Proceedings of the 2010 ACM workshop on Advanced video streaming techniques for peer-to-peer networks and social networking
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2010 ACM Workshop on Advanced Video Streaming Techniques for Peer-to-Peer Networks and Social Networking, held within ACM Multimedia 2010, in Florence, Italy. The call for papers attracted 30 submissions (two redirected from the main conference) from Australia, Asia, Canada, Europe, and the United States of America. The program committee accepted 15 papers covering a variety of topics, all in the context of peer-to-peer: Multi-source video distribution; modeling end-to-end delay; piece-picking for layered/scalable content; prefetching and upload strategies; QoE improvements for multiple description video transmission; cache optimization; network coding improving packet jitter; analytical approach to model adaptive video streaming; access control to BitTorrent swarms; group communication with layer-aware FEC; streaming with LT codes; design and evaluation of an optimized overlay topology; APIs and library. Furthermore, George Wright (Head of Prototyping, BBC Research and Development) provides an invited talk entitled "Audio/visual content and metadata delivered over the open Internet using P2P-Next: some experiences from a broadcaster's perspective."
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".