Notice bibliographique
Résumé
Currently, the entire world is struggling against the virulent pandemic COVID-19. Unfortunately, each of us is affected, either overtly or covertly. Our conference, 2020 4th International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2020) is not an exception. In April, mass gatherings are not permitted by the government. It is uncertain when the COVID-19 would end, so it remains unclear for postponement time, while many scholars and researchers wanted to attend this long waited conference and have academic exchanges with their peers. Therefore, in order to actively respond the call of the government, and meet author’s request, the AIACT 2020, which was planned to be held in Hangzhou, China from April 24 to 26, 2020, is changed to be held on April 25, 2020 online through Zoom software. This approach not only reduces people gathering, but also meets their communication needs. Each keynote speech lasts 40 minutes, invited speech lasts 30 minutes and author presentation lasts 15 minutes. Each presentation is packed with question and answer part. There is lively discussion at the meeting, which promotes the academic exchange. The success and prosperity of the conference is reflected high level of the papers received. AIACT 2020 became an effective communication platform for all the participants over the world. AIACT 2020 was organized by Hong Kong Society of Mechanical Engineers, sponsored by York University. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments and successful practices in Artificial Intelligence, Automation and Control Technologies. More than 70 participants attended the meeting, they were from USA, Japan, Australia, Singapore, Greece, New Zealand, India, Canada, Turkey, Germany, China and more. Five renowned speakers given speeches about their latest research and reports. They are: Prof. Bin He, from Shanghai University, China; Prof. Sheng Guo, from Beijing Jiaotong University, China; Prof. Dan Zhang, from York University, Canada; Prof. Jinsong Bao, from Donghua University, China; Dr. Haijun Shan, from Zhejiang Lab, China. The conference also had 2 technical sessions and 1 poster session. The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 3 chapters: 1. Machine Learning, Computer Vision and Natural Language Processing; 2. Algorithm, Neural Network; 3. Robotics, Control, Fault Detective, Testing, Others. We would like to acknowledge all of those who supported AIACT 2020. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organizing and peer review of the papers. We sincerely hope that AIACT 2020 will be a forum for excellent discussions that will put forward new ideas and promote collaborative researches. We are sure that the proceedings will serve as an important research source of references and the knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. Prof. Dan Zhang Conference Chairman
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,550 | 0,398 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».