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Record W3118144132 · doi:10.1145/3412841.3442001

SecureJS compiler

2021· preprint· en· W3118144132 on OpenAlexfundno aff
Yoonseok Ko, Tamara Rezk, Manuel Serrano

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsnot available
FundersAgence Nationale de la RechercheEuropean CommissionCanadian Institute of Steel Construction
KeywordsComputer scienceCompilerIsolation (microbiology)JavaScriptImplementationNode (physics)EnforcementOperating systemComputer securityProgramming languageEngineeringPolitical science

Abstract

fetched live from OpenAlex

The memory isolation mechanism plays an essential role to provide security enforcement in JavaScript programs. Existing secure interaction libraries such as Google Caja, SES, and VM2 rely on built-in memory isolation mechanisms provided by Node.js and browsers, yet most of the other engines such as JerryScript and Duktape, which are implementations for IoT devices, do not support such isolation mechanisms.

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)0.001
metaresearch head score (Gemma)0.004
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: Software · Consensus signal: Software
Teacher disagreement score0.090
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0900.089

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 head0.037
GPT teacher head0.275
Teacher spread0.238 · 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
GenreSoftware

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

Citations2
Published2021
Admission routes1
Has abstractyes

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