Proceedings of the 2009 workshop on Ambient media computing
Bibliographic record
Abstract
It is our great pleasure to welcome you to ACM 2009 Workshop on Ambient Media Computing (AMC'09). This workshop is collocated with ACM Multimedia 2009 and consists of two parts: Part I on Sensory Media Systems and Applications, and Part II on Media Data Integration. In response to the call for papers, the workshop received 18 submissions throughout the world. After rigorous and careful review processes, the program committee accepted a total of 10 papers, with 5 papers for Part I, and 5 papers for Part II. In addition, the program features a keynote by Balakrishnan Prabhakaran (University of Texas at Dallas) on Enriching User Experience with Intuitive Interactions and Immersive Environments and an invited talk by Zhengyou Zhang (Microsoft Research) on Audio-Visual Analysis for Event Understanding. We hope that these proceedings will serve as a valuable reference for researchers and developers who are interested in ambient media computing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.023 |
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 source (direct Gemma or distilled Codex), 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".