Proceedings of the 2010 ACM workshop on Social, adaptive and personalized multimedia interaction and access
Bibliographic record
Abstract
It is our great pleasure to welcome you to the International Workshop on Social, Adaptive and Personalized Multimedia Interaction and Access (SAPMIA 2010). This year's workshop is supported by several European Research Projects on State of the Art topics in Multimedia and attempts to provide a forum to disseminate work that explicitly exploits the synergy between multimedia content analysis, personalisation, and next generation networking and community aspects of social networks. This workshop attempts to present the new scenery in multimedia networking, as this is identified through the integration of multimedia content analysis techniques with information derived from users, networked communities, and context awareness, in a mission to present, discuss and develop new adaptation and personalization approaches from which users of multimedia can benefit. The call for papers attracted submissions from Asia, Canada and Europe. The program committee accepted 15 papers that cover a variety of topics, including interactive multimedia systems, adaptive browsing, and user interfaces, collaborative search, personalized access to multimedia content, robust and scalable multimedia content distribution, content-based recommendation, semantic technologies for multimedia content personalization and adaptation, and social multimedia applications. In addition, the program includes a keynote speech by Touradj Ebrahimi entitled "QoE of video streaming in P2P/social networks".
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 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".