Notice bibliographique
Résumé
Currently the entire world is struggling against the virulent pandemic COVID-19. Unfortunately, each of us is affected, either directly or indirectly. Our conference, 2020 International Conference on Industrial Applications of Big Data and Artificial Intelligence (BDAI 2020) was not an exception. In November, mass gatherings are not permitted by the government in China to protect people. It is uncertain when the COVID-19 will 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 BDAI 2020, which was planned to be held in Shenzhen, China from November 26 to 29, 2020, was changed to be held on November 26, 2020 online through Tencent VooV software. This approach not only avoids people gathering, but also meets their communication needs. Each keynote speech lasted 40 minutes, invited speech 30 minutes and authors presentation 15 minutes. Each presentation was included with questions and answers. There was lively discussion at the conference, which promotes the academic exchange. The success and prosperity of the conference is reflected high level of the papers received. BDAI 2020 became an effective communication platform for all the participants over the world. BDAI 2020 was organized by Hong Kong Society of Mechanical Engineers. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments, and successful practices in Industrial Applications of Big Data and Artificial Intelligence. More than 40 participants attended the conference, they were from USA, Australia, UK, Malaysia, South Korea, India, Swiss, China and more. Four renowned speakers given speeches about their latest research and reports. They are: Prof. Dan Zhang, York University, Canada; Prof. DP Sharma, AMUIT under UNDP & Academic Ambassador, Cloud Computing (AI), IBM, USA; Prof. Amir H. Gandomi, University of Technology Sydney, Australia; Assoc. Prof. Simon James Fong, University of Macau, Macau S.A.R., China. The conference also had 1 technical session and 1 poster sessions. The conference proceeding is a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 2 chapters: 1. Big Data and AI Technologies; 2. Big Data and AI Applications. We would like to acknowledge all of those who supported BDAI 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 organization and peer review of the papers. We sincerely hope that BDAI 2020 will be a forum for excellent discussions that will put forward new ideas and promote collaborative research. 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. Finally, we would like to thank the organization and committee for all their hard work and support and hope to meet you all in person at our next conference. 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».