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Record W2512398025 · doi:10.1145/2957265.2961863

Spin-lock gesture authentication for mobile devices

2016· article· en· W2512398025 on OpenAlexaff
Yomna Aly, Cosmin Munteanu, Stefania Raimondo, Alan Yusheng Wu, Molly Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceUsabilityLock (firearm)Lightweight Extensible Authentication ProtocolAuthentication (law)Authentication protocolChip Authentication ProgramComputer securityMulti-factor authenticationMobile devicePasswordEmail authenticationHuman–computer interactionWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

The security of authentication mechanisms on touchscreen mobile devices is often at risk due to various usability barriers. The most common authentication step mobile users go through is the lock screen that blocks access to the entire device. This is subject to a usability-vs-security tension: users want quick and easy access to their phones while ensuring it is protected from unauthorized access. Since ease-of-use is a critical attribute for any new authentication mechanism, we propose an authentication system inspired by an intuitive real-life paradigm, the single dial combination (spin) lock, and evaluate its usability and user acceptance. We show that a Spin-lock authentication is perceived by users as more enjoyable than other lock methods. We use this to discuss implications for the design of mobile authentication mechanisms and suggest situations for which real-life inspired locking systems may be preferable.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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 designBench or experimental
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

Citations7
Published2016
Admission routes1
Has abstractyes

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