Towards understanding expression for tele-operation
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it