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Record W33420468

Mid-air text input techniques for very large wall displays

2009· article· en· W33420468 on OpenAlex
Garth Shoemaker, Leah Findlater, Jessica Q. Dawson, Kellogg S. Booth

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 British Columbia
Fundersnot available
KeywordsVisibilityComputer scienceHandwritingSpace (punctuation)Human–computer interactionArtificial intelligenceComputer visionComputer graphics (images)OpticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Traditional text input modalities, namely keyboards, are often not appropriate for use when standing in front of very large wall displays. Direct interaction techniques, such as handwriting, are better, but are not well suited to situations where users are not in close physical proximity to the display. We discuss the potential of mid-air interaction techniques for text input on very large wall displays, and introduce two factors, distance-dependence and visibility-dependence, which are useful for segmenting the design space of mid-air techniques. We then describe three techniques that were designed with the goal of exploring the design space, and present a comparative evaluation of those techniques. Questions raised by the evaluation were investigated further in a second evaluation focusing on distance-dependence. The two factors of distance- and visibility-dependence can guide the design of future text input techniques, and our results suggest that distance-independent techniques may be best for use with very large wall displays.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.518

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 science0.0010.000
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations53
Published2009
Admission routes1
Has abstractyes

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