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Record W1992459170 · doi:10.1109/hpcc.2010.107

A Software Self-Organizing Middleware for Smart Spaces Based on Fuzzy Logic

2010· preprint· en· W1992459170 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
Typepreprint
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSoftware deploymentComputer scienceMiddleware (distributed applications)Fuzzy logicAutonomic computingUbiquitous computingSoftwareContext (archaeology)Message oriented middlewareSoftware engineeringDistributed computingHuman–computer interactionSoftware architectureArtificial intelligenceProgramming languageOperating system

Abstract

fetched live from OpenAlex

Autonomic Computing Self-Configuration /Organization can simplify the complexity of software deployment, management and utilization in pervasive environments such as smart spaces. Software management complexity in pervasive environments is a serious problem, which leads to high management costs and slow down the broad utilization of the pervasive technologies. This paper presents our work on a self-organization middleware that reduce user manipulations and reasoning during software deployment and organization in smart spaces. Our approach is based on Fuzzy Logic and semantic description in order to support the self-organization reasoning on the environment context.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.238
Teacher spread0.212 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

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