novel visibility restoration approach that
is based on Bi-Histogram modification, and which
integrates a haze density estimation module and a haze
formation removal module for effective and accurate
estimation of haze density in the transmission map. As
our experimental results demonstrate, the proposed
approach achieves superior visibility restoration
efficacy in comparison with the other state-of-the-art
approaches based on both qualitative and quantitative
evaluations. The proposed approach proves effective and
accurate in terms of both background and foreground
restoration of various hazy scenarios.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Combi:2015:IAT,
author = "Carlo Combi and Jiming Liu",
title = "Introduction to the {ACM TIST} Special Issue on
Intelligent Healthcare Informatics",
journal = j-TIST,
volume = "6",
number = "4",
pages = "51:1--51:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2791398",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kim:2015:AAR,
author = "Eunju Kim and Sumi Helal and Chris Nugent and Mark
Beattie",
title = "Analyzing Activity Recognition Uncertainties in Smart
Home Environments",
journal = j-TIST,
volume = "6",
number = "4",
pages = "52:1--52:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2651445",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In spite of the importance of activity recognition
(AR) for intelligent human-computer interaction in
emerging smart space applications, state-of-the-art AR
technology is not ready or adequate for real-world
deployments due to its insufficient accuracy. The
accuracy limitation is directly attributed to
uncertainties stemming from multiple sources in the AR
system. Hence, one of the major goals of AR research is
to improve system accuracy by minimizing or managing
the uncertainties encountered throughout the AR
process. As we cannot manage uncertainties well without
measuring them, we must first quantify their impact.
Nevertheless, such a quantification process is very
challenging given that uncertainties come from diverse
and heterogeneous sources. In this article, we propose
an approach, which can account for multiple uncertainty
sources and assess their impact on AR systems. We
introduce several metrics to quantify the various
uncertainties and their impact. We then conduct a
quantitative impact analysis of uncertainties utilizing
data collected from actual smart spaces that we have
instrumented. The analysis is intended to serve as
groundwork for developing ``diagnostic'' accuracy
measures of AR systems capable of pinpointing the
sources of accuracy loss. This is to be contrasted with
the currently used accuracy measures.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Soto-Mendoza:2015:DPS,
author = "Valeria Soto-Mendoza and J. Antonio
Garc{\'\i}a-Mac{\'\i}as and Edgar Ch{\'a}vez and Ana I.
Mart{\'\i}nez-Garc{\'\i}a and Jes{\'u}s Favela and
Patricia Serrano-Alvarado and Mayth{\'e} R.
Z{\'u}{\~n}iga Rojas",
title = "Design of a Predictive Scheduling System to Improve
Assisted Living Services for Elders",
journal = j-TIST,
volume = "6",
number = "4",
pages = "53:1--53:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2736700",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "As the number of older adults increases, and with it
the demand for dedicated care, geriatric residences
face a shortage of caregivers, who themselves
experience work overload, stress, and burden. We
conducted a long-term field study in three geriatric
residences to understand the work conditions of
caregivers with the aim of developing technologies to
assist them in their work and help them deal with their
burdens. From this study, we obtained relevant
requirements and insights to design, implement, and
evaluate two prototypes for supporting caregivers'
tasks (e.g., electronic recording and automatic
notifications) in order to validate the feasibility of
their implementation in situ and their technical
requirements. The evaluation in situ of the prototypes
was conducted for a period of 4 weeks. The results of
the evaluation, together with the data collected from 6
months of use, motivated the design of a predictive
schedule, which was iteratively improved and evaluated
in participative sessions with caregivers. PRESENCE,
the predictive schedule we propose, triggers real-time
alerts of risky situations (e.g., falls, entering
off-limits areas such as the infirmary or the kitchen)
and informs caregivers of routine tasks that need to be
performed (e.g., medication administration, diaper
change, etc.). Moreover, PRESENCE helps caregivers to
record caring tasks (such as diaper changes or
medication) and well-being assessments (such as the
mood) that are difficult to automate. This facilitates
caregiver's shift handover and can help to train new
caregivers by suggesting routine tasks and by sending
reminders and timely information about residents. It
can be seen as a tool to reduce the workload of
caregivers and medical staff. Instead of trying to
substitute the caregiver with an automatic caring
system, as proposed by others, we propose our
predictive schedule system that blends caregiver
assessments and measurements from sensors. We show the
feasibility of predicting caregiver tasks and a
formative evaluation with caregivers that provides
preliminary evidence of its utility.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Champaign:2015:EPC,
author = "John Champaign and Robin Cohen and Disney Yan Lam",
title = "Empowering Patients and Caregivers to Manage
Healthcare Via Streamlined Presentation of {Web}
Objects Selected by Modeling Learning Benefits Obtained
by Similar Peers",
journal = j-TIST,
volume = "6",
number = "4",
pages = "54:1--54:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700480",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we introduce a framework for
selecting web objects (texts, videos, simulations) from
a large online repository to present to patients and
caregivers, in order to assist in their healthcare.
Motivated by the paradigm of peer-based intelligent
tutoring, we model the learning gains achieved by users
when exposed to specific web objects in order to
recommend those objects most likely to deliver benefit
to new users. We are able to show that this streamlined
presentation leads to effective knowledge gains, both
through a process of simulated learning and through a
user study, for the specific application of caring for
children with autism. The value of our framework for
peer-driven content selection of health information is
emphasized through two additional roles for peers:
attaching commentary to web objects and proposing
subdivided objects for presentation, both of which are
demonstrated to deliver effective learning gains, in
simulations. In all, we are offering an opportunity for
patients to navigate the deep waters of excessive
online information towards effective management of
healthcare, through content selection influenced by
previous peer experiences.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2015:UHC,
author = "Haodong Yang and Christopher C. Yang",
title = "Using Health-Consumer-Contributed Data to Detect
Adverse Drug Reactions by Association Mining with
Temporal Analysis",
journal = j-TIST,
volume = "6",
number = "4",
pages = "55:1--55:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Since adverse drug reactions (ADRs) represent a
significant health problem all over the world, ADR
detection has become an important research topic in
drug safety surveillance. As many potential ADRs cannot
be detected though premarketing review, drug safety
currently depends heavily on postmarketing
surveillance. Particularly, current postmarketing
surveillance in the United States primarily relies on
the FDA Adverse Event Reporting System (FAERS).
However, the effectiveness of such spontaneous
reporting systems for ADR detection is not as good as
expected because of the extremely high underreporting
ratio of ADRs. Moreover, it often takes the FDA years
to complete the whole process of collecting reports,
investigating cases, and releasing alerts. Given the
prosperity of social media, many online health
communities are publicly available for health consumers
to share and discuss any healthcare experience such as
ADRs they are suffering. Such
health-consumer-contributed content is timely and
informative, but this data source still remains
untapped for postmarketing drug safety surveillance. In
this study, we propose to use (1) association mining to
identify the relations between a drug and an ADR and
(2) temporal analysis to detect drug safety signals at
the early stage. We collect data from MedHelp and use
the FDA's alerts and information of drug labeling
revision as the gold standard to evaluate the
effectiveness of our approach. The experiment results
show that health-related social media is a promising
source for ADR detection, and our proposed techniques
are effective to identify early ADR signals.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ullah:2015:ERL,
author = "Md Zia Ullah and Masaki Aono and Md Hanif Seddiqui",
title = "Estimating a Ranked List of Human Genetic Diseases by
Associating Phenotype-Gene with Gene-Disease Bipartite
Graphs",
journal = j-TIST,
volume = "6",
number = "4",
pages = "56:1--56:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700487",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With vast amounts of medical knowledge available on
the Internet, it is becoming increasingly practical to
help doctors in clinical diagnostics by suggesting
plausible diseases predicted by applying data and text
mining technologies. Recently, Genome-Wide Association
Studies ( GWAS ) have proved useful as a method for
exploring phenotypic associations with diseases.
However, since genetic diseases are difficult to
diagnose because of their low prevalence, large number,
and broad diversity of symptoms, genetic disease
patients are often misdiagnosed or experience long
diagnostic delays. In this article, we propose a method
for ranking genetic diseases for a set of clinical
phenotypes. In this regard, we associate a
phenotype-gene bipartite graph ( PGBG ) with a
gene-disease bipartite graph ( GDBG ) by producing a
phenotype-disease bipartite graph ( PDBG ), and we
estimate the candidate weights of diseases. In our
approach, all paths from a phenotype to a disease are
explored by considering causative genes to assign a
weight based on path frequency, and the phenotype is
linked to the disease in a new PDBG. We introduce the
Bidirectionally induced Importance Weight ( BIW )
prediction method to PDBG for approximating the weights
of the edges of diseases with phenotypes by considering
link information from both sides of the bipartite
graph. The performance of our system is compared to
that of other known related systems by estimating
Normalized Discounted Cumulative Gain ( NDCG ), Mean
Average Precision ( MAP ), and Kendall's tau metrics.
Further experiments are conducted with well-known TF $
\cdot $ IDF, BM25, and Jenson-Shannon divergence as
baselines. The result shows that our proposed method
outperforms the known related tool Phenomizer in terms
of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it
performs worse than Phenomizer in terms of Kendall's
tau-b metric at the top-10 ranks. It also turns out
that our proposed method has overall better performance
than the baseline methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Antonelli:2015:MCM,
author = "Dario Antonelli and Elena Baralis and Giulia Bruno and
Luca Cagliero and Tania Cerquitelli and Silvia Chiusano
and Paolo Garza and Naeem A. Mahoto",
title = "{MeTA}: Characterization of Medical Treatments at
Different Abstraction Levels",
journal = j-TIST,
volume = "6",
number = "4",
pages = "57:1--57:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700479",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Physicians and health care organizations always
collect large amounts of data during patient care.
These large and high-dimensional datasets are usually
characterized by an inherent sparseness. Hence,
analyzing these datasets to figure out interesting and
hidden knowledge is a challenging task. This article
proposes a new data mining framework based on
generalized association rules to discover
multiple-level correlations among patient data.
Specifically, correlations among prescribed
examinations, drugs, and patient profiles are
discovered and analyzed at different abstraction
levels. The rule extraction process is driven by a
taxonomy to generalize examinations and drugs into
their corresponding categories. To ease the manual
inspection of the result, a worthwhile subset of rules
(i.e., nonredundant generalized rules) is considered.
Furthermore, rules are classified according to the
involved data features (medical treatments or patient
profiles) and then explored in a top-down fashion: from
the small subset of high-level rules, a drill-down is
performed to target more specific rules. The
experiments, performed on a real diabetic patient
dataset, demonstrate the effectiveness of the proposed
approach in discovering interesting rule groups at
different abstraction levels.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Motai:2015:SCD,
author = "Yuichi Motai and Dingkun Ma and Alen Docef and
Hiroyuki Yoshida",
title = "Smart Colonography for Distributed Medical Databases
with Group Kernel Feature Analysis",
journal = j-TIST,
volume = "6",
number = "4",
pages = "58:1--58:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668136",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Computer-Aided Detection (CAD) of polyps in Computed
Tomographic (CT) colonography is currently very limited
since a single database at each hospital/institution
doesn't provide sufficient data for training the CAD
system's classification algorithm. To address this
limitation, we propose to use multiple databases,
(e.g., big data studies) to create multiple
institution-wide databases using distributed computing
technologies, which we call smart colonography. Smart
colonography may be built by a larger colonography
database networked through the participation of
multiple institutions via distributed computing. The
motivation herein is to create a distributed database
that increases the detection accuracy of CAD diagnosis
by covering many true-positive cases. Colonography data
analysis is mutually accessible to increase the
availability of resources so that the knowledge of
radiologists is enhanced. In this article, we propose a
scalable and efficient algorithm called Group Kernel
Feature Analysis (GKFA), which can be applied to
multiple cancer databases so that the overall
performance of CAD is improved. The key idea behind the
proposed GKFA method is to allow the feature space to
be updated as the training proceeds with more data
being fed from other institutions into the algorithm.
Experimental results show that GKFA achieves very good
classification accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kim:2015:RPR,
author = "Mi-Young Kim and Ying Xu and Osmar R. Zaiane and Randy
Goebel",
title = "Recognition of Patient-Related Named Entities in Noisy
Tele-Health Texts",
journal = j-TIST,
volume = "6",
number = "4",
pages = "59:1--59:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2651444",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We explore methods for effectively extracting
information from clinical narratives that are captured
in a public health consulting phone service called
HealthLink. Our research investigates the application
of state-of-the-art natural language processing and
machine learning to clinical narratives to extract
information of interest. The currently available data
consist of dialogues constructed by nurses while
consulting patients by phone. Since the data are
interviews transcribed by nurses during phone
conversations, they include a significant volume and
variety of noise. When we extract the patient-related
information from the noisy data, we have to remove or
correct at least two kinds of noise: explicit noise,
which includes spelling errors, unfinished sentences,
omission of sentence delimiters, and variants of terms,
and implicit noise, which includes non-patient
information and patient's untrustworthy information. To
filter explicit noise, we propose our own biomedical
term detection/normalization method: it resolves
misspelling, term variations, and arbitrary
abbreviation of terms by nurses. In detecting temporal
terms, temperature, and other types of named entities
(which show patients' personal information such as age
and sex), we propose a bootstrapping-based pattern
learning process to detect a variety of arbitrary
variations of named entities. To address implicit
noise, we propose a dependency path-based filtering
method. The result of our denoising is the extraction
of normalized patient information, and we visualize the
named entities by constructing a graph that shows the
relations between named entities. The objective of this
knowledge discovery task is to identify associations
between biomedical terms and to clearly expose the
trends of patients' symptoms and concern; the
experimental results show that we achieve reasonable
performance with our noise reduction methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ding:2015:LRN,
author = "Wenkui Ding and Xiubo Geng and Xu-Dong Zhang",
title = "Learning to Rank from Noisy Data",
journal = j-TIST,
volume = "7",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2576230",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Learning to rank, which learns the ranking function
from training data, has become an emerging research
area in information retrieval and machine learning.
Most existing work on learning to rank assumes that the
training data is clean, which is not always true,
however. The ambiguity of query intent, the lack of
domain knowledge, and the vague definition of relevance
levels all make it difficult for common annotators to
give reliable relevance labels to some documents. As a
result, the relevance labels in the training data of
learning to rank usually contain noise. If we ignore
this fact, the performance of learning-to-rank
algorithms will be damaged. In this article, we propose
considering the labeling noise in the process of
learning to rank and using a two-step approach to
extend existing algorithms to handle noisy training
data. In the first step, we estimate the degree of
labeling noise for a training document. To this end, we
assume that the majority of the relevance labels in the
training data are reliable and we use a graphical model
to describe the generative process of a training query,
the feature vectors of its associated documents, and
the relevance labels of these documents. The parameters
in the graphical model are learned by means of maximum
likelihood estimation. Then the conditional probability
of the relevance label given the feature vector of a
document is computed. If the probability is large, we
regard the degree of labeling noise for this document
as small; otherwise, we regard the degree as large. In
the second step, we extend existing learning-to-rank
algorithms by incorporating the estimated degree of
labeling noise into their loss functions. Specifically,
we give larger weights to those training documents with
smaller degrees of labeling noise and smaller weights
to those with larger degrees of labeling noise. As
examples, we demonstrate the extensions for McRank,
RankSVM, RankBoost, and RankNet. Empirical results on
benchmark datasets show that the proposed approach can
effectively distinguish noisy documents from clean
ones, and the extended learning-to-rank algorithms can
achieve better performances than baselines.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2015:LSB,
author = "Fan Liu and Jinhui Tang and Yan Song and Liyan Zhang
and Zhenmin Tang",
title = "Local Structure-Based Sparse Representation for Face
Recognition",
journal = j-TIST,
volume = "7",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2733383",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article presents a simple yet effective face
recognition method, called local structure-based sparse
representation classification (LS\_SRC). Motivated by
the ``divide-and-conquer'' strategy, we first divide
the face into local blocks and classify each local
block, then integrate all the classification results to
make the final decision. To classify each local block,
we further divide each block into several overlapped
local patches and assume that these local patches lie
in a linear subspace. This subspace assumption reflects
the local structure relationship of the overlapped
patches, making sparse representation-based
classification (SRC) feasible even when encountering
the single-sample-per-person (SSPP) problem. To lighten
the computing burden of LS\_SRC, we further propose the
local structure-based collaborative representation
classification (LS\_CRC). Moreover, the performance of
LS\_SRC and LS\_CRC can be further improved by using
the confusion matrix of the classifier. Experimental
results on four public face databases show that our
methods not only generalize well to SSPP problem but
also have strong robustness to occlusion; little pose
variation; and the variations of expression,
illumination, and time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Groves:2015:OAT,
author = "William Groves and Maria Gini",
title = "On Optimizing Airline Ticket Purchase Timing",
journal = j-TIST,
volume = "7",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2733384",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Proper timing of the purchase of airline tickets is
difficult even when historical ticket prices and some
domain knowledge are available. To address this
problem, we introduce an algorithm that optimizes
purchase timing on behalf of customers and provides
performance estimates of its computed action policy.
Given a desired flight route and travel date, the
algorithm uses machine-learning methods on recent
ticket price quotes from many competing airlines to
predict the future expected minimum price of all
available flights. The main novelty of our algorithm
lies in using a systematic feature-selection technique,
which captures time dependencies in the data by using
time-delayed features, and reduces the number of
features by imposing a class hierarchy among the raw
features and pruning the features based on in-situ
performance. Our algorithm achieves much closer to the
optimal purchase policy than other existing decision
theoretic approaches for this domain, and meets or
exceeds the performance of existing feature-selection
methods from the literature. Applications of our
feature-selection process to other domains are also
discussed.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Dong:2015:NMR,
author = "Yongsheng Dong and Dacheng Tao and Xuelong Li",
title = "Nonnegative Multiresolution Representation-Based
Texture Image Classification",
journal = j-TIST,
volume = "7",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2738050",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Effective representation of image texture is important
for an image-classification task. Statistical modelling
in wavelet domains has been widely used to image
texture representation. However, due to the intraclass
complexity and interclass diversity of textures, it is
hard to use a predefined probability distribution
function to fit adaptively all wavelet subband
coefficients of different textures. In this article, we
propose a novel modelling approach, Heterogeneous and
Incrementally Generated Histogram (HIGH), to indirectly
model the wavelet coefficients by use of four local
features in wavelet subbands. By concatenating all the
HIGHs in all wavelet subbands of a texture, we can
construct a nonnegative multiresolution vector (NMV) to
represent a texture image. Considering the NMV's high
dimensionality and nonnegativity, we further propose a
Hessian regularized discriminative nonnegative matrix
factorization to compute a low-dimensional basis of the
linear subspace of NMVs. Finally, we present a texture
classification approach by projecting NMVs on the
low-dimensional basis. Experimental results show that
our proposed texture classification method outperforms
seven representative approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:MKM,
author = "Bowei Chen and Jun Wang and Ingemar J. Cox and Mohan
S. Kankanhalli",
title = "Multi-Keyword Multi-Click Advertisement Option
Contracts for Sponsored Search",
journal = j-TIST,
volume = "7",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2743027",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In sponsored search, advertisement (abbreviated ad)
slots are usually sold by a search engine to an
advertiser through an auction mechanism in which
advertisers bid on keywords. In theory, auction
mechanisms have many desirable economic properties.
However, keyword auctions have a number of limitations
including: the uncertainty in payment prices for
advertisers; the volatility in the search engine's
revenue; and the weak loyalty between advertiser and
search engine. In this article, we propose a special ad
option that alleviates these problems. In our proposal,
an advertiser can purchase an option from a search
engine in advance by paying an upfront fee, known as
the option price. The advertiser then has the right,
but no obligation, to purchase among the prespecified
set of keywords at the fixed cost-per-clicks (CPCs) for
a specified number of clicks in a specified period of
time. The proposed option is closely related to a
special exotic option in finance that contains multiple
underlying assets (multi-keyword) and is also
multi-exercisable (multi-click). This novel structure
has many benefits: advertisers can have reduced
uncertainty in advertising; the search engine can
improve the advertisers' loyalty as well as obtain a
stable and increased expected revenue over time. Since
the proposed ad option can be implemented in
conjunction with the existing keyword auctions, the
option price and corresponding fixed CPCs must be set
such that there is no arbitrage between the two
markets. Option pricing methods are discussed and our
experimental results validate the development. Compared
to keyword auctions, a search engine can have an
increased expected revenue by selling an ad option.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Font:2015:AIT,
author = "Frederic Font and Joan Serr{\`a} and Xavier Serra",
title = "Analysis of the Impact of a Tag Recommendation System
in a Real-World Folksonomy",
journal = j-TIST,
volume = "7",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2743026",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative tagging systems have emerged as a
successful solution for annotating contributed
resources to online sharing platforms, facilitating
searching, browsing, and organizing their contents. To
aid users in the annotation process, several tag
recommendation methods have been proposed. It has been
repeatedly hypothesized that these methods should
contribute to improving annotation quality and reducing
the cost of the annotation process. It has been also
hypothesized that these methods should contribute to
the consolidation of the vocabulary of collaborative
tagging systems. However, to date, no empirical and
quantitative result supports these hypotheses. In this
work, we deeply analyze the impact of a tag
recommendation system in the folksonomy of Freesound, a
real-world and large-scale online sound sharing
platform. Our results suggest that tag recommendation
effectively increases vocabulary sharing among users of
the platform. In addition, tag recommendation is shown
to contribute to the convergence of the vocabulary as
well as to a partial increase in the quality of
annotations. However, according to our analysis, the
cost of the annotation process does not seem to be
effectively reduced. Our work is relevant to increase
our understanding about the nature of tag
recommendation systems and points to future directions
for the further development of those systems and their
analysis.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cheng:2015:HBS,
author = "Fan-Chieh Cheng and Bo-Hao Chen and Shih-Chia Huang",
title = "A Hybrid Background Subtraction Method with Background
and Foreground Candidates Detection",
journal = j-TIST,
volume = "7",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2746409",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Background subtraction for motion detection is often
used in video surveillance systems. However,
difficulties in bootstrapping restrict its development.
This article proposes a novel hybrid background
subtraction technique to solve this problem. For
performance improvement of background subtraction, the
proposed technique not only quickly initializes the
background model but also eliminates unnecessary
regions containing only background pixels in the object
detection process. Furthermore, an embodiment based on
the proposed technique is also presented. Experimental
results verify that the proposed technique allows for
reduced execution time as well as improvement of
performance as evaluated by Recall, Precision, F1, and
Similarity metrics when used with state-of-the-art
background subtraction methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Muntean:2015:LPM,
author = "Cristina Ioana Muntean and Franco Maria Nardini and
Fabrizio Silvestri and Ranieri Baraglia",
title = "On Learning Prediction Models for Tourists Paths",
journal = j-TIST,
volume = "7",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2766459",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we tackle the problem of predicting
the ``next'' geographical position of a tourist, given
her history (i.e., the prediction is done accordingly
to the tourist's current trail) by means of supervised
learning techniques, namely Gradient Boosted Regression
Trees and Ranking SVM. The learning is done on the
basis of an object space represented by a 68-dimension
feature vector specifically designed for
tourism-related data. Furthermore, we propose a
thorough comparison of several methods that are
considered state-of-the-art in recommender and trail
prediction systems for tourism, as well as a popularity
baseline. Experiments show that the methods we propose
consistently outperform the baselines and provide
strong evidence of the performance and robustness of
our solutions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2015:WHP,
author = "Yinting Wang and Mingli Song and Dacheng Tao and Yong
Rui and Jiajun Bu and Ah Chung Tsoi and Shaojie Zhuo
and Ping Tan",
title = "{Where2Stand}: a Human Position Recommendation System
for Souvenir Photography",
journal = j-TIST,
volume = "7",
number = "1",
pages = "9:1--9:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2770879",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "People often take photographs at tourist sites and
these pictures usually have two main elements: a person
in the foreground and scenery in the background. This
type of ``souvenir photo'' is one of the most common
photos clicked by tourists. Although algorithms that
aid a user-photographer in taking a well-composed
picture of a scene exist [Ni et al. 2013], few studies
have addressed the issue of properly positioning human
subjects in photographs. In photography, the common
guidelines of composing portrait images exist. However,
these rules usually do not consider the background
scene. Therefore, in this article, we investigate
human-scenery positional relationships and construct a
photographic assistance system to optimize the position
of human subjects in a given background scene, thereby
assisting the user in capturing high-quality souvenir
photos. We collect thousands of well-composed portrait
photographs to learn human-scenery aesthetic
composition rules. In addition, we define a set of
negative rules to exclude undesirable compositions.
Recommendation results are achieved by combining the
first learned positive rule with our proposed negative
rules. We implement the proposed system on an Android
platform in a smartphone. The system demonstrates its
efficacy by producing well-composed souvenir photos.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hennes:2015:MLS,
author = "Daniel Hennes and Steven {De Jong} and Karl Tuyls and
Ya'akov (Kobi) Gal",
title = "Metastrategies in Large-Scale Bargaining Settings",
journal = j-TIST,
volume = "7",
number = "1",
pages = "10:1--10:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2774224",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article presents novel methods for representing
and analyzing a special class of multiagent bargaining
settings that feature multiple players, large action
spaces, and a relationship among players' goals, tasks,
and resources. We show how to reduce these interactions
to a set of bilateral normal-form games in which the
strategy space is significantly smaller than the
original settings while still preserving much of their
structural relationship. The method is demonstrated
using the Colored Trails (CT) framework, which
encompasses a broad family of games and has been used
in many past studies. We define a set of heuristics
(metastrategies) in multiplayer CT games that make
varying assumptions about players' strategies, such as
boundedly rational play and social preferences. We show
how these CT settings can be decomposed into canonical
bilateral games such as the Prisoners' Dilemma, Stag
Hunt, and Ultimatum games in a way that significantly
facilitates their analysis. We demonstrate the
feasibility of this approach in separate CT settings
involving one-shot and repeated bargaining scenarios,
which are subsequently analyzed using evolutionary
game-theoretic techniques. We provide a set of
necessary conditions for CT games for allowing this
decomposition. Our results have significance for
multiagent systems researchers in mapping large
multiplayer CT task settings to smaller, well-known
bilateral normal-form games while preserving some of
the structure of the original setting.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:SSI,
author = "Jia-Dong Zhang and Chi-Yin Chow",
title = "Spatiotemporal Sequential Influence Modeling for
Location Recommendations: a Gravity-based Approach",
journal = j-TIST,
volume = "7",
number = "1",
pages = "11:1--11:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2786761",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommending to users personalized locations is an
important feature of Location-Based Social Networks
(LBSNs), which benefits users who wish to explore new
places and businesses to discover potential customers.
In LBSNs, social and geographical influences have been
intensively used in location recommendations. However,
human movement also exhibits spatiotemporal sequential
patterns, but only a few current studies consider the
spatiotemporal sequential influence of locations on
users' check-in behaviors. In this article, we propose
a new gravity model for location recommendations,
called LORE, to exploit the spatiotemporal sequential
influence on location recommendations. First, LORE
extracts sequential patterns from historical check-in
location sequences of all users as a Location-Location
Transition Graph (L$^2$ TG), and utilizes the L$^2$ TG
to predict the probability of a user visiting a new
location through the developed additive Markov chain
that considers the effect of all visited locations in
the check-in history of the user on the new location.
Furthermore, LORE applies our contrived gravity model
to weigh the effect of each visited location on the new
location derived from the personalized attractive force
(i.e., the weight) between the visited location and the
new location. The gravity model effectively integrates
the spatiotemporal, social, and popularity influences
by estimating a power-law distribution based on (i) the
spatial distance and temporal difference between two
consecutive check-in locations of the same user, (ii)
the check-in frequency of social friends, and (iii) the
popularity of locations from all users. Finally, we
conduct a comprehensive performance evaluation for LORE
using three large-scale real-world datasets collected
from Foursquare, Gowalla, and Brightkite. Experimental
results show that LORE achieves significantly superior
location recommendations compared to other
state-of-the-art location recommendation techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guan:2015:DML,
author = "Tao Guan and Yuesong Wang and Liya Duan and Rongrong
Ji",
title = "On-Device Mobile Landmark Recognition Using Binarized
Descriptor with Multifeature Fusion",
journal = j-TIST,
volume = "7",
number = "1",
pages = "12:1--12:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2795234",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Along with the exponential growth of high-performance
mobile devices, on-device Mobile Landmark Recognition
(MLR) has recently attracted increasing research
attention. However, the latency and accuracy of
automatic recognition remain as bottlenecks against its
real-world usage. In this article, we introduce a novel
framework that combines interactive image segmentation
with multifeature fusion to achieve improved MLR with
high accuracy. First, we propose an effective vector
binarization method to reduce the memory usage of image
descriptors extracted on-device, which maintains
comparable recognition accuracy to the original
descriptors. Second, we design a location-aware fusion
algorithm that can fuse multiple visual features into a
compact yet discriminative image descriptor to improve
on-device efficiency. Third, a user-friendly
interaction scheme is developed that enables
interactive foreground/background segmentation to
largely improve recognition accuracy. Experimental
results demonstrate the effectiveness of the proposed
algorithms for on-device MLR applications.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2016:EFC,
author = "Kun Zhang and Zhikun Wang and Jiji Zhang and Bernhard
Sch{\"o}lkopf",
title = "On Estimation of Functional Causal Models: General
Results and Application to the Post-Nonlinear Causal
Model",
journal = j-TIST,
volume = "7",
number = "2",
pages = "13:1--13:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700476",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Compared to constraint-based causal discovery, causal
discovery based on functional causal models is able to
identify the whole causal model under appropriate
assumptions [Shimizu et al. 2006; Hoyer et al. 2009;
Zhang and Hyv{\"a}rinen 2009b]. Functional causal
models represent the effect as a function of the direct
causes together with an independent noise term.
Examples include the linear non-Gaussian acyclic model
(LiNGAM), nonlinear additive noise model, and
post-nonlinear (PNL) model. Currently, there are two
ways to estimate the parameters in the models:
dependence minimization and maximum likelihood. In this
article, we show that for any acyclic functional causal
model, minimizing the mutual information between the
hypothetical cause and the noise term is equivalent to
maximizing the data likelihood with a flexible model
for the distribution of the noise term. We then focus
on estimation of the PNL causal model and propose to
estimate it with the warped Gaussian process with the
noise modeled by the mixture of Gaussians. As a
Bayesian nonparametric approach, it outperforms the
previous one based on mutual information minimization
with nonlinear functions represented by multilayer
perceptrons; we also show that unlike the ordinary
regression, estimation results of the PNL causal model
are sensitive to the assumption on the noise
distribution. Experimental results on both synthetic
and real data support our theoretical claims.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:OSC,
author = "Jiuyong Li and Thuc Duy Le and Lin Liu and Jixue Liu
and Zhou Jin and Bingyu Sun and Saisai Ma",
title = "From Observational Studies to Causal Rule Mining",
journal = j-TIST,
volume = "7",
number = "2",
pages = "14:1--14:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2746410",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Randomised controlled trials (RCTs) are the most
effective approach to causal discovery, but in many
circumstances it is impossible to conduct RCTs.
Therefore, observational studies based on passively
observed data are widely accepted as an alternative to
RCTs. However, in observational studies, prior
knowledge is required to generate the hypotheses about
the cause-effect relationships to be tested, and hence
they can only be applied to problems with available
domain knowledge and a handful of variables. In
practice, many datasets are of high dimensionality,
which leaves observational studies out of the
opportunities for causal discovery from such a wealth
of data sources. In another direction, many efficient
data mining methods have been developed to identify
associations among variables in large datasets. The
problem is that causal relationships imply
associations, but the reverse is not always true.
However, we can see the synergy between the two
paradigms here. Specifically, association rule mining
can be used to deal with the high-dimensionality
problem, whereas observational studies can be utilised
to eliminate noncausal associations. In this article,
we propose the concept of causal rules (CRs) and
develop an algorithm for mining CRs in large datasets.
We use the idea of retrospective cohort studies to
detect CRs based on the results of association rule
mining. Experiments with both synthetic and real-world
datasets have demonstrated the effectiveness and
efficiency of CR mining. In comparison with the
commonly used causal discovery methods, the proposed
approach generally is faster and has better or
competitive performance in finding correct or sensible
causes. It is also capable of finding a cause
consisting of multiple variables-a feature that other
causal discovery methods do not possess.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Leiva:2016:GGG,
author = "Luis A. Leiva and Daniel Mart{\'\i}n-Albo and
R{\'e}jean Plamondon",
title = "Gestures {\`a} Go Go: Authoring Synthetic Human-Like
Stroke Gestures Using the Kinematic Theory of Rapid
Movements",
journal = j-TIST,
volume = "7",
number = "2",
pages = "15:1--15:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2799648",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Training a high-quality gesture recognizer requires
providing a large number of examples to enable good
performance on unseen, future data. However, recruiting
participants, data collection, and labeling, etc.,
necessary for achieving this goal are usually time
consuming and expensive. Thus, it is important to
investigate how to empower developers to quickly
collect gesture samples for improving UI usage and user
experience. In response to this need, we introduce
Gestures {\`a} Go Go ( g3), a web service plus an
accompanying web application for bootstrapping stroke
gesture samples based on the kinematic theory of rapid
human movements. The user only has to provide a gesture
example once, and g3 will create a model of that
gesture. Then, by introducing local and global
perturbations to the model parameters, g3 generates
from tens to thousands of synthetic human-like samples.
Through a comprehensive evaluation, we show that
synthesized gestures perform equally similar to
gestures generated by human users. Ultimately, this
work informs our understanding of designing better user
interfaces that are driven by gestures.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Minkov:2016:EEU,
author = "Einat Minkov",
title = "Event Extraction using Structured Learning and Rich
Domain Knowledge: Application across Domains and Data
Sources",
journal = j-TIST,
volume = "7",
number = "2",
pages = "16:1--16:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2801131",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We consider the task of record extraction from text
documents, where the goal is to automatically populate
the fields of target relations, such as scientific
seminars or corporate acquisition events. There are
various inferences involved in the record-extraction
process, including mention detection, unification, and
field assignments. We use structured learning to find
the appropriate field-value assignments. Unlike
previous works, the proposed approach generates
feature-rich models that enable the modeling of domain
semantics and structural coherence at all levels and
across fields. Given labeled examples, such an approach
can, for instance, learn likely event durations and the
fact that start times should come before end times.
While the inference space is large, effective learning
is achieved using a perceptron-style method and simple,
greedy beam decoding. A main focus of this article is
on practical aspects involved in implementing the
proposed framework for real-world applications. We
argue and demonstrate that this approach is favorable
in conditions of data shift, a real-world setting in
which models learned using a limited set of labeled
examples are applied to examples drawn from a different
data distribution. Much of the framework's robustness
is attributed to the modeling of domain knowledge. We
describe design and implementation details for the case
study of seminar event extraction from email
announcements, and discuss design adaptations across
different domains and text genres.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2016:PAT,
author = "Kun Zhang and Jiuyong Li and Elias Bareinboim and
Bernhard Sch{\"o}lkopf and Judea Pearl",
title = "Preface to the {ACM TIST} Special Issue on Causal
Discovery and Inference",
journal = j-TIST,
volume = "7",
number = "2",
pages = "17:1--17:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2840720",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shan:2016:SBS,
author = "Na Shan and Xiaogang Dong and Pingfeng Xu and Jianhua
Guo",
title = "Sharp Bounds on Survivor Average Causal Effects When
the Outcome Is Binary and Truncated by Death",
journal = j-TIST,
volume = "7",
number = "2",
pages = "18:1--18:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700498",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In randomized trials with follow-up, outcomes may be
undefined for individuals who die before the follow-up
is complete. In such settings, Frangakis and Rubin
[2002] proposed the ``principal stratum effect'' or
``Survivor Average Causal Effect'' (SACE), which is a
fair treatment comparison in the subpopulation that
would have survived under either treatment arm. Many of
the existing results for estimating the SACE are
difficult to carry out in practice. In this article,
when the outcome is binary, we apply the symbolic
Balke-Pearl linear programming method to derive simple
formulas for the sharp bounds on the SACE under the
monotonicity assumption commonly used by many
researchers.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2016:SIC,
author = "Hua Chen and Peng Ding and Zhi Geng and Xiao-Hua
Zhou",
title = "Semiparametric Inference of the Complier Average
Causal Effect with Nonignorable Missing Outcomes",
journal = j-TIST,
volume = "7",
number = "2",
pages = "19:1--19:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668135",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Noncompliance and missing data often occur in
randomized trials, which complicate the inference of
causal effects. When both noncompliance and missing
data are present, previous papers proposed moment and
maximum likelihood estimators for binary and normally
distributed continuous outcomes under the latent
ignorable missing data mechanism. However, the latent
ignorable missing data mechanism may be violated in
practice, because the missing data mechanism may depend
directly on the missing outcome itself. Under
noncompliance and an outcome-dependent nonignorable
missing data mechanism, previous studies showed the
identifiability of complier average causal effect for
discrete outcomes. In this article, we study the
semiparametric identifiability and estimation of
complier average causal effect in randomized clinical
trials with both all-or-none noncompliance and
outcome-dependent nonignorable missing continuous
outcomes, and propose a two-step maximum likelihood
estimator in order to eliminate the infinite
dimensional nuisance parameter. Our method does not
need to specify a parametric form for the missing data
mechanism. We also evaluate the finite sample property
of our method via extensive simulation studies and
sensitivity analysis, with an application to a
double-blinded psychiatric clinical trial.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Luo:2016:BDI,
author = "Peng Luo and Zhi Geng",
title = "Bounds on Direct and Indirect Effects of Treatment on
a Continuous Endpoint",
journal = j-TIST,
volume = "7",
number = "2",
pages = "20:1--20:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668134",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Direct effect of a treatment variable on an endpoint
variable and indirect effect through a mediate variable
are important concepts for understanding a causal
mechanism. However, the randomized assignment of
treatment is not sufficient for identifying the direct
and indirect effects, and extra assumptions and
conditions are required, such as the sequential
ignorability assumption without unobserved confounders
or the sequential potential ignorability assumption.
But these assumptions may not be credible in many
applications. In this article, we consider the bounds
on controlled direct effect, natural direct effect, and
natural indirect effect without these extra
assumptions. Cai et al. [2008] presented the bounds for
the case of a binary endpoint, and we extend their
results to the general case for an arbitrary
endpoint.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2016:CDD,
author = "Furui Liu and Laiwan Chan",
title = "Causal Discovery on Discrete Data with Extensions to
Mixture Model",
journal = j-TIST,
volume = "7",
number = "2",
pages = "21:1--21:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700477",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we deal with the causal discovery
problem on discrete data. First, we present a causal
discovery method for traditional additive noise models
that identifies the causal direction by analyzing the
supports of the conditional distributions. Then, we
present a causal mixture model to address the problem
that the function transforming cause to effect varies
across the observations. We propose a novel method
called Support Analysis (SA) for causal discovery with
the mixture model. Experiments using synthetic and real
data are presented to demonstrate the performance of
our proposed algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Flaxman:2016:GPI,
author = "Seth R. Flaxman and Daniel B. Neill and Alexander J.
Smola",
title = "{Gaussian} Processes for Independence Tests with
Non-iid Data in Causal Inference",
journal = j-TIST,
volume = "7",
number = "2",
pages = "22:1--22:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2806892",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In applied fields, practitioners hoping to apply
causal structure learning or causal orientation
algorithms face an important question: which
independence test is appropriate for my data? In the
case of real-valued iid data, linear dependencies, and
Gaussian error terms, partial correlation is
sufficient. But once any of these assumptions is
modified, the situation becomes more complex.
Kernel-based tests of independence have gained
popularity to deal with nonlinear dependencies in
recent years, but testing for conditional independence
remains a challenging problem. We highlight the
important issue of non-iid observations: when data are
observed in space, time, or on a network, ``nearby''
observations are likely to be similar. This fact biases
estimates of dependence between variables. Inspired by
the success of Gaussian process regression for handling
non-iid observations in a wide variety of areas and by
the usefulness of the Hilbert--Schmidt Independence
Criterion (HSIC), a kernel-based independence test, we
propose a simple framework to address all of these
issues: first, use Gaussian process regression to
control for certain variables and to obtain residuals.
Second, use HSIC to test for independence. We
illustrate this on two classic datasets, one spatial,
the other temporal, that are usually treated as iid. We
show how properly accounting for spatial and temporal
variation can lead to more reasonable causal graphs. We
also show how highly structured data, like images and
text, can be used in a causal inference framework using
a novel structured input/output Gaussian process
formulation. We demonstrate this idea on a dataset of
translated sentences, trying to predict the source
language.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fire:2016:LPC,
author = "Amy Fire and Song-Chun Zhu",
title = "Learning Perceptual Causality from Video",
journal = j-TIST,
volume = "7",
number = "2",
pages = "23:1--23:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2809782",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Perceptual causality is the perception of causal
relationships from observation. Humans, even as
infants, form such models from observation of the world
around them [Saxe and Carey 2006]. For a deeper
understanding, the computer must make similar models
through the analogous form of observation: video. In
this article, we provide a framework for the
unsupervised learning of this perceptual causal
structure from video. Our method takes action and
object status detections as input and uses heuristics
suggested by cognitive science research to produce the
causal links perceived between them. We greedily modify
an initial distribution featuring independence between
potential causes and effects by adding dependencies
that maximize information gain. We compile the learned
causal relationships into a Causal And-Or Graph, a
probabilistic and-or representation of causality that
adds a prior to causality. Validated against human
perception, experiments show that our method correctly
learns causal relations, attributing status changes of
objects to causing actions amid irrelevant actions. Our
method outperforms Hellinger's $ \chi^2$-statistic by
considering hierarchical action selection, and
outperforms the treatment effect by discounting
coincidental relationships.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Demeshko:2016:NCS,
author = "Marina Demeshko and Takashi Washio and Yoshinobu
Kawahara and Yuriy Pepyolyshev",
title = "A Novel Continuous and Structural {VAR} Modeling
Approach and Its Application to Reactor Noise
Analysis",
journal = j-TIST,
volume = "7",
number = "2",
pages = "24:1--24:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2710025",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A vector autoregressive model in discrete time domain
(DVAR) is often used to analyze continuous time,
multivariate, linear Markov systems through their
observed time series data sampled at discrete
timesteps. Based on previous studies, the DVAR model is
supposed to be a noncanonical representation of the
system, that is, it does not correspond to a unique
system bijectively. However, in this article, we
characterize the relations of the DVAR model with its
corresponding Structural Vector AR (SVAR) and
Continuous Time Vector AR (CTVAR) models through a
finite difference method across continuous and discrete
time domain. We further clarify that the DVAR model of
a continuous time, multivariate, linear Markov system
is canonical under a highly generic condition. Our
analysis shows that we can uniquely reproduce its SVAR
and CTVAR models from the DVAR model. Based on these
results, we propose a novel Continuous and Structural
Vector Autoregressive (CSVAR) modeling approach to
derive the SVAR and the CTVAR models from their DVAR
model empirically derived from the observed time series
of continuous time linear Markov systems. We
demonstrate its superior performance through some
numerical experiments on both artificial and real-world
data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hours:2016:CAS,
author = "Hadrien Hours and Ernst Biersack and Patrick Loiseau",
title = "A Causal Approach to the Study of {TCP} Performance",
journal = j-TIST,
volume = "7",
number = "2",
pages = "25:1--25:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2770878",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Communication networks are complex systems whose
operation relies on a large number of components that
work together to provide services to end users. As the
quality of these services depends on different
parameters, understanding how each of them impacts the
final performance of a service is a challenging but
important problem. However, intervening on individual
factors to evaluate the impact of the different
parameters is often impractical due to the high cost of
intervention in a network. It is, therefore, desirable
to adopt a formal approach to understand the role of
the different parameters and to predict how a change in
any of these parameters will impact performance. The
approach of causality pioneered by J. Pearl provides a
powerful framework to investigate these questions. Most
of the existing theory is non-parametric and does not
make any assumption on the nature of the system under
study. However, most of the implementations of causal
model inference algorithms and most of the examples of
usage of a causal model to predict intervention rely on
assumptions such linearity, normality, or discrete
data. In this article, we present a methodology to
overcome the challenges of working with real-world data
and extend the application of causality to complex
systems in the area of telecommunication networks, for
which assumptions of normality, linearity and discrete
data do no hold. Specifically, we study the performance
of TCP, which is the prevalent protocol for reliable
end-to-end transfer in the Internet. Analytical models
of the performance of TCP exist, but they take into
account the state of network only and disregard the
impact of the application at the sender and the
receiver, which often influences TCP performance. To
address this point, we take as application the file
transfer protocol (FTP), which uses TCP for reliable
transfer. Studying a well-understood protocol such as
TCP allows us to validate our approach and compare its
results to previous studies. We first present and
evaluate our methodology using TCP traffic obtained via
network emulation, which allows us to experimentally
validate the prediction of an intervention. We then
apply the methodology to real-world TCP traffic sent
over the Internet. Throughout the article, we compare
the causal approach for studying TCP performance to
other approaches such as analytical modeling or
simulation and and show how they can complement each
other.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Belem:2016:BRE,
author = "Fabiano M. Bel{\'e}m and Carolina S. Batista and
Rodrygo L. T. Santos and Jussara M. Almeida and Marcos
A. Gon{\c{c}}alves",
title = "Beyond Relevance: Explicitly Promoting Novelty and
Diversity in Tag Recommendation",
journal = j-TIST,
volume = "7",
number = "3",
pages = "26:1--26:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2801130",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The design and evaluation of tag recommendation
methods has historically focused on maximizing the
relevance of the suggested tags for a given object,
such as a movie or a song. However, relevance by itself
may not be enough to guarantee recommendation
usefulness. Promoting novelty and diversity in tag
recommendation not only increases the chances that the
user will select ``some'' of the recommended tags but
also promotes complementary information (i.e., tags),
which helps to cover multiple aspects or topics related
to the target object. Previous work has addressed the
tag recommendation problem by exploiting at most two of
the following aspects: (1) relevance, (2) explicit
topic diversity, and (3) novelty. In contrast, here we
tackle these three aspects conjointly, by introducing
two new tag recommendation methods that cover all three
aspects of the problem at different levels. Our first
method, called Random Forest with topic-related
attributes, or RF$_t$, extends a relevance-driven tag
recommender based on the Random Forest ( RF )
learning-to-rank method by including new tag attributes
to capture the extent to which a candidate tag is
related to the topics of the target object. This
solution captures topic diversity as well as novelty at
the attribute level while aiming at maximizing
relevance in its objective function. Our second method,
called Explicit Tag Recommendation Diversifier with
Novelty Promotion, or xTReND, reranks the
recommendations provided by any tag recommender to
jointly promote relevance, novelty, and topic
diversity. We use RF$_t$ as a basic recommender applied
before the reranking, thus building a solution that
addresses the problem at both attribute and objective
levels. Furthermore, to enable the use of our solutions
on applications in which category information is
unavailable, we investigate the suitability of using
latent Dirichlet allocation (LDA) to automatically
generate topics for objects. We evaluate all tag
recommendation approaches using real data from five
popular Web 2.0 applications. Our results show that
RF$_t$ greatly outperforms the relevance-driven RF
baseline in diversity while producing gains in
relevance as well. We also find that our new xTReND
reranker obtains considerable gains in both novelty and
relevance when compared to that same baseline while
keeping the same relevance levels. Furthermore,
compared to our previous reranker method, xTReD, which
does not consider novelty, xTReND is also quite
effective, improving the novelty of the recommended
tags while keeping similar relevance and diversity
levels in most datasets and scenarios. Comparing our
two new proposals, we find that xTReND considerably
outperforms RF$_t$ in terms of novelty and diversity
with only small losses (under 4\%) in relevance.
Overall, considering the trade-off among relevance,
novelty, and diversity, our results demonstrate the
superiority of xTReND over the baselines and the
proposed alternative, RF$_t$. Finally, the use of
automatically generated latent topics as an alternative
to manually labeled categories also provides
significant improvements, which greatly enhances the
applicability of our solutions to applications where
the latter is not available.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Paik:2016:PDM,
author = "Jiaul H. Paik",
title = "Parameterized Decay Model for Information Retrieval",
journal = j-TIST,
volume = "7",
number = "3",
pages = "27:1--27:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2800794",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article proposes a term weighting scheme for
measuring query-document similarity that attempts to
explicitly model the dependency between separate
occurrences of a term in a document. The assumption is
that, if a term appears once in a document, it is more
likely to appear again in the same document. Thus, as
the term appears again and again, the information
content of the subsequent occurrences decreases
gradually, since they are more predictable. We
introduce a parameterized decay function to model this
assumption, where the initial contribution of the term
can be determined using any reasonable term
discrimination factor. The effectiveness of the
proposed model is evaluated on a number of recent web
test collections of varying nature. The experimental
results show that the proposed model significantly
outperforms a number of well known retrieval models
including a recently proposed strong Term Frequency and
Inverse Document Frequency (TF-IDF) model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:MCA,
author = "Zhifeng Li and Dihong Gong and Qiang Li and Dacheng
Tao and Xuelong Li",
title = "Mutual Component Analysis for Heterogeneous Face
Recognition",
journal = j-TIST,
volume = "7",
number = "3",
pages = "28:1--28:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2807705",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Heterogeneous face recognition, also known as
cross-modality face recognition or intermodality face
recognition, refers to matching two face images from
alternative image modalities. Since face images from
different image modalities of the same person are
associated with the same face object, there should be
mutual components that reflect those intrinsic face
characteristics that are invariant to the image
modalities. Motivated by this rationality, we propose a
novel approach called Mutual Component Analysis (MCA)
to infer the mutual components for robust heterogeneous
face recognition. In the MCA approach, a generative
model is first proposed to model the process of
generating face images in different modalities, and
then an Expectation Maximization (EM) algorithm is
designed to iteratively learn the model parameters. The
learned generative model is able to infer the mutual
components (which we call the hidden factor, where
hidden means the factor is unreachable and invisible,
and can only be inferred from observations) that are
associated with the person's identity, thus enabling
fast and effective matching for cross-modality face
recognition. To enhance recognition performance, we
propose an MCA-based multiclassifier framework using
multiple local features. Experimental results show that
our new approach significantly outperforms the
state-of-the-art results on two typical application
scenarios: sketch-to-photo and infrared-to-visible face
recognition.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ye:2016:GIL,
author = "Jintao Ye and Zhao Yan Ming and Tat Seng Chua",
title = "Generating Incremental Length Summary Based on
Hierarchical Topic Coverage Maximization",
journal = j-TIST,
volume = "7",
number = "3",
pages = "29:1--29:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2809433",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Document summarization is playing an important role in
coping with information overload on the Web. Many
summarization models have been proposed recently, but
few try to adjust the summary length and sentence order
according to application scenarios. With the popularity
of handheld devices, presenting key information first
in summaries of flexible length is of great convenience
in terms of faster reading and decision-making and
network consumption reduction. Targeting this problem,
we introduce a novel task of generating summaries of
incremental length. In particular, we require that the
summaries should have the ability to automatically
adjust the coverage of general-detailed information
when the summary length varies. We propose a novel
summarization model that incrementally maximizes topic
coverage based on the document's hierarchical topic
model. In addition to the standard Rouge-1 measure, we
define a new evaluation metric based on the similarity
of the summaries' topic coverage distribution in order
to account for sentence order and summary length.
Extensive experiments on Wikipedia pages, DUC 2007, and
general noninverted writing style documents from
multiple sources show the effectiveness of our proposed
approach. Moreover, we carry out a user study on a
mobile application scenario to show the usability of
the produced summary in terms of improving judgment
accuracy and speed, as well as reducing the reading
burden and network traffic.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2016:PCM,
author = "Dingqi Yang and Daqing Zhang and Bingqing Qu",
title = "Participatory Cultural Mapping Based on Collective
Behavior Data in Location-Based Social Networks",
journal = j-TIST,
volume = "7",
number = "3",
pages = "30:1--30:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2814575",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Culture has been recognized as a driving impetus for
human development. It co-evolves with both human belief
and behavior. When studying culture, Cultural Mapping
is a crucial tool to visualize different aspects of
culture (e.g., religions and languages) from the
perspectives of indigenous and local people. Existing
cultural mapping approaches usually rely on large-scale
survey data with respect to human beliefs, such as
moral values. However, such a data collection method
not only incurs a significant cost of both human
resources and time, but also fails to capture human
behavior, which massively reflects cultural
information. In addition, it is practically difficult
to collect large-scale human behavior data.
Fortunately, with the recent boom in Location-Based
Social Networks (LBSNs), a considerable number of users
report their activities in LBSNs in a participatory
manner, which provides us with an unprecedented
opportunity to study large-scale user behavioral data.
In this article, we propose a participatory cultural
mapping approach based on collective behavior in LBSNs.
First, we collect the participatory sensed user
behavioral data from LBSNs. Second, since only local
users are eligible for cultural mapping, we propose a
progressive ``home'' location identification method to
filter out ineligible users. Third, by extracting three
key cultural features from daily activity, mobility,
and linguistic perspectives, respectively, we propose a
cultural clustering method to discover cultural
clusters. Finally, we visualize the cultural clusters
on the world map. Based on a real-world LBSN dataset,
we experimentally validate our approach by conducting
both qualitative and quantitative analysis on the
generated cultural maps. The results show that our
approach can subtly capture cultural features and
generate representative cultural maps that correspond
well with traditional cultural maps based on survey
data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jia:2016:LPT,
author = "Yantao Jia and Yuanzhuo Wang and Xiaolong Jin and
Xueqi Cheng",
title = "Location Prediction: a Temporal-Spatial {Bayesian}
Model",
journal = j-TIST,
volume = "7",
number = "3",
pages = "31:1--31:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2816824",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In social networks, predicting a user's location
mainly depends on those of his/her friends, where the
key lies in how to select his/her most influential
friends. In this article, we analyze the theoretically
maximal accuracy of location prediction based on
friends' locations and compare it with the practical
accuracy obtained by the state-of-the-art location
prediction methods. Upon observing a big gap between
the theoretical and practical accuracy, we propose a
new strategy for selecting influential friends in order
to improve the practical location prediction accuracy.
Specifically, several features are defined to measure
the influence of the friends on a user's location,
based on which we put forth a sequential
random-walk-with-restart procedure to rank the friends
of the user in terms of their influence. By dynamically
selecting the top N most influential friends of the
user per time slice, we develop a temporal-spatial
Bayesian model to characterize the dynamics of friends'
influence for location prediction. Finally, extensive
experimental results on datasets of real social
networks demonstrate that the proposed influential
friend selection method and temporal-spatial Bayesian
model can significantly improve the accuracy of
location prediction.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:VFE,
author = "Xiaoyan Li and Tongliang Liu and Jiankang Deng and
Dacheng Tao",
title = "Video Face Editing Using Temporal-Spatial-Smooth
Warping",
journal = j-TIST,
volume = "7",
number = "3",
pages = "32:1--32:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2819000",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Editing faces in videos is a popular yet challenging
task in computer vision and graphics that encompasses
various applications, including facial attractiveness
enhancement, makeup transfer, face replacement, and
expression manipulation. Directly applying the existing
warping methods to video face editing has the major
problem of temporal incoherence in the synthesized
videos, which cannot be addressed by simply employing
face tracking techniques or manual interventions, as it
is difficult to eliminate the subtly temporal
incoherence of the facial feature point localizations
in a video sequence. In this article, we propose a
temporal-spatial-smooth warping (TSSW) method to
achieve a high temporal coherence for video face
editing. TSSW is based on two observations: (1) the
control lattices are critical for generating warping
surfaces and achieving the temporal coherence between
consecutive video frames, and (2) the temporal
coherence and spatial smoothness of the control
lattices can be simultaneously and effectively
preserved. Based upon these observations, we impose the
temporal coherence constraint on the control lattices
on two consecutive frames, as well as the spatial
smoothness constraint on the control lattice on the
current frame. TSSW calculates the control lattice (in
either the horizontal or vertical direction) by
updating the control lattice (in the corresponding
direction) on its preceding frame, i.e., minimizing a
novel energy function that unifies a data-driven term,
a smoothness term, and feature point constraints. The
contributions of this article are twofold: (1) we
develop TSSW, which is robust to the subtly temporal
incoherence of the facial feature point localizations
and is effective to preserve the temporal coherence and
spatial smoothness of the control lattices for editing
faces in videos, and (2) we present a new unified video
face editing framework that is capable for improving
the performances of facial attractiveness enhancement,
makeup transfer, face replacement, and expression
manipulation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:MNS,
author = "Zechao Li and Jinhui Tang and Xueming Wang and Jing
Liu and Hanqing Lu",
title = "Multimedia News Summarization in Search",
journal = j-TIST,
volume = "7",
number = "3",
pages = "33:1--33:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2822907",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "It is a necessary but challenging task to relieve
users from the proliferative news information and allow
them to quickly and comprehensively master the
information of the whats and hows that are happening in
the world every day. In this article, we develop a
novel approach of multimedia news summarization for
searching results on the Internet, which uncovers the
underlying topics among query-related news information
and threads the news events within each topic to
generate a query-related brief overview. First, the
hierarchical latent Dirichlet allocation (hLDA) model
is introduced to discover the hierarchical topic
structure from query-related news documents, and a new
approach based on the weighted aggregation and max
pooling is proposed to identify one representative news
article for each topic. One representative image is
also selected to visualize each topic as a complement
to the text information. Given the representative
documents selected for each topic, a time-bias maximum
spanning tree (MST) algorithm is proposed to thread
them into a coherent and compact summary of their
parent topic. Finally, we design a friendly interface
to present users with the hierarchical summarization of
their required news information. Extensive experiments
conducted on a large-scale news dataset collected from
multiple news Web sites demonstrate the encouraging
performance of the proposed solution for news
summarization in news retrieval.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hardegger:2016:SUB,
author = "Michael Hardegger and Daniel Roggen and Alberto
Calatroni and Gerhard Tr{\"o}ster",
title = "{S-SMART}: a Unified {Bayesian} Framework for
Simultaneous Semantic Mapping, Activity Recognition,
and Tracking",
journal = j-TIST,
volume = "7",
number = "3",
pages = "34:1--34:??",
month = 
