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This book constitutes the revised selected papers of the 8th International Workshop on Algorithms and Computation, WALCOM 2014, held in Chennai, India, in February 2014. The 29 full papers presented together with 3 invited talks were carefully reviewed and selected from 62 submissions. The papers are organized in topical sections on computational geometry, algorithms and approximations, distributed computing and networks, graph algorithms, complexity and bounds, and graph embeddings and drawings.

The growth of a global digital economy has enabled rapid communication, instantaneous movement of funds, and availability of vast amounts of information. With this come challenges such as the vulnerability of digitalized sociotechnological systems (STSs) to destructive events (earthquakes, disease events, terrorist attacks). Similar issues arise for disruptions to complex linked natural and social systems (from changing climates, evolving urban environments, etc.). This book explores new approaches to the resilience of sociotechnological and natural-social systems in a digital world of big data, extraordinary computing capacity, and rapidly developing methods of Artificial Intelligence. Most of the book's papers were presented at the Workshop on Big Data and Systems Analysis held at the International Institute for Applied Systems Analysis in Laxenburg, Austria in February, 2020. Their authors are associated with the Task Group “Advanced mathematical tools for data-driven applied systems analysis” created and sponsored by CODATA in November, 2018. The world-wide COVID-19 pandemic illustrates the vulnerability of our healthcare systems, supply chains, and social infrastructure, and confronts our notions of what makes a system resilient. We have found that use of AI tools can lead to problems when unexpected events occur. On the other hand, the vast amounts of data available from sensors, satellite images, social media, etc. can also be used to make modern systems more resilient. Papers in the book explore disruptions of complex networks and algorithms that minimize departure from a previous state after a disruption; introduce a multigrammatical framework for the technological and resource bases of today's large-scale industrial systems and the transformations resulting from disruptive events; and explain how robotics can enhance pre-emptive measures or post-disaster responses to increase resiliency. Other papers explore current directions in data processing and handling and principles of FAIRness in data; how the availability of large amounts of data can aid in the development of resilient STSs and challenges to overcome in doing so. The book also addresses interactions between humans and built environments, focusing on how AI can inform today's smart and connected buildings and make them resilient, and how AI tools can increase resilience to misinformation and its dissemination.

In this book, the latest achievements of the computation time analysis theory and practical applications of intelligent algorithms are set out. There are five chapters: (1) new method of intelligent algorithm computation time analysis; (2)Application of intelligent algorithms in computer vision; (3)Application of intelligent algorithms in logistics scheduling; (4)Application of intelligent algorithms in software testing; and (5) application of intelligent algorithm in multi-objective optimization. The content of each chapter is supported by papers published in top journals. The authors introduce the work of each part, which mainly includes a brief introduction (mainly for readers to understand) and academic discussion (rigorous theoretical and experimental support), in a vivid and interesting way through excellent pictures and literary compositions. To help readers learn and make progress together, each part of this book provides relevant literature, code, experimental data, and so on. - Integrates the theoretical analysis results of intelligent algorithms, which is convenient for the majority of researchers to deeply understand the theoretical analysis results of intelligent algorithms and further supplement and improve the theoretical research of intelligent algorithms - Opens up readers'understanding of the theoretical level of intelligent algorithms and spreads the inherent charm of intelligent algorithms - Integrates the diverse knowledge of society and provides a more comprehensive and scientific knowledge of intelligent algorithm theory

This book constitutes the refereed proceedings of the 23rd Annual Symposium on Combinatorial Pattern Matching, CPM 2012, held in Helsinki, Finland, in July 2012. The 33 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 60 submissions. The papers address issues of searching and matching strings and more complicated patterns such as trees, regular expressions, graphs, point sets, and arrays. The goal is to derive non-trivial combinatorial properties of such structures and to exploit these properties in order to either achieve superior performance for the corresponding computational problems or pinpoint conditions under which searches cannot be performed efficiently. The meeting also deals with problems in computational biology, data compression and data mining, coding, information retrieval, natural language processing, and pattern recognition.

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications.

This volume contains the proceedings of the Workshop on On-line Algorithms held at the DIMACS Center at Rutgers University in February 1991. Presenting results in the theory of on-line algorithms, the articles discuss a broad range of problems. Most of the papers are based on competitive (worst-case) analysis of on-line algorithms, but some papers consider alternative approaches to on-line analysis. A critical question examined by some of the authors is how to modify competitive analysis to better reconcile the theory and practice of on-line algorithms. Many of the papers examine the ways in which randomization can be used to yield algorithms with improved performance. This book is aimed primarily at specialists in algorithm analysis, but most of the articles present clear expositions of previous work.

This book is a celebration of Leslie Lamport's work on concurrency, interwoven in four-and-a-half decades of an evolving industry: from the introduction of the first personal computer to an era when parallel and distributed multiprocessors are abundant. His works lay formal foundations for concurrent computations executed by interconnected computers. Some of the algorithms have become standard engineering practice for fault tolerant distributed computing – distributed systems that continue to function correctly despite failures of individual components. He also developed a substantial body of work on the formal specification and verification of concurrent systems, and has contributed to the development of automated tools applying these methods. Part I consists of technical chapters of the book and a biography. The technical chapters of this book present a retrospective on Lamport's original ideas from experts in the field. Through this lens, it portrays their long-lasting impact. The chapters cover timeless notions Lamport introduced: the Bakery algorithm, atomic shared registers and sequential consistency; causality and logical time; Byzantine Agreement; state machine replication and Paxos; temporal logic of actions (TLA). The professional biography tells of Lamport's career, providing the context in which his work arose and broke new grounds, and discusses LaTeX – perhaps Lamport's most influential contribution outside the field of concurrency. This chapter gives a voice to the people behind the achievements, notably Lamport himself, and additionally the colleagues around him, who inspired, collaborated, and helped him drive worldwide impact. Part II consists of a selection of Leslie Lamport's most influential papers. This book touches on a lifetime of contributions by Leslie Lamport to the field of concurrency and on the extensive influence he had on people working in the field. It will be of value to historians of science, and to researchers and students who work in the area of concurrency and who are interested to read about the work of one of the most influential researchers in this field.

Swarm Intelligence (SI) is one of the most important and challenging paradigms under the umbrella of computational intelligence. It focuses on the research of collective behaviours of a swarm in nature and/or social phenomenon to solve complicated and difficult problems which cannot be handled by traditional approaches. Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will be inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.

Swarm Intelligence (SI) is one of the most important and challenging paradigms under the umbrella of computational intelligence. It focuses on the research of collective behaviours of a swarm in nature and/or social phenomenon to solve complicated and difficult problems which cannot be handled by traditional approaches. Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will be inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.

This volume contains papers presented at the DIMACS workshop on Specification of Parallel Algorithms, held in May 1994 at Princeton University. The goal of the workshop was to bring together some of the best researchers in parallel languages, algorithms, and systems to present and discuss recent developments in their areas of expertise. Among the topics discussed were new specification techniques for concurrent and distributed systems, behavioral and operational specification techniques, new parallel language and system abstractions, novel concurrent architectures and systems, large-scale parallel systems, specification tools and environments, and proof techniques for concurrent systems.

This book constitutes the refereed proceedings of the First Mediterranean Conference on Algorithms, MedAlg 2012, held in Kibbutz Ein Gedi, Israel, in December 2012. The 18 papers presented were carefully reviewed and selected from 44 submissions. The conference papers focus on the design, engineering, theoretical and experimental performance analysis of algorithms for problems arising in different areas of computation. Topics covered include: communications networks, combinatorial optimization and approximation, parallel and distributed computing, computer systems and architecture, economics, game theory, social networks and the World Wide Web.

The proceedings represent the state of knowledge in the area of algorithmic differentiation (AD). The 31 contributed papers presented at the AD2012 conference cover the application of AD to many areas in science and engineering as well as aspects of AD theory and its implementation in tools. For all papers the referees, selected from the program committee and the greater community, as well as the editors have emphasized accessibility of the presented ideas also to non-AD experts. In the AD tools arena new implementations are introduced covering, for example, Java and graphical modeling environments or join the set of existing tools for Fortran. New developments in AD algorithms target the efficiency of matrix-operation derivatives, detection and exploitation of sparsity, partial separability, the treatment of nonsmooth functions, and other high-level mathematical aspects of the numerical computations to be differentiated. Applications stem from the Earth sciences, nuclear engineering, fluid dynamics, and chemistry, to name just a few. In many cases the applications in a given area of science or engineering share characteristics that require specific approaches to enable AD capabilities or provide an opportunity for efficiency gains in the derivative computation. The description of these characteristics and of the techniques for successfully using AD should make the proceedings a valuable source of information for users of AD tools.

This book constitutes the refereed proceedings of the 15th International Conference on Theory and Applications of Satisfiability Testing, SAT 2012, held in Trento, Italy, in June 2012. The 29 revised full papers, 7 tool papers, and 16 poster papers presented together with 2 invited talks were carefully reviewed and selected from 112 submissions (88 full, 10 tool and 14 poster papers). The papers are organized in topical sections on stochastic local search, theory, quantified Boolean formulae, applications, parallel and portfolio approaches, CDCL SAT solving, MAX-SAT, cores interpolants, complexity analysis, and circuits and encodings.

This book constitutes the refereed proceedings of the 6th International Frontiers of Algorithmics Workshop, FAW 2012, and the 8th International Conference on Algorithmic Aspects in Information and Management, AAIM 2012, jointly held in Beijing, China, in May 2012. The 33 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on algorithms and data structures, algorithmic game theory and incentive analysis, biomedical imaging algorithms, communication networks and optimization, computational learning theory, knowledge discovery, and data mining, experimental algorithmic methodologies, optimization algorithms in economic and operations research, pattern recognition algorithms and trustworthy algorithms and trustworthy software.

“Software Tools and Algorithms for Biological Systems'is composed of a collection of papers received in response to an announcement that was widely distributed to academicians and practitioners in the broad area of computational biology and software tools. Also, selected authors of accepted papers of BIOCOMP'09 proceedings (International Conference on Bioinformatics and Computational Biology: July 13-16, 2009; Las Vegas, Nevada, USA) were invited to submit the extended versions of their papers for evaluation.

This Festschrift volume, published in honor of Kurt Mehlhorn on the occasion of his 60th birthday, contains 28 papers written by his former Ph.D. students and colleagues as well as by his former Ph.D. advisor, Bob Constable. The volume's title is a translation of the title of Kurt Mehlhorn's first book,'Effiziente Algorithmen', published by Teubner-Verlag in 1977. This Festschrift demonstrates how the field of algorithmics has developed and matured in the decades since then. The papers included in this volume are organized in topical sections on models of computation and complexity; sorting and searching; combinatorial optimization with applications; computational geometry and geometric graphs; and algorithm engineering, exactness and robustness.

Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using.Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.Summarization patterns: get a top-level view by summarizing and grouping dataFiltering patterns: view data subsets such as records generated from one userData organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easierJoin patterns: analyze different datasets together to discover interesting relationshipsMetapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same jobInput and output patterns: customize the way you use Hadoop to load or store data'A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop.'--Tom White, author of Hadoop: The Definitive Guide

When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success.This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You'll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website.Learn the basics of A/B testing—and recognize when it's better to use bandit algorithmsDevelop a unit testing framework for debugging bandit algorithmsGet additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials

This volume provides approaches and solutions to challenges occurring at the interface of research fields such as, e.g., data analysis, data mining and knowledge discovery, computer science, operations research, and statistics. In addition to theory-oriented contributions various application areas are included. Moreover, traditional classification research directions concerning network data, graphs, and social relationships as well as statistical musicology describe examples for current interest fields tackled by the authors. The book comprises a total of 55 selected papers presented at the Joint Conference of the German Classification Society (GfKl), the German Association for Pattern Recognition (DAGM), and the Symposium of the International Federation of Classification Societies (IFCS) in 2011.​

“Automatic Program Development” is a tribute to Robert Paige (1947-1999), our accomplished and respected colleague, and moreover our good friend, whose untimely passing was a loss to our academic and research community. We have collected the revised, updated versions of the papers published in his honor in the Higher-Order and Symbolic Computation Journal in the years 2003 and 2005. Among them there are two papers by Bob: (i) a retrospective view of his research lines, and (ii) a proposal for future studies in the area of the automatic program derivation. The book also includes some papers by members of the IFIP Working Group 2.1 of which Bob was an active member. All papers are related to some of the research interests of Bob and, in particular, to the transformational development of programs and their algorithmic derivation from formal specifications. “Automatic Program Development” offers a renewed stimulus for continuing and deepening Bob's research visions. A familiar touch is given to the book by some pictures kindly provided to us by his wife Nieba, the personal recollections of his brother Gary and some of his colleagues and friends.

This book constitutes the refereed proceedings of the 23rd International Symposium on Algorithms and Computation, ISAAC 2012, held in Taipei, Taiwan, in December 2012. The 68 revised full papers presented together with three invited talks were carefully reviewed and selected from 174 submissions for inclusion in the book. This volume contains topics such as graph algorithms; online and streaming algorithms; combinatorial optimization; computational complexity; computational geometry; string algorithms; approximation algorithms; graph drawing; data structures; randomized algorithms; and algorithmic game theory.

Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications. This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.

This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, held in Natal, Brazil, in August 2012. The 100 revised full papers presented were carefully reviewed and selected from more than 200 submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.

When scientists analyze datasets in a search for underlying phenomena, patterns or causal factors, their first step is often an automatic or semi-automatic search for structures in the data. Of these feature-extraction methods, topological ones stand out due to their solid mathematical foundation. Topologically defined structures—as found in scalar, vector and tensor fields—have proven their merit in a wide range of scientific domains, and scientists have found them to be revealing in subjects such as physics, engineering, and medicine. Full of state-of-the-art research and contemporary hot topics in the subject, this volume is a selection of peer-reviewed papers originally presented at the fourth Workshop on Topology-Based Methods in Data Analysis and Visualization, TopoInVis 2011, held in Zurich, Switzerland. The workshop brought together many of the leading lights in the field for a mixture of formal presentations and discussion. One topic currently generating a great deal of interest, and explored in several chapters here, is the search for topological structures in time-dependent flows, and their relationship with Lagrangian coherent structures. Contributors also focus on discrete topologies of scalar and vector fields, and on persistence-based simplification, among other issues of note. The new research results included in this volume relate to all three key areas in data analysis—theory, algorithms and applications.

This book represents the refereed proceedings of the Ninth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Warsaw (Poland) in August 2010. These biennial conferences are major events for Monte Carlo and the premiere event for quasi-Monte Carlo research. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. The reader will be provided with information on latest developments in these very active areas. The book is an excellent reference for theoreticians and practitioners interested in solving high-dimensional computational problems arising, in particular, in finance and statistics.

This book has a single purpose: to help everyone become computational thinkers. Computational thinking (CT) is thinking informed by the digital age, and a computational thinker is someone who can apply that thinking everywhere and anywhere. Through practical examples and easy-to-grasp terminology, this book is a guide to navigating the digital world and improving one's efficiency, productivity, and success immediately.Given its pervasiveness, knowledge and experience of computation is a cornerstone of productivity, and improved thinking will lead to advances in every aspect of one's life. In this way, CT can be thought of as the mutual reinforcement of thinking and knowledge of computation in the digital age. Comprising a rich collection of self-contained articles that can be read separately, and illustrated by pictures, images and article-end crossword puzzles, this book is an engaging and accessible route to ‘Becoming a Computational Thinker'and achieving ‘Success in the Digital Age'.Aimed at the general reader, this book provides insights that can be applied across the full spectrum of industries and practices, helping readers to not only adapt and function in the digital world but also take advantage of new technologies and even innovate new ways doing things.Additional online resources are available at https://computize.org/CTer/

Computational intelligence-based optimization methods, also known as metaheuristic optimization algorithms, are a popular topic in mathematical programming.These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effective and renowned algorithms in the literature. These algorithms are not only practical but also provide thought-provoking theoretical ideas to help readers understand how they solve optimization problems. Each chapter includes a brief review of the algorithm's background and the fields it has been used in.Additionally, Python code is provided for all algorithms at the end of each chapter, making this book a valuable resource for beginner and intermediate programmers looking to understand these algorithms.

In an ever-evolving educational landscape, traditional methods face unprecedented challenges. The Impact of ChatGPT on Higher Education takes you on a trailblazing journey into ChatGPT's transformative potential and the ethical considerations in higher education. Authored by experts at the forefront of educational transformation and technology's impact on learning, this book offers invaluable insights for educators, leaders, policymakers, and AI enthusiasts. Dive deep with the authors as they navigate from theory to practice, unravelling power dynamics, social structures, and ChatGPT's profound influence. Real-world examples and a captivating case study from MEF University provide tangible evidence of ChatGPT's impact on education. Explore how ChatGPT raises critical questions about course planning, assessments, teaching, and AI's role in education. The authors illuminate issues related to academic honesty, ethics, bias, misinformation, cost, equity, and data privacy. As AI technologies continue to evolve and impact education, The Impact of ChatGPT on Higher Education provides valuable guidance and insights for educators and researchers seeking to harness the power of ChatGPT in their work.

'Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students'--

Explore the essentials of computer programming and algorithmic thinking with Visual Basic. This comprehensive course is designed for beginners to master the core concepts and practical applications.Key FeaturesComprehensive coverage of Visual Basic and algorithms with practical exercises and examplesIntroduction to programming fundamentals, & in-depth exploration of advanced structuresIntroduction to arrays, subprograms, and object-oriented programmingBook DescriptionThis course begins with a fundamental overview of how computers operate, setting a solid foundation for your learning. You'll then delve into the essentials of Visual Basic, exploring integrated development environments and necessary software packages. As you progress, you'll tackle basic algorithmic concepts, variables, constants, and how to handle input and output efficiently. Moving forward, the course introduces you to control structures, starting with sequence control, and advancing through various decision structures, including single, dual, and multiple-alternative decisions. You'll gain practical experience with flowcharts and decision-making processes, equipping you with the skills to manage complex programming scenarios. The latter part of the course focuses on loop control structures, both simple and nested, and teaches you to implement them effectively through practical exercises and flowcharts. Finally, you'll explore advanced topics such as data structures, including one-dimensional and two-dimensional arrays, and dictionaries. The course also covers subprograms and object-oriented programming, ensuring you have a comprehensive understanding of Visual Basic. With a practical approach, this course is designed to build your confidence in programming, enabling you to tackle real-world problems with ease.What you will learnUnderstand how computers work and the basics of Visual BasicInstall and configure essential software packagesUse variables, constants, and handle input/output effectivelyApply operators and create trace tablesImplement sequence, decision, and loop control structuresExplore object-oriented programming and file handlingWho this book is forThis course is ideal for a wide range of learners. Complete beginners with no prior programming experience will find it particularly beneficial, as it starts from the basics and builds up gradually. High school and college students looking to strengthen their understanding of programming fundamentals will also benefit from this comprehensive guide. Additionally, professionals from non-technical fields who wish to acquire programming skills for career advancement or personal interest will find the course accessible and rewarding.

Welcome to the economy of algorithms. It's here and it's growing. In the past few years, we have been flooded with examples of impressive technology. Algorithms have been around for hundreds of years, but they have only recently begun to ‘escape'our understanding. When algorithms perform certain tasks, they're not just as good as us, they're becoming infinitely better, and, at the same time, massively more surprising. We are so impressed by what they can do that we give them a lot of agency. But because they are so hard to comprehend, this leads to all kinds of unintended consequences. In the 20th century, things were simple: we had the economy of corporations. In the first two decades of the 21st century, we saw the emergence of the economy of people, otherwise known as the digital economy, enabled by the internet. Now we're seeing a new economy take shape: the economy of algorithms.

META-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systems—generally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed systems will find this book indispensable. Meta-Heuristic Algorithms for Advanced Distributed Systems readers will also find: Analysis of security issues, distributed system design, stochastic optimization techniques, and more Detailed discussion of meta-heuristic techniques such as the genetic algorithm, particle swam optimization, and many others Applications of optimized distribution systems in healthcare and other key??industries Meta-Heuristic Algorithms for Advanced Distributed Systems is ideal for academics and researchers studying distributed systems, their design, and their applications.

A friendly, fully-illustrated introduction to the most important computer programming algorithms.Master the most widely used algorithms and be fully prepared when you're asked about them at your next job interview. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, you'll actually enjoy learning about algorithms with this fun and friendly guide! In Grokking Algorithms, Second Edition you will discover: Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP-complete and greedy algorithms Performance trade-offs between algorithms Exercises and code samples in every chapter Over 400 illustrations with detailed walkthroughs The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn't have to be complicated or boring! This revised second edition contains brand new coverage of trees, including binary search trees, balanced trees, B-trees and more. You'll also discover fresh insights on data structure performance that takes account of modern CPUs. Plus, the book's fully annotated code samples have been updated to Python 3. Foreword by Daniel Zingaro. About the technology The algorithms you use most often have already been discovered, tested, and proven. Grokking Algorithms, Second Edition makes it a breeze to learn, understand, and use them. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, it's the perfect way to unlock the power of algorithms in your everyday work and prepare for your next coding interview—no math required! About the book Grokking Algorithms, Second Edition teaches you important algorithms to speed up your programs, simplify your code, and solve common programming problems. Start with tasks like sorting and searching, then build your skills to tackle advanced problems like data compression and artificial intelligence. You'll even learn to compare the performance tradeoffs between algorithms. Plus, this new edition includes fresh coverage of trees, NP-complete problems, and code updates to Python 3. What's inside Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP-complete and greedy algorithms Exercises and code samples in every chapter About the reader No advanced math or programming skills required. About the author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents 1 Introduction to algorithms 2 Selection sort 3 Recursion 4 Quicksort 5 Hash tables 6 Beadth-first search 7 Trees 8 Balanced trees 9 Dijkstra's algorithm 10 Greedy algorithms 11 Dynamic programming 12 k-nearest neighbors 13 where to go next

Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry. Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar, thus solving optimization problems using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters to name a few. This handbook provides readers with in-depth analysis of this algorithm and existing methods in the literature to cope with such challenges. The authors and editors also propose several improvements, variants and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book. Provides in-depth analysis of equations, mathematical models and mechanisms of the Whale Optimization Algorithm Proposes different variants of the Whale Optimization Algorithm to solve binary, multiobjective, noisy, dynamic and combinatorial optimization problems Demonstrates how to design, develop and test different hybrids of Whale Optimization Algorithm Introduces several application areas of the Whale Optimization Algorithm, focusing on sustainability Includes source code from applications and algorithms that is available online

Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms Key FeaturesLearn how to wrangle different types of datasets and analytics problems into networksLeverage graph theoretic algorithms to analyze data efficientlyApply the skills you gain to solve a variety of problems through case studies in PythonPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWe are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You'll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you'll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you'll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you'll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.What you will learnTransform different data types, such as spatial data, into network formatsExplore common network science tools in PythonDiscover how geometry impacts spreading processes on networksImplement machine learning algorithms on network data featuresBuild and query graph databasesExplore new frontiers in network science such as quantum algorithmsWho this book is forIf you're a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations.

Quantum algorithms are among the most important, interesting, and promising innovations in information and communication technology. They pose a major threat to today's cybersecurity and at the same time promise great benefits by potentially solving previously intractable computational problems with reasonable effort. The theory of quantum algorithms is based on advanced concepts from computer science, mathematics, and physics. Introduction to Quantum Algorithms offers a mathematically precise exploration of these concepts, accessible to those with a basic mathematical university education, while also catering to more experienced readers. This comprehensive book is suitable for self-study or as a textbook for one- or two-semester introductory courses on quantum computing algorithms. Instructors can tailor their approach to emphasize theoretical understanding and proofs or practical applications of quantum algorithms, depending on the course's goals and timeframe.

Unlock the power of Python with this comprehensive guide, “Python and Algorithmic Thinking for the Complete Beginner.” It covers everything from computer basics to advanced decision and loop control structures.Key FeaturesComprehensive coverage from basic computer operations to advanced programming conceptsStep-by-step progression of each topic, along with tips and tricks to enhance coding efficiencyIn-depth exploration of Python and algorithmic thinking with exercises and practical examplesBook DescriptionThis course is meticulously designed to take beginners on a journey through the fascinating world of Python programming and algorithmic thinking. The initial chapters lay a strong foundation, starting with the basics of how computers operate, moving into Python programming, and familiarizing learners with integrated development environments like IDLE and Visual Studio Code. Further, the course delves into essential programming constructs such as variables, constants, input/output handling, and operators. You'll gain practical experience with trace tables, sequence control structures, and decision control structures through comprehensive exercises and examples. The curriculum emphasizes hands-on learning with chapters dedicated to manipulating numbers, strings, and understanding complex mathematical expressions. By mastering these concepts, you'll be well-prepared to tackle more advanced topics. The final chapters introduce you to object-oriented programming and file manipulation, rounding out your skill set. Throughout the course, practical tips and tricks are provided to enhance your coding efficiency and problem-solving skills. By the end of this course, you will have a robust understanding of Python programming and the ability to apply algorithmic thinking to solve real-world problems.What you will learnUnderstand how computers work and the basics of Python programmingInstall and use integrated development environments (IDEs)Develop skills in decision and loop control structuresManipulate data using lists, dictionaries, and stringsApply algorithmic thinking to solve complex problemsGain proficiency in object-oriented programming & file manipulationWho this book is forThis course is ideal for absolute beginners with no prior programming experience. Basic computer literacy is required, but no specific knowledge of programming or algorithms is necessary. It is also suitable for individuals looking to refresh their Python skills and enhance their understanding of algorithmic thinking. High school and college students interested in programming, professionals seeking to upskill, and hobbyists eager to learn a new programming language will all find value in this course.

This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms—supported by motivating examples—that bring meaning to the problems faced by computer programmers. The idea of computational complexity is introduced, demonstrating what can and cannot be computed efficiently at scale, helping programmers make informed judgements about the algorithms they use. The easy-to-read text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.Topics and features:Includes introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective coursesProvides learning goals, review questions, and programming exercises in each chapter, as well as numerous examplesPresents a primer on Python for those coming from a different language backgroundAdds a new chapter on multiprocessing with Python using the DragonHPC multinode implementation of multiprocessing (includes a tutorial)Reviews the use of hashing in sets and maps, and examines binary search trees, tree traversals, and select graph algorithmsOffers downloadable programs and supplementary files at an associated website to help studentsStudents of computer science will find this clear and concise textbook invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.Dr. Kent D. Lee is a Professor Emeritus of Computer Science at Luther College, Decorah, Iowa, USA. He is the author of the successful Springer books, Python Programming Fundamentals, and Foundations of Programming Languages.Dr. Steve Hubbard is a Professor Emeritus of Mathematics and Computer Science at Luther College.

Computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, and the process of imaging introduce constraints and properties that are key to solving some of these tasks. In the presence of noisy observations and other uncertainties, the algorithms make use of statistical methods for robust inference. In this paper, we highlight the role of geometric constraints in statistical estimation methods, and how the interplay of geometry and statistics leads to the choice and design of algorithms. In particular, we illustrate the role of imaging, illumination, and motion constraints in classical vision problems such as tracking, structure from motion, metrology, activity analysis and recognition, and appropriate statistical methods used in each of these problems.

Explore essential quantum computing algorithms and master concepts intuitively with minimal math expertise requiredKey FeaturesLearn the fundamentals with an introduction to matrix arithmeticWrite quantum computing programs in Qiskit—IBM's publicly available quantum computing websiteEmail your questions directly to the author—no question is too elementaryPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNavigate the quantum computing spectrum with this book, bridging the gap between abstract, math-heavy texts and math-avoidant beginner guides. Unlike intermediate-level books that often leave gaps in comprehension, this all-encompassing guide offers the missing links you need to truly understand the subject. Balancing intuition and rigor, this book empowers you to become a master of quantum algorithms. No longer confined to canned examples, you'll acquire the skills necessary to craft your own quantum code. Quantum Computing Algorithms is organized into four sections to build your expertise progressively. The first section lays the foundation with essential quantum concepts, ensuring that you grasp qubits, their representation, and their transformations. Moving to quantum algorithms, the second section focuses on pivotal algorithms — specifically, quantum key distribution and teleportation. The third section demonstrates the transformative power of algorithms that outpace classical computation and makes way for the fourth section, helping you to expand your horizons by exploring alternative quantum computing models. By the end of this book, quantum algorithms will cease to be mystifying as you make this knowledge your asset and enter a new era of computation, where you have the power to shape the code of reality.What you will learnDefine quantum circuitsHarness superposition and entanglement to solve classical problemsGain insights into the implementation of quantum teleportationExplore the impact of quantum computing on cryptographyTranslate theoretical knowledge into practical skills by writing and executing code on real quantum hardwareExpand your understanding of this domain by uncovering alternative quantum computing modelsWho this book is forThis book is for individuals familiar with algebra and computer programming, eager to delve into modern physics concepts. Whether you've dabbled in introductory quantum computing material or are seeking deeper insights, this quantum computing book is your gateway to in-depth exploration.

Artificial Intelligence was meant to be the great social equalizer that helps promote fairness by removing human bias from the equation, but is this true? Given that the policing and judicial systems can display human bias, this book explores how the technology they use can also reflect these prejudices. From healthcare services to social scoring in exams, to applying for and getting loans, AI outcomes often restrict those most in need of these services. Through personal stories from an esteemed Black Data Scientist and AI expert, this book attempts to demystify the algorithmic black box. AI pervades all aspects of modern society and affects everyone within it, yet its internal biases are rarely confronted. This book advises readers on what they can do to fight against it, including the introduction of a proposed AI Bill of Rights, whilst also providing specific recommendations for AI developers and technologists. https://hiddeninwhitesight.com/

This book demonstrates the need for and the value of interdisciplinary research in addressing important societal challenges associated with the widespread use of algorithmic decision-making. Algorithms are increasingly being used to make decisions in various domains such as criminal justice, medicine, and employment. While algorithmic tools have the potential to make decision-making more accurate, consistent, and transparent, they pose serious challenges to societal interests. For example, they can perpetuate discrimination, cause representational harm, and deny opportunities.The Societal Impacts of Algorithmic Decision-Making presents several contributions to the growing body of literature that seeks to respond to these challenges, drawing on techniques and insights from computer science, economics, and law. The author develops tools and frameworks to characterize the impacts of decision-making and incorporates models of behavior to reason about decision-making in complex environments. These technical insights are leveraged to deepen the qualitative understanding of the impacts of algorithms on problem domains including employment and lending.The social harms of algorithmic decision-making are far from being solved. While easy solutions are not presented here, there are actionable insights for those who seek to deploy algorithms responsibly. The research presented within this book will hopefully contribute to broader efforts to safeguard societal values while still taking advantage of the promise of algorithmic decision-making.

Das wertschöpfende Potenzial von Business Analytics und Daten im Unternehmen nutzen.„Dieses Buch liefert einen strukturierten Überblick über die verschiedenen Prozessschritte von Business Analytics. Im Fokus stehen dabei weniger technisch-mathematische, sondern vielmehr anwendungsspezifische Aspekte, was das Buch vor allem im betriebswirtschaftlichen Kontext zu einer wertbringenden Lektüre macht! Hervorragend als Einstieg für einen generellen Überblick & zur Einordnung des Themas Business Analytics.“ Timo Buck, Head of Applied Data Science, ratiopharm GmbH Warum schöpfen manche Unternehmen das Potenzial von Business Analytics voll aus und andere dagegen nicht? Der Grund hierfür ist nicht etwa eine mangelnde Kompetenz bei der Anwendung von Algorithmen. Vielmehr werden andere Aspekte von Business Analytics nicht ausreichend beachtet! Dazu gehören die Interpretation und Visualisierung der Ergebnisse der Algorithmen, aber auch die organisatorische Verortung der Analytics-Funktion. Dieses Buch vermittelt Führungskräften alle wesentlichen Aspekte von Business Analytics in kompakter Form. Den Rahmen dazu bilden vier Grundfragen: Für welche betriebswirtschaftlichen Probleme sollen die knappen Analytics-Ressourcen eingesetzt werden? Welche Ressourcen, also Daten, IT und Personal, sind zur Lösung der Probleme notwendig? Mit welchen Algorithmen können die Probleme gelöst werden? Wie müssen die Ergebnisse der Algorithmen interpretiert und visualisiert werden, damit Führungskräfte sie korrekt einsetzen können? Eine Besonderheit dieses Buches sind die zahlreichen Praxisbeispiele. In diesen realen Beispielen werden sämtliche Aspekte von Business Analytics verdeutlicht. Durch eine Mischung unterschiedlicher Branchen und Größenklassen werden tiefgehende Einsichten möglich. Über den Autor: Dr. Mischa Seiter ist Professor für Wertschöpfungs- und Netzwerkmanagement am Institut für Business Analytics der Universität Ulm und wissenschaftlicher Leiter des International Performance Research Institute. Er verantwortet den berufsbegleitenden Master-Studiengang „Business Analytics“, ist Mitautor des Standardwerks „Controlling“ sowie Gründer des Podcasts „Controlling-Vordenker“.

Data structure and algorithms are two of the most important aspects of computer science. Data structure and algorithms help in understanding the nature of the problem at a deeper level and thereby a better understanding of the world. Learning data structure and algorithms will help you become a better programmer. This book provides a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth, which makes their design and analysis accessible to all levels of readers. This book provides with an enjoyable introduction to the field of algorithms.

Amidst the rampant use of algorithmization enabled by AI, the common theme of AI systems is the human factor. Humans play an essential role in designing, developing, and operationalizing AI systems. We have a remit to ensure those systems run transparently, perform equitably, value our privacy, and effectively fulfill human needs. This book takes an interdisciplinary approach to contribute to the ongoing development of human–AI interaction with a particular focus on the'human'dimension and provides insights to improve the design of AI that could be genuinely beneficial and effectively used in society. The readers of this book will benefit by gaining insights into various perspectives about how AI has impacted people and society and how it will do so in the future, and understanding how we can design algorithm systems that are beneficial, legitimate, usable by humans, and designed considering and respecting human values. This book provides a horizontal set of guidelines and insight into how humans can be empowered by making choices about AI designs that allow them meaningful control over AI. Designing meaningful AI experiences has garnered great attention to address responsibility gaps and mitigate them by establishing conditions that enable the proper attribution of responsibility to humans. This book helps us understand the possibilities of what AI systems can do and how they can and should be integrated into our society.

This book comprehensively assesses the growing importance of project data for project scheduling, risk analysis and control. It discusses the relevance of project data for both researchers and professionals, and illustrates why the collection, processing and use of such data is not as straightforward as most people think. The theme of this book is known in the literature as data-driven project management and includes the discussion of using computer algorithms, human intuition, and project data for managing projects under risk. The book reviews the basic components of data-driven project management by summarizing the current state-of-the-art methodologies, including the latest computer and machine learning algorithms and statistical methodologies, for project risk and control. It highlights the importance of artificial project data for academics, and describes the specific requirements such data must meet. In turn, the book discusses a wide variety of statistical methods available to generate these artificial data and shows how they have helped researchers to develop algorithms and tools to improve decision-making in project management. Moreover, it examines the relevance of project data from a professional standpoint and describes how professionals should collect empirical project data for better decision-making. Finally, the book introduces a new approach to data collection, generation, and analysis for creating project databases, making it relevant for academic researchers and professional project managers alike.

p>If you thought data structures and algorithms were all just theory, you're missing out on what they can do for your Python code. Learn to use Big O notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code's efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. Every chapter features practice exercises to give you the hands-on information you need to master data structures and algorithms for your day-to-day work.Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. The Python edition uses Python exclusively for all code examples, exercise, and solutions.Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions.Use these techniques today to make your Python code faster and more scalable.

Computational complexity is critical in analysis of algorithms and is important to be able to select algorithms for efficiency and solvability. Algorithm and Design Complexity initiates with discussion of algorithm analysis, time-space trade-off, symptotic notations, and so forth. It further includes algorithms that are definite and effective, known as computational procedures. Further topics explored include divide-and-conquer, dynamic programming, and backtracking.Features: Includes complete coverage of basics and design of algorithms Discusses algorithm analysis techniques like divide-and-conquer, dynamic programming, and greedy heuristics Provides time and space complexity tutorials Reviews combinatorial optimization of Knapsack problem Simplifies recurrence relation for time complexity This book is aimed at graduate students and researchers in computers science, information technology, and electrical engineering.

This comprehensive compendium describes a parametric model and algorithmic theory to represent geometric entities with dependent uncertainties between them. The theory, named Linear Parametric Geometric Uncertainty Model (LPGUM), is an expressive and computationally efficient framework that allows to systematically study geometric uncertainty and its related algorithms in computer geometry.The self-contained monograph is of great scientific, technical, and economic importance as geometric uncertainty is ubiquitous in mechanical CAD/CAM, robotics, computer vision, wireless networks and many other fields. Geometric models, in contrast, are usually exact and do not account for these inaccuracies.This useful reference text benefits academics, researchers, and practitioners in computer science, robotics, mechanical engineering and related fields.

Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary.