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Record W4402452715 · doi:10.11159/mhci24.108

Human Computer Interaction Based Cao's Kite Craft Experience in Virtual Reality

2024· article· en· W4402452715 on OpenAlexvenueno aff
Zongze Li, Wei Song, Hou Shu, Ruxin Wang, Lingbo Kang, Mengfan Gao

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsnot available
FundersBeijing Institute of Graphic Communication
KeywordsKiteCraftVirtual realityComputer scienceHuman–computer interactionImmersion (mathematics)Computer graphics (images)Visual artsArtMathematics

Abstract

fetched live from OpenAlex

Cao's Kite craft was generated in 18th century and now becomes one of the representative Chinese national intangible cultural heritage items.Cao's kite craft includes four basic skills and we focus on the skill of tying the framework in this paper.Especially, in the step roasting bamboo strips of tying the framework, we improve the mesh deformation algorithm to achieve better effects.In the interactive realization of virtual kite craft, we implement all three steps of tying the framework.By the human computer interactive design in this paper, the immersion and engagement of the experiencer are further enhanced in virtual reality.

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 score0.306
Threshold uncertainty score0.368

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 studies0.0000.000
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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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