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Record W2911464248

Proceedings of the 10th International Symposium on Smart Graphics

2007· article· en· W2911464248 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

VenueSmart Graphics · 2007
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGraphicsComputer scienceIBMComputer graphicsEvent (particle physics)WatsonHuman–computer interactionMultimediaComputer graphics (images)Artificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Welcome to the International Symposium on Smart Graphics! It will bring together people from the fields of Computer Graphics, Graphics Design, Cognitive Psychology and Artificial Intelligence, all working on different aspects of computer generated graphics. After a very successful AAAI Spring Symposium on Smart Graphics in 2000 the organizing committee decided to turn the event into a self-contained symposium. Last year's event (Smart Graphics 2001) attracted our target of 30 attendees. This was despite an unfortunate clash with the ACM I3DG conference. This year we aim to increase the number of attendees without creating a conference atmosphere, the number of attendees will be capped at 50. Seeing the Smart Graphics initiative taking off so dynamically, we are expecting a great number of varied and interdisciplinary submissions, and we are looking forward to the 2002 symposium, which is generously hosted by the IBM T.J. Watson Research Center.Advances and breakthroughs in the area of Computer Graphics have made visual media a major ingredient of the modern user interface, and it is likely that graphics will play a dominant role in the way people communicate and interact with computers in the future. Especially the evolution of computing towards more and more pervasive and distributed devices pose new and challenging problems for the effective use of graphics. We believe that intelligent behavior and graphics will provide the technical core of next generation interfaces. But in order to make those interfaces successful, principles and findings from Cognitive Psychology and Graphics Design are equally important to reflect the user's needs and abilities.Until recently there has been very little overlap between the Cognitive Psychology, Computer Graphics, AI and Graphics Design communities. The Smart Graphics Symposium intends to close these gaps. Recent advances in Computer Graphics have allowed AI researchers to integrate graphics in their systems (without being burdened by low-level issues such as image rendering) and graphics acceleration hardware has become affordable and is now available for a broad range of platforms. On the other hand, many AI techniques have matured to the point of being usable by non specialists. Furthermore, these very techniques are likely to be the vehicle by which both principles from Graphics Design and the results of research in cognitive aspects of visual representations, will be integrated in next generation graphical interfaces.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.000
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 head0.250
Teacher spread0.236 · 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