Bo Liu – författare

Molecular Dynamic Simulation: Fundamentals and Applications explains the basic principles of MD simulation and explores its recent developments and roles in advanced modeling approaches.

The implementation of MD simulation and its application to various aspects of materials science and engineering including mechanical, thermal, mass transportation, and physical/chemical reaction problems are illustrated. Innovative modeling techniques that apply MD to explore the mechanics of typical nanomaterials and nanostructures and to characterize crystalline, amorphous, and liquid systems are also presented.

The rich research experience of the authors in MD simulation will ensure that the readers are provided with both an in-depth understanding of MD simulation and clear technical guidance.

How could a teenager who did not have a whirlpool grow up amidst the scorn and ridicule of his tribe members?

Entering the martial way through battle, how would one reach the peak of the martial way? My Dao, the Dao of Heaven! Zhan Tian! Battle! World Conqueror! Fight! Fight! Fight! Dazzling and colorful Five Elements true energy, dazzling martial skills, and a thousand years of extinct savage beasts … Everyone, please support this new book. Those who were paying attention to War Emperor could add a QQ group: 11468430 [put away]

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 issuesto 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.

A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways.  In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed.

We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantificationof the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications.

In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

This book provides state-of-the-art Face De-Identification techniques and privacy protection methods, while highlighting the challenges faced in safeguarding personal information. It presents three innovative image privacy protection approaches, including differential private k-anonymity, identity differential privacy guarantee and personalized and invertible Face De-Identification. In addition, the authors propose a novel architecture for reversible Face Video De-Identification, which utilizes deep motion flow to ensure seamless privacy protection across video frames.

This book is a compelling exploration of the rapidly evolving field of Face De-Identification and privacy protection in the age of advanced AI-based face recognition technology and pervasive surveillance. This insightful book embarks readers on a journey through the intricate landscape of facial recognition, artificial intelligence, social network and the challenges posed by the digital footprint left behind by individuals in their daily lives. The authors also explore emerging trends in privacy protection and discuss future research directions.

Researchers working in computer science, artificial intelligence, machine learning, data privacy and cybersecurity as well as advanced-level students majoring in computers science will find this book useful as reference or secondary text. Professionals working in the fields of  biometrics, data security, software development and facial recognition technology as well as  policymakers and government officials will also want to purchase this book. 

Computational intelligence techniques are becoming more and more important for automated problem solving nowadays. Due to the growing complexity of industrial applications and the increasingly tight time-to-market requirements, the time available for thorough problem analysis and development of tailored solution methods is decreasing. There is no doubt that this trend will continue in the foreseeable future. Hence, it is not surprising that robust and general automated problem solving methods with satisfactory performance are needed.

This conference proceedings is a collection of the accepted papers of ISCC2023 - the 7th International Conference on Information Science, Communication and Computing held in Chongqing, China, 2-5 June 2023. The topics focus on intelligent information science and technology, artificial intelligence and intelligent systems, cloud computing and big data, smart computing and communication technology, wireless network, and cyber security. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows, and undergraduate and graduate students who need to build a knowledge base of the latest advances and state of the practice in the topics covered by this conference proceedings. This will enable them to build, maintain and manage systems of high reliability and complexity. We would like to thank the authors for their hard work and dedication, and the reviewers for ensuring that only the highest quality papers were selected.