Liang Zhao

Li Lianjun

A comprehensive treatment of Chinese maritime law and judicial practice, this book covers both substantive law and procedure law of maritime law in mainland China. This is a professional book for both academics and practitioners in the field of maritime law. Including analysis of and comment on judicial practice from the Supreme People’s Court, Higher People’s Courts and ten maritime courts, as well as a whitepaper of Chinese maritime adjudication for 30 years (1984-2014), this brings to an English-speaking audience for the first time some of the most technical aspects of maritime law. It is therefore an invaluable resource for all those interested in maritime law in China.

Maritime Law and Practice in China

Huajun Chen

Cungeng Cao

Junaid Arshad

Taufiq Asyhari

Yonghao Wang

The five-volume set LNCS 14884, 14885, 14886, 14887 & 14888 constitutes the refereed deadline proceedings of the 17th International Conference on Knowledge Science, Engineering and Management, KSEM 2024, held in Birmingham, UK, during August 16–18, 2024. The 160 full papers presented in these proceedings were carefully reviewed and selected from 495 submissions. The papers are organized in the following topical sections: Volume I: Knowledge Science with Learning and AI (KSLA) Volume II: Knowledge Engineering Research and Applications (KERA) Volume III: Knowledge Management with Optimization and Security (KMOS) Volume IV: Emerging Technology Volume V: Special Tracks

17th International Conference, KSEM 2024, Birmingham, UK, August 16–18, 2024, Proceedings, Part III

Knowledge Science, Engineering and Management

Sherif Sakr

Athman Bouguettaya

Anna Liu

In practice, the design and architecture of a cloud varies among cloud providers. We present a generic evaluation framework for the performance, availability and reliability characteristics of various cloud platforms. We describe a generic benchmark architecture for cloud databases, specifically NoSQL database as a service. It measures the performance of replication delay and monetary cost. Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. The specifications of existing service level agreements (SLA) for cloud services are not designed to flexibly handle even relatively straightforward performance and technical requirements of consumer applications. We present a novel approach for SLA-based management of cloud-hosted databases from the consumer perspective and an end-to-end framework for consumer-centric SLA management of cloud-hosted databases. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted software applications.

Cloud Data Management

Lipo Wang

Yong Liu

Zhengtao Yu

This book discusses the recent advances in natural computation, fuzzy systems and knowledge discovery. Presenting selected, peer-reviewed papers from the 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019), held in Kunming, China, from 20 to 22 July 2019, it is a useful resource for researchers, including professors and graduate students, as well as R&D staff in industry.

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

Yu Song

Daojian Cheng

The first book offering a global overview of fundamental microfluidics and the wide range of possible applications, for example, in chemistry, biology, and biomedical science. As such, it summarizes recent progress in microfluidics, including its origin and development, the theoretical fundamentals, and fabrication techniques for microfluidic devices. The book also comprehensively covers the fluid mechanics, physics and chemistry as well as applications in such different fields as detection and synthesis of inorganic and organic materials. A useful reference for non-specialists and a basic guideline for research scientists and technicians already active in this field or intending to work in microfluidics.

Fundamentals, Devices, and Applications

Microfluidics

Jian Pei

Peng Cui

Lingfei Wu

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

Graph Neural Networks: Foundations, Frontiers, and Applications

Israel Tojal Da Silva

Joel Correa Da Rosa

Rodrigo Drummond

Jianjiong Gao

Advances in AI‐Based Tools for Personalized Cancer Diagnosis, Prognosis and Treatment

Volume 2

Liang Zhao

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