Bibliographic record
Abstract
The International Conference on Algorithms, Network and Computer Technology (ICANCT) is an annually held conference, it aims to provide an ideal platform for bringing together researchers, practitioners, scholars, professors and engineers from all around the world to exchange the newest research results and stimulate the scientific innovations. The 2nd International Conference on Algorithms, Network and Computer Technology (ICANCT 2023) was held online on December 22th, 2023. The conference program mainly consisted of keynote speeches, oral presentations and E-poster presentations. ICANCT 2023 has 5 distinguished keynote speakers including: Prof. Witold Pedrycz from University of Alberta, Canada; Prof. Shunli Wang from Southwest University of Science and Technology, China; Prof. Anand Nayyar from Duy Tan University, Vietnam; Prof. Sudan Jha from Kathmandu University, Nepal and Prof. YAM Sheung Chi Phillip from The Chinese University of HongKong, China. They have given keynote speeches on related topics of Algorithms, Network and Computer Technology. All accepted papers in the proceedings went through a 3-stage double-blind peer-review process: first round of review for the full paper, second round of review of the revised paper, final decision on the paper. Offering a current overview of the latest research and applications in Algorithms, Network and Computer Technology, this book will be of interest to all those working in the field. On behalf of the Organizing committee, we would like to give sincere gratitude to all the keynote speakers, peer reviewers, authors and everyone who contributed to ICANCT 2023. We owe gratitude to all the committee members for providing necessary help and support throughout the conference. At last, we acknowledge the publication support of Journal of Physics: Conference Series. List of Committee Members are available in this Pdf.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.521 | 0.387 |
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".