会议信息

ICCSE'' 2019: International Conference on Crowd Science and Engineering

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截稿日期:
2019-06-21 已截稿
通知日期:
2019-07-26
会议日期:
2019-10-18
会议地点:
Jinan, China
届数:
4
浏览: 18327   关注: 0   参加: 1
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ICCSE''
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会伴指数 (CP-I)

40.7 / 100
全站第 4,937 名 / 共 5,759 个会议 · 前 86%
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
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会议传承 (20%)
43
社区关注 (10%)
8
资料公开度 (15%)
25

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置信度 45% —— 分数中有多大比例来自实际观测到的数据,而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-10-25

征稿

ICCSE'' 2019 (International Conference on Crowd Science and Engineering) is an academic conference held in Jinan, China on 2019-10-18. The paper submission deadline is 2019-06-21. Acceptance notifications are sent on 2019-07-26.

On behalf of the Organizing Committee, we are honoured to introduce you to the 4th International Conference on Crowd Science and Engineering (ICCSE 2019). Crowd Science refers to the science that underlies online crowd-powered ecosystems. It focuses on the design and analysis of information processing systems in which humans participate to contribute their intelligence, effort, time and/or resources. In 2016, the first International Conference on Crowd Science and Engineering (ICCSE) was held on the beautiful campus of The University of British Columbia, Vancouver, Canada. In 2017, the second ICCSE conference was held on the beautiful campus of Tsinghua University, Beijing, China. In 2018, the third ICCSE conference was held on the beautiful campus of Nanyang Technological University, Singapore. In 2019, the forth ICCSE conference will be hosted by Shandong University, in Jinan, China. In ICCSE’19, researchers, industry practitioners and policy makers will gather to explore the transformative potential of crowd science research and how to engineer efficient systems that combine the respective strengths of humans and machines to open up new possibilities. ICCSE’19 now invites you to participate and contribute to this international forum for the dissemination and exchange of up-to-date scientific information on theoretical and applied areas of crowd science and engineering. The related fields and application areas relevant to ICCSE’19 include, but are not limited to: Theory: • Models and methods for Crowd Science and Engineering • Trust and incentives • The cloud and the crowd • Crowdsourcing • Mental model • AI (learning, reasoning and knowledge) • Big Data Sensing: • Crowd sensing • Internet of Things • Intelligent sensing • Online sensing • Mobile sensing • Social sensing • Citizen sensing • Ubiquitous sensing • Smart city Context: • Mobile context • Social context • Opportunity context • Trajectory context • Unobtrusive context • Context-aware algorithm • Context-aware representation • Context-aware reasoning • Context-aware recommendation • Machine learning for context awareness Cognition: • Cognition based decision making • Online learning • Deep learning • Transfer learning • Temporal cognition • Perception • Attention • Memory • Affect, Behaviour and cognition • Analytics Interaction: • Crowd AI • Crowd behaviour • Brain-computer interface • Harnessing the crowd in human-computer interaction • Crowdsourcing human-robot interaction • Human computation • Crowd mobilization • Crowd visualization • Emotion and personality Practice: • Tools and platforms to support Crowd Science and Engineering • Crowd-sourced design and engineering • Massive Open Online Courses (MOOCs) • Citizen science • Digital sharing economy • Crowdfunding • Crowdsourcing well-being • Productive aging and e-healthcare • Gamification • Crowdsourcing in e-government • Industrial crowdsourcing The ICCSE’17 and ICCSE’18 Proceedings have been published in ACM Digital Library. Therefore, all papers included in ICCSE’17 and ICCSE’18 Proceedings are EI Indexed and CPCI-S Indexed. The ICCSE’19 organizing committee shall continue to put up the request to ACM for the inclusion of the ICCSE’19 Proceedings in ACM Digital Library. If such request is approved by ACM, all papers included in the ICCSE’19 Proceedings will also be EI Indexed and CPCI-S Indexed. Best Paper Award and Best Student Paper Award will be conferred at banquet
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