This book places the topic of the state and society in the context of modern development in China over the past century, investigating the dynamic relation and internal tension between the state’s power enhancement and society’s vitality activation instead of simply regarding the country and society as two separate entities. Building a modern country and activating the people’s vitality involves three closely linked and mutually supporting aspects: establishing the identity recognition of the people to unite the nation; adjusting the organizational system of the society to promote mobilization and institute a social incentive system; and determining dominant strategies and means for the interaction between the country and society to address social-governance issues. This book carefully sheds light on the logic behind China’s roundabout strategy for building a modern country and motivating the vitality of its people.
In terms of China’s current situation, the prevention and control of land degradation and the development of innovative sustainable land management activities lie within the purview of public works. Further, public-private partnerships (PPPs) hold considerable potential for application in this field. Inner Mongolia is one of the Chinese provinces hardest hit by land degradation. Fortunately, after years of dedicated efforts, meaningful achievements have been made: the increasing participation of the people as a whole, combined with growing investments in land degradation prevention and ecological construction on the part of private enterprises, has to some extent compensated for the lack of government involvement. Further, Inner Mongolia has been a pioneer in the use of PPPs for the prevention and control of land degradation, which has yielded numerous ecological, social and economic benefits. To better promote the development of ecological construction and expand the participation in land degradation control, this book systematically studies the use of PPPs in the Inner Mongolia autonomous region, drawing on field investigations and case analyses to do so. Its main goal is to explore a public-private partnership model that can effectively expand the scale of investment in land degradation prevention and sustainable land management.
This book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner. The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates.
This book constitutes the refereed proceedings of the 22nd Conference on Artificial Intelligence, Canadian AI 2009, held in Kelowna, Canada, in May 2009. The 15 revised full papers presented together with 19 revised short papers, 8 papers from the graduate student symposium and the abstracts of 3 keynote presentations were carefully reviewed and selected from 63 submissions. The papers present original high-quality research in all areas of Artificial Intelligence and apply historical AI techniques to modern problem domains as well as recent techniques to historical problem settings.
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