Bibliographic record
Abstract
2025 9th International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2025), was successfully held in Sapporo, Japan from February 17 to 21, 2025, which was organized by Hong Kong Society of Mechanical Engineers(HKSME) and Shanghai Jiao Tong University, supported by Norwegian University of Science and Technology. Considering that some participants could not attend in person, the conference was adjusted as hybrid conference, as a combination of on-line and off-line conference. The conference accepted 33 papers, including countries like Malaysia, Japan, China, India, Thailand, Australia, Singapore,Canada, etc. Four renowned speakers delivered speeches about their latest research. They are Prof. Edwin K. P. Chong from Colorado State University, USA; Prof. Shugen Ma from The Hong Kong University of Science and Technology, China; Prof. Graziano Chesi from The University of Hong Kong, HKSAR,China and Prof. Haibin DUAN from Beihang University,China, delivered excellent speeches, sharing their latest and insightful research ideas. The conference also includes 3 technical sessions and one poster session. Each presenter was given 10-15 minutes to deliver their presentation, including 2 minutes Q&A. Two awards, one best oral presentation award and one best poster presentation award were selected by the end of conference. There is a lively discussion at the conference, which promotes academic exchange, which makes AIACT 2025 an effective communication platform for all the participants all over the world. List of COMMITTEES is 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 | 0.001 | 0.008 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.649 | 0.516 |
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".