Titlebook: Computer Supported Cooperative Work and Social Computing; 18th CCF Conference, Yuqing Sun,Tun Lu,Bowen Du Conference proceedings 2024 The E

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1865-0929 tive Mechanisms, Models, Approaches, Algorithms and Systems; Crowd Intelligence and Crowd Cooperative Computing; Cooperative Evolutionary Computation and Human-like Intelligent Collaboration;  Domain-Specific Collaborative Applications..978-981-99-9639-1978-981-99-9640-7Series ISSN 1865-0929 Series E-ISSN 1865-0937

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1865-0929 puting, ChineseCSCW 2023 held in Harbin, China, in August 2023.. The 54 full papers and 28 short papers presented in these proceedings were carefully reviewed and selected from 221 submissions. The papers are organized in the following topical sections: Social Media and Online Communities; Collabora

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https://doi.org/10.1007/978-981-99-9640-7artificial intelligence; data security; distributed systems; query languages; neural networks; network se

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How Do Scholars and Academics Differ?etween intersections is a significant challenge. Reinforcement learning is widely used in the field of intelligent traffic signal control because it is good at dealing with sequence decision problems. The current reinforcement learning based approach makes phase decisions through coordinated coopera

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Small Presses and Their Reader Communities, and skill enhancement opportunities of workers who are with lower skill levels. Consequently, as the number of completed tasks increases, skill disparities among workers have worsened. This can be primarily attributed to the platform’s heavy reliance on high-skilled workers, which makes it difficul

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Small Presses and Their Reader Communities,have focused on the location privacy protection of individual users and ignored the location privacy of group users, who will work together to complete an LBS task through collaborative computing. A small number of existing researchers have applied k-anonymity techniques to group location privacy pr

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https://doi.org/10.1007/978-3-031-28982-8the challenges of overload, task recommendation is presented to deal with the above issue. The existing MCS task recommendation methods only consider the workers themselves, without taking the potential relationships between workers and workers and tasks, which may fail to grasp the long-term behavi