Bibliographic record
Abstract
The JACIII was first published in 1997, and 2017 marks its 20th anniversary. During the last two decades, the research fields in computational intelligence have rapidly evolved owing to the spread of the Internet, performance improvement of computers, and accumulation of scientific knowledge. To celebrate this 20th anniversary, we have selected 6 important research areas from the JACIII scope, and invited outstanding researchers from each of these areas to contribute papers about the progress and major topics in those areas during the past 20 years. Submitted paper went through a peer-review process by distinguished professors to further improve the quality. The research areas selected were computational intelligence, fuzzy intelligence, intelligent robots, artificial intelligence and web intelligence, data mining, and smart grids. Each of those paper covers broad topics appeared in the research areas, from which readers could grasp what happened during the past 20 years. We also hope readers could find some hints about future directions of their own researches towards the next 20 years. Invited Paper 1: Computational Intelligence: Retrospection and Future Author: Witold Pedrycz (University of Alberta, Canada) Invited Paper 2: Fuzzy Inference: Its Past and Prospects Authors: Kiyohiko Uehara (Ibaraki University, Japan) and Kaoru Hirota (Beijing Institute of Technology, China) Invited Paper 3: Relationship Between Human and Robot in Nonverbal Communication Authors: Yukiko Nakagawa and Noriaki Nakagawa (RT Corporation, Japan) Invited Paper 4: Web Intelligence and Artificial Intelligence Author: Yasufumi Takama (Tokyo Metropolitan University, Japan) Invited Paper 5: A Review of Data Mining Techniques and Applications Authors: Ratchakoon Pruengkarn, Kok Wai Wong, and Chun Che Fung (Murdoch University, Australia) Invited Paper 6: Development and Current State of Smart Grids: A Review Author: Ken Nagasaka (Tokyo University of Agriculture and Technology, Japan)
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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 | 0.001 | 0.006 |
| 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 | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.885 | 0.798 |
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".