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Record W2243124917 · doi:10.1145/2807442.2807489

Gunslinger

2015· article· en· W2243124917 on OpenAlexaff
Ming-Yu Liu, Mathieu Nancel, Daniel Vogel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGestureCursor (databases)Computer scienceUsabilityFocus (optics)Human–computer interactionMulti-touchInput deviceGesture recognition3D interactionInteraction techniqueBody languageComputer visionArtificial intelligenceVirtual realityCommunicationComputer hardwarePsychology

Abstract

fetched live from OpenAlex

We describe Gunslinger, a mid-air interaction technique using barehand postures and gestures. Unlike past work, we explore a relaxed arms-down position with both hands interacting at the sides of the body. It features "hand-cursor" feedback to communicate recognized hand posture, command mode and tracking quality; and a simple, but flexible hand posture recognizer. Although Gunslinger is suitable for many usage contexts, we focus on integrating mid-air gestures with large display touch input. We show how the Gunslinger form factor enables an interaction language that is equivalent, coherent, and compatible with large display touch input. A four-part study evaluates Midas Touch, posture recognition feedback, pointing and clicking, and general usability.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.070
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0700.023

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 head0.071
GPT teacher head0.264
Teacher spread0.193 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations107
Published2015
Admission routes1
Has abstractyes

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