Proceedings of the 8th international conference on Multimodal interfaces
Bibliographic record
Abstract
Multimodal interaction is a domain of research that is based on the intuition that humans bring a broad bandwidth of interactive resources to bear in our interactions with other people and with our environment (and computers). It is a rich ground for interdisciplinary research that spans the detection and tracking of human behavior, the production of visual, physical, and audible signals for human consumption, the system architectures, approaches and theories for integrating these varied inputs and outputs, and the experimental methods and evaluations for such multimodal interfaces. As such, multimodal interfaces invite insights from such fields as human-computer interaction, spoken language understanding, natural language understanding, image processing, computer vision, pattern recognition, experimental psychology, psycholinguistics, social psychology, computer-supported cooperative work.These proceedings include the papers accepted for presentation at the Eighth International Conference on Multimodal Interfaces (ICMI'06) held in Banff, Canada on the November 2-4, 2006. These proceedings are published by ACM.The papers included in these proceedings were selected from 102 contributions with 81 full papers submitted by researchers worldwide. A full double-blind review process was employed. Each paper was allocated for review to four members of the Program Committee, with one serving as the primary reviewer. There were 104 reviewers, each of whom reviewed at least one paper. The process yielded 18 acceptances for oral presentations, and 22 for poster presentations. These papers represent some of the latest developments in the research of multimodal interfaces.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.152 | 0.065 |
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".