Bibliographic record
Abstract
Considering the unpredictable situation of COVID-19, the pandemic-related nationwide lockdowns, other coordinated restrictive measures, as well as the health and safety of our participants and members of our research community is of top priority to the organizing committee. 2022 2nd International Conference on Electrical, Electronics and Computing Technology (EECT 2022) planned to be held from March 25th-27th, 2022 in Shanghai, China was changed to be held virtually through Zoom software. EECT 2022 is to bring together innovative academics and industrial experts in the field of electrical, electronics and computing technology to a common forum. The primary goal of the conference is to promote research and developmental activities in electrical, electronics and Computing Technology and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference was divided into three sessions, including keynote speeches, oral presentations, and poster presentations. In the keynote presentation’s part, we have 5 renowned professors present their insightful speeches. Each speaker allocated 40-50 minutes including 5min for Q&A to hold their speeches. Prof. Om P. Malik from University of Calgary, Prof. Graziano Chesi from The University of Hong Kong, Prof. Nikolaos Freris from University of Science and Technology of China, Prof. Farhad Shahnia from Murdoch University and Dr. Michael Waltl from TU Wien. For oral presentations, authors were given approximately 10-15 minutes to perform their oral presentations one by one. EECT 2022 received more than 91 manuscripts, and 43 submissions have been accepted. All accepted papers under the Journal’s Peer Review Policy were strictly selected based on originality, significance, relevance, and contribution to the area after being peer-reviewed. EECT 2022 follows a double-blind reviewing process to make the review process as fair as possible, both reviewers’ and authors’ identities remain anonymous. We would like to acknowledge all editors, staff, committee members, reviewers, and authors who contributed to the conference. The help and contribution of each individual and institution were instrumental in the success of the conference. Each individual and institutional help were very important for the success of this conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers. EECT 2022 Committee Conference Committee are available in the 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 | 0.010 |
| 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 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.521 | 0.379 |
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".