This book features a collection of high-quality research papers presented at the 9th International Symposium on Applied Computing for Software and Smart systems (ACSS 2022), to be held during September 09-10, 2022 in Kolkata, India. The book presents innovative works by undergraduate, graduate students and as well as PhD scholars. The emphasis of the workshop is on software and smart systems and research outcomes on other relevant areas pertaining to advancement of computing.

Almost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.

The book presents a succession of RISC-V processor implementations in increasing difficulty (non pipelined, pipelined, deeply pipelined, multithreaded, multicore). Each implementation is shown as an HLS (High Level Synthesis) code in C++ which can really be synthesized and tested on an FPGA based development board (such a board can be freely obtained from the Xilinx University Program targeting the university professors). The book can be useful for three reasons. First, it is a novel way to introduce computer architecture. The codes given can serve as labs for a processor architecture course. Second, the book content is based on the RISC-V Instruction Set Architecture, which is an open-source machine language promised to become the machine language to be taught, replacing DLX and MIPS. Third, all the designs are implemented through the High Level Synthesis, a tool which is able to translate a C program into an IP (Intellectual Property). Hence, the book can serve to engineers willing to implement processors on FPGA and to researchers willing to develop RISC-V based hardware simulators.

Algorithms were always an important part of many branches in the sciences. In many manuals and handbooks, algorithms of problems of computational mathematics are focused on the manual performance or by means of a calculator. In this book, descriptions of algorithms, their solutions and main characteristics are discussed. The present work is the outcome of many years of the authors' work on solving different problems and tasks from domains of instruction making, metrology, system analysis, ecology, data analysis from ecology, agriculture, medicine and creation of corresponding universal computer packages and systems.

This book features research presented and discussed during the Research & Innovation Forum (Rii Forum) 2020. As such, this book offers a unique insight into emerging topics, issues and developments pertinent to the fields of technology, innovation and education and their social impact. Papers included in this book apply inter- and multi-disciplinary approaches to query such issues as technology-enhanced teaching and learning, smart cities, information systems, cognitive computing and social networking. What brings these threads of the discussion together is the question of how advances in computer science - which are otherwise largely incomprehensible to researchers from other fields - can be effectively translated and capitalized on so as to make them beneficial for society as a whole. In this context, Rii Forum and Rii Forum proceedings offer an essential venue where diverse stakeholders, including academics, the think tank sector and decision-makers, can engage in a meaningful dialogue with a view to improving the applicability of advances in computer science.

The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.

This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of things (IoT), and information security.

A remarkable look at how the growth, technology, and politics of high-frequency trading have altered global financial markets In today's financial markets, trading floors on which brokers buy and sell shares face-to-face have increasingly been replaced by lightning-fast electronic systems that use algorithms to execute astounding volumes of transactions. Trading at the Speed of Light tells the story of this epic transformation. Donald MacKenzie shows how in the 1990s, in what were then the disreputable margins of the US financial system, a new approach to trading-automated high-frequency trading or HFT-began and then spread throughout the world. HFT has brought new efficiency to global trading, but has also created an unrelenting race for speed, leading to a systematic, subterranean battle among HFT algorithms. In HFT, time is measured in nanoseconds (billionths of a second), and in a nanosecond the fastest possible signal-light in a vacuum-can travel only thirty centimeters, or roughly a foot. That makes HFT exquisitely sensitive to the length and transmission capacity of the cables connecting computer servers to the exchanges' systems and to the location of the microwave towers that carry signals between computer datacenters. Drawing from more than 300 interviews with high-frequency traders, the people who supply them with technological and communication capabilities, exchange staff, regulators, and many others, MacKenzie reveals the extraordinary efforts expended to speed up every aspect of trading. He looks at how in some markets big banks have fought off the challenge from HFT firms, and how exchanges sometimes engineer technical systems to favor certain types of algorithms over others. Focusing on the material, political, and economic characteristics of high-frequency trading, Trading at the Speed of Light offers a unique glimpse into its influence on global finance and where it could lead us in the future.

A ONE-STOP, DEFINITIVE GUIDE TO DATA MODELING FOR IT PROFESSIONALS

Data Modeling Fundamentals is a comprehensive guide to the foundation and principles behind data modeling and its essential role in successful database design. The only book specifically addressing the essentials of data modeling for IT professionals, it includes:

An overview of data modeling concepts, techniques, and symbols

Anatomy of a comprehensive data model describing the various components

Extensive details of entities, attributes, identifiers, and relationships

Transition from data modeling to database design and implementation, datanormalization, and modeling for decision-support systems

Quality data modeling and agile data modeling in practice

Trends in data modeling methodologies and techniques

Chapter summaries with review questions and exercises at the conclusion of each chapter help readers gauge their understanding of the material presented. Complete with real-world examples, references, and an abundance of practical tips and suggestions, Data Modeling Fundamentals is an indispensable resource for both novice and experienced data modelers.

This book presents various computational and cognitive modeling approaches in the areas of health, education, finance, theenvironment, engineering, commerce and industry. Gathering selected conference papers presented atthe International Conference on Trends in Computational and Cognitive Engineering (TCCE), it sharescutting-edge insights and ideas from mathematicians, engineers, scientists and researchers anddiscusses fresh perspectives on problem solving in a range of research areas.

The two-volume set LNCS 12415 and 12416 constitutes the refereed proceedings of of the 19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020. The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; bioinformatics, biometrics and medical applications; artificial intelligence in modeling and simulation. The papers included in the second volume are organized in the following four parts: computer vision, image and speech analysis; data mining; various problems of artificial intelligence; agent systems, robotics and control. *The conference was held virtually due to the COVID-19 pandemic.

The gap between theoretical ideas and messy reality, as seen in Neal Stephenson, Adam Smith, and Star Trek. We depend on-we believe in-algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations-the marriage vow, the shaman's curse-do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm-in practical terms, "a method for solving a problem"-has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking. Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopedie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things. If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of "algorithmic reading" and scholarship that attends to process, spearheading a new experimental humanities.

Michael Goodrich and Roberto Tamassia, authors of the successful, Data Structures and Algorithms in Java, 2/e, have written Algorithm Engineering, a text designed to provide a comprehensive introduction to the design, implementation and analysis of computer algorithms and data structures from a modern perspective. This book offers theoretical analysis techniques as well as algorithmic design patterns and experimental methods for the engineering of algorithms.
Market: Computer Scientists; Programmers.

Software Engineering: The Current Practice teaches students basic software engineering skills and helps practitioners refresh their knowledge and explore recent developments in the field, including software changes and iterative processes of software development. After a historical overview and an introduction to software technology and models, the book discusses the software change and its phases, including concept location, impact analysis, refactoring, actualization, and verification. It then covers the most common iterative processes: agile, directed, and centralized processes. The text also journeys through the software life span from the initial development of software from scratch to the final stages that lead toward software closedown. For ProfessionalsThe book gives programmers and software managers a unified view of the contemporary practice of software engineering. It shows how various developments fit together and fit into the contemporary software engineering mosaic. The knowledge gained from the book allows practitioners to evaluate and improve the software engineering processes in their projects. For InstructorsInstructors have several options for using this classroom-tested material. Designed to be run in conjunction with the lectures, ideas for student projects include open source programs that use Java or C++ and range in size from 50 to 500 thousand lines of code. These projects emphasize the role of developers in a classroom-tailored version of the directed iterative process (DIP). For StudentsStudents gain a real understanding of software engineering processes through the lectures and projects. They acquire hands-on experience with software of the size and quality comparable to that of industrial software. As is the case in the industry, students work in teams but have individual assignments and accountability.

This book features high-quality research papers presented at Third Doctoral Symposium on Computational Intelligence (DoSCI 2022), organized by Institute of Engineering and Technology (IET), AKTU, Lucknow, India, on March 5, 2022. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high performance computing, biomedical computing, and decision support and decision making.

This fourth edition of Robert Sedgewick and Kevin Wayne's Algorithms is the leading textbook on algorithms today and is widely used in colleges and universities worldwide. This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.

The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts.

The companion web site, algs4.cs.princeton.edu contains An online synopsisFull Java implementationsTest dataExercises and answersDynamic visualizationsLecture slidesProgramming assignments with checklistsLinks to related material

The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants.

Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.

Matching problems with preferences are all around us: they arise when agents seek to be allocated to one another on the basis of ranked preferences over potential outcomes. Efficient algorithms are needed for producing matchings that optimise the satisfaction of the agents according to their preference lists. In recent years there has been a sharp increase in the study of algorithmic aspects of matching problems with preferences, partly reflecting the growing number of applications of these problems worldwide. The importance of the research area was recognised in 2012 through the award of the Nobel Prize in Economic Sciences to Alvin Roth and Lloyd Shapley. This book describes the most important results in this area, providing a timely update to The Stable Marriage Problem: Structure and Algorithms (D Gusfield and R W Irving, MIT Press, 1989) in connection with stable matching problems, whilst also broadening the scope to include matching problems with preferences under a range of alternative optimality criteria.

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams.

The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets.

This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.

When it comes to artificial intelligence, we either hear of a paradise on earth or of our imminent extinction. It's time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we'll be discussing these issues long after the last page is turned.

Taking a novel, more appealing approach than current texts, An Integrated Introduction to Computer Graphics and Geometric Modeling focuses on graphics, modeling, and mathematical methods, including ray tracing, polygon shading, radiosity, fractals, freeform curves and surfaces, vector methods, and transformation techniques. The author begins with fractals, rather than the typical line-drawing algorithms found in many standard texts. He also brings the turtle back from obscurity to introduce several major concepts in computer graphics.

Supplying the mathematical foundations, the book covers linear algebra topics, such as vector geometry and algebra, affine and projective spaces, affine maps, projective transformations, matrices, and quaternions. The main graphics areas explored include reflection and refraction, recursive ray tracing, radiosity, illumination models, polygon shading, and hidden surface procedures. The book also discusses geometric modeling, including planes, polygons, spheres, quadrics, algebraic and parametric curves and surfaces, constructive solid geometry, boundary files, octrees, interpolation, approximation, Bezier and B-spline methods, fractal algorithms, and subdivision techniques.

Making the material accessible and relevant for years to come, the text avoids descriptions of current graphics hardware and special programming languages. Instead, it presents graphics algorithms based on well-established physical models of light and cogent mathematical methods.

This classroom-tested textbook describes the design and implementation of software for distributed real-time systems, using a bottom-up approach. The text addresses common challenges faced in software projects involving real-time systems, and presents a novel method for simply and effectively performing all of the software engineering steps. Each chapter opens with a discussion of the core concepts, together with a review of the relevant methods and available software. This is then followed with a description of the implementation of the concepts in a sample kernel, complete with executable code. Topics and features: introduces the fundamentals of real-time systems, including real-time architecture and distributed real-time systems; presents a focus on the real-time operating system, covering the concepts of task, memory, and input/output management; provides a detailed step-by-step construction of a real-time operating system kernel, which is then used to test various higher level implementations; describes periodic and aperiodic scheduling, resource management, and distributed scheduling; reviews the process of application design from high-level design methods to low-level details of design and implementation; surveys real-time programming languages and fault tolerance techniques; includes end-of-chapter review questions, extensive C code, numerous examples, and a case study implementing the methods in real-world applications; supplies additional material at an associated website. Requiring only a basic background in computer architecture and operating systems, this practically-oriented work is an invaluable study aid for senior undergraduate and graduate-level students of electrical and computer engineering, and computer science. The text will also serve as a useful general reference for researchers interested in real-time systems.

Desktop Grid Computing presents common techniques used in numerous models, algorithms, and tools developed during the last decade to implement desktop grid computing. These techniques enable the solution of many important sub-problems for middleware design, including scheduling, data management, security, load balancing, result certification, and fault tolerance. The book's first part covers the initial ideas and basic concepts of desktop grid computing. The second part explores challenging current and future problems. Each chapter presents the sub-problems, discusses theoretical and practical issues, offers details about implementation and experiments, and includes references to further reading and notes. One of the first books to give a thorough and up-to-date presentation of this topic, this resource describes various approaches and models as well as recent trends that underline the evolution of desktop grids. It balances the theory of designing desktop grid middleware and architecture with applications and real-world deployment on large-scale platforms.

Introduction to Chemical Engineering Analysis Using Mathematica, Second Edition reviews the processes and designs used to manufacture, use, and dispose of chemical products using Mathematica, one of the most powerful mathematical software tools available for symbolic, numerical, and graphical computing. Analysis and computation are explained simultaneously. The book covers the core concepts of chemical engineering, ranging from the conservation of mass and energy to chemical kinetics. The text also shows how to use the latest version of Mathematica, from the basics of writing a few lines of code through developing entire analysis programs. This second edition has been fully revised and updated, and includes analyses of the conservation of energy, whereas the first edition focused on the conservation of mass and ordinary differential equations.

This book includes high-quality research papers presented at the First International Conference on Human-Centric Smart Computing (ICHCSC 2022), organized by the University of Engineering and Management, Jaipur, India, on 27-29 April 2022. The topics covered in the book are human-centric computing, hyper connectivity, and data science. The book presents innovative work by leading academics, researchers, and experts from industry.

The big challenge for a successful AI project isn't deciding which problems you can solve. It's deciding which problems you should solve. In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries. Key Features * Selecting the right AI project to meet specific business goals * Economizing resources to deliver the best value for money * How to measure the success of your AI efforts in the business terms * Predict if you are you on the right track to deliver your intended business results For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required. About the technology Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Managing Successful AI Projects sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It's filled with practical techniques for running data science programs that ensure they're cost effective and focused on the right business goals. Veljko Krunic is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.