Bibliographic record
Abstract
2024 8th International Conference on Data Mining, Communications and Information Technology (DMCIT 2024), was successfully held on May 25, 2024, which was organized by Asia Pacific Institute of Science and Engineering (APISE), media supported by Internet of Things Technologies, Modern Electronics Technique, Journal of Xidian University, Journal of Information and Intelligence, OPTICAL COMMUNICATION TECHNOLOGY, Telecommunication Engineering. The conference was held in Hong Kong from May 24-26, 2024 as planned. Considering that some participants could not attend in person, the conference was adjusted as a hybrid conference, as a combination of on-line and off-line conference. The proceedings of this year’s edition comprised three main categories: “1. Advanced Methods and Algorithms”; “2. Applied Technologies in Various Domains”; “3. Case Studies and Practical Implementations”. All these submissions were rigorously reviewed by the Program Committee. The conference attracted 45 submissions in total, and out of 27 papers were accepted, including countries like Canada, France, China, India, New Zealand, Spain, Thailand, etc. On the conference day, 8 oral presentations and 18 poster presentations were arranged according to the participants’ choices. Each presenter was given 15 minutes to deliver their presentation, including 3 minutes Q&A. Two awards, one best oral presentation award and one best poster presentation award were selected by the end of the conference. The conference was inaugurated with the esteemed Dr. Simon Fong from the University of Macau, Macau S.A.R., China, and Dr. Ka-Chun Wong from the City University of Hong Kong, Hong Kong S.A.R., China, both delivering outstanding opening remarks. The conference was honored by the presence of four eminent keynote speakers who graced the event with their distinguished speeches. Professor Xianbin Wang from Western University, Canada, Professor Raymond Chi-Wing Wong from The Hong Kong University of Science and Technology, Hong Kong, Professor Steven Guan from Xi’an Jiaotong-Liverpool University, China, and Professor Chin-Chen Chang from Feng Chia University, Taiwan ROC, each shared their latest and profoundly insightful research perspectives. The technical session and poster session were formally presided over by Prof. Jiwat Ram, who delivered exemplary and thought-provoking remarks. The DMCIT 2024 conference is dedicated to showcasing the most recent findings and scholarly work in the realms of Data Mining, Communications, Information Technology, and associated fields. Through a combination of oral presentations and poster sessions, the event facilitates a platform for participants to engage in the exchange of innovative concepts, forge professional or academic alliances, and seek out international collaborators for prospective joint ventures. We express our collective gratitude to all participants for their invaluable contributions. The shared knowledge and spirited discussions have been the lifeblood of our gathering, igniting a passion for innovation and collaboration. We are deeply inspired by the intellectual curiosity and the pursuit of knowledge that have been the defining attributes of our conference. The synergy of ideas and the collaborative spirit have laid a robust foundation for the ongoing evolution and progress within our disciplines. Looking forward, we anticipate the enduring impact of this conference, confident that the seeds of thought sown here will flourish into a bountiful harvest of academic achievements. 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 | 0.011 |
| 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 | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.537 | 0.408 |
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".