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Beschreibung
The two-volume set CCIS 1491 and 1492 constitutes the refereed post-conferenceproceedings of the 16th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2021, held in Xiangtan, China, November 26–28, 2021. The conference was held in a hybrid mode i.e. online and on-site in Xiangtan due to the COVID-19 crisis.
The 65 revised full papers and 22 revised short papers were carefully reviewed and selected from 242 submissions. The papers are organized in the following topical sections:
Volume I:
Collaborative Mechanisms, Models, Approaches, Algorithms and Systems; Cooperative Evolutionary Computation and Human-like Intelligent Collaboration; Domain-Specific Collaborative Applications;
Volume II:
Crowd Intelligence and Crowd Cooperative Computing; Social Media and Online Communities.
The 65 revised full papers and 22 revised short papers were carefully reviewed and selected from 242 submissions. The papers are organized in the following topical sections:
Volume I:
Collaborative Mechanisms, Models, Approaches, Algorithms and Systems; Cooperative Evolutionary Computation and Human-like Intelligent Collaboration; Domain-Specific Collaborative Applications;
Volume II:
Crowd Intelligence and Crowd Cooperative Computing; Social Media and Online Communities.
16th CCF Conference, ChineseCSCW 2021, Xiangtan, China, November 26–28, 2021, Revised Selected Papers, Part II
Details
| Verlag | Springer Singapore |
| Ersterscheinung | 23. Juli 2022 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 826 Gramm |
| Format | Softcover |
| ISBN-13 | 9789811945489 |
| Auflage | 1st ed. 2022 |
| Seiten | 528 |
Schlagwörter
Angewandte Informatik, Computer and Information Systems Applications, Computer Application in Social and Behavioral Sciences, Computer Engineering and Networks, Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften, Computerhardware, Computers and Society, Database Management, Datenbanken, Digital- und Informationstechnologien: soziale und ethische Aspekte, Machine Learning, Maschinelles Lernen
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