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This book constitutes the refereed proceedings of the 7th National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, NCVPRIPG 2019, held in Hubballi, India, in December 2019. The 55 revised full papers 3 short papers presented in this volume were carefully reviewed and selected from 210 submissions. The papers are organized in topical sections on vision and geometry, learning and vision, image processing and document analysis, detection and recognition.
This comprehensive and interdisciplinary Handbook illustrates the patterns of class transformation in China since 1949, situating them in their historical context. Presenting detailed case studies of social stratification and class formation in a wide range of settings, the expert international contributors provide invaluable insights into multiple aspects of China’s economy, polity and society. The Handbook on Class and Social Stratification in China explores critical contemporary topics which are rarely put in perspective or schematized, therefore placing it at the forefront of progressive scholarship. These include; • state power as a determinant of life chances • women’s social mobility in relation to marriage • the high school entrance exam as a class sorter • class stratification in relation to health • China’s rural migrant workers and labour politics. Eminently readable, this systematic exploration of class and stratification will appeal to scholars and researchers with an interest in class formation, status attainment, social inequality, mobility, development, social policy and politics in China and Asia.
The three-volume set LNCS 12305, 12306, and 12307 constitutes the refereed proceedings of the Third Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020, held virtually in Nanjing, China, in October 2020. The 158 full papers presented were carefully reviewed and selected from 402 submissions. The papers have been organized in the following topical sections: Part I: Computer Vision and Application, Part II: Pattern Recognition and Application, Part III: Machine Learning.
A unified and systematic description of analysis and decision problems within a wide class of uncertain systems, described by traditional mathematical methods and by relational knowledge representations. Prof. Bubnicki takes a unique approach to stability and stabilization of uncertain systems.
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