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• Part I. An Introduction to the Techniques: 1. An introduction to approximation algorithms
• 2. Greedy algorithms and local search
• 3. Rounding data and dynamic programming
• 4. Deterministic rounding of linear programs
• 5. Random sampling and randomized rounding of linear programs
• 6. Randomized rounding of semidefinite programs
• 7. The primal-dual method
• 8. Cuts and metrics
• Part II. Further Uses of the Techniques: 9. Further uses of greedy and local search algorithms
• 10. Further uses of rounding data and dynamic programming
• 11. Further uses of deterministic rounding of linear programs
• 12. Further uses of random sampling and randomized rounding of linear programs
• 13. Further uses of randomized rounding of semidefinite programs
• 14. Further uses of the primal-dual method
• 15. Further uses of cuts and metrics
• 16. Techniques in proving the hardness of approximation
• 17. Open problems
• Appendix A. Linear programming
• Appendix B. NP-completeness.
• (source: Nielsen Book Data)

Discrete optimization problems are everywhere, from traditional operations research planning (scheduling, facility location and network design); to computer science databases; to advertising issues in viral marketing. Yet most such problems are NP-hard; unless P = NP, there are no efficient algorithms to find optimal solutions. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first section is devoted to a single algorithmic technique applied to several different problems, with more sophisticated treatment in the second section. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithm courses, it will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.
(source: Nielsen Book Data)

• Getting started. Introducing algorithms ; Considering algorithm design ; Using python to work with algorithms ; Introducing python for algorithm programming ; Performing essential data manipulations using python
• Understanding the need to sort and search. Structuring data ; Arranging and searching data
• Exploring the world of graphs. Understanding graph basics ; Reconnecting the dots ; Discovering graph secrets ; Getting the right web page
• Struggling with big data. Managing big data ; Parallelizing operations ; Compressing data
• Challenging difficult problems. Working with greedy algorithms ; Relying on dynamic programming ; Using randomized algorithms ; Performing local serach ; Employing linear programming ; Considering heuristics
• The part of tens. Ten algorithms that are changing the world ; Ten algorithmic problems yet to solve.

Discover how algorithms shape and impact our digital world All data, big or small, starts with algorithms. Algorithms are mathematical equations that determine what we see--based on our likes, dislikes, queries, views, interests, relationships, and more--online. They are, in a sense, the electronic gatekeepers to our digital, as well as our physical, world. This book demystifies the subject of algorithms so you can understand how important they are business and scientific decision making. Algorithms for Dummies is a clear and concise primer for everyday people who are interested in algorithms and how they impact our digital lives. Based on the fact that we already live in a world where algorithms are behind most of the technology we use, this book offers eye-opening information on the pervasiveness and importance of this mathematical science--how it plays out in our everyday digestion of news and entertainment, as well as in its influence on our social interactions and consumerism. Readers even learn how to program an algorithm using Python! Become well-versed in the major areas comprising algorithms Examine the incredible history behind algorithms Get familiar with real-world applications of problem-solving procedures Experience hands-on development of an algorithm from start to finish with Python If you have a nagging curiosity about why an ad for that hammock you checked out on Amazon is appearing on your Facebook page, you'll find Algorithm for Dummies to be an enlightening introduction to this integral realm of math, science, and business.
(source: Nielsen Book Data)

• Ethics at the Service of Digital Technology
• The Code is Ethics and Ethics is the Code
• The Framework for Algorithmic Processing
• Conclusion
• Appendix
• List of Abbreviations.

The technical progress illustrated by the development of Artificial Intelligence (AI), Big Data technologies, the Internet of Things (IoT), online platforms, NBICs, autonomous expert systems, and the Blockchain let appear the possibility of a new world and the emergence of a fourth industrial revolution centered around digital data. Therefore, the advent of digital and its omnipresence in our modern society create a growing need to lay ethical benchmarks against this new religion of data, the "dataisme".

It's been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain. How will open data initiatives respond to new concerns about privacy, inclusion, and artificial intelligence? And what can we learn from the last decade in order to deliver impact where it is most needed? The State of Open Data brings together over 60 authors from around the world to address these questions and to take stock of the real progress made to date across sectors and around the world, uncovering the issues that will shape the future of open data in the years to come.

• PREFACE ix ACKNOWLEDGMENTS xiii CONTRIBUTORS xv I PRELIMINARIES
• 1 A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms 3 Nasimul Noman and Hitoshi Iba
• 2 Mathematical Models and Computational Methods for Inference of Genetic Networks 30 Tatsuya Akutsu
• 3 Gene Regulatory Networks: Real Data Sources and Their Analysis 49 Yuji Zhang II EAs FOR GENE EXPRESSION DATA ANALYSIS AND GRN RECONSTRUCTION
• 4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms 69 Alan Wee-Chung Liew
• 5 Inference of Vohradsk' y s Models of Genetic Networks Using a Real-Coded Genetic Algorithm 96 Shuhei Kimura
• 6 GPU-Powered Evolutionary Design of Mass-Action-Based Models of Gene Regulation 118 Marco S. Nobile, Davide Cipolla, Paolo Cazzaniga and Daniela Besozzi
• 7 Modeling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi-Objective Setups 151 Spencer Angus Thomas, Yaochu Jin, Emma Laing and Colin Smith
• 8 Reconstruction of Large-Scale Gene Regulatory Network Using S-system Model 185 Ahsan Raja Chowdhury and Madhu Chetty III EAs FOR EVOLVING GRNs AND REACTION NETWORKS
• 9 Design Automation of Nucleic Acid Reaction System Simulated by Chemical Kinetics Based on Graph Rewriting Model 213 Ibuki Kawamata and Masami Hagiya
• 10 Using Evolutionary Algorithms to Study the Evolution of Gene Regulatory Networks Controlling Biological Development 240 Alexander Spirov and David Holloway
• 11 Evolving GRN-inspired In Vitro Oscillatory Systems 269 Quang Huy Dinh, Nathanael Aubert, Nasimul Noman, Hitoshi Iba and Yannic Rondelez IV APPLICATION OF GRN WITH EAs
• 12 Artificial Gene Regulatory Networks for Agent Control 301 Sylvain Cussat-Blanc, Jean Disset, St'ephane Sanchez and Yves Duthen
• 13 Evolving H-GRNs for Morphogenetic Adaptive Pattern Formation of Swarm Robots 327 Hyondong Oh and Yaochu Jin
• 14 Regulatory Representations in Architectural Design 362 Daniel Richards and Martyn Amos
• 15 Computing with Artificial Gene Regulatory Networks 398 Michael A. Lones INDEX.
• (source: Nielsen Book Data)

Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
(source: Nielsen Book Data)

• Chapter 1 Introduction to Grid Computing
• chapter 2 Job Submission
• chapter 3 Schedulers
• chapter 4 Security Concepts
• chapter 5 Grid Security
• chapter 6 System Infrastructure I: Web Services
• chapter 7 System Infrastructure II: Grid Computing Services / SERVICES
• chapter 8 User-Friendly Interfaces
• chapter 9 Grid-Enabling Applications.

Designed for senior undergraduate and first-year graduate students, Grid Computing: Techniques and Applications shows professors how to teach this subject in a practical way. Extensively classroom-tested, it covers job submission and scheduling, Grid security, Grid computing services and software tools, graphical user interfaces, workflow editors, .
(source: Nielsen Book Data)
Designed for senior undergraduate and first-year graduate students, Grid Computing: Techniques and Applications shows professors how to teach this subject in a practical way. Extensively classroom-tested, it covers job submission and scheduling, Grid security, Grid computing services and software tools, graphical user interfaces, workflow editors, and Grid-enabling applications. The book begins with an introduction that discusses the use of a Grid computing Web-based portal. It then examines the underlying action of job submission using a command-line interface and the use of a job scheduler. After describing both general Internet security techniques and specific security mechanisms developed for Grid computing, the author focuses on Web services technologies and how they are adopted for Grid computing. He also discusses the advantages of using a graphical user interface over a command-line interface and presents a graphical workflow editor that enables users to compose sequences of computational tasks visually using a simple drag-and-drop interface. The final chapter explains how to deploy applications on a Grid. The Grid computing platform offers much more than simply running an application at a remote site. It also enables multiple, geographically distributed computers to collectively obtain increased speed and fault tolerance. Illustrating this kind of resource discovery, this practical text encompasses the varied and interconnected aspects of Grid computing, including how to design a system infrastructure and Grid portal. Supplemental Web Resources The author's Web site offers various instructional resources, including slides and links to software for programming assignments. Many of these assignments do not require access to a Grid platform. Instead, the author provides step-by-step instructions for installing open-source software to deploy and test Web and Grid services, a Grid computing workflow editor to design and test workflows, and a Grid computing portal to deploy portlets.
(source: Nielsen Book Data)

In twenty-five concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through analogies and metaphors.This book bridges the gap that exists between purely technical books about the blockchain and purely business-focused books. It does so by explaining both the technical concepts that make up the blockchain and their role in business-relevant applications.What you'll learn: what the blockchain is; why it is needed and what problem it solves; why there is so much excitement about the blockchain and its potential; major components and their purpose; how various components of the blockchain work and interact; limitations, why they exist, and what has been done to overcome them; and major application scenarios.Who this book is for: Everyone who wants to get a general idea of what blockchain technology is, how it works, and how it will potentially change the financial system as we know it.

• Matrices and their properties
• Elimination methods for linear systems
• Determinants
• Matrix factorizations and canonical forms
• Iterative methods for linear systems
• Eigenvalues and eigenvectors
• Computing machines
• Software for numerical linear algebra
• Miscellaneous topics
• Lives and works

This expansive volume describes the history of numerical methods proposed to solve linear algebra problems, from antiquity to the present day. In the book, the authors focus on methods for solving linear systems of equations and eigenvalue problems, describe the interplay between numerical methods and the computing tools available at the time, and include 78 biographies of important contributors to the field

• Frontmatter
• Preface
• Contents
• Committees for Pattern Recognition: Infeasible Systems of Linear Inequalities, Hyperplane Arrangements, and Realizable Oriented Matroids
• 1. Oriented Matroids, the Pattern Recognition Problem, and Tope Committees
• 2. Boolean Intervals
• 3. Dehn-Sommerville Type Relations
• 4. Farey Subsequences
• 5. Blocking Sets of Set Families, and Absolute Blocking Constructions in Posets
• 6. Committees of Set Families, and Relative Blocking Constructions in Posets
• 7. Layers of Tope Committees
• 8. Three-Tope Committees
• 9. Halfspaces, Convex Sets, and Tope Committees
• 10. Tope Committees and Reorientations of Oriented Matroids
• 11. Topes and Critical Committees
• 12. Critical Committees and Distance Signals
• 13. Symmetric Cycles in the Hypercube Graphs
• Bibliography
• List of Notation
• Index.

Pattern Recognition on Oriented Matroids covers a range of innovative problems in combinatorics, poset and graph theories, optimization, and number theory that constitute a far-reaching extension of the arsenal of committee methods in pattern recognition. The groundwork for the modern committee theory was laid in the mid-1960s, when it was shown that the familiar notion of solution to a feasible system of linear inequalities has ingenious analogues which can serve as collective solutions to infeasible systems. A hierarchy of dialects in the language of mathematics, for instance, open cones in the context of linear inequality systems, regions of hyperplane arrangements, and maximal covectors (or topes) of oriented matroids, provides an excellent opportunity to take a fresh look at the infeasible system of homogeneous strict linear inequalities - the standard working model for the contradictory two-class pattern recognition problem in its geometric setting. The universal language of oriented matroid theory considerably simplifies a structural and enumerative analysis of applied aspects of the infeasibility phenomenon. The present book is devoted to several selected topics in the emerging theory of pattern recognition on oriented matroids: the questions of existence and applicability of matroidal generalizations of committee decision rules and related graph-theoretic constructions to oriented matroids with very weak restrictions on their structural properties; a study (in which, in particular, interesting subsequences of the Farey sequence appear naturally) of the hierarchy of the corresponding tope committees; a description of the three-tope committees that are the most attractive approximation to the notion of solution to an infeasible system of linear constraints; an application of convexity in oriented matroids as well as blocker constructions in combinatorial optimization and in poset theory to enumerative problems on tope committees; an attempt to clarify how elementary changes (one-element reorientations) in an oriented matroid affect the family of its tope committees; a discrete Fourier analysis of the important family of critical tope committees through rank and distance relations in the tope poset and the tope graph; the characterization of a key combinatorial role played by the symmetric cycles in hypercube graphs. Contents Oriented Matroids, the Pattern Recognition Problem, and Tope Committees Boolean Intervals Dehn-Sommerville Type Relations Farey Subsequences Blocking Sets of Set Families, and Absolute Blocking Constructions in Posets Committees of Set Families, and Relative Blocking Constructions in Posets Layers of Tope Committees Three-Tope Committees Halfspaces, Convex Sets, and Tope Committees Tope Committees and Reorientations of Oriented Matroids Topes and Critical Committees Critical Committees and Distance Signals Symmetric Cycles in the Hypercube Graphs.
(source: Nielsen Book Data)

• List of Figures xiii
• List of Tables xix
• Preface xxiii
• Acknowledgments xxv
• Chapter 1 Survey 1
• 1.1 Introduction of Kernel Analysis 1
• 1.2 Kernel Offline Learning 2
• 1.2.1 Choose the Appropriate Kernels 3
• 1.2.2 Adopt KA into the Traditionally Developed Machine Learning Techniques 6
• 1.2.3 Structured Database with Kernel 9
• 1.3 Distributed Database with Kernel 12
• 1.3.1 Multiple Database Representation 12
• 1.3.2 Kernel Selections Among Heterogeneous Multiple Databases 13
• 1.3.3 Multiple Database Representation KA Applications to Distributed Databases 14
• 1.4 Kernel Online Learning 16
• 1.4.1 Kernel-Based Online Learning Algorithms 16
• 1.4.2 Adopt Online KA Framework into the Traditionally Developed Machine Learning Techniques 17
• 1.4.3 Relationship Between Online Learning and Prediction Techniques 21
• 1.5 Prediction with Kernels 22
• 1.5.1 Linear Prediction 22
• 1.5.2 Kalman Filter 23
• 1.5.3 Finite-State Model 23
• 1.5.4 Autoregressive Moving Average Model 24
• 1.5.5 Comparison of Four Models 25
• 1.6 Future Direction and Conclusion 26
• References 26
• Chapter 2 Offline Kernel Analysis 41
• 2.1 Introduction 41
• 2.2 Kernel Feature Analysis 43
• 2.2.1 Kernel Basics 43
• 2.2.2 Kernel Principal Component Analysis (KPCA) 45
• 2.2.3 Accelerated Kernel Feature Analysis (AKFA) 46
• 2.2.4 Comparison of the Relevant Kernel Methods 48
• 2.3 Principal Composite Kernel Feature Analysis (PC-KFA) 49
• 2.3.1 Kernel Selections 49
• 2.3.2 Kernel Combinatory Optimization 52
• 2.4 Experimental Analysis 54
• 2.4.1 Cancer Image Datasets 54
• 2.4.2 Kernel Selection 56
• 2.4.3 Kernel Combination and Reconstruction 58
• 2.4.4 Kernel Combination and Classification 59
• 2.4.5 Comparisons of Other Composite Kernel Learning Studies 60
• 2.4.6 Computation Time 61
• 2.5 Conclusion 62
• References 63
• Chapter 3 Group Kernel Feature Analysis 69
• 3.1 Introduction 69
• 3.2 Kernel Principal Component Analysis (KPCA) 71
• 3.3 Kernel Feature Analysis (KFA) for Distributed Databases 73
• 3.3.1 Extract Data-Dependent Kernels Using KFA 73
• 3.3.2 Decomposition of Database Through Data Association via Recursively Updating Kernel Matrices 75
• 3.4 Group Kernel Feature Analysis (GKFA) 78
• 3.4.1 Composite Kernel: Kernel Combinatory Optimization 79
• 3.4.2 Multiple Databases Using Composite Kernel 81
• 3.5 Experimental Results 83
• 3.5.1 Cancer Databases 83
• 3.5.2 Optimal Selection of Data-Dependent Kernels 84
• 3.5.3 Kernel Combinatory Optimization 84
• 3.5.4 Composite Kernel for Multiple Databases 86
• 3.5.5 K-NN Classification Evaluation with ROC 87
• 3.5.6 Comparison of Results with Other Studies on Colonography 89
• 3.5.7 Computational Speed and Scalability Evaluation of GKFA 90
• 3.6 Conclusions 91
• References 92
• Chapter 4 Online Kernel Analysis 97
• 4.1 Introduction 97
• 4.2 Kernel Basics: A Brief Review 99
• 4.2.1 Kernel Principal Component Analysis 99
• 4.2.2 Kernel Selection 100
• 4.3 Kernel Adaptation Analysis of PC-KFA 101
• 4.4 Heterogeneous vs. Homogeneous Data for Online PC-KFA 102
• 4.4.1 Updating the Gram Matrix of the Online Data 103
• 4.4.2 Composite Kernel for Online Data 104
• 4.5 Long-Term Sequential Trajectories with Self-Monitoring 104
• 4.5.1 Reevaluation of Large Online Data 105
• 4.5.2 Validation of Decomposing Online Data into Small Chunks 106
• 4.6 Experimental Results 107
• 4.6.1 Cancer Datasets 107
• 4.6.2 Selection of Optimum Kernel and Composite Kernel for Offline Data 108
• 4.6.3 Selection of Optimum Kernel and Composite Kernel for the New Online Sequences 110
• 4.6.4 Classification of Heterogeneous Versus Homogeneous Data 111
• 4.6.5 Online Learning Evaluation of Long-term Sequence 112
• 4.6.6 Evaluation of Computational Time 116
• 4.7 Conclusions 117
• References 117
• Chapter 5 Cloud Kernel Analysis 121
• 5.1 Introduction 121
• 5.2 Cloud Environments 123
• 5.2.1 Server Specifications of Cloud Platforms 123
• 5.2.2 Cloud Framework of KPCA for AMD 124
• 5.3 AMD for Cloud Colonography 125
• 5.3.1 AMD Concept 125
• 5.3.2 Data Configuration of AMD 125
• 5.3.3 Implementation of AMD for Two Cloud Cases 129
• 5.3.4 Parallelization of AMD 132
• 5.4 Classification Evaluation of Cloud Colonography 135
• 5.4.1 Databases with Classification Criteria 135
• 5.4.2 Classification Results 137
• 5.5 Cloud Computing Performance 140
• 5.5.1 Cloud Computing Setting with Cancer Databases 140
• 5.5.2 Computation Time 142
• 5.5.3 Memory Usage 144
• 5.5.4 Running Cost 145
• 5.5.5 Parallelization 145
• 5.6 Conclusions 146
• References 147
• Chapter 6 Predictive Kernel Analysis 153
• 6.1 Introduction 153
• 6.2 Kernel Basics 154
• 6.2.1 KPCA and AKFA 155
• 6.3 Stationary Data Training 157
• 6.3.1 Kernel Selection 157
• 6.3.2 Composite Kernel: Kernel Combinatory Optimization 159
• 6.4 Longitudinal Nonstationary Data with Anomaly/Normal Detection 160
• 6.4.1 Updating the Gram Matrix Based on Nonstationary Longitudinal Data 160
• 6.4.2 Composite Kernel for Nonstationary Data 162
• 6.5 Longitudinal Sequential Trajectories for Anomaly Detection and Prediction 163
• 6.5.1 Anomaly Detection of Nonstationary Small Chunks Datasets 164
• 6.5.2 Anomaly Prediction of Long-Time Sequential Trajectories 167
• 6.6 Classification Results 169
• 6.6.1 Cancer Datasets 169
• 6.6.2 Selection of Optimum Kernel and Composite Kernel for Stationary Data 170
• 6.6.3 Comparisons with Other Kernel Learning Methods 172
• 6.6.4 Anomaly Detection for the Nonstationary Data 174
• 6.7 Longitudinal Prediction Results 175
• 6.7.1 Large Nonstationary Sequential dataset for Anomaly Detection 175
• 6.7.2 Time Horizontal Prediction for Risk Factor Analysis of Anomaly Long-Time Sequential Trajectories 178
• 6.7.3 Computational Time for Complexity Evaluation 179
• 6.8 Conclusions 180
• References 181
• Chapter 7 Conclusion 185
• Appendix A 189
• Appendix B Representative Matlab codes 195
• B.1 Accelerated Kernel Feature Analysis 196
• B.2 Experimental Evaluations 198
• B.3 Group Kernel Analysis 201
• B.4 Online Composite Kernel Analysis 206
• B.5 Online Data Sequences Control 208
• B.6 Alignment Factor 209
• B.7 Cloud Kernel Analysis 210
• B.8 Plot Computation Time 211
• B.9 Parallelization 212
• Index 215.
• (source: Nielsen Book Data)

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.
(source: Nielsen Book Data)

• 1 Introduction
• 2 Representations of the Integers
• 3 Computing with Integers
• 4 Approximate Representations of the Real Numbers
• 5 Computing with Errors
• 6 Graphs
• 7 Simple Graph Algorithms
• 8 Sorting Algorithms
• 9 Optimal Trees and Paths
• 10 Matchings and Network Flows
• 11 Gaussian Elimination
• Bibliography
• Index.

Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.

• Cover; Table of Contents; Preface;
• Chapter 1: Understanding your Project; Reasons for conversion; Functional reasons; Technical reasons; Understanding the functionality; The application; Business process; User interaction; User roles; Understanding the technicality; Components; Architecture; Forms builder; Modules and iterations; Modules; Iterations; Summary;
• Chapter 2: Preparing your Forms Conversion; Get your stuff!; Creating XML files; The Forms2XML conversion tool; Forms Modules; Object Libraries; Forms Menus; Report Files; PL/SQL Libraries; Understanding XML; The target database; Summary

• Chapter 3: Create your Forms Conversion projectGetting started; Creating the project; Adding additional sources; The project page; Editing the project; Deleting the project; Editing project details; Applicability; Set application defaults; Summary;
• Chapter 4: Planning your Project; The project page; Inside our project; Component; Count; Equivalent component; Implementation Details; Included; File Name; Applicable; What we need to do; Blocks; Triggers; Lists of Values; Alerts; Program Units; Component annotations; Completion status; Assign developers; Project planning; Using annotations

• Applicability and completenessAssignees; Tags; Summary;
• Chapter 5: Getting your Logic Right!; Pre-generation editing; Investigating; Data blocks; Block items; Original versus Enhanced Query; Triggers; Custom Query; Generation; Editing; Analyzing business logic; Alerts; Program units; Libraries; Triggers; Summary;
• Chapter 6: Generating your Application; Setting the project; Start the generation; Application design models; Check the pages; Adding pages; Selecting a theme; Create the application; Run the application; Summary;
• Chapter 7: Reviewing and Customizing your Application; The home page

• Lists of ValuesValidations; Back to the project page; Titles and names; Summary;
• Chapter 8: Delivering your Application; Steps in application delivery; Integrating modules and applications; Authentication integration; Integrating with Oracle Single Sign-On; User acceptance; Training; Deploying; Exporting the application; Importing the application; Summary; Index

Packed with plentiful screenshots and clear explanations, this book is a step-by-step tutorial that demonstrates a successful Oracle Forms to Oracle APEX Conversion project. This book is for Oracle Forms developers who want to migrate to Oracle APEX. The book will also be useful for Oracle APEX developers who want to convert their older Forms applications to Oracle APEX applications.
(source: Nielsen Book Data)

• Multi-agent systems.- swarm robotics.- fuzzy logic approaches.- planning and routing problems.- recommendation in social media.- predication.- classification.- finding patterns.- image enhancement.- deep learning.- theories and models of swarm intelligence.- ant colony optimization.- particle swarm optimization.- artificial bee colony algorithms.- genetic algorithms.- differential evolution.- fireworks algorithm.- bacterial foraging optimization.- artificial immune system.- hydrologic cycle optimization.- other swarm-based optimization algorithms.- hybrid optimization algorithms.- multi-objective optimization.- large-scale global optimization. .
• (source: Nielsen Book Data)

The two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections namely: multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; predication; classification; finding patterns; image enhancement; deep learning; theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization.
(source: Nielsen Book Data)

• 1. From the philosophy of trace to digital traces
• 2. Formalism associated with algorithmic projections
• 3. Connected objects, a location's ubiquity level and the user's algorithmic consent
• 4. On the value of data and algorithmic projection
• 5. False data and fictitious algorithmic projections
• 6. High-impact cyber-operations built on fictitious algorithmic projections
• 7. Prospective epilogue: global algorithmic projection and NBIC convergence.

Individual's digital fingerprints interacts with the different algorithms they encounter throughout life. Centered on the human user, this formalism makes it possible to distinguish the voluntary projections of an individual and their systemic projections (suffered, metadata), both open (public) and closed. This book discusses the ubiquity of place and the algorithmic consent of a user.
From Digital Traces to Algorithmic Projections describes individual digital fingerprints in interaction with the different algorithms they encounter throughout life. Centered on the human user, this formalism makes it possible to distinguish the voluntary projections of an individual and their systemic projections (suffered, metadata), both open (public) and closed. As the global algorithmic projection of an individual is now the focus of attention (Big Data, neuromarketing, targeted advertising, sentiment analysis, cybermonitoring, etc.) and is used to define new concepts, this resource discusses the ubiquity of place and the algorithmic consent of a user.

As a cookbook, this book enables you to create APEX web applications and to implement features with immediately usable recipes that unleash the powerful functionality of Oracle APEX 4.0. Each recipe is presented as a separate, standalone entity and reading of other prior recipes is not required. It can be seen as a reference and a practical guide to APEX development. This book is aimed both at developers new to the APEX environment and at intermediate developers. More advanced developers will also gain from the information at hand. If you are new to APEX you will find recipes to start development. If you are an experienced user you will find ways to work smarter and more easily with APEX and enhance your applications. A little knowledge of PL/SQL, HTML and JavaScript is assumed.
(source: Nielsen Book Data)

• Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: Performance Thoughts; Understanding performance; Performance as a requirement; Performance engineering; Performance aspects; Latency; Throughput; Resource usage; Availability/reliability; Scalability; Efficiency; Class of applications; Case study: performance aspects of a desktop application; Case study: performance aspects of a mobile application; Case study: performance aspects of a server application; Performance concerns as time changes; Technical overview

• Multithreaded programmingParallel programming; Distributed computing; Grid computing; Summary;
• Chapter 2: Architecting High-performance .NET Code; Software architecture; Performance concerns about architecture; Object-oriented design principles; Single responsibility principle; The open-closed principle; The Liskov substitution principle; That interface segregation principle; The dependency inversion principle; Common designs and architectures; Layered architecture; Performance concerns; Model-View-Controller and ASP.NET MVC; Performance concerns; Model-View-ViewModel and XAML

• Performance concerns3-tier architecture; Performance concerns; Service-Oriented Architecture (SOA); Standardized service contract; Service loose coupling; Service abstraction; Service reusability; Service autonomy; Service statelessness; Service discoverability; Service composability; Performance concerns; Architecture comparison; Common platform architectures; Architecting desktop applications; Architecting mobile applications; Architecting web applications; Architecting cloud web applications; Performance considerations; Caching, when and where; PLINQ everywhere; Inversion of Control (IoC)

• Lazy loadingReusability of code; Agnostic versus idiom-powered implementation; Short coding; Remote computation; Cloud versus on-premise applications; Summary;
• Chapter 3: CLR Internals; Introduction to CLR; Memory management; Garbage collection; Large object heap; Collection tuning; Working with AppDomains; IDisposable interface; Threading; Multithreading synchronization; Locks; Signaling locks; Drawbacks of locks; Exception handling; Summary;
• Chapter 4: Asynchronous Programming; Understanding asynchronous programming; Asynchronous programming theory; Asynchronous Programming Model (APM)

• Event-based Asynchronous Pattern (EAP)Task-based Asynchronous Pattern (TAP); Task creation; Task synchronization; Task exception handling; Task cancellation; Task continuation; Task factories; Task UI synchronization; Async/await; Summary;
• Chapter 5: Programming for Parallelism; Parallel programming; Task parallelism; Data parallelism; Task parallelism with TPL; Data parallelism with TPL; ThreadPool tuning; Parallel execution abortion; Partitions; Sliding parallel programming; Integrated querying with LINQ; Data parallelism with PLINQ; Partitioning optimization; Summary

If you are a .NET developer with an understanding of application development, but want to learn how to optimize the performance of your applications, this is the book for you. Basic knowledge of C# is expected.
(source: Nielsen Book Data)

• Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: Performance Thoughts; Understanding performance; Performance as a requirement; Performance engineering; Performance aspects; Latency; Throughput; Resource usage; Availability/reliability; Scalability; Efficiency; Class of applications; Case study: performance aspects of a desktop application; Case study: performance aspects of a mobile application; Case study: performance aspects of a server application; Performance concerns as time changes; Technical overview

• Multithreaded programmingParallel programming; Distributed computing; Grid computing; Summary;
• Chapter 2: Architecting High-performance .NET Code; Software architecture; Performance concerns about architecture; Object-oriented design principles; Single responsibility principle; The open-closed principle; The Liskov substitution principle; That interface segregation principle; The dependency inversion principle; Common designs and architectures; Layered architecture; Performance concerns; Model-View-Controller and ASP.NET MVC; Performance concerns; Model-View-ViewModel and XAML

• Performance concerns3-tier architecture; Performance concerns; Service-Oriented Architecture (SOA); Standardized service contract; Service loose coupling; Service abstraction; Service reusability; Service autonomy; Service statelessness; Service discoverability; Service composability; Performance concerns; Architecture comparison; Common platform architectures; Architecting desktop applications; Architecting mobile applications; Architecting web applications; Architecting cloud web applications; Performance considerations; Caching, when and where; PLINQ everywhere; Inversion of Control (IoC)

• Lazy loadingReusability of code; Agnostic versus idiom-powered implementation; Short coding; Remote computation; Cloud versus on-premise applications; Summary;
• Chapter 3: CLR Internals; Introduction to CLR; Memory management; Garbage collection; Large object heap; Collection tuning; Working with AppDomains; IDisposable interface; Threading; Multithreading synchronization; Locks; Signaling locks; Drawbacks of locks; Exception handling; Summary;
• Chapter 4: Asynchronous Programming; Understanding asynchronous programming; Asynchronous programming theory; Asynchronous Programming Model (APM)

• Event-based Asynchronous Pattern (EAP)Task-based Asynchronous Pattern (TAP); Task creation; Task synchronization; Task exception handling; Task cancellation; Task continuation; Task factories; Task UI synchronization; Async/await; Summary;
• Chapter 5: Programming for Parallelism; Parallel programming; Task parallelism; Data parallelism; Task parallelism with TPL; Data parallelism with TPL; ThreadPool tuning; Parallel execution abortion; Partitions; Sliding parallel programming; Integrated querying with LINQ; Data parallelism with PLINQ; Partitioning optimization; Summary

If you are a .NET developer with an understanding of application development, but want to learn how to optimize the performance of your applications, this is the book for you. Basic knowledge of C# is expected.
(source: Nielsen Book Data)

Wyci?gnij najlepsze wnioski z dost?pnych danych!Maszyna my?l?ca jak cz?owiek to marzenie ludzko?ci. Dzi?ki dzisiejszej wiedzy i dost?pnym narz?dziom wci?? przybli?amy si? do jego spe?nienia. Zastanawiasz si?, jak nauczy? maszyn? my?lenia? Jak sprawi?, ?eby podejmowa?a trafne decyzje oraz przewidywa?a najbli?sz? przysz?o?? na podstawie przygotowanych modeli? Na to i wiele innych pyta? odpowiada ta wspania?a ksi??ka. Dzi?ki niej poznasz j?zyk R, nauczysz si? eksplorowa? dost?pne dane, okre?la? warto?? mediany i odchylenia standardowego oraz wizualizowa? powi?zania mi?dzy kolumnami. Gdy opanujesz.