Proceedings of the 3rd International on Workshop on Physical Analytics
Bibliographic record
Abstract
Welcome to the 3rd International Workshop on Physical Analytics (WPA) being held in Singapore on June 26, 2016. We are honored to serve as the chairs for this latest WPA edition that continues in the tradition of previous workshops in the series that were also co-located with MobiSys. Broadly speaking, WPA is motivated by the observations that people spend a significant part of their daily lives performing a variety of activities in the physical world---travelling to places (including commuting to/from work using public or private transport), dwelling and engaging in various activities at various locations (e.g., exercising in the gym, eating at restaurants and food courts), interacting with various physical objects and artefacts (e.g., touching or picking up products at a retail store, or browsing through books and magazines at a library), being subject to various audiovisual stimuli (e.g., listening to announcements at transit hubs, watching advertisements on public displays or movies on TV) and interacting with other people (in groups, as part of crowds or one-on-one). These activities and interactions contain a wealth of information about user behavior, preferences, attitudes and interests, that, if harnessed, can benefit both users and consumer-facing businesses. While research has been underway in utilizing various sensing and analytics tools to capture and annotate such behavior (e.g., profile smoking episodes using wearable devices or monitor consumer reactions to advertising content via video analysis), the vast majority of such research focuses on exploring individual sensing techniques targeted at specific activities, and is scattered across various academic forums. The goal of this workshop series is to offer a unified forum to explore both (a) the technologies (current and emerging) that can enable unobtrusive capture of such individual and collective physical world behavior, and (b) the realworld commercial applications and services that leverage upon such understanding of physical world behavior. By bringing together researchers and practitioners from industry to have a continuing conversation on Physical Analytics, our goal is to help coalesce a research agenda for our community. This year we are particularly honored to have Kyle Jamieson (Princeton University) giving the workshop keynote. We look forward to hearing Kyle's perspective on what new innovations in high-precision localization, and breakthroughs in wireless networking more generally, will mean for the physical analytics area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".