MétaCan
Menu
Back to cohort
Record W2000623079 · doi:10.1145/1180639.1180732

Motion swarms

2006· article· en· W2000623079 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMotion (physics)Computer scienceField (mathematics)MultimediaHuman–computer interactionMovement (music)Computer graphics (images)Interactive artArtificial intelligenceArtAestheticsMathematics

Abstract

fetched live from OpenAlex

We create interactive art that can be enjoyed by groups such as audiences at public events with the intent to encourage communication with those around us as we play with the art. Video systems are an attractive mechanism to provide interaction with artwork. However, public spaces are complex environments for video analysis systems. Interaction becomes even more difficult when the art is viewed by large groups of people. We describe a video system for interaction with art in public spaces and with large audiences using a model-free, appearance-based approach. Our system extracts parameters that describe the field of motion seen by a camera, and then imposes structure on the scene by introducing a swarm of particles that moves in reaction to the motion field. Constraints placed on the particle movement impose further structure on the motion field. The artistic display reacts to the particles in a manner that is interesting and predictable for participants. We demonstrate our video interaction system with a series of interactive art installations tested with the assistance of a volunteer audience.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.137

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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