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Record W2395465087 · doi:10.1145/2858036.2858052

DualKey

2016· article· en· W2395465087 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
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceSelection (genetic algorithm)Process (computing)Term (time)Artificial intelligenceKey (lock)Identification (biology)Computer visionMachine learningOperating system

Abstract

fetched live from OpenAlex

Fast and accurate access to keys for text entry remains an open question for miniature screens. Existing works typically use a cumbersome two-step selection process, first to zero-in on a particular zone and second to make the key selection. We introduce DualKey, a miniature screen text entry technique with a single selection step that relies on finger identification. We report on the results of a 10 day longitudinal study with 10 participants that evaluated speed, accuracy, and learning. DualKey outperformed the existing techniques on long-term performance with a speed of 19.6 WPM. We then optimized the keyboard layout for reducing finger switching time based on the study data. A second 10 day study with eight participants showed that the new sweqty layout improved upon DualKey even further to 21.59 WPM for long-term speed, was comparable to existing techniques on novice speed and outperformed existing techniques on novice accuracy rate.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
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 science0.0000.000
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations80
Published2016
Admission routes1
Has abstractyes

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