End-system architecture for distributed networked multimedia applications: issues, trends and future directions
Bibliographic record
Abstract
The emerging broadband computer networks (wired and wireless) are likely to have numerous multimedia applications, such as videoconferencing, interactive games, and collaborative computing standard features in desktop PCs and portables. In recent years there have been significant developments in the field of multimedia coding algorithms and their VLSI implementations along with the development of high speed networking technology (e.g., ATM, FDDI, fast Ethernet). But the end-system architecture (software and hardware) as a whole is lagging behind these advancements. Optimized hardware and software architectures are needed for end systems that will guarantee predictable performance of real-time multimedia applications according to user provided QoS. In this paper, we identify the major end-system hardware and software requirements for networked multimedia applications. Focusing mainly on hardware and operating system levels, we advocate an integrated end-system architecture rather than some ad-hoc solutions for real-time multimedia computing, in addition to general purpose computing in a distributed environment. A core-based system architecture is proposed. We observe that interfacing with high speed networks, inter-device level data transfer, power efficiency and in addition, system resource utilization are some of the issues that need serious consideration in an end-system architecture.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".