Bibliographic record
Abstract
The International Conference on Control Theory and Applications 2022 (ICoCTA 2022), organized by the Hong Kong Society of Robotics and Automation (HKSRA), was held virtually (originally scheduled in Chengdu, China) in June 10-12, 2022. ICoCTA 2022 aims at bringing together researchers, engineers, scientists and industry professionals in a unique platform and present their stimulating research and knowledge transfer ideas in control theory and applications. Control theory and engineering have witnessed dramatic achievements, which have made possible space travel and communication satellites, have assisted in the design of safe and efficient aircraft, ships, trains, and cars, have helped in developing cleaner chemical processes while addressing environmental concerns. The conference included two full days of technical sessions consisting of keynote lectures, oral presentations, and poster presentations. 13 papers were selected from 28 submissions after peer review, and all the authors participated in ICoCTA 2022 with presentations and poster. In the keynote speech session, we were greatly honor to have invited Professor Romeo Ortega, IEEE Fellow, Full Professor at ITAM, Mexico; Professor Petros Ioannou, A.V. ‘BAL’ Chair Professor, University of Southern California; Prof.YangQuan Chen, University of California, Merced; Professor Anthony G Cohn, University of Leeds, UK; Prof. Yang Shi, Department of Mechanical Engineering, University of Victoria, Victoria, Canada; Professor Hyo-Sung Ahn, Distributed Control & Autonomous Systems Lab. (DCASL), School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Korea; Professor Ioannis Pitas, Department of Informatics, University of Thessaloniki, Greece for addressing impressive and insightful speeches to the audience. List of ICoCTA 2022 Committees are available in this pdf.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.570 | 0.390 |
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".