2018 11th International Conference on Computer and Electrical Engineering
Bibliographic record
Abstract
Preface We are very pleased to welcome all of you to 11th International Conference on Computer and Electrical Engineering held in Tokyo, Japan during October 12-14, 2018. ICCEE was started in Phuket Island, Thailand in the year of 2008 and after the success of the first edition, it has been held annually from 2009 to 2017 in Dubai (UAE), Chengdu (China), Singapore, Hong Kong, Paris (France), Geneva (Switzerland), Paris (France),Barcelona (Spain), and Edmonton (Canada). With the successful experience over the past 10 years, This year, ICCEE starts off for the new decade. The goal of ICCEE2018 is to discuss the latest research and results of scientists and engineers from academic side and industrial side related to computer and electrical engineering topics. All the papers were subjected to peer-review by conference committee members and international reviewers. We had 55 submissions and accepted 30 high quality papers related to computer and electrical engineering such as data science, software engineering, image analysis, computer science, control technology, electronic power technology and so on. The acceptance rate was 54.5%. The attendees are from various regions such as Asia, Oceania, Europe and Africa. We also have five invited speakers from US, India and Japan. The proceedings give an exciting and wide-ranging discussion of the topics presented at ICCEE2018. The ICCEE2018 has been organized in the program chapters as: Data Science and Software Engineering; Image analysis and processing technology; Computer Science and Information Engineering; Electronics and Communication Engineering; Power machinery & measurement and control technology; Electronic power technology and energy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.182 | 0.136 |
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".