JQOS: a QoS-based Internet videoconferencing system using the Java media framework (JMF)
Bibliographic record
Abstract
The heterogeneity feature of the Internet makes it hard for a single real-time multicast stream with unique or static quality of service (QoS) support to provide good service to all receivers. Applying network-level QoS guarantees or application-level QoS adaptations are often considered effective solutions to this issue. However, the current proposed protocols or schemes have problems in scalability and implementation. This paper proposes a simple but efficient application-level dynamic QoS control mechanism and demonstrates it via implementing a QoS-based Internet videoconferencing system using Sun Microsystems' Java media framework (JMF). The dynamic QoS self-adaptation model has achieved the goal in receiving clear audio and visually useful video. The intelligent QoS request-handling algorithm may adjust the source transmission to satisfy most of, if not all, session receivers' requests. The receiver's QoS self-adaptation ability allows session receivers to apply their individual interests on the receiving streams.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".