This volume brings to English-language readers the results of an important long-term project of historians from China and Japan addressing contentious issues in their shared modern histories. Originally published simultaneously in Chinese and Japanese in 2006, the thirteen essays in this collection focus renewed attention on a set of political and historiographical controversies that have steered and stymied Sino-Japanese relations from the mid-nineteenth century through World War II to the present. These in-depth contributions explore a range of themes, from prewar diplomatic relations and conflicts, to wartime collaboration and atrocity, to postwar commemorations and textbook debates—all while grappling with the core issue of how history has been researched, written, taught, and understood in both countries. In the context of a wider trend toward cross-national dialogues over historical issues, this volume can be read as both a progress report and a case study of the effort to overcome contentious problems of history in East Asia.
With the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.
In the extension of the Japanese empire in the 1930s and 1940s, technology, geo-strategy, and institutions were closely intertwined in empire building. The central argument of this study of the development of a communications network linking the far-flung parts of the Japanese imperium is that modern telecommunications not only served to connect these territories but, more important, made it possible for the Japanese to envision an integrated empire in Asia. Even as the imperial communications network served to foster integration and strengthened Japanese leadership and control, its creation and operation exacerbated long-standing tensions and created new conflicts within the government, the military, and society in general.
Collaboration among scholars has always been recognized as a fundamental feature of scientific discovery. The ever-increasing diversity among disciplines and complexity of research problems makes it even more compelling to collaborate in order to keep up with the fast pace of innovation and advance knowledge. Along with the rapidly developing Internet communication technologies and the increasing popularity of the social web, we have observed many important developments of scholarly collaboration on the academic social web. In this book, we review the rapid transformation of scholarly collaboration on various academic social web platforms and examine how these platforms have facilitated academics throughout their research lifecycle—from forming ideas, collecting data, and authoring articles to disseminating findings. We refer to the term "academic social web platforms" in this book as a category of Web 2.0 tools or online platforms (such as CiteULike, Mendeley, Academia.edu, and ResearchGate) that enable and facilitate scholarly information exchange and participation. We will also examine scholarly collaboration behaviors including sharing academic resources, exchanging opinions, following each other's research, keeping up with current research trends, and, most importantly, building up their professional networks. Inspired by the model developed Olson et al. [2000] on factors for successful scientific collaboration, our examination of the status of scholarly collaboration on the academic social web has four emphases: technology readiness, coupling work, building common ground, and collaboration readiness. Finally, we talk about the insights and challenges of all these online scholarly collaboration activities imposed on the research communities who are engaging in supporting online scholarly collaboration. This book aims to help researchers and practitioners understand the development of scholarly collaboration on the academic social web, and to build up an active community of scholars who are interested in this topic.
With the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.
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