Henry Horng-Shing Lu – författare

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

This book discusses the theory and practice of random number generators that are useful for computer simulation and computer security applications.  Random numbers are ubiquitous in computation. They are used in randomized algorithms to perform sampling or choose randomly initialized parameters or perform Markov Chain Monte Carlo (MCMC). They are also used in computer security applications for various purposes such as cryptographic nuances or in authenticators.  In practice, the random numbers used by any of these applications are from a pseudo-random sequence. These pseudo-random sequences are generated by RNGs (random number generators). This book discusses the theory underlying such RNGs, which are used by all programmers. However, few try to understand the theory behind them.  This topic is an active area of research, particularly when the generators are used for cryptographic applications. The authors introduce readers to RNGs, how they are judged for quality, the mathematical and statistical theory behind them, as well as provide details on how these can be implemented in any programming language. The book discusses non-linear transformations that use classical linear generators for cryptographic applications and how to optimize to make such generators more efficient. In addition, the book provides up-to-date research on RNGs including a modern class of efficient RNGs and shows how to search for new RNGs with good quality and how to parallelize these RNGs.

This book discusses the theory and practice of random number generators that are useful for computer simulation and computer security applications.  Random numbers are ubiquitous in computation. They are used in randomized algorithms to perform sampling or choose randomly initialized parameters or perform Markov Chain Monte Carlo (MCMC). They are also used in computer security applications for various purposes such as cryptographic nuances or in authenticators.  In practice, the random numbers used by any of these applications are from a pseudo-random sequence. These pseudo-random sequences are generated by RNGs (random number generators). This book discusses the theory underlying such RNGs, which are used by all programmers. However, few try to understand the theory behind them.  This topic is an active area of research, particularly when the generators are used for cryptographic applications. The authors introduce readers to RNGs, how they are judged for quality, the mathematical and statistical theory behind them, as well as provide details on how these can be implemented in any programming language. The book discusses non-linear transformations that use classical linear generators for cryptographic applications and how to optimize to make such generators more efficient. In addition, the book provides up-to-date research on RNGs including a modern class of efficient RNGs and shows how to search for new RNGs with good quality and how to parallelize these RNGs.

This handbook delves into the multifaceted domain of blockchain technology and its applications, offering insights across technological, economic, and practical dimensions. It is divided into several parts, reflecting analysis techniques specific to different fields of application, including digital finance, security and supply chains, among others.The book begins with a foundational exploration of blockchain technology, providing a comprehensive understanding of its core mechanisms. The analysis extends to cutting-edge topics such as Non-Fungible Tokens (NFTs) and visualization technologies, which reveal the dynamic interplay between digital assets and technological innovation.Cryptocurrency liquidity and market dynamics are also examined, employing principles like Metcalfe's Law and Log-Periodic Power Laws to unravel complex market behaviors. Empirical studies shed light on volatility forecasting and the impact of sudden market jumps, enriching the predictive frameworks for cryptocurrency trading.In the realm of blockchain analytics, advanced methodologies like process mining are applied to uncover patterns in blockchain applications, while network analytics provide tools for fraud detection and heuristic evaluation. Decision-making in blockchain transactions is explored through the lens of mempool dynamics, providing practical insights for optimizing blockchain operations.The narrative extends to business applications, highlighting strategies for blockchain-driven development and the legal and financial implications of Distributed Ledger Technology (DLT). Blockchain’s transformative potential in healthcare is showcased, alongside a survey of federated learning frameworks that leverage blockchain for secure and decentralized machine learning.Security remains a pivotal theme, with a focus on the role of blockchain in cybersecurity and compliance functions. The investigation addresses decentralized finance (DeFi) vulnerabilities through case studies on attacks, offering lessons to enhance system robustness. Finally, the text envisions a future where decentralized models democratize foundational technologies, and blockchain facilitates innovations in maritime supply chains, driving efficiency and transparency in global logistics.This compilation serves as a gateway to understanding the theoretical and practical dimensions of blockchain, emphasizing its evolving role in reshaping industries and economic systems.

This handbook delves into the multifaceted domain of blockchain technology and its applications, offering insights across technological, economic, and practical dimensions. It is divided into several parts, reflecting analysis techniques specific to different fields of application, including digital finance, security and supply chains, among others.The book begins with a foundational exploration of blockchain technology, providing a comprehensive understanding of its core mechanisms. The analysis extends to cutting-edge topics such as Non-Fungible Tokens (NFTs) and visualization technologies, which reveal the dynamic interplay between digital assets and technological innovation.Cryptocurrency liquidity and market dynamics are also examined, employing principles like Metcalfe's Law and Log-Periodic Power Laws to unravel complex market behaviors. Empirical studies shed light on volatility forecasting and the impact of sudden market jumps, enriching the predictive frameworks for cryptocurrency trading.In the realm of blockchain analytics, advanced methodologies like process mining are applied to uncover patterns in blockchain applications, while network analytics provide tools for fraud detection and heuristic evaluation. Decision-making in blockchain transactions is explored through the lens of mempool dynamics, providing practical insights for optimizing blockchain operations.The narrative extends to business applications, highlighting strategies for blockchain-driven development and the legal and financial implications of Distributed Ledger Technology (DLT). Blockchain’s transformative potential in healthcare is showcased, alongside a survey of federated learning frameworks that leverage blockchain for secure and decentralized machine learning.Security remains a pivotal theme, with a focus on the role of blockchain in cybersecurity and compliance functions. The investigation addresses decentralized finance (DeFi) vulnerabilities through case studies on attacks, offering lessons to enhance system robustness. Finally, the text envisions a future where decentralized models democratize foundational technologies, and blockchain facilitates innovations in maritime supply chains, driving efficiency and transparency in global logistics.This compilation serves as a gateway to understanding the theoretical and practical dimensions of blockchain, emphasizing its evolving role in reshaping industries and economic systems.

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.