Bibliographic record
Abstract
The organizing committee of the 6th International Conference on Electrical Engineering, Control and Robotics (EECR 2020) and the Workshop the 3rd International Conference on Intelligent Control and Computing (ICICC 2020) aimed to facilitate interaction among participants for the latest progress and development in the fields of electrical engineering, intelligent control and robotics. Through the conference and workshop, the committee intends to enhance the sharing of individual experiences in their expertise and to encourage interdisciplinary and international collaborations. The conference and workshop were held in Holiday Inn Express Xiamen Airport Zone, Xiamen, China, during January 10-12, 2020, co-organized by Sichuan Institute of Electronics and Huaqiao University, with the support of Concordia University, the South China University of Technology, the University of Electronic Science and Technology of China, Southwest Minzu University and others. Despite the high quality of most of the submissions, the final proceedings of EECR 2020 and ICICC 2020. includes 59 papers, which were selected after a thorough reviewing process and were resented at the conference and workshop. The contributions of the technical program committee and the referees are deeply appreciated. Most of all, we would like to express our sincere thanks to the authors for submitting their most recent works and the organizing committee for their enormous efforts to turn this event into a smoothly running meeting. We sincerely hope that this publication will prove to be an important resource for the scientific community. With our warmest regards, Chun-Yi Su Concordia University, Canada
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.433 | 0.305 |
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".