This book constitutes the refereed proceedings of the Third EAI International Conference on Smart Grid and Internet of Things, SGIoT 2019, held in TaiChung, Taiwan, in November 2019. The 10 papers presented were carefully reviewed and selected from 22 submissions and present results on how to achieve more efficient use of resources based largely on the IoT-based machine-to-machine (M2M) interactions of millions of smart meters and sensors in the smart grid specific communication networks such as home area networks, building area networks, and neighborhood area networks. The smart grid also encompasses IoT technologies, which monitor transmission lines, manage substations, integrate renewable energy generation. Through these technologies, the authorities can smartly identify outage problems, and intelligently schedule the power generation and delivery to the customers. Furthermore, the smart grid should teach us a valuable lesson that security must be designed in from the start of any IoT deployment.

This book constitutes the refereed post-conference proceedings of the 6th International Workshop on Accelerator Programming Using Directives, WACCPD 2019, held in Denver, CO, USA, in November 2019.The 7 full papers presented have been carefully reviewed and selected from 13 submissions. The papers share knowledge and experiences to program emerging complex parallel computing systems. They are organized in the following three sections: porting scientific applications to heterogeneous architectures using directives; directive-based programming for math libraries; and performance portability for heterogeneous architectures.

Un'eccellente introduzione agli algoritmi, alla loro struttura, a come modificano i dati, alla computabilita e alla complessita, il libro e scritto in una forma allo stesso tempo elegante e schietto che fa si che possa essere considerato sia un valido testo per un corso introduttivo di Informatica, sia un tesoro da custodire per i programmatori provetti e i progettisti di software.

Floating-point arithmetic is ubiquitous in modern computing, as it is the tool of choice to approximate real numbers. Due to its limited range and precision, its use can become quite involved and potentially lead to numerous failures. One way to greatly increase confidence in floating-point software is by computer-assisted verification of its correctness proofs. This book provides a comprehensive view of how to formally specify and verify tricky floating-point algorithms with the Coq proof assistant. It describes the Flocq formalization of floating-point arithmetic and some methods to automate theorem proofs. It then presents the specification and verification of various algorithms, from error-free transformations to a numerical scheme for a partial differential equation. The examples cover not only mathematical algorithms but also C programs as well as issues related to compilation.

This book is the first one that provides a solid bridge between algorithmic information theory and statistical mechanics. Algorithmic information theory (AIT) is a theory of program size and recently is also known as algorithmic randomness. AIT provides a framework for characterizing the notion of randomness for an individual object and for studying it closely and comprehensively. In this book, a statistical mechanical interpretation of AIT is introduced while explaining the basic notions and results of AIT to the reader who has an acquaintance with an elementary theory of computation. A simplification of the setting of AIT is the noiseless source coding in information theory. First, in the book, a statistical mechanical interpretation of the noiseless source coding scheme is introduced. It can be seen that the notions in statistical mechanics such as entropy, temperature, and thermal equilibrium are translated into the context of noiseless source coding in a natural manner. Then, the framework of AIT is introduced. On this basis, the introduction of a statistical mechanical interpretation of AIT is begun. Namely, the notion of thermodynamic quantities, such as free energy, energy, and entropy, is introduced into AIT. In the interpretation, the temperature is shown to be equal to the partial randomness of the values of all these thermodynamic quantities, where the notion of partial randomness is a stronger representation of the compression rate measured by means of program-size complexity. Additionally, it is demonstrated that this situation holds for the temperature itself as a thermodynamic quantity. That is, for each of all the thermodynamic quantities above, the computability of its value at temperature T gives a sufficient condition for T to be a fixed point on partial randomness. In this groundbreaking book, the current status of the interpretation from both mathematical and physical points of view is reported. For example, a total statistical mechanical interpretation of AIT that actualizes a perfect correspondence to normal statistical mechanics can be developed by identifying a microcanonical ensemble in the framework of AIT. As a result, the statistical mechanical meaning of the thermodynamic quantities of AIT is clarified. In the book, the close relationship of the interpretation to Landauer's principle is pointed out.

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

This quick reference is a condensed guide to the essential data structures, algorithms, and functions provided by the C++17 Standard Library. It does not explain the C++ language or syntax, but is accessible to anyone with basic C++ knowledge or programming experience. Even the most experienced C++ programmer will learn a thing or two from it and find it a useful memory-aid. It is hard to remember all the possibilities, details, and intricacies of the vast and growing Standard Library. This handy reference guide is therefore indispensable to any C++ programmer. It offers a condensed, well-structured summary of all essential aspects of the C++ Standard Library. No page-long, repetitive examples or obscure, rarely used features. Instead, everything you need to know and watch out for in practice is outlined in a compact, to-the-point style, interspersed with practical tips and well-chosen, clarifying examples. This new edition is updated to include all Standard Library changes in C++17, including the new vocabulary types std::string_view, any, optional, and variant; parallel algorithms; the file system library; specialized mathematical functions; and more. What You Will Learn Gain the essentials that the C++ Standard Library has to offer Use containers to efficiently store and retrieve your data Inspect and manipulate your data with algorithms See how lambda expressions allow for elegant use of algorithms Discover what the standard string class provides and how to use it Write localized applications Work with file and stream-based I/O Prevent memory leaks with smart pointers Write safe and efficient multi-threaded code using the threading libraries Who This Book Is For All C++ programmers, irrespective of their proficiency with the language or the Standard Library. A secondary audience is developers who are new to C++, but not new to programming, and who want to learn more about the C++ Standard Library in a quick, condensed manner.

Algorithms are the hidden methods that computers apply to process information and make decisions. Nowadays, our lives are run by algorithms. They determine what news we see. They influence which products we buy. They suggest our dating partners. They may even be determining the outcome of national elections. They are creating, and destroying, entire industries. Despite mounting concerns, few know what algorithms are, how they work, or who created them. Poems that Solve Puzzles tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and inspirational events behind the genesis of the world's most important algorithms. Professor Chris Bleakley recounts tales of ancient lost inscriptions, Victorian steam-driven contraptions, top secret military projects, penniless academics, hippy dreamers, tech billionaires, superhuman artificial intelligences, cryptocurrencies, and quantum computing. Along the way, the book explains, with the aid of clear examples and illustrations, how the most influential algorithms work. Compelling and impactful, Poems that Solve Puzzles tells the story of how algorithms came to revolutionise our world.

Effective decision-making while trading off the constraints and conflicting multiple objectives under rapid technological developments, massive generation of data, and extreme volatility is of paramount importance to organizations to win over the time-based competition today. While agility is a crucial issue, the firms have been increasingly relying on evidence-based decision-making through intelligent decision support systems driven by computational intelligence and automation to achieve a competitive advantage.  The decisions are no longer confined to a specific functional area. Instead, business organizations today find actionable insight for formulating future courses of action by integrating multiple objectives and perspectives. Therefore, multi-objective decision-making plays a critical role in businesses and industries. In this regard, the importance of Operations Research (OR) models and their applications enables the firms to derive optimum solutions subject to various constraints and/or objectives while considering multiple functional areas of the organizations together. Hence, researchers and practitioners have extensively applied OR models to solve various organizational issues related to manufacturing, service, supply chain and logistics management, human resource management, finance, and market analysis, among others. Further, OR models driven by AI have been enabled to provide intelligent decision-support frameworks for achieving sustainable development goals. The present issue provides a unique platform to showcase the contributions of the leading international experts on production systems and business from academia, industry, and government to discuss the issues in intelligent manufacturing, operations management, financial management, supply chain management, and Industry 4.0 in the Artificial Intelligence era. Some of the general (but not specific) scopes of this proceeding entail OR models such as Optimization and Control, Combinatorial Optimization, Queuing Theory, Resource Allocation Models, Linear and Nonlinear Programming Models, Multi-objective and multi-attribute Decision Models, Statistical Quality Control along with AI, Bayesian Data Analysis, Machine Learning and Econometrics and their applications vis-à-vis AI & Data-driven Production Management, Marketing and Retail Management, Financial Management, Human Resource Management, Operations Management, Smart Manufacturing & Industry 4.0, Supply Chain and Logistics Management, Digital Supply Network, Healthcare Administration, Inventory Management, consumer behavior, security analysis, and portfolio management and sustainability.   The present issue shall be of interest to the faculty members, students, and scholars of various engineering and social science institutions and universities, along with the practitioners and policymakers of different industries and organizations.

An Introduction to Mathematical Cryptography provides an introduction to public key cryptography and underlying mathematics that is required for the subject. Each of the eight chapters expands on a specific area of mathematical cryptography and provides an extensive list of exercises.

It is a suitable text for advanced students in pure and applied mathematics and computer science, or the book may be used as a self-study. This book also provides a self-contained treatment of mathematical cryptography for the reader with limited mathematical background.

The 20th century saw tremendous achievements and progress in science and
technology. Undoubtedly, computers and computer-related technologies acted as
one of vital catalysts for accelerating this progress in the latter half of
the century. The contributions of mathematical sciences have been equally
profound, and the synergy between mathematics and computer science has played a
key role in accelerating the progress of both fields as well as science
and engineering.

Mathematical sciences will undoubtedly continue to play this vital role in this
new century. In particular, mathematical modeling and numerical simulation will
continue to be among the essential methodologies for solving massive and
complex problems that arise in science, engineering and manufacturing. Underpinning this all from a sound, theoretical perspective will be numerical algorithms. In recognition of this observation, this volume focuses on the
following specific topics.

(1) Fundamental numerical algorithms
(2) Applications of numerical algorithms
(3) Emerging technologies.

The articles included in this issue by experts on advanced scientific and
engineering computations from numerous countries elucidate state-of-the-art achievements in these three topics from various angles and suggest the future directions. Although we cannot hope to cover all the aspects in scientific and engineering computations, we hope that the articles will interest, inform and inspire members of the science and engineering community.

This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17-19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.

In today s digital environment, distributed systems are increasingly present in a wide variety of environments, ranging from public software applications to critical systems. Distributed Systems introduces the underlying concepts, the associated design techniques and the related security issues. Distributed Systems: Design and Algorithms, is dedicated to engineers, students, and anyone familiar with algorithms and programming, who want to know more about distributed systems. These systems are characterized by: several components with one or more threads, possibly running on different processors; asynchronous communications with possible additional assumptions (reliability, order preserving, etc.); local views for every component and no shared data between components. This title presents distributed systems from a point of view dedicated to their design and their main principles: the main algorithms are described and placed in their application context, i.e. consistency management and the way they are used in distributed file-systems.

Algorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems.

This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java.

The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions.

Multiagent systems (MAS) are one of the most exciting and the fastest growing domains in the intelligent resource management and agent-oriented technology, which deals with modeling of autonomous decisions making entities. Recent developments have produced very encouraging results in the novel approach of handling multiplayer interactive systems. In particular, the multiagent system approach is adapted to model, control, manage or test the operations and management of several system applications including multi-vehicles, microgrids, multi-robots, where agents represent individual entities in the network. Each participant is modeled as an autonomous participant with independent strategies and responses to outcomes. They are able to operate autonomously and interact pro-actively with their environment. In recent works, the problem of information consensus is addressed, where a team of vehicles communicate with each other to agree on key pieces of information that enable them to work together in a coordinated fashion. The problem is challenging because communication channels have limited range and there are possibilities of fading and dropout. The book comprises chapters on synchronization and consensus in multiagent systems. It shows that the joint presentation of synchronization and consensus enables readers to learn about similarities and differences of both concepts. It reviews the cooperative control of multi-agent dynamical systems interconnected by a communication network topology. Using the terminology of cooperative control, each system is endowed with its own state variable and dynamics. A fundamental problem in multi-agent dynamical systems on networks is the design of distributed protocols that guarantee consensus or synchronization in the sense that the states of all the systems reach the same value. It is evident from the results that research in multiagent systems offer opportunities for further developments in theoretical, simulation and implementations. This book attempts to fill this gap and aims at presenting a comprehensive volume that documents theoretical aspects and practical applications.

This book presents best selected research papers presented at the International Conference on Computer Networks, Big Data and IoT (ICCBI 2020), organized by Vaigai College Engineering, Madurai, Tamil Nadu, India, during 15-16 December 2020. The book covers original papers on computer networks, network protocols and wireless networks, data communication technologies and network security. The book is a valuable resource and reference for researchers, instructors, students, scientists, engineers, managers and industry practitioners in those important areas.

Complexity theory aims to understand and classify computational problems, especially decision problems, according to their inherent complexity. This book uses new techniques to expand the theory for use with counting problems. The authors present dichotomy classifications for broad classes of counting problems in the realm of P and NP. Classifications are proved for partition functions of spin systems, graph homomorphisms, constraint satisfaction problems, and Holant problems. The book assumes minimal prior knowledge of computational complexity theory, developing proof techniques as needed and gradually increasing the generality and abstraction of the theory. This volume presents the theory on the Boolean domain, and includes a thorough presentation of holographic algorithms, culminating in classifications of computational problems studied in exactly solvable models from statistical mechanics.

String matching is one of the oldest algorithmic techniques, yet still one of the most pervasive in computer science. The past 20 years have seen technological leaps in applications as diverse as information retrieval and compression. This copiously illustrated collection of puzzles and exercises in key areas of text algorithms and combinatorics on words offers graduate students and researchers a pleasant and direct way to learn and practice with advanced concepts. The problems are drawn from a large range of scientific publications, both classic and new. Building up from the basics, the book goes on to showcase problems in combinatorics on words (including Fibonacci or Thue-Morse words), pattern matching (including Knuth-Morris-Pratt and Boyer-Moore like algorithms), efficient text data structures (including suffix trees and suffix arrays), regularities in words (including periods and runs) and text compression (including Huffman, Lempel-Ziv and Burrows-Wheeler based methods).

This book presents fundamental new techniques for understanding and processing geospatial data. These "spatial gems" articulate and highlight insightful ideas that often remain unstated in graduate textbooks, and which are not the focus of research papers. They teach us how to do something useful with spatial data, in the form of algorithms, code, or equations. Unlike a research paper, Spatial Gems, Volume 1 does not focus on "Look what we have done!" but rather shows "Look what YOU can do!" With contributions from researchers at the forefront of the field, this volume occupies a unique position in the literature by serving graduate students, professional researchers, professors, and computer developers in the field alike.

Discover how today's forex market works and understand the essential risks in forex algo trading and how to mitigate them Key Features * Research and build trading applications without advanced Python programming skills * Dive into professional fx trading and make your algo trading apps more adequate for the real market * Develop simple yet efficient backtesting applications which help you keep the expectations realistic Book Description Algo trading, especially in the forex market, has long been very popular among programmers and especially Python professionals because of the visible simplicity to start. However, statistics say that only less than 5% of traders are able to survive in this market. One of the reasons is that developers of algo trading applications do not take into consideration many important features of this market and have their live results radically differ from expectations The book is a comprehensive guide to anything market-related: data, orders, trading venues, and risk. From the programming side, it explains the general architecture of trading applications, focuses on systemic risk management, covers many de-facto industry standards such as FIX protocol, TA-lib, and scikit-learn gives practical examples of using basic pattern recognition to identify market behavior, explains how to create realistic tests, and finally considers a number of sample apps with full code By the end of this book, you will learn to retrieve market data, clean it up and filter it, compress it into various formats, apply trading logic, emulate the execution of orders, and test the trading app before trading live What you will learn * Run reliable backtesting emulating real-world conditions * Translate trading ideas into code * Understand the essentials of fundamental and technical analysis * Use popular technical analysis and machine learning libraries * Connect to data sources and check the integrity of market data * Use API and FIX protocol to send orders Who This Book Is For This book is for Financial traders and Python developers who are interested in forex trading. Academic researchers who want to focus on practical applications will also find this book useful. On the other hand, this book can be a fit for even established fx market professionals who would like to take the first steps in algo trading. In order to learn from this book, you should have a solid understanding of OOP and Python in particular with a general understanding of network protocols and interfaces. No advanced knowledge about markets and trading is required