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Learning Communities — Understanding Information Flow in Human Networks
BT Technology Journal
Human dynamics: computation for organizations
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Proceedings of the 13th annual ACM international conference on Multimedia
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Proceedings of the 9th international conference on Multimodal interfaces
Practical asynchronous neighbor discovery and rendezvous for mobile sensing applications
Proceedings of the 6th ACM conference on Embedded network sensor systems
Neary: conversation field detection based on similarity of auditory situation
Proceedings of the 10th workshop on Mobile Computing Systems and Applications
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
ISWPC'09 Proceedings of the 4th international conference on Wireless pervasive computing
Predicting flow state in daily work through continuous sensing of motion rhythm
INSS'09 Proceedings of the 6th international conference on Networked sensing systems
ACM Transactions on Intelligent Systems and Technology (TIST)
Modelling and analyzing multimodal dyadic interactions using social networks
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Pervasive sensing to model political opinions in face-to-face networks
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
mConverse: inferring conversation episodes from respiratory measurements collected in the field
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Conversational inverse information for context-based retrieval of personal experiences
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
Predicting creativity in the wild: experience sample and sociometric modeling of teams
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Collaborative personal speaker identification: A generalized approach
Pervasive and Mobile Computing
MyConverse: recognising and visualising personal conversations using smartphones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Knowledge of how groups of people interact is important in many disciplines, e.g. organizational behavior, social network analysis, knowledge management and ubiquitous computing. Existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained (available from chat and email logs), or have been forced to rely on questionnaires, surveys or diaries to get data on face-to-face interactions between people. The aim of this thesis is to automatically model face-to-face interactions within a community. The first challenge was to collect rich and unbiased sensor data of natural interactions. The “sociometer”, a specially designed wearable sensor package, was built to address this problem by unobtrusively measuring face-to-face interactions between people. Using the sociometers, 1518 hours of wearable sensor data from 23 individuals was collected over a two-week period (66 hours per person). This thesis develops a computational framework for learning the interaction structure and dynamics automatically from the sociometer data. Low-level sensor data are transformed into measures that can be used to learn socially relevant aspects of people's interactions—e.g. identifying when people are talking and whom they are talking to. The network structure is learned from the patterns of communication among people. The dynamics of a person's interactions, and how one person's dynamics affects the other's style of interaction are also modeled. Finally, a person's style of interaction is related to the person's role within the network. The algorithms are evaluated by comparing the output against hand-labeled and survey data. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)