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Record W2003785947 · doi:10.1145/2047196.2047257

The 1line keyboard

2011· article· en· W2003785947 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
KeywordsKeystroke loggingComputer scienceGestureVirtual keyboardText entrySpace (punctuation)Computer graphics (images)Human–computer interactionSpeech recognitionComputer hardwareArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

Current soft QWERTY keyboards often consume a large portion of the screen space on portable touchscreens. This space consumption can diminish the overall user experi-ence on these devices. In this paper, we present the 1Line keyboard, a soft QWERTY keyboard that is 140 pixels tall (in landscape mode) and 40% of the height of the native iPad QWERTY keyboard. Our keyboard condenses the three rows of keys in the normal QWERTY layout into a single line with eight keys. The sizing of the eight keys is based on users' mental layout of a QWERTY keyboard on an iPad. The system disambiguates the word the user types based on the sequence of keys pressed. The user can use flick gestures to perform backspace and enter, and tap on the bezel below the keyboard to input a space. Through an evaluation, we show that participants are able to quickly learn how to use the 1Line keyboard and type at a rate of over 30 WPM after just five 20-minute typing sessions. Using a keystroke level model, we predict the peak expert text entry rate with the 1Line keyboard to be 66--68 WPM.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.833

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)0.0000.001

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 head0.028
GPT teacher head0.223
Teacher spread0.195 · 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

Citations105
Published2011
Admission routes1
Has abstractyes

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