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Record W3088665199 · doi:10.20380/gi2020.19

Bend or PIN: Studying Bend Password Authentication with People with Vision Impairment

2020· article· en· W3088665199 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

VenueCanada Human-Computer Communications Society · 2020
Typearticle
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsPasswordUsabilityLearnabilityAuthentication (law)Visual impairmentComputer scienceHuman–computer interactionGestureBiometricsInternet privacyComputer securityComputer visionPsychology

Abstract

fetched live from OpenAlex

People living with vision impairment can be vulnerable to attackers when entering passwords on their smartphones, as their technology is more 'observable'. While researchers have proposed tangible interactions such as bend input as an alternative authentication method, limited work have evaluated this method with people with vision impairment. This paper extends previous work by presenting our user study of bend passwords with 16 participants who live with varying levels of vision impairment or blindness. Each participant created their own passwords using both PIN codes and BendyPass, a combination of bend gestures performed on a flexible device. We explored whether BendyPass does indeed offer greater opportunity over PINs and evaluated the usability of both. Our findings show bend passwords have learnability and memorability potential as a tactile authentication method for people with vision impairment, and could be faster to enter than PINs. However, BendyPass still has limitations relating to security and usability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.259
Teacher spread0.222 · 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