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Record W2044498192 · doi:10.1145/2556288.2557397

Crossing-based selection with direct touch input

2014· article· en· W2044498192 on OpenAlex
Yuexing Luo, Daniel Vogel

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 Waterloo
Fundersnot available
KeywordsStylusComputer scienceTask (project management)Selection (genetic algorithm)Adaptation (eye)Level crossingFitts's lawComputer visionMulti-touchArtificial intelligencePhysicsEngineeringOptics

Abstract

fetched live from OpenAlex

Fundamental performance results for crossing-based selec-tion tasks with direct touch input are presented. A close adaptation of Accot and Zhai's indirect stylus crossing ex-periment reveals similar trends for direct touch input: touch crossing task time is faster or equivalent to touch pointing; continuous selection of large orthogonal crossing targets is most effective; and continuous selection of small collinear targets is least effective. Unlike indirect stylus and mouse crossing, not every kind of direct touch pointing perfor-mance is modeled accurately with standard Fitts' law. Instead, Fitts' law, used previously for touch pointing with small targets, is used to more accurately model discrete touch crossing with a directionally constrained target. In addition, visual touch feedback is shown to have a strong effect on absolute accuracy. Our work empirically validates touch crossing as a practical and efficient selection technique, and motivates the exploration of novel forms of expressive multi-touch crossing.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.237

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 head0.007
GPT teacher head0.230
Teacher spread0.224 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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