V International Scientific and Practical Conference “Virtual Simulation, Prototyping and Industrial Design 2018”
Bibliographic record
Abstract
V International Scientific and Practical Conference “VIRTUAL SIMULATION, PROTOTYPING AND INDUSTRIAL DESIGN – 2018” (VSPID-2018) 14-16 November, 2018 Tambov, Russian Federation The Vth International Scientific and Practical Conference “VIRTUAL SIMULATION, PROTOTYPING AND INDUSTRIAL DESIGN – 2018” (VSPID-2018) is an annual scientific event joining experienced and young scientists dealing with research of methods and algorithms used for virtual modeling and prototyping. The VSPID-2018 Conference was focused on the presentation and discussion of research achievements and future developments in the field of virtual simulation, prototyping, and industrial design. The VSPID-2018 included the following scientific topics: (1) Virtual modeling, visualization and prototyping of technical systems; (2) Virtual modeling, visualization and prototyping of social objects; (3) Virtual modeling in geographic information systems of territories management: development and use; (4) Design and application of computer simulation models, virtual simulators and machine vision systems; (5) Creation of e-learning materials using virtual simulation and prototyping; (6) Industrial design; (7) Modern information processing; (8) Modeling of processes of teaching and management in education; and (9) Mathematical modeling of molecular systems. The issue demonstrates potential and prospective methods, algorithms, and approaches which may be profitable leverage for elaborating and applying virtual models and prototypes in design, modernization, manufacturing sectors, as well as in cultural and architectural preservation. VSPID-2018 was jointly organized by Tambov State Technical University and Tambov Region’s Government (Russia). The Organizing Committee included professors and specialists from Russian Academy of Sciences, Torino Polytechnic Institute (Italy), University of Soka (Japan), Amberg-Weiden University of Applied Sciences (Germany), University of Münster (Germany), Tambov State Technical University (Russia), Moscow State Automobile and Road Technical University (Russia), Moscow State University (Russia) and other institutions. All submitted manuscripts went through the review process. We want to thank all reviewers from Russia, China, Turkey, Australia, Poland, Spain, Iran, USA, Germany, Slovakia, Colombia, Italy, India, Korea, Canada, Ukraine, Czech Republic, for their time and highly professional comments. We deeply believe that their reviews gave opportunity to improve the scientific quality of the presented papers which may be useful for academic, scientific and industrial sectors. We would like to thank all participants for their contribution, sponsors for the financial support, and Program Committee members for their huge efforts in organization and holding of the VSPID-2018 Conference. The conference was supported by the Russian Foundation for Basic Research (project 18-07-20042/18) and Administration of the Tambov Region’s Government (project 1-NM-18). Organizing Committee: Dr. Vladimir Nemtinov, Tambov State Technical University, Tambov, Russia Editor-in-chief of the Issue: Prof. Sergei Gorlatch, Westfaelische Wilhelms-Universität, Münster, Germany
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 0.031 |
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".