Bibliographic record
Abstract
Abstract The 2020 4th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2020) was held on February 21-23, 2020. Since 2017, this annual international conference has been held for three times, and this time, the fourth conference was held via online platform due to the COVID-19 crisis, which is different from the previous traditional way. Despite the distance, online IWAACE 2020 enables experts and scholars in the field of Advanced Algorithms and Control Engineering to continue to communicate and discuss the state-of-the-art research with each other. It provides a flexible way for scholars and practitioners to enhance academic exchange and cooperation. We were honored to have Dr. Yuanzhu Chen, Head of Department of Computer Science, Memorial University of Newfoundland, Canada, to chair IWAACE 2020. Our Technical Program Committee constitutes more than 40 experts in the field of Advanced Algorithms and Control Engineering from home and abroad. During the conference, we were pleased to invite three distinguished experts to present their insightful speeches. Prof. Xinguo Yu from University of Wollongong, Australia, shared his study on Automatic Problem Solving for Basic Education. Assoc. Prof. Xiang Zhou from City University of Hong Kong, China, held a speech on the topic of Machine Learning and Control Theory. Dr. Badrul Hisham bin Ahmad from Universiti Teknikal Malaysia Melaka, Malaysia, talked about Design and Development of VHF FRONT-END for lightning interferometer system. List of More details of the virtual conference format, Committee members 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.640 | 0.483 |
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".