By Ma Zhixin, Xu Yusheng, Tharam S. Dillon (auth.), Longbing Cao, Hiroshi Motoda, Jaideep Srivastava, Ee-Peng Lim, Irwin King, Philip S. Yu, Wolfgang Nejdl, Guandong Xu, Gang Li, Ya Zhang (eds.)
This e-book constitutes the completely refereed lawsuits of the foreign Workshops on habit and Social Informatics and Computing, BSIC 2013, held as collocated occasion of IJCAI 2013, in Beijing, China in August 2013 and the foreign Workshop on habit and Social Informatics, BSI 2013, held as satellite tv for pc workshop of PAKDD 2013, in Gold Coast, Australia, in April 2013. The 23 papers provided have been conscientiously reviewed and chosen from fifty eight submissions. The papers learn quite a lot of thoughts and techniques for behavior/social-oriented analyses together with behavioral and social interplay and community, behavioral/social styles, behavioral/social affects, the formation of behavioral/social-oriented teams and collective intelligence and behavioral/social intelligence emergence.
Read or Download Behavior and Social Computing: International Workshop on Behavior and Social Informatics, BSI 2013, Gold Coast, QLD, Australia, April 14-17, 2013 and International Workshop on Behavior and Social Informatics and Computing, BSIC 2013, Beijing, China, Augus PDF
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Extra resources for Behavior and Social Computing: International Workshop on Behavior and Social Informatics, BSI 2013, Gold Coast, QLD, Australia, April 14-17, 2013 and International Workshop on Behavior and Social Informatics and Computing, BSIC 2013, Beijing, China, Augus
Store a record of the hotspot into the database, with attribute tag ID, user ID, date and time, and number of activities. Here, tag ID can be empty, which means that user does not stay at any hotspot in an hour. Number of activities also can be empty, meaning that user have no activity. There should be 24 records for each user in a day, representing the user’s activities within a day. But these records should remove shocking pairs and noise points, to form a hotspot sequence. 2 Stability and Periodicity of Individual Activities Individual activity includes activity hotspot and activity intensity.
LNCS, vol. 5385, pp. 639–652. Springer, Heidelberg (2008) 3. : Experiments in social computation. Communications of the ACM 55(10), 56– 67 (2012) 4. : A behavioral study of bargaining in social networks. In: Proceedings of the 11th ACM Conference on Electronic Commerce, pp. 243–252. ACM (2010) 5. : Cooperation and contagion in web-based, networked public goods experiments. PLoS One 6(3), e16836 (2011) 6. : The spread of behavior in an online social network experiment. Science 329(5996), 1194–1197 (2010) 7.
BSIC/BSI 2013, LNAI 8178, pp. 30–43, 2013. © Springer International Publishing Switzerland 2013 Using Mobile Phone Location Data for Urban Activity Analysis 31 real-world cellphone log data to demonstrate their framework. Based on 226 daily GPS traces of 101 subjects,  develops a mobility model that captures the effect of human mobility patterns characterized by some fundamental statistical functions.  proposes a MAST (Movement, Action, and Situation over Time) probabilistic model for using mobile phone sensors to analyze and predict user behavior patterns.