This book covers and makes four major contributions: 1) analyzing and surveying the pros and cons of current approaches for identifying rumor sources on complex networks; 2) proposing a novel approach to identify rumor sources in time-varying networks; 3) developing a fast approach to identify multiple rumor sources; 4) proposing a community-based method to overcome the scalability issue in this research area. These contributions enable rumor source identification to be applied effectively in real-world networks, and eventually diminish rumor damages, which the authors rigorously illustrate in this book. In the modern world, the ubiquity of networks has made us vulnerable to various risks. For instance, viruses propagate throughout the Internet and infect millions of computers. Misinformation spreads incredibly fast in online social networks, such as Facebook and Twitter. Infectious diseases, such as SARS, H1N1 or Ebola, have spread geographically and killed hundreds of thousands people. In essence, all of these situations can be modeled as a rumor spreading through a network, where the goal is to find the source of the rumor so as to control and prevent network risks. So far, extensive work has been done to develop new approaches to effectively identify rumor sources. However, current approaches still suffer from critical weaknesses. The most serious one is the complex spatiotemporal diffusion process of rumors in time-varying networks, which is the bottleneck of current approaches. The second problem lies in the expensively computational complexity of identifying multiple rumor sources. The third important issue is the huge scale of the underlying networks, which makes it difficult to develop efficient strategies to quickly and accurately identify rumor sources. These weaknesses prevent rumor source identification from being applied in a broader range of real-world applications. This book aims to analyze and address these issues to make rumor source identification more effective and applicable in the real world. The authors propose a novel reverse dissemination strategy to narrow down the scale of suspicious sources, which dramatically promotes the efficiency of their method. The authors then develop a Maximum-likelihood estimator, which can pin point the true source from the suspects with high accuracy. For the scalability issue in rumor source identification, the authors explore sensor techniques and develop a community structure based method. Then the authors take the advantage of the linear correlation between rumor spreading time and infection distance, and develop a fast method to locate the rumor diffusion source. Theoretical analysis proves the efficiency of the proposed method, and the experiment results verify the significant advantages of the proposed method in large-scale networks. This book targets graduate and post-graduate students studying computer science and networking. Researchers and professionals working in network security, propagation models and other related topics, will also be interested in this book.

Following underwater acoustic channel modeling, this book investigates the relationship between coherence time and transmission distances. It considers the power allocation issues of two typical transmission scenarios, namely short-range transmission and medium-long range transmission. For the former scenario, an adaptive system is developed based on instantaneous channel state information. The primary focus is on cooperative dual-hop orthogonal frequency division multiplexing (OFDM).This book includes the decomposed fountain codes designed to enable reliable communications with higher energy efficiency. It covers the Doppler Effect, which improves packet transmission reliability for effective low-complexity mirror-mapping-based intercarrier interference cancellation schemes capable of suppressing the intercarrier interference power level. Designed for professionals and researchers in the field of underwater acoustic communications, this book is also suitable for advanced-level students in electrical engineering or computer science.

Overcoming many challenges, data mining has already established discipline capability in many domains. ""Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches"" discusses advances in modern data mining research in today's rapidly growing global and technological environment. A critical mass of the most sought after knowledge, this publication serves as an important reference tool to leading research within information search and retrieval techniques.

Linear Systems: Non-Fragile Control and Filtering presents the latest research results and a systematic approach to designing non-fragile controllers and filters for linear systems. The authors combine the algebraic Riccati technique, the linear matrix inequality (LMI) technique, and the sensitivity analysis method to establish a set of new non-fragile (insensitive) control methods. This proposed method can optimize the closed-loop system performance and make the designed controllers or filters tolerant of coefficient variations in controller or filter gain matrices. A Systematic Approach to Designing Non-Fragile Controllers and Filters for Linear Systems The text begins with developments and main research methods in non-fragile control. It then systematically presents novel methods for non-fragile control and filtering of linear systems with respect to additive/multiplicative controller/filter gain uncertainties. The book introduces the algebraic Riccati equation technique to solve additive/multiplicative norm-bounded controller/filter gain uncertainty, and proposes a structured vertex separator to deal with the numerical problem resulting from interval-bounded coefficient variations. It also explains how to design insensitive controllers and filters in the framework of coefficient sensitivity theory. Throughout, the book includes numerical examples to demonstrate the effectiveness of the proposed design methods. More Effective Design Methods for Non-Fragile Controllers and Filters The design and analysis tools described will help readers to better understand and analyze parameter uncertainties and to design more effective non-fragile controllers and filters. Providing a coherent approach, this book is a valuable reference for researchers, graduate students, and anyone who wants to explore the area of non-fragile control and filtering.

This book constitutes the proceedings of the 11th International Conference on Internet and Distributed Computing Systems, IDCS 2018, held in Tokyo, Japan, in October 2018. The 21 full papers presented together with 5 poster and 2 short papers in this volume were carefully reviewed and selected from 40 submissions. This conference desired to look for inspiration in diverse areas (e.g., infrastructure and system design, software development, big data, control theory, artificial intelligence, IoT, self-adaptation, emerging models, paradigms, applications and technologies related to Internet-based distributed systems) to develop new ways to design and mange such complex and adaptive computation resources.

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 constitutes the refereed proceedings of the 11th International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TridentCom 2016, held in Hangzhou, China, in June 2016. The 16 papers were carefully selected from 25 submissions and provide a forum about technologies of big data, cyber physical systems and computer communications. The papers cover future Internet and software defined networks, network testbed design and implementation, testbed for network applications, and QoS/QoE in networks.

This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

This book constitutes the refereed proceedings of the 4th International Symposium on Cyberspace Safety and Security (CSS 2012), held in Melbourne, Australia, in December 2012. The 30 revised full papers presented together with 7 invited talks were carefully reviewed and selected from 105 submissions. The papers cover the following topics: mobile security, cyberspace attacks and defense, security application adn systems, network and cloud security, wireless security, security protocols and models.

This book constitutes the refereed proceedings of the International Conference on Data and Knowledge Engineering, ICDKE 2012, held in Wuyishan, Fujian, China, in November 2012. The conference was co-located with the 6th International Conference on Network and System Security, NSS 2012. The 13 revised full papers of ICDKE 2012 were carefully reviewed and selected from 53 submissions. The papers cover the following topics: artificial intelligence and data engineering; knowledge discovery and data management; information extraction and retrieval and data security.

This book constitutes the refereed proceedings of the 5th International Conference on Internet and Distributed Computing Systems, IDCS 2012, held in Wuyishan, Fujian, China, in November 2012. The conference was co-located with the 6th International Conference on Network and System Security, NSS 2012. The 22 revised full papers of IDCS 2012 presented were carefully reviewed and selected from 80 submissions. The papers cover the following topics: ad-hoc and sensor networks; internet and Web technologies; network operations and management; information infrastructure; resilience; fault tolerance and availability.

The two volume set LNCS 7439 and 7440 comprises the proceedings of the 12th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2012, as well as some workshop papers of the CDCN 2012 workshop which was held in conjunction with this conference. The 40 regular paper and 26 short papers included in these proceedings were carefully reviewed and selected from 156 submissions. The CDCN workshop attracted a total of 19 original submissions, 8 of which are included in part II of these proceedings. The papers cover many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental results, and commercial components and systems.

The two volume set LNCS 7439 and 7440 comprises the proceedings of the 12th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2012, as well as some workshop papers of the CDCN 2012 workshop which was held in conjunction with this conference. The 40 regular paper and 26 short papers included in these proceedings were carefully reviewed and selected from 156 submissions. The CDCN workshop attracted a total of 19 original submissions, 8 of which are included in part II of these proceedings. The papers cover many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental results, and commercial components and systems.

Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results gleaned from a decade's research.

  This book constitutes the refereed proceedings of the 16th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2003, held in Halifax, Canada in June2003. The 30 revised full papers and 24 revised short papers presented were carefully reviewed and selected from 106 submissions. The papers are organized in topical sections on knowledge representation, search, constraint satisfaction, machine learning and data mining, AI and Web applications, reasoning under uncertainty, agents and multi-agent systems, AI and bioinformatics, and AI and e-commerce.  

Software similarity and classification is an emerging topic with wide applications. It is applicable to the areas of malware detection, software theft detection, plagiarism detection, and software clone detection. Extracting program features, processing those features into suitable representations, and constructing distance metrics to define similarity and dissimilarity are the key methods to identify software variants, clones, derivatives, and classes of software. Software Similarity and Classification reviews the literature of those core concepts, in addition to relevant literature in each application and demonstrates that considering these applied problems as a similarity and classification problem enables techniques to be shared between areas. Additionally, the authors present in-depth case studies using the software similarity and classification techniques developed throughout the book.

This book constitutes revised and selected papers from the 6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020, held in Tianjin, China, in September 2020. The 38 full papers presented in this volume were carefully reviewed and selected from a total of 111 submissions. The papers are organized according to the topical sections on big data security; social networks; privacy-preserving and security.

This two -volume set, LNCS 10366 and 10367, constitutes the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.

This two -volume set, LNCS 10366 and 10367, constitutes the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.

This book constitutes the refereed proceedings of the 8th International Conference on Grid and Pervasive Computing, GPC 2016, held in Seoul, Korea, in May 2016. The 20 revised papers were carefully reviewed and selected from 94 submissions. The conference contains various aspects including green computing, cloud computing, virtualisation, data and storage, and network security.

Linear Systems: Non-Fragile Control and Filtering presents the latest research results and a systematic approach to designing non-fragile controllers and filters for linear systems. The authors combine the algebraic Riccati technique, the linear matrix inequality (LMI) technique, and the sensitivity analysis method to establish a set of new non-fragile (insensitive) control methods. This proposed method can optimize the closed-loop system performance and make the designed controllers or filters tolerant of coefficient variations in controller or filter gain matrices. A Systematic Approach to Designing Non-Fragile Controllers and Filters for Linear Systems The text begins with developments and main research methods in non-fragile control. It then systematically presents novel methods for non-fragile control and filtering of linear systems with respect to additive/multiplicative controller/filter gain uncertainties. The book introduces the algebraic Riccati equation technique to solve additive/multiplicative norm-bounded controller/filter gain uncertainty, and proposes a structured vertex separator to deal with the numerical problem resulting from interval-bounded coefficient variations. It also explains how to design insensitive controllers and filters in the framework of coefficient sensitivity theory. Throughout, the book includes numerical examples to demonstrate the effectiveness of the proposed design methods. More Effective Design Methods for Non-Fragile Controllers and Filters The design and analysis tools described will help readers to better understand and analyze parameter uncertainties and to design more effective non-fragile controllers and filters. Providing a coherent approach, this book is a valuable reference for researchers, graduate students, and anyone who wants to explore the area of non-fragile control and filtering.