Cooperation stimulation in peer-to-peer video streaming
Bibliographic record
Abstract
In the previous chapter, we used colluders in multimedia fingerprinting as an example to study the necessary conditions for colluders to cooperate with one another, and investigated how attackers negotiate with one another and reach an agreement. In this chapter, we consider P2P networks, investigate how users in P2P systems cooperate with one another to form a social network, and study the optimal cooperation strategies. As introduced in Chapter 3, mesh-pull–based P2P video streaming is one of the largest types of multimedia social networks on the Internet and has enjoyed many successful deployments. However, because of the voluntary participation nature and limited resources, users' full cooperation cannot be guaranteed. In addition, users in P2P live streaming systems are strategic and rational, and they are likely to manipulate any incentive systems (for example, by cheating) to maximize their payoffs. As such, in this chapter, we study the incentives for users in video streaming systems to collaborate with one another and design the optimal cooperation strategies. Furthermore, with recent developments in wireless communication and networking technologies and the popularity of powerful mobile devices, low-cost and high-quality–service wireless local area networks (WLANs) are becoming rapidly ingrained in our daily lives via public hotspots, access points, digital home networks, and many others. Users in the same WLAN form a wireless social network; such wireless social networks have many unique properties that make cooperation stimulation more challenging.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".