Special Issue on Soft Computing for Modeling and Simulation
Bibliographic record
Abstract
Welcome to this special issue on Soft Computing for Modeling and Simulation of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). We are pleased to introduce 5 selected papers and 4 regular papers presented at NASTEC (North-American Simulation Technology Conference), which is a series of conferences initiated by Eurosis after in-depth discussions with Prof. Mokhtar Beldjehem and North-American Simulationists, Soft Computing Scientists, addressing issues regarding the interplays and synergies between Modeling, Simulation and Soft Computing. The first NASTEC 2008 was organized at Mc Gill University, Montré{e}al, Canada, which is its birth place. It has attracted Simulationists, researchers and practitioners of Soft Computing, attendees from academic, industry and government agencies in an exchange of ideas and shared experiences. The intent of the NASTEC'2008 event is to nurture the spirit of cooperation and strive to improve the quality of life in this global village through excellence in hybrid Soft Computing research and education by engineering of next-generation intelligent hybrid Soft Computing systems for Modeling, Simulation, Simulation-based and Data-driven Software Engineering, Web-centric Computing, E-learning and Virtual Reality Systems at the service and for the benefits of the humankind. Computer Simulation is being acknowledged as the “third leg” of scientific discovery and analysis, along with theory and experimentation. Simulation Technology aims at building the “software digital factory.” The fields of Modeling, Simulation, and Soft Computing in general have made significant progress; parts of them were reflected in the present Special issue. This issue was able to attract top-level and forefront research. The themes center on novel issues in connection with Modeling, Simulation, Soft Computing, Simulation-based Software Engineering, Web-centric Computing, Virtual Reality Systems, their interplays and synergies. We are grateful to a number of people without whom we would not have been able to put this special issue together. They include our NASTEC'08 IPC and JACIII reviewers for making this special issue possible; they have done an excellent job: We got 4.5 reviews per paper on the average. We are grateful to authors of selected papers who have considered JACIII as the target for their work, and even though we could not accommodate every submission in this issue, we hope that the reviews will be helpful to many people. We are also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the NASTEC 2008 conference staff for inviting us to guest-edit this Journal. Last, but not least, we are indebted to the staff of JACIII and Fuji Technology Press for making this a reality.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.159 | 0.049 |
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".