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Record W2112773017 · doi:10.1145/2556288.2557316

Let's kick it

2014· article· en· W2112773017 on OpenAlexaff
Ricardo Jota, Pedro Lopes, Daniel Wigdor, Joaquim Jorge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
FundersFundação para a Ciência e a Tecnologia
KeywordsFoot (prosody)GestureTappingComputer scienceModalitiesSpace (punctuation)Human–computer interactionSurface (topology)Computer graphics (images)Computer visionEngineeringMechanical engineeringGeometryMathematics

Abstract

fetched live from OpenAlex

Large-scale touch surfaces have been widely studied in literature and adopted for public installations such as interactive billboards. However, current designs do not take into consideration that touching the interactive surface at different heights is not the same; for body-height displays, the bottom portion of the screen is within easier reach of the foot than the hand. We explore the design space of foot input on vertical surfaces, and propose three distinct interaction modalities: hand, foot tapping, and foot gesturing. Our design exploration pays particular attention to areas of the touch surface that were previously overlooked: out of hand's reach and close to the floor. We instantiate our design space with a working prototype of an interactive surface, in which we are able to distinguish between finger and foot tapping and extend the input area beyond the bottom of the display to support foot gestures.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.203
Threshold uncertainty score0.678

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0050.007
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.2030.115

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 head0.237
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2014
Admission routes1
Has abstractyes

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