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Record W2051674079 · doi:10.1145/2335356.2335367

Do you see your password?

2012· article· en· W2051674079 on OpenAlex

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
TopicUser Authentication and Security Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsPasswordComputer scienceRecallCognitive passwordAuthentication (law)Information retrievalArtificial intelligenceOne-time passwordPassword policyComputer securityPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

Text-based password systems are the authentication mechanism most commonly used on computer systems. Graphical passwords have recently been proposed because the pictorial-superiority effect suggests that people have better memory for images. The most widely advocated graphical password systems are based on recognition rather than recall. This approach is favored because recognition is a more effective manner of retrieval than recall, exhibiting greater accuracy and longevity of material. However, schemes such as these combine both the use of graphical images and the use of recognition as a retrieval mechanism. This paper reports on a study that sought to address this confound by exploring the recognition of text as a novel means of authentication. We hypothesized that there would be significant differences between text recognition and text recall conditions. Our study, however, showed that the conditions were comparable; we found no significant difference in memorability. Furthermore, text recognition required more time to authenticate successfully.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.999

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)0.0000.002

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 head0.039
GPT teacher head0.279
Teacher spread0.240 · 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

Citations56
Published2012
Admission routes1
Has abstractyes

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