The First International Workshop on Computational Intelligence and Big Data for Scientific and Technological Resources and Services (STRS’2020)

There has been an explosive growth of a wide variety of scientific and technological resources in both research communities and industry sectors. The former includes experimental/simulation datasets, intellectual products such as publications and patents, etc. and the latter includes transaction records, business models, processes, and workflows, etc. Such resources have not been extensively explored and utilized by end users mainly because: i) they are widely distributed at various geographical locations such as laboratories, universities, companies, etc. and are typically isolated from each other, ii) there is a lack of suitable models, mechanisms, and protocols for their sharing, reuse, exchange, and distribution, and iii) their support for the real economy requires intelligent aggregation and information retrieval of big data from disparate sources, which are complex and challenging. The rapid advance in high-performance computing and the pervasive use of machine learning and data mining have made it now possible to process and analyze large volumes of high-dimensional, multimodal, heterogeneous, and structured/ unstructured data. As a result, the above situation is also being revolutionized by the power of computational intelligence and big data in the area of scientific and technological services. Particularly, many data-driven techniques and service models have been proposed or are under active development for cross-platform resource integration, demand prediction, product recommendation, coordinated search, intelligent matching, operation optimization, etc. to improve business performance and research productivity based on scientific and technological resources. The goal of this workshop is to provide a forum for an interactive dialog between researchers in scientific domains and technologists and practitioners in industry to discuss, promote, and demonstrate how recent developments of artificial intelligence and big data technologies can be used to integrate and explore various scientific and technological resources to improve business intelligence and accelerate scientific innovation. It is envisioned that the combination of big data with a large collection of computational intelligence algorithms will reach the level of true intelligence in the aggregation, sharing, and utilization of scientific and technological resources to serve the real economy. We welcome original research articles in all aspects of computational intelligence and big data towards the development of scientific and technological services and the support for complex ecosystems of distributed resources.

The topics of the workshop include but not limited to:
• AI-assisted resource aggregation and integration
• Intelligent discovery of scientific and technological resources
• Models and protocols for sharing and reuse of scientific and technological resources
• Big data analysis of distributed scientific and technological resources
• Complex giant systems of distributed resources
• Pattern recognition for scientific and technological resources
• Data-driven technology for scientific and technological resources
• Language inference technology for scientific and technological resources
• Collaborative analysis and mining of multi-core value chains
• Precise search
• Intelligent matching
• Intelligent transactions
• Intelligent service technology
• Evaluation optimization technology
• On-demand customization for regional scientific and technological resources
• Intelligent services of regional scientific and technological resources
• Intelligent learning and mining of regional service value chains
• Cloud platform support for scientific and technological resources

Workshop Co-Chairs
• Chase Wu, New Jersey Institute of Technology, USA,
• Celimuge Wu, The University of Electro-Communications, Japan
• Yanchao Yin, Kunming University of Science and Technology, China

Submissions Guidelines and PROCEEDINGS
Manuscripts should be prepared in 10-point font using the IEEE 8.5" x 11" two-column format. All papers should be in PDF format, and submitted electronically at Paper Submission Link. A full paper must not exceed the stated length (including all figures, tables and references). Submitted papers must present original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines may be rejected without review. Also submissions received after the due date, exceeding length limit, or not appropriately structured may also not be considered. Authors may contact the Workshop Chair for further information or clarification. All submissions are blind peer-reviewed by at least three reviewers. Accepted papers will appear in the Conference Proceedings, and be published by the IEEE Computer Society Conference Publishing Services and be submitted to IEEE Xplore for inclusion.
Submitted papers must include original work, and must not be under consideration for another conference or journal. Submission of regular papers must not exceed 6 pages and must follow the IEEE paper format. Please include up to 7 keywords, complete postal and e-mail address, and fax and phone numbers of the corresponding author. Authors of accepted papers are expected to present their work at the conference. Submitted papers that are deemed of good quality but that could not be accepted as regular papers will be accepted as short papers.

Important Dates:
• Submission deadline: July 31, 2020
• Notification of acceptance: August 15, 2020
• Camera-ready papers due: August 25, 2020
• Registration deadline: September 4, 2020

Submission system 

PC members

- Jun Guo, NWU
- Chase Wu, NJIT
- Keping Yu, Waseda University
- Xianfu Chen, VTT Technical Research Centre of Finland
- Martin Musicante, Federal University of Rio Grande do Norte
- Weizhi Liao, University of Electronic Science and Technology of China
- Yangjian Ji, Zhejiang University
- Yanchao Yin, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
- Aiqin Hou, Northwest University
- Xinjian Gu, Zhejiang University
- Mahdi Zargayouna, Univ Gustave Eiffel, France
- Fei Qiao, Tsinghua Univ.
- Dazhao Cheng, UNC Charlotte
- Xinzheng Niu, University of Electronic Science and Technology of China
- Genoveva Vargas Solar, CNRS-LIG-LAFMIA
- Liu Xin Dalian, University of Technology
- Daqing Yun, Harrisburg University
- Xiaoyan Wang, Ibaraki University
- Yi Gu, Middle Tennessee State University
- Jianwu Wang, Department of Information Systems, University of Maryland, Baltimore County
- Celimuge Wu, University of Electro- Communications,
- Yanchao Yin, unming University of Science and Technology, Kunming, Yunnan 650500, China
- Zhi Liu, Waseda University
- Hedi Karray, INP-ENIT
- Ali Akoglu, University of Arizona
- Chau Yuen, Singapore University of Technology and Design
- Soufiene Djahel, Manchester Metropolitan University
- Rui Yin, Zhejiang University City College
- Di Zhang, Zhengzhou University