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Record W2293306693 · doi:10.5555/1999416.1999477

DEVS-based modeling of a human motion data synthesis and control system

2010· article· en· W2293306693 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.

Bibliographic record

VenueSummer Computer Simulation Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceAnimationDEVSMotion captureMotion (physics)Human motionComputer animationArtificial intelligenceMotion controlData modelingEvent (particle physics)Computer visionControl engineeringSimulationModeling and simulationRobotComputer graphics (images)EngineeringDatabase

Abstract

fetched live from OpenAlex

In this paper we present a DEVS-based model for a human motion data synthesis system. The model includes basic components representing the behavior of a human motion control system (brain, spinal cord, and body). The system is designed to produce the output in the form of motion capture data. The proposed model and the output data produced can be widely applied in different areas such as robotics and multimedia (animation). The simulations carried out illustrate the performance of the model, which is capable of mimicking a human motion control system. The model is capable of taking into account minute features related to actor characteristics and even noise. Furthermore we show how discrete event modeling is a suitable means for representing human motion data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.736
Threshold uncertainty score0.433

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 communication0.0000.000
Open science0.0000.000
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 head0.069
GPT teacher head0.274
Teacher spread0.206 · 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