Goal of the workshop:
Compared with traditional datasets, big data brings about new opportunities for discovering new values, helps us to gain an in-depth understanding of the hidden values, and also incurs new challenges. This workshop is concerned about the challenges in effectively storage, processing, and analytics of the explosive data for developing cognitive robotics.
Workshop theme:
In the past decade, the computer and information industry has experienced rapid changes in both platform scale and scope of applications. Computers, smart phones, clouds, social networks, and supercomputers demand not only high performance but also a high degree of machine intelligence.
To face these new computing and communication changes, we must upgrade the clouds and the computing ecosystem with new capabilities, such as machine learning, IoT sensing, data analytics, and cognitive machines mimicking human intelligence. The main topics of CCR 2020 include big data, robotics, cloud computing, cognitive computing, cloud security, data analytics, mobile big data, cognitive learning, machine learning, etc.
Scope of the workshop:
Researchers and practitioners all over the world, from both academia and industry, working in the areas of big data and cognitive robot are invited to discuss state of the art theoretical research, technology development, and practical implications. Topics of interest include, but are not limited to, the following:
- Innovative architecture, infrastructure, techniques and testbeds for big data and cognitive robotics
- Cognitive computing, affective computing, machine learning and other novel tools, services, technologies, algorithms and methods for robotics
- Individual and social behavior analysis, contextual data management and mining for big data
- Multi-modal information fusion in cognitive robotics
- Intelligent and interactive interface for cognitive services and applications
- Machine intelligence assisted by big data and cognitive computing
- Privacy protected discovery and adaptation
- Standardization and implementation challenges
ERiC Laboratory 