Computer Science

This book highlights the design, use and structure of blockchain systems and decentralized ledger technologies (B/DLT) for use in the construction industry. Construction remains a fragmented change-resistant industry with chronic problems of underproductivity and a very low digitization factor compared to other fields. In parallel, the convergence, embedding and coordination of digital technologies in the physical world provides a unique opportunity for the construction industry to leap ahead and adopt fourth industrial revolution technologies. Within this context, B/DLT are an excellent fit for the digitization of the construction industry. B/DLT are effective in this as they organize and align digital and physical supply chains, produce stigmergic coordination out of decentralization, enable the governance of complex projects for multiple stakeholders, while enabling the creation of a new class of business models and legal instruments for construction.

Content distribution, i.e., distributing digital content from one node to another node or multiple nodes, is the most fundamental function of the Internet. Since Amazon’s launch of EC2 in 2006 and Apple’s release of the iPhone in 2007, Internet content distribution has shown a strong trend toward polarization. On the one hand, considerable investments have been made in creating heavyweight, integrated data centers (“heavy-cloud”) all over the world, in order to achieve economies of scale and high flexibility/efficiency of content distribution. On the other hand, end-user devices (“light-end”) have become increasingly lightweight, mobile and heterogeneous, creating new demands concerning traffic usage, energy consumption, bandwidth, latency, reliability, and/or the security of content distribution. Based on comprehensive real-world measurements at scale, we observe that existing content distribution techniques often perform poorly under the abovementioned new circumstances.

Motivated by the trend of “heavy-cloud vs. light-end,” this book is dedicated to uncovering the root causes of today’s mobile networking problems and designing innovative cloud-based solutions to practically address such problems. Our work has produced not only academic papers published in prestigious conference proceedings like SIGCOMM, NSDI, MobiCom and MobiSys, but also concrete effects on industrial systems such as Xiaomi Mobile, MIUI OS, Tencent App Store, Baidu PhoneGuard, and WiFi.com. A series of practical takeaways and easy-to-follow testimonials are provided to researchers and practitioners working in mobile networking and cloud computing. In addition, we have released as much code and data used in our research as possible to benefit the community.

Mobile crowdsensing is a new sensing paradigm that utilizes the intelligence of a crowd of individuals to collect data for mobile purposes by using their portable devices, such as smartphones and wearable devices. Commonly, individuals are incentivized to collect data to fulfill a crowdsensing task released by a data requester. This “sensing as a service” elaborates our knowledge of the physical world by opening up a new door of data collection and analysis. However, with the expansion of mobile crowdsensing, privacy issues urgently need to be solved.

In this book, we discuss the research background and current research process of privacy protection in mobile crowdsensing. In the first chapter, the background, system model, and threat model of mobile crowdsensing are introduced. The second chapter discusses the current techniques to protect user privacy in mobile crowdsensing. Chapter three introduces the privacy-preserving content-based task allocation scheme. Chapter fourfurther introduces the privacy-preserving location-based task scheme. Chapter five presents the scheme of privacy-preserving truth discovery with truth transparency. Chapter six proposes the scheme of privacy-preserving truth discovery with truth hiding. Chapter seven summarizes this monograph and proposes future research directions.

In summary, this book introduces the following techniques in mobile crowdsensing: 1) describe a randomizable matrix-based task-matching method to protect task privacy and enable secure content-based task allocation; 2) describe a multi-clouds randomizable matrix-based task-matching method to protect location privacy and enable secure arbitrary range queries; and 3) describe privacy-preserving truth discovery methods to support efficient and secure truth discovery. These techniques are vital to the rapid development of privacy-preserving in mobile crowdsensing.

The rise of Android malware poses a significant threat to users’ information security and privacy. Malicious software can inflict severe harm on users by employing various tactics, including deception, personal information theft, and device control. To address this issue, both academia and industry are continually engaged in research and development efforts focused on detecting and countering Android malware.

This book guides professionals and students from various backgrounds to use machine learning in their own fields with low-code platform KNIME and without coding. Many people from various industries need use machine learning to solve problems in their own domains. However, machine learning is often viewed as the domain of programmers, especially for those who are familiar with Python. It is too hard for people from different backgrounds to learn Python to use machine learning. KNIME, the low-code platform, comes to help. KNIME helps people use machine learning in an intuitive environment, enabling everyone to focus on what to do instead of how to do.

This book helps the readers gain an intuitive understanding of the basic concepts of machine learning through illustrations to practice machine learning in their respective fields. The author provides a practical guide on how to participate in Kaggle completions with KNIME to practice machine learning techniques.

This book presents challenges, bad/best practices, experiences, tools, gaps, and future directions in the field. This will guide future generations of experts moving forward on DevOps and testing automation for complex CPSs.

This volume contains the selected proceedings of the International Conference on Information Security, Privacy, and Digital Forensics (ICISPD 2023). The content highlights novel contributions and recent developments in areas such as cyber-attacks and defenses, computer forensics, cybersecurity database forensics, cyber threat intelligence, data analytics for security, anonymity, penetration testing, incident response, Internet of Things security, malware and botnets, social media security, humanitarian forensics, software and media piracy, crime analysis, and hardware security, among others. This volume will serve as a valuable resource for researchers in both industry and academia who are working in the fields of security, privacy, and digital forensics from both technological and social perspectives.

The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023.

The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis