Sip-Based Protocol for P2P Large-Scale Multiparty VoIP (MVoIP) Conference Support
Bibliographic record
Abstract
Even if both traditional centralized and decentralized models can support small conferences, their deployment for large number of participants is less obvious. In fact, while server-based centralized conference facilitates control and administration operations, supporting centralized media processing in large scale conference causes system overhead on the mixer. On the other hand, decentralized solutions that use multi end-system media processors will introduce an overhead to control conference floors and users membership. Our solution introduces a new model that enable multi-host media process support while the conference control and management is kept simplified and centralized around the administrator. To do that, we build two different meshed networks to enable both voice audio distribution between participants and general conference control. Our conference system includes different components that enable conference creation/destruction, user addition/removal, media assignment and speech floor control. We introduce an abstract protocol message that implements membership operations. We also discuss the implementation of system components and we detail their mapping to SIP.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".