Design of a virtual environment aided by a model‐based formal approach using DEVS
Bibliographic record
Abstract
Abstract Virtual environment (VE) is a modern computer technique that aims to provide an attracting and meaningful human–computer interacting platform, which can essentially help the human users to learn, to play or to be trained in a ‘like‐real’ situation. Recent advances in VE techniques have resulted in their being widely used in many areas, in particular, the E‐learning‐based training applications. Many researchers have developed the techniques for designing and implementing the 3D virtual environment; however, the existing approaches cannot fully catch up the increasing complexity of modern VE applications. In this paper, we designed and implemented a very attracting web‐based 3D virtual environment application that aims to help the training practice of personnel working in the radiology department of a hospital. Furthermore, we presented a model‐based formal approach using discrete event system specification (DEVS) to help us in validating the X3D components' behavior. As a step further, DEVS also helps to optimize our design through simulating the design alternatives. Copyright © 2009 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".