Recent technology trends involving the combination of mobile networks and cloud computing have offered new chances for mobile network providers to use specific carrier-cloud services. These advancements will enhance the utilization of the mobile cloud in industry and corporate settings. Mobile Networks and Cloud Computing Convergence for Progressive Services and Applications is a fundamental source for the advancement of knowledge, application, and practice in the interdisciplinary areas of mobile network and cloud computing. By addressing innovative concepts and critical issues, this book is essential for researchers, practitioners, and students interested in the emerging field of vehicular wireless networks.

This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book.

This book constitutes the refereed proceedings of the 7th International Conference on Cloud Computing, Security, Privacy in New Computing Environments, CloudComp 2016, and the First EAI International Conference SPNCE 2016, both held in Guangzhou, China, in November and December 2016.The proceedings contain 10 full papers selected from 27 submissions and presented at CloudComp 2016 and 12 full papers selected from 69 submissions and presented at SPNCE 2016. CloudComp 2016 presents recent advances and experiences in clouds, cloud computing and related ecosystems and business support. SPNCE 2016 focuses on security and privacy aspects of new computing environments including mobile computing, big data, cloud computing and other large-scale environments.

This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book.

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

With the development of Internet and rapid growths of the network bandwidth, the quality of network transmission has a significant improvement. However, in the pluralistic network applications, the dramatic network bandwidth may be occupied by some of computers or users. It affects the communication QoS of general network users or applications. How to deal with the unfair network bandwidth usage problem becomes a critical issue. The network bandwidth management can be realized by packet buffer management or output packet scheduling. In this book, we formulate the packet admission control of buffer management as the MDP optimization problem. All of the received packets are classified into three types. According to the QoS of packet class and traffic condition, the allowing policy derived by the MDP decides whether accept or discard the received packet for getting an optimal packet buffer management. With computer simulation, we demonstrate that the MDP-based buffer management can confine the buffer occupancy of bad-user class packet, and decrease the discarding rate of real-time and good- user class packet significantly.