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Record W2136064925 · doi:10.1109/.2006.1629437

Unstructured Peer-to-Peer Session over IP using SIP

2006· article· en· W2136064925 on OpenAlexaff
Khashayar Khavari, Chuen Liang, Ali Tizghadam, Farid Fadaie, Nadeem Abji, Ramy Farha, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSession Initiation ProtocolComputer scienceScalabilitySession (web analytics)Peer-to-peerSIP trunkingComputer networkService providerVoice over IPService (business)Modular designProtocol (science)ServerThe InternetWorld Wide WebDatabaseOperating system

Abstract

fetched live from OpenAlex

Data and telephone service providers have started considering migration to an IP based environment. This paper presents an unstructured peer-to-peer approach to initiating and maintaining sessions over IP using session initiation protocol (SIP). The offered solution is completely modular and is compatible with traditional server-client based approaches using SIP. The peer-to-peer nature of our design allows it to be highly scalable and self managing with low maintenance costs, making it an attractive solution for service providers

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2006
Admission routes1
Has abstractyes

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