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Record W2090125559 · doi:10.1145/2484402.2484410

RAFR

2013· article· en· W2090125559 on OpenAlex
Sumanta Sarkar, Reihaneh Safavi–Naini, Liang Feng Zhang

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
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRedundancy (engineering)Computer scienceCloud computingErasure codeData redundancyCloud storageDatabaseOperating systemDecoding methodsAlgorithm

Abstract

fetched live from OpenAlex

Cloud storage services have been increasingly popular in recent years. To provide guarantee against possible drive failures and crashes, data must be stored with redundancy on multiple drives. In this paper, we propose Remote Assessment of File Redundancy (RAFR), with the goal of providing guarantee to users that the cloud has stored data in k + Ψ drives, for some Ψ < 0, and so up to Ψ drive failures can be tolerated. In RAFR, cloud encodes the data using an (n, k) MDS erasure code and stores it in n drives. The user can verify that at least k drives are holding the encoded data and so the data is recoverable. Our analysis shows that the value of Ψ is n -- k -- ω(log n).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.995

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.006

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 head0.015
GPT teacher head0.231
Teacher spread0.215 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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