Starting from ICCSE 2024, ICCSE will adopt a new model of calling for research papers throughout the year. Each year, ICCSE will have four rounds of research paper submissions, with each round involving four stages of reviewing to allow for revisions. Notification dates are approximate and the times are based on “Anywhere on Earth” (AoE) time.
First Round
- Submission due: March 1st
- Notification for authors (Accept/Revise/Reject): April 1st
- Revision due: April 15th
- Notification to authors (Accept/Reject): May 1st
- Camera-ready copy due: June 1st
Second Round
- Submission due: June 1st
- Notification for authors (Accept/Revise/Reject): July 1st
- Revision due: July 15th
- Notification to authors (Accept/Reject): August 1st
- Camera-ready copy due: September 1st
Third Round
- Submission due: September 1st
- Notification for authors (Accept/Revise/Reject): October 1st
- Revision due: October 15th
- Notification to authors (Accept/Reject): November 1st
- Camera-ready copy due: December 1st
Fourth Round
- Submission due: December 1st
- Notification for authors (Accept/Revise/Reject): January 1st
- Revision due: January 15th
- Notification to authors (Accept/Reject): February 1st
- Camera-ready copy due: March 1st
ICCSE’24 solicits original work submitted as a regular paper of up to 8 pages (inclusive of references) in ACM format (URL: https://www.acm.org/publications/proceedings-template). Accepted papers will be planned to apply for the ICCSE’24 Proceedings in ACM Digital Library. The authors of a selected number of top quality papers will be invited to extend their papers for further review and possible publication in International Journal of Crowd Science (IJCS, , indexed and abstracted by EI Compendex, Scopus, DOAJ, among others.) or special issues of other reputable journals. Submissions can be made via the EasyChair platform, and inquiries can be directed to iccse@crowdscience.org.
Relevant fields and application areas for ICCSE’24 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 |