MétaCan
Menu
Back to cohort
Record W2113820952 · doi:10.1145/1809842.1809858

Role-based access control (RBAC) in Java via proxy objects using annotations

2010· article· en· W2113820952 on OpenAlexaff
Jeff Zarnett, Mahesh Tripunitara, Patrick Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceJavaRole-based access controlAccess controlProxy (statistics)InvocationProgramming languageDatabaseComputer security

Abstract

fetched live from OpenAlex

We propose a new approach for applying Role-Based Access Control (RBAC) to methods in objects in the Java programming language. In our approach, a policy implementer (usually a developer) annotates methods, interfaces, and classes with roles. Our system automatically creates proxy objects which only contain methods to which a client is authorized access based on the role specifications. Potentially untrusted clients that use Remote Method Invocation (RMI) then receive proxy objects rather than the originals.

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)0.020
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0060.010
Open science0.0030.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.002

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 head0.020
GPT teacher head0.342
Teacher spread0.322 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations13
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicAccess Control and TrustFrench-language works237,207