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Record W2406610395 · doi:10.1145/2858036.2858355

Finger-Aware Shortcuts

2016· article· en· W2406610395 on OpenAlex
Jingjie Zheng, 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
KeywordsComputer scienceKey (lock)Service (business)Identification (biology)Formative assessmentArtificial intelligenceComputer visionHuman–computer interactionComputer security

Abstract

fetched live from OpenAlex

We evaluate and demonstrate finger, hand, and posture identification as keyboard shortcuts. By detecting the hand and finger used to press a key, and open or closed hand postures, a key press can have multiple command mappings. A formative study reveals performance and preference patterns when using different fingers and postures to press a key. The results are used to develop a computer vision algorithm to identify fingers and hands on a keyboard captured by a built-in lap top camera and reflector. This algorithm is built into a background service to enable system-wide finger-aware shortcut keys in any application. A controlled experiment uses the service to compare the performance of Finger-Aware Shortcuts with existing methods. The results show Finger-Aware Shortcuts are comparable with a common class of shortcuts using multiple modifier keys. Finally, application demonstrations illustrate different use cases and mappings for Finger-Aware Shortcuts and extend the idea to two-handed key presses, continuous parameter control, and menu selection.

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 score0.982
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations37
Published2016
Admission routes1
Has abstractyes

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