This book constitutes the refereed proceedings of the 11th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2020, held in Hangzhou, China, in July 2020. The 24 full papers and 5 short papers presented were carefully reviewed and selected from 36 submissions. They are organized in topical sections on machine learning; multi-agent system; recommendation system; social computing; brain computer integration; pattern recognition; and computer vision and image understanding.

This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.

This book traces the intimate connections between Britain and China throughout the nineteenth century and argues for China's central impact on the British visual imagination. Chang brings together an unusual group of primary sources to investigate how nineteenth-century Britons looked at and represented Chinese people, places, and things, and how, in the process, ethnographic, geographic, and aesthetic representations of China shaped British writers' and artists' vision of their own lives and experiences. For many Britons, China was much more than a geographical location; it was also a way of seeing and being seen that could be either embraced as creative inspiration or rejected as contagious influence. In both cases, the idea of China's visual difference stood in negative contrast to Britain's evolving sense of the visual and literary real. To better grasp what Romantic and Victorian writers, artists, and architects were doing at home, we must also understand the foreign "objects" found in their midst and what they were looking at abroad.

This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.