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Record W3100553681 · doi:10.22215/etd/2018-13237

Bend Passwords for People with Vision Impairment

2018· dissertation· en· W3100553681 on OpenAlex
Daniella Briotto Faustino

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
Typedissertation
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPasswordUsabilityComputer securityAuthentication (law)Internet privacyHuman–computer interactionComputer scienceGestureEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Passwords help people avoid unauthorized access to their personal devices but are not without challenges, like memorability and shoulder surfing attacks. Little is known about how people with vision impairment assure their digital security in mobile contexts. We conducted an online survey with 325 people who are blind or have low vision and found they are concerned about entering passwords in public because of the risk of others observing their passwords. We also found PINs, commonly required on smartphones, are considered insecure and poorly accessible. To solve those issues, we investigated the usability of bend passwords, a recently proposed method for authentication that uses a combination of pre-defined bend gestures performed on a flexible device. We designed a new deformable prototype and ran a user study with 16 vision-impaired participants, finding that bend passwords are as easy to learn and memorize as PINs, but are faster to enter than PINs.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score0.573

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)0.0000.000

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 head0.007
GPT teacher head0.272
Teacher spread0.265 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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