Fusion multimodale pour les systemes d'interaction
Bibliographic record
Abstract
Researchers in computer science and computer engineering devote now a significant part of their efforts in communication and interaction between human and machine. Indeed, with the advent of real-time multimodal and multimedia processing, computer is no longer seen only as a calculation tool, but as a machine of communication processing, a machine that accompanies, assists or promotes many activities in daily life. A multimodal interface allows a more flexible and natural interaction between a user and a computing system. It extends the capabilities of this system to better match the natural communication means of human beings. In such interactive system, fusion engines are the fundamental components that interpret input events whose meaning can vary according to a given context. Fusion of events from various communication sources, such as speech, pen, text, gesture, etc. allow the richness of human-machine interaction. This research will allow a better understanding of the multimodal fusion and interaction, by the construction of a fusion engine using technologies of semantic web domain. The aim is to develop an expert fusion system for multimodal human-machine interaction that will lead to design a monitoring tool for normal persons, seniors and handicaps to ensure their support, at home or outside.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.016 |
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; both teacher heads agree on what is shown here.
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".