MétaCan
Menu
Back to cohort
Record W2095179437 · doi:10.1145/1753846.1754114

Investigating user account control practices

2010· article· en· W2095179437 on OpenAlexaff
Sara Motiee, Kirstie Hawkey, Konstantin Beznosov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer sciencePrivilege (computing)Control (management)MalwareHuman–computer interactionLimit (mathematics)Face (sociological concept)User experience designComputer securityWorld Wide WebInternet privacyArtificial intelligence

Abstract

fetched live from OpenAlex

Non-administrator user accounts and the user account control (UAC) approach of Windows Vista are two practical solutions to limit the damage of malware infection. UAC in Windows Vista supports usage of lower privilege accounts; a UAC prompt allows users to raise their privileges when required. We conducted a user study and contextual interviews to understand the motives and challenges participants face when using different user accounts and the UAC approach. Most participants were not aware of or motivated to employ low-privileged accounts. Moreover, most did not understand or carefully consider the prompts.

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)0.012
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.299
Teacher spread0.282 · 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 designObservational
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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Malware Detection TechniquesFrench-language works237,207