Bibliographic record
Abstract
2019 International Conference on Intelligent Transportation and Vehicle Engineering (ICITVE 2019), hosted by School of Automotive and Transportation Engineering, Heilongjiang Institute of Technology, was successfully held in Chongqing, China during December 6-8, 2019. ICITVE 2019 aims to provide a platform for experts, scholars, and practitioners in the field of Intelligent Transportation and Vehicle Engineering to share the latest research results, discuss existing problems and challenges, explore cutting-edge technologies, and enhance their cooperation. We are honored to have Prof. Baixue Fu, Dean of School of Automotive and Transportation Engineering, Heilongjiang Institute of Technology, China, and Prof. Traian Mazilu from University Politehnica of Bucharest, Romania to jointly chair this conference. And we invited over 30 excellent scholars form east and west to constitute the Academic Committee of ICITVE 2019. During the conference, five remarkable experts gave excellent keynote speeches related to the field of Intelligent Transportation and Vehicle Engineering on site. Prof. Devinder Yadav from University of Nottingham Ningbo, China discussed emission control at airports and advances in alternate propulsion in civil aviation industry. Assoc.Prof. Seyed Mohammadreza Ghadiri from Malaysia University of Science & Technology, shared his research on Speed Limit Obedience Factor (SLOF) A New Countermeasure to Improve the Efficiency of The Advisory Intelligent Speed Adaptation. Prof. Said Easa from Ryerson University Toronto, Canada held a speech on Smart Mobility: Challenges and Solutions. Prof. Fangwu Ma from Jilin University, China discussed the Outlook of Ecosystem of New Mobility. And Senior Engineer Chengyong Niu, Project Supervisor from Chongqing Vehicle Test & Research Institute Co., Ltd, China had a speech on Test and application challenges for autonomous vehicle at level 3 and below. Thanks to these wonderful speeches given by the distinguished experts, the conference has formed a lively and heated academic vibe on site. We are happy to present the proceedings of ICITVE 2019 featured the new advances and research results in the fields of transportation and vehicle engineering research. All papers have been through rigid review process to comply with the requirements of international publication. We would to like to express our sincere gratitude to all the participants attending the conference, who made this conference a great success. We want to thank our honorific chairmen for hosting this conference, the distinguished keynote speakers for giving insightful speeches, and all authors for submitting their study paper. We hope this conference and the proceedings of it could serve as a good reference for those who work in the field of Intelligent Transportation and Vehicle Engineering. We aim to hold this conference every year to make it an convenient platform for people to share views and experiences in intelligent transportation and vehicle engineering and related areas. We would like to see you next year. Thank you. Committee of ICITVE 2019 List of Committee members are available in this 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 | 0.001 | 0.007 |
| 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 | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.580 | 0.447 |
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".