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Record W2141679368 · doi:10.1109/ccece.2003.1226032

An adaptive framework for multimedia messaging services over wireless networks and the Internet

2004· article· en· W2141679368 on OpenAlexaff
Aisha Syed, Mrinal Mandal, Ali Zeineddine, F.F. Rahime

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceComputer networkThe InternetMultimediaMulti-frequency networkWirelessBandwidth (computing)IP Multimedia SubsystemWireless networkWorld Wide WebQuality of serviceTelecommunicationsHeterogeneous network

Abstract

fetched live from OpenAlex

Messaging services have become a popular tool for communication over wireless networks and the Internet. With improvements in network bandwidth and growing user requirements, they have evolved into multimedia messaging services (MMS) that allow communication of multimedia data among users as well as service providers and their clients. The existing MMS framework standardized by the 3GPP and WAP Forum employs several components with the MMS proxy-relay (MPR) as a core element. The MPR provides messaging, browsing, content retrieval and storage services to clients by relaying messages and coordinating tasks with other components in the framework. This centralized function of routing messages and serving multimedia content on a first-come-first-served basis leads to inefficient load-balancing and traffic management across the framework. We propose an adaptive MMS framework and discuss the concept of caching proxies that utilize a priority-based scheme to better balance the payload, reduce congestion in network paths as well as minimize delays on future incoming requests to the MPR.

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 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 score0.972
Threshold uncertainty score0.271

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 science0.0010.000
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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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.

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

Citations1
Published2004
Admission routes1
Has abstractyes

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