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Record W2897592681 · doi:10.1145/3234695.3241032

Bend Passwords on BendyPass

2018· article· en· W2897592681 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPasswordGestureComputer scienceAuthentication (law)Computer securityCognitive passwordHuman–computer interactionHaptic technologyPassword strengthArtificial intelligenceOne-time password

Abstract

fetched live from OpenAlex

People with vision impairment are concerned about entering passwords in public as accessibility features (e.g. screen readers and screen magnifiers) make their passwords more vulnerable to attackers. This project aims to use bend passwords to solve this accessibility issue, as they are harder to observe than PINs. Bend passwords are a recently proposed method for user authentication that uses a combination of predefined bend and fold gestures performed on a flexible device. Our inexpensive prototype called BendyPass is made of silicone, with flex sensors able to capture and verify bend passwords, a vibration motor for gesture input haptic feedback, and a button to delete the last gesture or confirm the password. Bend passwords entered on BendyPass provide a tactile method for user authentication, designed to reduce the vulnerability to attackers and help people with vision impairment to better protect their personal information.

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: Empirical · Consensus signal: none
Teacher disagreement score0.916
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 science0.0010.000
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 head0.016
GPT teacher head0.257
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

Citations13
Published2018
Admission routes2
Has abstractyes

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