Bibliographic record
Abstract
On-demand media streaming has gained considerable attention in recent years owing to its promising usage in a rich set of Internet-based services such as movies on demand and distance learning. However, the current solutions for on-demand media streaming, primarily based on the client-server model, are not economical and not scalable due to the high cost in deploying the servers and the heavy media traffic overload on the servers. We propose a scalable cost-effective P2P solution, called Bit-Vampire, that aggregates the peerspsila storage and bandwidth to facilitate on-demand media streaming. In this talk, we will discuss the state-of-the-art of P2P on-demand video streaming and in particular present a novel category overlay P2P architecture that enables efficient content search, called COOL (CategOry OverLay) search, and the concept of Bit-Vampire that makes use of COOL search and a scheduling algorithm for efficient concurrent media streaming from multiple peers. Other related practical issues such as efficient pre-release distribution for flash crowd congestion avoidance are also discussed. A movie streaming demo is also presented to illustrate the application of Bit-Vampire that will enable the ldquoInternet DVD playerrdquo technology in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".