Tools for Graphical Specification and Visualization of DEVS Models
Bibliographic record
Abstract
We introduce advanced graphical modeling and visualization facilities for Discrete Event System Specification (DEVS) modeling and simulation (M&S) in the CD++ environment. The objective is to provide general users with a variety of easy-to-use environments to facilitate the model analysis process and thereby promoting the adoption of M&S by a wider community of practitioners and researchers. CD++Modeler allows users without much experience in software development to construct rather complex DEVS models and to analyze simulation data using 2D graphics. We also introduce a graphical platform called MAPS designed specifically for urban traffic systems, and other advanced 3D animation tools (CD++/VRML, CD++/Maya, CD++DEVSView, and CD++/Blender) based on both commercial and open-source software packages. We elaborate on the design of these toolkits and demonstrate their capabilities as well as relative merits and limitations with realistic applications. Following a highly modular approach, the resulting architecture can be easily extended to incorporate other modeling and visualization techniques in future development. We show that these facilities can reduce the model development cost significantly, lower the learning curve for general users, and improve the comprehension of continuously evolving models, making them suitable for efficient decision making.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".