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Record W1986967248 · doi:10.1145/1710035.1710040

User-centric service provisioning for IMS

2009· article· en· W1986967248 on OpenAlexaff
Salekul Islam, Jean‐Charles Grégoire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsIP Multimedia SubsystemComputer scienceProvisioningComputer networkCore networkService (business)Context (archaeology)ServerSession (web analytics)World Wide WebQuality of service

Abstract

fetched live from OpenAlex

The IP Multimedia Subsystem (IMS) is a converged framework for delivering voice, video and data communication services to mobile and fixed users. The present operator-centric IMS model, which assume a single operator for the access network, IMS core and application servers, restricts if and how users can access services beyond the IMS core network. The present IMS model, by limiting the subscribers' choice might be rejected by many end users. User-centric service provisioning should establish the users' control and thus commence the customer interest for IMS. In this paper, we study the problems that a user-centric IMS architecture should address, and broaden the scope of our previously designed, third-party service enabled IMS model [21] by showing its use in user-centric service provisioning. Third-party offered service subscription and session setup procedures are explained with two use cases. The Users' profile management and privacy endurance, possible terminal implications are also discussed in the context of a user-centric IMS model.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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