Proceedings of the 3rd international workshop on Affective interaction in natural environments
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 3rd International Workshop on Affective Interaction in Natural Environments -- AFFINE 2010. AFFINE follows a number of successful workshops and events commencing in 2008. A key aim of AFFINE is the identification and investigation of significant open issues in real-time, affect-aware applications 'in the wild' and especially in embodied interaction, for example, with robots or virtual agents. AFFINE seeks to bring together researchers working on the real-time interpretation of user behaviour with those who are concerned with social robot and virtual agent interaction frameworks. The call for papers attracted several submissions from Europe, Asia, Africa, Canada and the United States. The program committee accepted 17 papers that cover a variety of topics, including multimodal human affect recognition, multimedia expression generation in robots and virtual agents, human-computer and human-robot interaction. In addition, the program includes a keynote talk by Prof. Antonio Camurri on the automated analysis of non-verbal expressive gesture and expressive social interaction in groups of users, for applications in novel multimodal interfaces and emerging user-centric media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".