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Record W2099952704 · doi:10.1109/mcom.2005.1509971

Accelerating embedded Java for mobile devices

2005· article· en· W2099952704 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 Communications Magazine · 2005
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer sciencePortingJavaEmbedded JavaOperating systemEmbedded systemSoftware deploymentCompilerVirtual machineMobile deviceReal time JavastrictfpSoftware

Abstract

fetched live from OpenAlex

With the proliferation of wireless devices, networks, and systems, the deployment of efficient embedded Java virtual machines is becoming a challenging and important research area. Accordingly, a plethora of acceleration techniques have been proposed. In this article we present a new acceleration technology that we developed for embedded Java virtual machines. Acceleration is achieved by the integration of a new selective dynamic compiler, which we called Armed E-Bunny, into the J2ME/CLDC (Java 2 Micro-Edition for Connected Limited Device Configuration) kilobyte virtual machine (KVM). The modified KVM is ported on a handheld PDA that is powered with embedded Linux. Experimental results demonstrate that we accomplished an important speedup (more than 360 percent) with respect to Sun's latest version of KVM. This experimentation was carried out using standard J2ME benchmarks.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.693
Threshold uncertainty score0.538

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 communication0.0000.001
Open science0.0030.000
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 head0.054
GPT teacher head0.334
Teacher spread0.279 · 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