Entity resolution is an essential tool in processing and analyzing data in order to draw precise conclusions from the information being presented. Further research in entity resolution is necessary to help promote information quality and improved data reporting in multidisciplinary fields requiring accurate data representation. Innovative Techniques and Applications of Entity Resolution draws upon interdisciplinary research on tools, techniques, and applications of entity resolution. This research work provides a detailed analysis of entity resolution applied to various types of data as well as appropriate techniques and applications and is appropriately designed for students, researchers, information professionals, and system developers.
The ideal reference book providing all the information needed to fully understand magnetic communications in a self-contained source, written by experts in the field. This book offers a comprehensive introduction to magnetic communication using easy-to-understand language to explain concepts throughout and introduces the theory step by step with examples. A careful balance of combined theoretical and practical perspective is given throughout the book with interdisciplinary and multidisciplinary considerations for in-depth and diverse understanding. This book covers the background, developments, fundaments, antennas, channels, performance, protocol related to magnetic communications as well as applications that are of current interest, such as IoT, MIMO and wireless power transfer. The figures of merit within magnetic communication system components are included, demonstrating how to both model and analyze them. This book will be of great benefit to graduate students, researchers, and electrical engineers working in the fields of wireless communications and the internet of things.
In both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as “dirty data.” Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing. Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers in the database and machine learning communities to industry practitioners. Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field.
This book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to “label” or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.
China's most important love comedy, Wang Shifu's Xixiangji, or The Story of the Western Wing, is a rollicking play that chronicles the adventures of the star-crossed lovers Oriole and Student Zhang. Since its appearance in the thirteenth century, it has enjoyed unparalleled popularity. The play has given rise to innumerable sequels, parodies, and rewritings; it has influenced countless later plays, short stories, and novels and has played a crucial role in the development of drama criticism. This translation of the full and complete text of the earliest extant version is available in paperback for the first time. The editors' introduction will inform students of Chinese cultural and literary traditions. This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1991. China's most important love comedy, Wang Shifu's Xixiangji, or The Story of the Western Wing, is a rollicking play that chronicles the adventures of the star-crossed lovers Oriole and Student Zhang. Since its appearance in the thirteenth cen
How has China's post-Deng leadership governed the country? How have the changing social and political environments shifted the bases of political legitimacy? What strategies has Jiang Zemin adopted to cope with new circumstances in order to strengthen his leadership? What are the challenges these new reform measures have generated for the leadership? And how have domestic concerns constrained the leadership's intention in China's foreign relations? These are some of the questions which this volume attempts to address.The authors agree that Jiang Zemin is not a man without any political initiative. He has struggled to establish his own style of leadership, and to strengthen the legitimacy of his leadership by setting forth new rules and institutions for political games and by finding new measures to cope with new challenges. This collection of articles shows the success Jiang and his colleagues have had in strengthening their leadership; how the different reform measures have strengthened Jiang's rule; and how the ongoing reform has created new challenges for his regime.
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