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Record W2029923333 · doi:10.1145/2470654.2481295

SeeSay and HearSay CAPTCHA for mobile interaction

2013· article· en· W2029923333 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 institutionsUniversity of Toronto
Fundersnot available
KeywordsCAPTCHAHearsayComputer scienceHuman–computer interactionModality (human–computer interaction)Text entryMobile deviceUsabilityWorld Wide Web

Abstract

fetched live from OpenAlex

Speech certainly has advantages as an input modality for smartphone applications, especially in scenarios where using touch or keyboard entry is difficult, on increasingly miniaturized devices where useable keyboards are difficult to accommodate, or in scenarios where only small amounts of text need to be input, such as when entering SMS texts or responding to a CAPTCHA challenge. In this paper, we propose two new alternative ways to design CAPTCHAs in which the user says the answer instead of typing it with (a) output stimuli provided visually (SeeSay) or (b) auditorily (HearSay). Our user study results show that SeeSay CAPTCHA requires less time to be solved and users prefer it over current text-based CAPTCHA methods.

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 score0.865
Threshold uncertainty score0.212

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)0.0000.000

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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

Citations35
Published2013
Admission routes1
Has abstractyes

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