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Call for Special Tracks

KSEM2018 conference provides a forum for engineers and scientists in academia, industry and government to present their latest research findings in major and emerging areas of Knowledge Science, Engineering and Management. All accepted papers of special tracks will be included in the proceeding.


Description and Topics of Interest:

This track is to provide a discussion of deep integration of network science and knowledge science, which refers to the network knowledge representation, learning and managements. The purpose here is to promote the development of knowledge science, engineering and managements via the newly developed techniques of network representation and learning, such as network embedding, community identification, structural prediction, to name but a few. This is to make researchers in both network and knowledge science sit together to give a significant and multidisciplinary discussion.

The issues on topics include, but not limited to:

  • Community detection and profiling
  • Network representation and learning
  • Knowledge graph embedding
  • Link prediction and dynamic network analysis
  • Recommendation systems in social networks
  • Knowledge discovering from social networks
  • Graph mining in social networks
  • Deep learning for social networks
  • Influence propagation in social networks
  • Topic model and summarization in social networks
  • Support vector machines and kernel methods in network and knowledge science

Track Chairs

  • Françoise Fogelman-Soulié, Tianjin University, francoise.soulie@outlook.com
  • Di Jin, Tianjin University, jindi@tju.edu.cn
  • Fuzhen Zhuang, Institute of Computing Technology, Chinese Academy of Sciences, zhuangfuzhen@ict.ac.cn


The purpose of this track is to provide a multi-discipline discussion of social knowledge analysis and management. We invite original submissions addressing all aspects of computational social science, social network analysis and management. The link between social data and information technology provides a fertile ground for researchers interested in the theoretical and empirical analysis of human behavior understanding, social data analysis and management. Data now collected about behavior-related information provide us an unprecedented opportunity to address both new and longstanding questions in computer and social science. This is a challenging problem where many issues are still open.

Issues on topics including, but not limited to:

  • Behavioral knowledge management
  • Social textual analysis and knowledge mapping
  • Heterogeneous knowledge analysis and mining
  • Dynamic knowledge analysis and management
  • Social opinion evolution and knowledge formation
  • Social knowledge-based competition
  • Social Network Mining
  • New models and algorithms for social knowledge analysis
  • Temporal evolution and dynamics of online social networks
  • Theoretical modeling for understanding emerged knowledge in social network

Track Chairs

  • Zhen Wang, nkzhenwang@163.com, Northwestern Polytechnical University
  • Chao Gao, cgao@swu.edu.cn, Southwest University

The purpose of this track is to provide an extensive and intensive focus on transfer learning and knowledge reusing.

Transfer learning (TL) is motivated by the fact that people can intelligently apply knowledge learned previously to solve new problems faster and better. In contrast to classical machine learning methods, TL exploit the knowledge discovered from one or more auxiliary source domains to facilitate predictive modeling consisting of different data patterns in the target domain.

We encourage submissions on the following (non-exhaustive) list of topics:

  • Transfer learning methods
  • Applications of transfer learning
  • Theory of knowledge reusability
  • Explicable of transfer learning

Track Chairs

  • Haiyang Jia, Jilin University, jiahy@jlu.edu.cn
  • Fuzhen Zhuang, Institute of Computing Technology, Chinese Academy of Sciences, zhuangfuzhen@ict.ac.cn

The purpose of this track is to provide a multi-discipline discussion of E-Finance, Financial Technologies (Fintech), and IT-Driven Financial Innovations. The link between finance and information technology provides a fertile ground for information systems researchers interested in the theoretical and empirical aspects of emerging and mature global financial markets, institutions and technology. The focus in IT is on technology tools, platforms, and ecosystems that make financial services or products more accessible, efficient, and affordable efficient, and affordable. a host of technologies that broadly influence the way financial payment, funding, lending, investing, trading, financial services, and currencies are conducted.

Issues on topics including, but not limited to:

  • Innovation and technology in global financial markets
  • Impact of financial innovation (products and services) on financial institutions and market structure
  • Fraud detection from financial big data
  • Social, algorithmic and high frequency trading)
  • Novel Fintech applications
  • Risk assessment in crowdfunding and p2p lending
  • Blockchain, bitcoin and alternative means of payments
  • Behavior and usage-based insurance
  • Governance, regulation and compliance of ICO and other financial innovations
  • Data and information asymmetries in financial markets
  • Artificial intelligence and its impact upon markets, firms and users e.g. Robo-advisors
  • The role of information technology and disruptive market events (e.g. Flash crashes)
  • Data management and data governance issues related to blockchain
  • Smart contracts-based business process management
  • Fraud detection from financial big data
  • Legal issues with smart contracts and blockchain platforms

Track Chairs

  • Jinyu Zhang, Nanjing University, zhjinyu@nju.edu.cn

Call of papers on all aspects of computing with constraints and satisfiability, including: theory, algorithms, environments, languages, models, systems, and applications such as decision making, resource allocation, scheduling, configuration, and planning.

Track Chairs

  • Jian Zhang, zj@ios.ac.cn, Institute of Software, Chinese Academy of Sciences
  • Yonggang Zhang, zhangyg@jlu.edu.cn, Jilin University
  • Shaowei Cai, caisw@ios.ac.cn, Institute of Software, Chinese Academy of Sciences

>> See Paper Submissions