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Record W2346847331 · doi:10.1145/2835641.2835649

HCI in performance arts and the case of Illimitable Space System's multimodal interaction and visualization

2015· article· en· W2346847331 on OpenAlexaffabout
Miao Song, Serguei A. Mokhov, Peter Grogono, Sudhir P. Mudur

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsConcordia University
Fundersnot available
KeywordsAnimationBeijingComputer scienceDanceVisualizationVariety (cybernetics)Performing artsMultimediaHuman–computer interactionThe artsSpace (punctuation)Computer graphics (images)Visual artsChinaArtificial intelligenceOperating systemArt

Abstract

fetched live from OpenAlex

The primary aim of this art paper is to present a case study of the use of modern 3D graphics and sensor technologies in interactive stage performances. Specifically, we present studies of interactive performances in which we have used the Illimitable Space System, a proof-of-concept tool box that offers configurable multimodal interaction via a variety of means. The animation and interaction are all done in real-time and can be of arbitrary duration while the system is up and running. Earlier ISS versions were exhibited in the end of 2012 and 2013 during Open House and Stewart Hall Expo-Science events, as well during the 2014 2-day Chinese New Year Gala performance during the Ascension dance at Concordia University, Montreal, Canada, and in the large Like Shadows theatre production in Beijing, China. We describe how ISS was configured and used in these events, and the valuable feedback obtained, confidence gained in interactive technology usage in performances, and lessons learned from them. All of which help us in making continuous improvements in ISS. As a result this paper includes the themes of these events, methods, theory, and history. Since technology has been used in stage performances from ancient times, we start with a brief historical background of technology in performance arts.

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 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: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.102

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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2015
Admission routes2
Has abstractyes

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