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Record W4249610758 · doi:10.1109/iv.2004.1320254

From ethno-mathematics to generative design: metapatterns and interactive methods for the creation of decorative art

2004· article· en· W4249610758 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

VenueProceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004. · 2004
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsConcordia University
Fundersnot available
KeywordsGenerative grammarComputer scienceRule-based machine translationGenerative DesignArtificial intelligenceNatural language processingPattern recognition (psychology)Engineering drawingEngineering

Abstract

fetched live from OpenAlex

The research discussed in This work focuses on the development of interactive methods to image and analyze the surface designs of cultural artifacts and the generation of new designs. The project is interdisciplinary and uses methodologies from aesthetic and cultural inquiry, mathematics and computer science. The results of the research are obtained by using a combination of tools, such as neural networks, pattern recognition techniques including edge detection, and pattern generating techniques such as shape grammars. The first phase of this research focuses on the analysis of Congolese Kuba cloth and Moroccan Zillij mosaics because each has a complete and complex surface pattern with very different characteristics. Our work has three main facets: the description of the geometric content of ethno-mathematical artifacts; the classification of this content; and the generation of grammatical rules for the creation of new designs based on the studied artifacts.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.667
Threshold uncertainty score0.906

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 communication0.0000.001
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 head0.060
GPT teacher head0.366
Teacher spread0.306 · 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