Bibliographic record
Abstract
With the advent of next-generation multimedia technologies such as very-low bit rate MPEG-4 codec, multimedia streaming of high-quality video and audio has become a near-term reality. The high compression ratio and error resilience offered by the MPEG-4 standard promise near-term popularity for rich contents and exceptional quality to consumers over affordable Internet connections, such as xDSL, cable modem and 3G wireless networks. Audio and video streaming applications are at the center of such scenarios; and Quality-of-Service (QoS) support in such applications is critical to their widespread acceptance.To the best of our knowledge, there has been no existing open-source MPEG-4 multimedia streaming applications in the academic community, which leads to the lack of research results using MPEG-4 streaming, especially with respect to Quality-of-Service support. In this work, we have implemented an open-source MPEG-4 multimedia streaming testbed in IP-based networks. In this paper, we show our experiences and lessons learned with such a testbed. First, we describe the algorithms and solutions used in our implemention testbed, emphasizing several critical issues. Second, through extensive experiments, we demonstrate measurements of bandwidth requirements and data loss for streaming a set of multimedia samples with different bit rates over UDP, which is ubiquitously available in the TCP/IP protocol stack on all consumer operating systems. Finally, future work for further improvements is also discussed.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".