Bibliographic record
Abstract
Currently the entire world is struggling against the virulent pandemic COVID-19. Unfortunately, each of us is affected, either directly or indirectly. Our conference, 2020 International Conference on Industrial Applications of Big Data and Artificial Intelligence (BDAI 2020) was not an exception. In November, mass gatherings are not permitted by the government in China to protect people. It is uncertain when the COVID-19 will end, so it remains unclear for postponement time, while many scholars and researchers wanted to attend this long-waited conference and have academic exchanges with their peers. Therefore, in order to actively respond the call of the government, and meet author’s request, the BDAI 2020, which was planned to be held in Shenzhen, China from November 26 to 29, 2020, was changed to be held on November 26, 2020 online through Tencent VooV software. This approach not only avoids people gathering, but also meets their communication needs. Each keynote speech lasted 40 minutes, invited speech 30 minutes and authors presentation 15 minutes. Each presentation was included with questions and answers. There was lively discussion at the conference, which promotes the academic exchange. The success and prosperity of the conference is reflected high level of the papers received. BDAI 2020 became an effective communication platform for all the participants over the world. BDAI 2020 was organized by Hong Kong Society of Mechanical Engineers. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments, and successful practices in Industrial Applications of Big Data and Artificial Intelligence. More than 40 participants attended the conference, they were from USA, Australia, UK, Malaysia, South Korea, India, Swiss, China and more. Four renowned speakers given speeches about their latest research and reports. They are: Prof. Dan Zhang, York University, Canada; Prof. DP Sharma, AMUIT under UNDP & Academic Ambassador, Cloud Computing (AI), IBM, USA; Prof. Amir H. Gandomi, University of Technology Sydney, Australia; Assoc. Prof. Simon James Fong, University of Macau, Macau S.A.R., China. The conference also had 1 technical session and 1 poster sessions. The conference proceeding is a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 2 chapters: 1. Big Data and AI Technologies; 2. Big Data and AI Applications. We would like to acknowledge all of those who supported BDAI 2020. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organization and peer review of the papers. We sincerely hope that BDAI 2020 will be a forum for excellent discussions that will put forward new ideas and promote collaborative research. We are sure that the proceedings will serve as an important research source of references and the knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. Finally, we would like to thank the organization and committee for all their hard work and support and hope to meet you all in person at our next conference. Prof. Dan Zhang Conference Chairman
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".