Bibliographic record
Abstract
The organizing committee of the 5th International Conference on Electrical Engineering, Control and Robotics (EECR 2019) aimed to facilitate interaction among participants for the latest progress and development in the fields of electrical engineering, control and robotics. Through this conference, the committee intends to enhance the sharing of individual experiences and expertise in electrical engineering, control and robotics with particular emphasis on the technical challenges associated with varied applications in these fields. The conference was held in Xihu Hotel of SCUT, Guangzhou, China, January 12-14, 2019, organized by Sichuan Institute of Electronics with the support of South China University of Technology, University of Electronic Science and Technology of China and Southwest Minzu University. Despite the high quality of most of the submissions, the final proceedings of EECR 2019 includes 62 papers, which were presented at the conference and that were selected after a thorough reviewing process. The authors of these papers come from different countries from Europe, Australia and Asia etc.. 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. Prof. Chun-Yi Su Concordia University, Canada Jan. 18, 2019
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.436 | 0.316 |
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".