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Record W2070071863 · doi:10.1016/j.intcom.2008.10.012

A run-time programmable simulator to enable multi-modal interaction with rigid-body systems

2008· article· en· W2070071863 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInteracting with Computers · 2008
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceDimpleHaptic technologyModalPerceptionSoftwareHaptic perceptionHuman–computer interactionSimulationComputer graphics (images)Programming language

Abstract

fetched live from OpenAlex

This paper describes DIMPLE, a software application for haptic force-feedback controllers which allows easy creation of interactive rigid-body simulations. DIMPLE makes extensive use of an established standard for control-rate transmission of audio control commands, which can be used to drive many simultaneous parameters of a given audio/visual synthesis engine. Because it is used with a high-level, visual multimedia programming language, DIMPLE allows fast and uncomplicated development of responsive, haptically-enabled virtual environments useful for fast prototyping of applications in fields where lower level programming skills may not be widespread. Examples of specific scenes constructed using DIMPLE are given, with applications to perception, HCI research, music, and multimedia. A pilot evaluation study was performed comparing DIMPLE to another implementation of a specific scene, which showed comparable results between subjects’ overall impressions of the simulation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.527
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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