Bibliographic record
Abstract
Organized by Beijing University of Technology, the 2023 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC2023) was successfully held both face to face & virtually in Dalian, China during April 14-16, 2023. Part of participants who were unable to actually attend due to travel and their schedules also delivered their speeches online through VooV Meeting. During the conference, the keynote speakers were each allocated 30 – 45 minutes to hold their speeches. After speech, there was a Q&A discussion between the speakers and participants. We believe that this conference is an important forum for the exchange of information and research results among us, who come from different countries, different educational and research institutes, and different research interests. All of the papers included in the proceedings were subjected to peer-review by conference committee members and international reviewers. 86 papers were accepted as full papers for presentation on the conference and publication in IPEC2023 Proceedings. These papers provide excellent advances of current research on the topics covering Image processing, computer vision, electronic technology, machine learning, signal and information processing, applied computer science and so on. The conference cannot be successful without the efforts of all conference committees, organizers, participants. We would like to express our deep gratitude and appreciation to all involved. Many thanks go as well to all of the reviewers who have helped us to maintain the high quality of manuscripts included in the Proceedings published by IOP Publishing. Finally, we sincerely hope you enjoy the scientific and technical contents of the conference and find a good opportunity to discuss ideas and future collaborations with other researchers and practitioners from institutions around the world. Ljiljana Trajković, Simon Fraser University, Canada Xuesong Xu, Hunan University of Technology and Business & Brandeis University, USA IPEC2023 General Chairs List of Conference 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 | 0.001 | 0.005 |
| 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 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.509 | 0.383 |
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".