Adaptive forward error correction for real-time groupware
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it