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Record W2079400051 · doi:10.1145/2512921.2512925

Semantic based networking of information in vehicular clouds based on dimensionality reduction

2013· article· en· W2079400051 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceExploitCloud computingThe InternetSemantics (computer science)Cluster analysisProtocol (science)Computer networkDistributed computingWorld Wide WebComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

Transport of information in vehicular clouds faces challenges due to intermittent connectivity and the fact that the already existing Internet protocol based transport solutions do not exploit the semantics of information to utilize the available contextual information. With the advent of Internet of Things and Machine to Machine communications, availability of contextual information through the wisdom of the crowd and ubiquity of sensors and devices calls for a shift towards networking of information beyond Internet Protocol (IP) level connectivity. We propose a novel approach for forwarding and discarding policy that can be utilized by content aware network elements. The proposed method makes use of multidimensional scaling techniques that leverages the spectral characteristics of information predicates. By evaluations and analysis we show that by considering the networking of information paradigm for vehicular clouds our proposed clustering technique yields a lower processing cost and complexity as the system scales.

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.

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 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: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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

Quick stats

Citations12
Published2013
Admission routes1
Has abstractyes

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