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Record W1590330054 · doi:10.1080/07370024.2012.678241

Multilingual Touchscreen Keyboard Design and Optimization

2012· article· en· W1590330054 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

VenueHuman-Computer Interaction · 2012
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStylusTouchscreenPinyinComputer scienceGermanHuman–computer interactionArtificial intelligenceEngineering drawingChinese charactersEngineeringLinguisticsComputer vision

Abstract

fetched live from OpenAlex

A keyboard design, once adopted, tends to have a longlasting and worldwide impact on daily user experience. There is a substantial body of research on touch-screen stylus keyboard optimization. Most of it has focused on English only. Applying rigorous mathematical optimization methods and addressing diacritic character design issues, this article expands this body of work to French, Spanish, German, and Chinese. More important and counter to the intuition that optimization by nature is necessarily specific to each language, this article demonstrates that it is possible to find common layouts that are highly optimized across multiple languages for stylus (or single finger) typing. We first obtained a layout that is highly optimized for both English and French input. We then obtained a layout that is optimized for English, French, Spanish, German, and Chinese pinyin simultaneously, reducing its stylus travel distance to about half of QWERTY's for all of the five languages. In comparison to QWERTY's 3.31, 3....

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: Methods · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score0.773

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 communication0.0000.003
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 head0.046
GPT teacher head0.312
Teacher spread0.266 · 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