Notice bibliographique
Résumé
The 2021 International Conference on Computers, Remote Sensing and Aerospace (CRSA 2021) was taken place on July 23-25, 2021 via online platform due to the pandemic of COVID-19. In light of the prevention and control of COVID-19 and travel restrictions worldwide, the organizer could not hold the conference physically, that’s why we have to adjust the format to online meeting to avoid personnel gatherings, so that the conference could go ahead as scheduled. This conference mainly focused on the latest research on "information science" and "artificial intelligence", and aims to gather experts, scholars, researchers and related practitioners in this field from all over the world to share research results, explore hot issues, and exchange new ideas, experience and technology. We warmly welcome experts and scholars in related fields to submit their new research or technical contributions to CRSA 2021, and share valuable experiences with scientists and scholars from all over the world! During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. During the conference, we were pleased to had Prof. Yuanzhu Chen, from School of Computing, Queen’s University, Canada as our Conference Chair. In the second part, we invited five professors as our keynote speakers. Prof. Yuanzhu Chen, our beloved Conference Chair, performed a speech: Fading Networks. Prof. Johan DEBAYLE, from Ecole Nationale Supérieure des Mines de Saint-Etienne, France. He presented an insightful speech: Morphological image processing for remote sensing applications. Our third keynote speaker, Prof. Devinder Yadav, from Aerospace / Faculty of Science and Engineering, University of Nottingham Ningbo China, China. His publications in scholarly international journals cover interdisciplinary areas, such as aviation regulations, flight safety, aircraft personnel licensing, airport engineering, higher education, airworthiness, aircraft maintenance, aviation management, and project management. Associate Professor. D. Jude Hemanth, from Karunya University/ Department of Electronics and Communication Engineering, India. He delivered a wonderful speech: Why Deep Learning techniques over conventional Machine Learning techniques? An introspection. Assoc. Prof. Anwar Ali, our finale keynote speaker, from Zhejiang Sci-Tech University, China. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge. The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Computational science, Remote Sensing, Aeroacoustics Science and more related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard. We would like to acknowledge all of those who supported CRSA 2021. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers. The Committee of CRSA 2021 Committee member are available in this pdf
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 | 0,012 |
| 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 | 0,004 | 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) | 0,555 | 0,407 |
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 ».