Adaptive forward error correction for real-time groupware
Bibliographic record
Abstract
Real-time distributed groupware sends several kinds of messages with varying quality-of-service requirements. However, standard network protocols do not provide the flexibility needed to support these different requirements (either providing too much reliability or too little), leading to poor performance on real-world networks. To address this problem, we investigated the use of an application-level networking technique called adaptive forward error correction (AFEC) for real-time groupware. AFEC can maintain a predefined level of reliability while avoiding the overhead of packet acknowledgement or retransmission. We analysed the requirements of typical real-time groupware systems and developed an AFEC technique to meet these needs. We tested the new technique in an experiment that measured message reliability and latency using TCP, plain UDP, UDP with non-adaptive FEC, and UDP with our AFEC scheme, under several simulated network conditions. Our results show that for awareness messages that can tolerate some loss, FEC approaches keep latency at nearly the plain-UDP level while dramatically improving reliability. In addition, adaptive FEC is the only technique that can maintain a specified level of reliability and also minimize delay as network conditions change. Our study shows that groupware AFEC can be a useful tool for improving the real-world performance and usability of real-time groupware.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".