Quan Z. Sheng

This book looks at conversational search in intelligent dialogue systems, as it investigates and addresses the challenges pertinent to effective context incorporation in conversational question answering (ConvQA). The authors explore the possibility of designing a scalable Conversational Question Answering Agent that can handle the challenges of incomplete/ambiguous questions, better able to relate to co-references to cope with the problems of effective weights and optimal threshold selection in vehicular networks. A fundamental emphasis is the understanding of ambiguous follow-up questions and the generation of contextual and question entities to fill in the missing information gaps. Key topics are studied, such as ‘hard history selection’ to filter out the context that is not relevant and performing a re-ranking of the selected turns based on their significance to answer the question as a part of the soft history selection process. This book aims to demonstrate that the history selection and modelling approaches proposed can effectively improve the performance of ConvQA models in different settings. The proposed models are compared with the state-of-the-art vis-à-vis different conversational datasets and provide new insights into conversational information retrieval. Through a systematic study of structured representations, entity-aware history selection, and open-domain passage retrieval using contrastive learning, this book presents a robust framework for advancing multi-turn QA systems. It is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.

This book examines the uses and potential risks of location-based services (LBS) in the context of big data, with a focus on location privacy protection methods. The growth of the mobile Internet and the popularity of smart devices have spurred the development of LBS and related mobile applications. However, the misuse of sensitive location data could compromise the physical and communication security of associated devices and nodes, potentially leading to privacy breaches. This book explores the potential risks to the location privacy of mobile users in the context of big data applications. It discusses the latest methods and implications of location privacy from different perspectives. The author offers case studies of three applications: statistical disclosure and privacy protection of location-based big data using a centralized differential privacy model; a user location perturbation mechanism based on a localized differential privacy model; and terminal location perturbation using a geo-indistinguishability model. Linking recent developments in three-dimensional positioning and artificial intelligence, the book also predicts future trends and provides insights into research issues in location privacy. This title will be a valuable resource for researchers, students, and professionals interested in location-based services, privacy computing and protection, wireless network security, and big data security.

This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain. IoV emerges as a powerful convergence, seamlessly amalgamating the Internet of Things (IoT) and the intelligent transportation systems (ITS). This is crucial not only for safety-critical applications but is also an indispensable resource for non-safety applications and efficient traffic flows. While this paradigm holds numerous advantages, the existence of malicious entities and the potential spread of harmful information within the network not only impairs its performance but also presents a danger to both passengers and pedestrians. Exploring the complexities arising from dynamicity and malicious actors, this book focuses primarily on modern trust management models designed to pinpoint and eradicate threats. This includes tackling the challenges regarding the quantification of trust attributes, corresponding weights of these attributes, and misbehavior detection threshold definition within the dynamic and distributed IoV environment. This will serve as an essential guide for industry professionals and researchers working in the areas of automotive systems and transportation networks. Additionally, it will also be useful as a supplementary text for students enrolled in courses covering cybersecurity, communication networks, and human factors in transportation. Sarah Ali Siddiqui is a CSIRO Early Research Career (CERC) Fellow in the Cyber Security Automation and Orchestration Team, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia. Adnan Mahmood is a Lecturer in Computing – IoT and Networking at the School of Computing, Macquarie University, Sydney, Australia. Quan Z. (Michael) Sheng is a Distinguished Professor and Head of the School of Computing, at Macquarie University, Sydney, Australia. Hajime Suzuki is a Principal Research Scientist at the Cybersecurity & Quantum Systems Group, Software and Computational Systems Research Program, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia. Wei Ni is a Principal Scientist at the Commonwealth Scientific and Industrial Research Organisation, a Technical Expert at Standards Australia, a Conjoint Pro-fessor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University, Sydney, Australia.

This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain. IoV emerges as a powerful convergence, seamlessly amalgamating the Internet of Things (IoT) and the intelligent transportation systems (ITS). This is crucial not only for safety-critical applications but is also an indispensable resource for non-safety applications and efficient traffic flows. While this paradigm holds numerous advantages, the existence of malicious entities and the potential spread of harmful information within the network not only impairs its performance but also presents a danger to both passengers and pedestrians. Exploring the complexities arising from dynamicity and malicious actors, this book focuses primarily on modern trust management models designed to pinpoint and eradicate threats. This includes tackling the challenges regarding the quantification of trust attributes, corresponding weights of these attributes, and misbehavior detection threshold definition within the dynamic and distributed IoV environment. This will serve as an essential guide for industry professionals and researchers working in the areas of automotive systems and transportation networks. Additionally, it will also be useful as a supplementary text for students enrolled in courses covering cybersecurity, communication networks, and human factors in transportation. Sarah Ali Siddiqui is a CSIRO Early Research Career (CERC) Fellow in the Cyber Security Automation and Orchestration Team, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia. Adnan Mahmood is a Lecturer in Computing – IoT and Networking at the School of Computing, Macquarie University, Sydney, Australia. Quan Z. (Michael) Sheng is a Distinguished Professor and Head of the School of Computing, at Macquarie University, Sydney, Australia. Hajime Suzuki is a Principal Research Scientist at the Cybersecurity & Quantum Systems Group, Software and Computational Systems Research Program, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia. Wei Ni is a Principal Scientist at the Commonwealth Scientific and Industrial Research Organisation, a Technical Expert at Standards Australia, a Conjoint Pro-fessor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University, Sydney, Australia.

This book, at first, explores the evolution of the IoT to SIoT and offers a comprehensive understanding of SIoT and trust management vis-à-vis SIoT. It subsequently envisages trust quantification models by employing key SIoT-specific trust features, including SIoT relationships (e.g., friendships, working relationships, and community-of-interest), direct observations, and indirect observations, to augment the idea of trust quantification of a SIoT object. Furthermore, diverse trust aggregation techniques, i.e., conventional weighted sum, machine learning, and artificial neural networks, are proposed so as to address the challenges of the trust aggregation. Finally, the book outlines the future research directions for emphasizing the importance of trustworthiness management in the evolving notion of the SIoT.

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection. This book:Presents a security framework model design named Behavioral Network Traffic Identification and Novelty Anomaly Detection for the IoT InfrastructuresHighlights recent advancements in machine learning, deep learning, and networking standards to boost Internet of Things securityBuilds a near real-time solution for identifying Internet of Things devices connecting to a network using their network traffic traces and providing them with sufficient access privilegesDevelops a robust framework for detecting IoT anomalous network trafficCovers an anti-malware solution for detecting malware targeting embedded devicesIt will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual’s historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries’ structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system. To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use a clustering technique toseparate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraints in the optimization task and achieves fast and accurate performance. For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated.

In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual’s historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries’ structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system. To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use a clustering technique toseparate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraints in the optimization task and achieves fast and accurate performance. For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated.

This book describes the design and implementation of Cloud Armor, a novel approach for credibility-based trust management and automatic discovery of cloud services in distributed and highly dynamic environments. This book also helps cloud users to understand the difficulties of establishing trust in cloud computing and the best criteria for selecting a service cloud. The techniques have been validated by a prototype system implementation and experimental studies using a collection of real world trust feedbacks on cloud services. The authors present the design and implementation of a novel protocol that preserves the consumers’ privacy, an adaptive and robust credibility model, a scalable availability model that relies on a decentralized architecture, and a cloud service crawler engine for automatic cloud services discovery. This book also analyzes results from a performance study on a number of open research issues for trust management in cloud environments including distribution of providers, geographic location and languages. These open research issues illustrate both an overview of the current state of cloud computing and potential future directions for the field. Trust Management in Cloud Services contains both theoretical and applied computing research, making it an ideal reference or secondary text book to both academic and industry professionals interested in cloud services. Advanced-level students in computer science and electrical engineering will also find the content valuable.

This book describes the design and implementation of Cloud Armor, a novel approach for credibility-based trust management and automatic discovery of cloud services in distributed and highly dynamic environments. This book also helps cloud users to understand the difficulties of establishing trust in cloud computing and the best criteria for selecting a service cloud. The techniques have been validated by a prototype system implementation and experimental studies using a collection of real world trust feedbacks on cloud services. The authors present the design and implementation of a novel protocol that preserves the consumers’ privacy, an adaptive and robust credibility model, a scalable availability model that relies on a decentralized architecture, and a cloud service crawler engine for automatic cloud services discovery. This book also analyzes results from a performance study on a number of open research issues for trust management in cloud environments including distribution of providers, geographic location and languages. These open research issues illustrate both an overview of the current state of cloud computing and potential future directions for the field. Trust Management in Cloud Services contains both theoretical and applied computing research, making it an ideal reference or secondary text book to both academic and industry professionals interested in cloud services. Advanced-level students in computer science and electrical engineering will also find the content valuable.