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Record W2770055653 · doi:10.1145/3152141.3152391

ThingsJS

2017· article· en· W2770055653 on OpenAlexafffund
Julien Gascon‐Samson, Mohammad Rafiuzzaman, Karthik Pattabiraman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMQTTMiddleware (distributed applications)JavaScriptDistributed computingScheduling (production processes)ScheduleAdaptation (eye)Internet of ThingsSoftware engineeringEmbedded systemOperating systemWorld Wide Web

Abstract

fetched live from OpenAlex

The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. Nowadays, such systems exhibit many important challenges across many dimensions. In this work, we propose ThingsJS, a rich Javascript-based middleware platform and runtime environment that abstracts the inherent complexity of such systems by providing a high-level framework for IoT system developers, built over Javascript. ThingsJS abstracts several large-scale distributed systems considerations, such as scheduling, monitoring and self-adaptation, by means of a rich constraint model, a multi-dimensional resource prediction approach and a SMT-based scheduler to properly schedule and manage the execution of high-level, large-scale distributed applications on heterogeneous physical IoT devices. ThingsJS also provides a rich inter-device communication framework built on top of the widely-used publish/subscribe/MQTT paradigm. Finally, ThingsJS also proposes a rich inter-device Javascript-based code migration framework to support the transparent migration of live IoT components between heterogeneous devices.

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 score0.909
Threshold uncertainty score0.480

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 science0.0010.001
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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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