A peer-to-peer framework for cost-effective on-demand media streaming
Bibliographic record
Abstract
This paper presents a novel peer-to-peer (P2P) framework for cost-effective on-demand media streaming, named BitVampire. BitVampire's primary design goal is to aggregate peers' storage and upstream bandwidths to facilitate on-demand media streaming. To achieve this goal, BitVampire splits published videos into segments and distributes them to different peers. When a peer (or a receiver) wants to watch a video, it (i) searches the corresponding segments, then (ii) selfishly determines the best subset of supplying peers and parallel downloads the desired media content from these peers in real-time mode. In BitVampire, participating peers help each other to get the desired content, thus powerful servers/proxies are not necessary, which makes it a cost-effective approach. To demonstrate the feasibility of BitVampire, We implemented a prototype using Java and JMF (Java Media Framework), and conducted some preliminary experiments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".