MétaCan
Menu
Back to cohort
Record W2313902655 · doi:10.1109/tnet.2012.2190296

Capability Reconciliation for a CSP Approach to Virtual Device Composition

2012· article· en· W2313902655 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

VenueIEEE/ACM Transactions on Networking · 2012
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceMobile ad hoc networkDistributed computingWireless ad hoc networkComposition (language)Service compositionComputer networkService (business)Node (physics)Constraint (computer-aided design)Mobile deviceService discoveryQuality of serviceTelecommunicationsWeb serviceOperating systemWirelessWorld Wide Web

Abstract

fetched live from OpenAlex

The dynamic composition of systems of networked appliances, or virtual devices, enables users to generate complex, strong, specific systems. Leading mobile ad hoc network (MANET)-based composition schemes currently make use of service discovery mechanisms that depend on periodic service advertising by controlled broadcast. The result is the unnecessary depletion of node resources such as battery and processing power. The dynamic and ad hoc nature of the discovery and composition of services is also not addressed by current schemes; this inevitably leads to capability differences that need to be reconciled when the input of one service is not compatible with the output of another. Our approach addresses the distributed constraint satisfaction problem in virtual device composition in MANETs; our simulation shows its effectiveness and efficiency.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.039
GPT teacher head0.269
Teacher spread0.230 · 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