Molecular Dynamic Simulation: Fundamentals and Applications&nbsp;explains the fundamentals of MD simulation and explores recent developments in advanced modeling approaches based on the MD method. The improvements in efficiency and accuracy delivered by this new research are explained to help readers apply them to a wide range of tasks. Details of the implementation of MD simulation are illustrated by presenting the applications of MD simulation in various aspects of materials study including mechanical, thermal, mass transportation, and absorption/desorption problems. Innovative methods of using MD to explore the mechanics of nano/micromaterials, and for the characterization of crystalline, amorphous and liquid materials are also presented. The rich research experience of the authors in molecular dynamic simulation will ensure that readers are provided with both an in-depth understanding of this method and clear technical guidance.

Molecular Dynamics Simulation: Fundamentals and Applications

Science & Math

Zhou, Kun

Liu, Bo

Physics

Elsevier

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 quantification of 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.

Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence

Computers & Technology

Liu, Wei

Zhou, Wanlei

Sreevallabh Chivukula, Aneesh

Yang, Xinghao

Computer Science

Springer

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 &nbsp;biometrics, data security, software development and facial recognition technology as well as &nbsp;policymakers and government officials will also want to purchase this book. &nbsp;

Face De-identification: Safeguarding Identities in the Digital Era

Song, Li

Cao, Jingyi

Wen, Yunqian

Xie, Rong

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.

Automated Design of Analog and High-frequency Circuits: A Computational Intelligence Approach (Studies in Computational Intelligence, 501)

Gielen, Georges

Fernández, Francisco V.

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.

Normalized Park Self Selection(Chinese Edition)

Culture and Arts Publishing House

Spring loser(Chinese Edition)

百花文艺出版社

Fun Robot (Robot principles of transmission line racing and production) middle school science series Jiangsu Province to expand school-based curriculum Collection(Chinese Edition)

Jiangsu Education Publishing House

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Books by Liu, Bo