Bibliographic record
Abstract
Human expressions contain important information during communication. Expressions are often used to quickly understand the basic underlying intent of a message being conveyed. This paper presents an approach that leverages human expression for remote tele-operation tasks and to augment shared multiparticipant environments with meaningful concepts. Taking advantage of this information helps to minimise the human time required to convey intent. Expressions are observed through hand gestures and facial expressions, basic primitives are identified using a fuzzy-hidden Markov model approach and sets of these primitives are used to infer intent using a domain specific conceptual-graph based knowledge system. Although dynamic hand gestures and basic facial expressions are used as sources of human expression, the flexibility exists to incorporate additional and alternate sources of human expression. The proposed approach to identify meaningful concepts from human expression can be a valuable tool in a multiparticipant collaborative environment. Multiparticipant multimedia collaboration benefits from this computer-assisted understanding approach, as culture-specific expressions can be automatically clarified to reduce ambiguity and misunderstanding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".