The main aim of the book is to give a review of all relevant information regarding a well-known and important problem of Feedback Arc Set (FAS). This review naturally also includes a history of the problem, as well as specific algorithms. To this point such a work does not exist: There are sources where one can find incomplete and perhaps untrustworthy information. With this book, information about FAS can be found easily in one place: formulation, description, theoretical background, applications, algorithms etc. Such a compendium will be of help to people involved in research, but also to...
The main aim of the book is to give a review of all relevant information regarding a well-known and important problem of Feedback Arc Set (FAS). This ...
This open access book provides information how to choose and collect the appropriate metrics for a software project in an organization. There are several kinds of metrics, based on the analysis of source code and developed for different programming paradigms such as structured programming and object-oriented programming (OOP). This way, the book follows three main objectives: (i) to identify existing and easily-collectible measures, if possible in the early phases of software development, for predicting and modeling both the traditional attributes of software systems and attributes...
This open access book provides information how to choose and collect the appropriate metrics for a software project in an organization. There are seve...
This SpringerBrief presents a brief introduction to probabilistic risk assessment (PRA), followed by a discussion of abnormal event detection techniques in industrial control systems (ICS). It also provides an introduction to the use of game theory for the development of cyber-attack response models and a discussion on the experimental testbeds used for ICS cyber security research. The probabilistic risk assessment framework used by the nuclear industry provides a valid framework to understand the impacts of cyber-attacks in the physical world. An introduction to the PRA techniques such as...
This SpringerBrief presents a brief introduction to probabilistic risk assessment (PRA), followed by a discussion of abnormal event detection techniqu...
Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question.This is...
Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encounter...
Wineinformatics is a new data science application with a focus on understanding wine through artificial intelligence. Thousands of new wine reviews are produced monthly, which benefits the understanding of wine through wine experts for winemakers and consumers. This book systematically investigates how to process human language format reviews and mine useful knowledge from a large volume of processed data.This book presents a human language processing tool named Computational Wine Wheel to process professional wine reviews and three novel Wineinformatics studies to analyze wine quality, price...
Wineinformatics is a new data science application with a focus on understanding wine through artificial intelligence. Thousands of new wine reviews ar...
This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book.In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality...
This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical appl...
An Introduction to the Machine Learning Empowered Intelligent Data Center NetworkingFundamentals of Machine Learning in Data Center Networks.This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks.Comprehensive Survey of AI-assisted...
An Introduction to the Machine Learning Empowered Intelligent Data Center NetworkingFundamentals of Machine Learning in Data Center Networks.This book...
