MétaCan
Menu
Back to cohort
Record W2197378127 · doi:10.1145/2807442.2807470

Tactile Animation by Direct Manipulation of Grid Displays

2015· article· en· W2197378127 on OpenAlexaff
Oliver Schneider, Ali Israr, Karon E. MacLean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceAnimationRendering (computer graphics)Computer animationComputer visionArtificial intelligenceWearable computerComputer graphics (images)GridTactile displayObject (grammar)Pipeline (software)

Abstract

fetched live from OpenAlex

Chairs, wearables, and handhelds have become popular sites for spatial tactile display. Visual animators, already expert in using time and space to portray motion, could readily transfer their skills to produce rich haptic sensations if given the right tools. We introduce the tactile animation object, a directly manipulated phantom tactile sensation. This abstraction has two key benefits: 1) efficient, creative, iterative control of spatiotemporal sensations, and 2) the potential to support a variety of tactile grids, including sparse displays. We present Mango, an editing tool for animators, including its rendering pipeline and perceptually-optimized interpolation algorithm for sparse vibrotactile grids. In our evaluation, professional animators found it easy to create a variety of vibrotactile patterns, with both experts and novices preferring the tactile animation object over controlling actuators individually.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.077
GPT teacher head0.306
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 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

Citations94
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicTactile and Sensory InteractionsFrench-language works237,207