Date: September 13th, 2014 (Saturday)
We plan to organize the workshop program along the following schedule. The timings may change slightly.
- 08:00 Workshop registration
- 08:45 Opening remarks and initial discussion
- 09:00 Keynote I: A Perfect Storm: Computation, Ubiquity, and Social Computing, John C. Thomas (!Problem Solving International)
- 10:00 Coffee break
- 10:30 Paper session I: New Sensing and Mining Opportunities, Nic Lane (Microsoft Research Asia, China)
- Andrea Cuttone, Sune Lehmann and Jakob Eg Larsen. Inferring Human Mobility from Sparse Low Accuracy Mobile Sensing Data
- Abhishek Mukherji, Vijay Srinivasan and Evan Welbourne. Adding Intelligence to Your Mobile Device via On-Device Sequential Pattern Mining
- Mina Sakamura, Takuro Yonezawa, Jin Nakazawa, Kazunori Takashio and Hideyuki Tokuda. LiPS: Linked Participatory Sensing for Optimizing Social Resource Allocation
- 11:45 Lunch break
- 12:45 Keynote II: Emotional Sentience, Mary Czerwinski (Microsoft Research)
- 13:45 Paper session II: Novel Applications in Everyday Social Life, Mirco Musolesi (University of Birmingham, UK)
- Veljko Pejovic and Mirco Musolesi. Anticipatory Mobile Computing for Behaviour Change Interventions
- Aleksandar Matic, Venet Osmani and Oscar Mayora. Mobile Monitoring of Formal and Informal Social Interactions at Workplace
- Chulhong Min, Saumay Pushp, Seungchul Lee, Inseok Hwang, Youngki Lee, Seungwoo Kang and Junehwa Song. Uncovering Embarrassing Moments In In-situ Exposure of Incoming Mobile Messages
- 15:00 Coffee break
- 15:30 Keynote III: Lessons Learnt@LiveLabs: Opportunities and Challenges in Practical Socio-Physical Sensing, Archan Misra (Singapore Management University)
- 16:30 Discussion
- 16:45 Closing remarks
Keynote Talk Details
Keynote I: John C. Thomas (!Problem Solving International)
A Perfect Storm: Computation, Ubiquity, and Social Computing
In this talk, I will present a framework that can be used to generate and extend useful applications in mobile computing. We will also look at the safeguards necessary to help ensure that such applications are humane as well as profitable. In particular, a consideration of users in terms of their characteristics, scales, roles, goals, special needs and values can be used in combination with a look at times and places at various scales, as well as objects, actors and situations. Doing so illustrates that there are many potential applications still to be designed, developed and deployed.
Keynote II: Mary Czerwinski (Microsoft Research)
In this talk I will describe novel systems and applications we are designing that perform mood detection and interventions in real time using mobile technology. We are exploring “sticky” user interface ideas to help users reflect on and manage their affective experiences. Many questions remain from current affective computing research in terms of how useful technology like this is over the long term and how valuable a mobile tracking system might be in real time (especially given the likelihood of misclassifications). In addition, we also are interested in intervention styles that can be used when negative or disruptive emotions are detected, whether in a car, at the desktop, or otherwise. Finally, we feel there is a huge opportunity in the remote familial space, or in a close social network, where knowing about the emotional health of separated loved ones or close friends comes in to play. These new research areas are tightly coupled to privacy issues. A few examples of applications from our research will be presented.
Keynote III: Archan Misra (Singapore Management University)
Lessons Learnt@LiveLabs: Opportunities and Challenges in Practical Socio-Physical Sensing
Since its operational launch in September 2013, LiveLabs has steadily grown in size, and now captures the longitudinal mobile sensor and usage data of several hundreds of participants on the SMU campus. I’ll first briefly describe the vision and the current operational capabilities of LiveLabs. I’ll then describe several examples of in-situ, context-aware behavioral experimentation that LiveLabs can enable, and also discuss practical challenges to executing such experimentation at the fidelity required for “scientific generalization”. Subsequently, I’ll provide several examples of socio-physical analytics, where we’ve used a combination of available demographic, location and behavioral data to make interesting inferences about individual and collective interactions in the physical world.