Numerical Algorithmic Science and Engineering (NAS&E), or more compactly,NumericalAlgorithmics, is the theoretical and empirical study and the practical implementation and application of algorithms for solvingfinite-dimensionalproblems of anumericnature. The variables of such problems are eitherdiscrete-valued, orcontinuous over the reals, or, and as is often the case, acombinationof the two, and they may or may not have an underlying network/graph structure. This re-emerging discipline of numerical algorithmics within computer science is the counterpart of the now well-established discipline...

Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete...

This book investigates intelligent network resource management for IoV, with the objective of maximizing the communication and computing performance of vehicle users. Focusing on two representative use cases in IoV, i.e., safety message broadcast and autonomous driving, the authors propose link-layer protocol design and application-layer computing task scheduling to achieve the objective given the unique characteristics and requirements of IoV. In particular, this book illustrates the challenges of resource management for IoV due to network dynamics, such as time-varying traffic intensity and...

Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a...

A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic...

Objective Information Theory (OIT) is proposed to represent and compute the information in a large-scale complex information system with big data in this monograph. To formally analyze, design, develop, and evaluate the information, OIT interprets the information from essential nature, measures the information from mathematical properties, and models the information from concept, logic, and physic. As the exemplified applications, Air Traffic Control System (ATCS) and Smart Court SoSs (System of Systems) are introduced for practical OITs.This Open Access book can be used as a technical...

Although deep learning models have achieved great progress in vision, speech, language, planning, control, and many other areas, there still exists a large performance gap between deep learning models and the human cognitive system. Many researchers argue that one of the major reasons accounting for the performance gap is that deep learning models and the human cognitive system process visual information in very different ways.To mimic the performance gap, since 2014, there has been a trend to model various cognitive mechanisms from cognitive neuroscience, e.g., attention, memory, reasoning,...

To be able to merge the psyche with the neural system has been a long-sought goal. There is much scientific literature on results from research on this topic, but the goal of this “booklet” is to present the subject in a nutshell and to attract a wider audience to this highly complex topic. Scientists often need years to grasp the scope and implications of merging the psyche with the neural system. Does that really have to be the case? What does the simulated model look like? What are the underlying philosophies? Can it be understood without mathematical formalism?Uniting the psyche and...