Bibliographic record
Abstract
2023 7th International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2023) is organized by Hong Kong Society of Mechanical Engineers. AIACT 2023 is a not-to-be-missed opportunity that distills the most current knowledge on a rapidly advancing discipline in one conference. Join key researchers and established professionals in the field of Artificial Intelligence, Automation and Control Technologies as they assess the current state-of-the-art and roadmap crucial areas for future research. For a combination of reasons, most of the authors could not attend offline conference. AIACT 2023, which was planned to be held in Kunming, China from February 24 to 26, 2023, was changed to a virtual event and was held on February 24, 2023 through Tencent VooV software. This approach not only reduces people’s travel, but also satisfies the need of communication. AIACT 2023 is targeted on providing opportunities to bring together the related researchers to share their most recent research achievements in these fields, and thus promote more advanced research. More than 30 participants attended the meeting, most of them were from China. Three renowned speakers given speeches about their latest research and reports. They are: Prof. Dan Zhang, from York University, Canada; Prof. Wenqiang Zhang, from Fudan University, China; Prof. Hongliu Yu, from University of Shanghai for Science and Technology, China. On the behalf of the conference organizing committee, I would like to express my great appreciation to the three keynote speakers (45 minutes each, including Q&A). In addition, the conference included one oral session and one poster session. In the oral session, each presentation was allotted 15 minutes. At the end of each session, the participants were engaged in discussions for future collaborations. A group photo was taken at the conference. List of 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 | 0.008 |
| 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 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.570 | 0.439 |
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".