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Record W1979046722 · doi:10.1109/icme.2003.1220967

Centralized peer-to-peer streaming with layered video

2003· article· en· W1979046722 on OpenAlexaff
Ivan Lee, Ling Guan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceScalabilityComputer networkCodecQuality of serviceDiscrete cosine transformMultiple description codingRobustness (evolution)ServerThe InternetPeer-to-peerVideo streamingDistributed computingReal-time computingComputer hardwareOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

Faster computational power and higher network bandwidth facilitates Internet applications to improve the way we live, work and play. For video streaming applications, it is a challenge to provide the scalability while maintaining the central manageability and the robustness. In this paper, we propose the centralized peer-to-peer streaming protocol (P2PSP), which features the centralized management, guaranteed perceived quality of service (PQoS), while decentralized the traffic loads. To evaluate the protocol, a layered video coding technique based on 3D discrete cosine transform (DCT) is designed. This codec applies self-organizing tree map (SOTM) to generate cascaded vector quantization codebooks. We demonstrate the effectiveness of the centralized P2PSP by comparing the video streaming simulations under the proposed architecture and the client/server architecture.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.296
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2003
Admission routes1
Has abstractyes

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