Bibliographic record
Abstract
Currently, the entire world is struggling against the virulent pandemic COVID-19. Unfortunately, each of us is affected, either overtly or covertly. Our conference, 2020 4th International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2020) is not an exception. In April, mass gatherings are not permitted by the government. It is uncertain when the COVID-19 would end, so it remains unclear for postponement time, while many scholars and researchers wanted to attend this long waited conference and have academic exchanges with their peers. Therefore, in order to actively respond the call of the government, and meet author’s request, the AIACT 2020, which was planned to be held in Hangzhou, China from April 24 to 26, 2020, is changed to be held on April 25, 2020 online through Zoom software. This approach not only reduces people gathering, but also meets their communication needs. Each keynote speech lasts 40 minutes, invited speech lasts 30 minutes and author presentation lasts 15 minutes. Each presentation is packed with question and answer part. There is lively discussion at the meeting, which promotes the academic exchange. The success and prosperity of the conference is reflected high level of the papers received. AIACT 2020 became an effective communication platform for all the participants over the world. AIACT 2020 was organized by Hong Kong Society of Mechanical Engineers, sponsored by York University. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments and successful practices in Artificial Intelligence, Automation and Control Technologies. More than 70 participants attended the meeting, they were from USA, Japan, Australia, Singapore, Greece, New Zealand, India, Canada, Turkey, Germany, China and more. Five renowned speakers given speeches about their latest research and reports. They are: Prof. Bin He, from Shanghai University, China; Prof. Sheng Guo, from Beijing Jiaotong University, China; Prof. Dan Zhang, from York University, Canada; Prof. Jinsong Bao, from Donghua University, China; Dr. Haijun Shan, from Zhejiang Lab, China. The conference also had 2 technical sessions and 1 poster session. The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 3 chapters: 1. Machine Learning, Computer Vision and Natural Language Processing; 2. Algorithm, Neural Network; 3. Robotics, Control, Fault Detective, Testing, Others. We would like to acknowledge all of those who supported AIACT 2020. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organizing and peer review of the papers. We sincerely hope that AIACT 2020 will be a forum for excellent discussions that will put forward new ideas and promote collaborative researches. We are sure that the proceedings will serve as an important research source of references and the knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. Prof. Dan Zhang Conference Chairman
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.550 | 0.398 |
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".