Computer und IT
Trustworthy AI
AN ESSENTIAL RESOURCE ON ARTIFICIAL INTELLIGENCE ETHICS FOR BUSINESS LEADERSIn Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI.Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents:* In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more * A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application * Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI.BEENA AMMANATH is a global thought leader in AI ethics and an award-winning senior technology executive with extensive experience across a variety of industries. She is currently Executive Director of the Global Deloitte AI Institute and Founder of Humans For AI. She has also worked with companies such as General Electric, Bank of America, Hewlett Packard Enterprise, Thomson Reuters, British Telecom, and more. She has served on the boards of several tech startups, nonprofits and universities.HTTP://WWW.BEENAMMANATH.COM/ForewordPrefaceAcknowledgmentsIntroduction1 A Primer on Modern AI2 Fair and Impartial3 Robust and Reliable4 Transparent5 Explainable6 Secure7 Safe8 Privacy9 Accountable10 Responsible11Trustworthy AI in Practice12 Looking ForwardIndex
Modern Parallel Programming with C++ and Assembly Language
Learn the fundamentals of x86 Single instruction multiple data (SIMD) programming using C++ intrinsic functions and x86-64 assembly language. This book emphasizes x86 SIMD programming topics and technologies that are relevant to modern software development in applications which can exploit data level parallelism, important for the processing of big data, large batches of data and related important in data science and much more.Modern Parallel Programming with C++ and Assembly Language is an instructional text that explains x86 SIMD programming using both C++ and assembly language. The book’s content and organization are designed to help you quickly understand and exploit the SIMD capabilities of x86 processors. It also contains an abundance of source code that is structured to accelerate learning and comprehension of essential SIMD programming concepts and algorithms.After reading this book, you will be able to code performance-optimized AVX, AVX2, and AVX-512 algorithms using either C++ intrinsic functions or x86-64 assembly language.WHAT YOU WILL LEARN* Understand the essential details about x86 SIMD architectures and instruction sets including AVX, AVX2, and AVX-512.* Master x86 SIMD data types, arithmetic instructions, and data management operations using both integer and floating-point operands.* Code performance-enhancing functions and algorithms that fully exploit the SIMD capabilities of a modern x86 processor.* Employ C++ intrinsic functions and x86-64 assembly language code to carry out arithmetic calculations using common programming constructs including arrays, matrices, and user-defined data structures.* Harness the x86 SIMD instruction sets to significantly accelerate the performance of computationally intense algorithms in applications such as machine learning, image processing, computer graphics, statistics, and matrix arithmetic.* Apply leading-edge coding strategies and techniques to optimally exploit the x86 SIMD instruction sets for maximum possible performance. WHO THIS BOOK IS FORIntermediate to advanced programmers/developers in general. Readers of this book should have previous programming experience with modern C++ (i.e., ANSI C++11 or later) and Assembly. Some familiarity with Microsoft’s Visual Studio or the GNU toolchain will be helpful. The target audience for Modern X86 SIMD Programming are experienced software developers, programmers and maybe some hobbyists.DANIEL KUSSWURM has over 35 years of professional experience as a software developer, computer scientist, and author. During his career, he has developed innovative software for medical devices, scientific instruments, and image processing applications. On many of these projects, he successfully employed C++ intrinsic functions, x86 assembly language, and SIMD programming techniques to significantly improve the performance of computationally intense algorithms or solve unique programming challenges. His educational background includes a BS in electrical engineering technology from Northern Illinois University along with an MS and PhD in computer science from DePaul University. Daniel Kusswurm is also the author of Modern X86 Assembly Language Programming (ISBN: 978-1484200650), Modern X86 Assembly Language Programming, Second Edition (ISBN: 978-1484240625), and Modern Arm Assembly Language Programming (ISBN: 978 1484262665), all published by Apress.Modern X86 SIMD Programming – Outline Page 1 of 7D. Kusswurm – F:\ModX86SIMD\Outline\ModernX86SIMD_Outline (v1).docxIntroductionThe Introduction presents an overview of the book and includes concise descriptions of each chapter. It also summaries thehardware and software tools required to use the book’s source code.OverviewTarget AudienceChapter DescriptionsSource CodeAdditional ResourcesChapter 1 – SIMD FundamentalsChapter 1 discusses SIMD fundamentals including data types, basic arithmetic, and common data manipulation operations.Understanding of this material is necessary for the reader to successfully comprehend the book’s subsequent chapters.What is SIMD?Simple C++ example (Ch01_01)Brief History of x86 SIMD Instruction Set ExtensionsMMXSSE – SSE4.2AVX, AVX2, and AVX-512SIMD Data TypesFundamental types128b, 256b, 512bInteger typesPacked i8, i16, i32, i64 (signed and unsigned)Floating-point typesPacked f16/b16, f32 and f64Little-endian storageSIMD ArithmeticIntegerAddition and subtractionWraparound vs. saturatedMultiplicationBitwise logicalFloating-pointAddition, subtraction, multiplication, division, sqrtHorizontal addition and subtractionFused multiply-accumulate (FMA)SIMD OperationsIntegerMin & maxComparesShuffles, permutations, and blends Size promotions and reductionsFloating-pointMin & maxComparesShuffles, permutations, and blendsSize promotions and reductionsModern X86 SIMD Programming – Outline Page 2 of 7D. Kusswurm – F:\ModX86SIMD\Outline\ModernX86SIMD_Outline (v1).docxMasked movesConditional execution and merging (AVX-512)SIMD Programming OverviewC++ compiler optionsC++ SIMD intrinsic functionsAssembly language functionsTesting for AVX, AVX2, and AVX-512Chapter 2 – AVX C++ Programming - Part 1Chapter 2 teaches AVX integer arithmetic and other operations using C++ intrinsic functions. It also discusses how to code afew simple image processing algorithms using C++ intrinsic functions and AVX instructions.Basic Integer ArithmeticAddition (Ch02_01)Subtraction (Ch02_02)Multiplication (Ch02_03)Common Integer OperationsBitwise logical operations (Ch02_04)Arithmetic and logical shifts (Ch02_05)Image Processing AlgorithmsPixel minimum and maximum (Ch02_06) Pixel mean (Ch02_07)Chapter 3 – AVX C++ Programming - Part 2Chapter 3 is similar to the previous chapter but emphasizes floating-point instead of integer values. This chapter alsoexplains how to employ C++ intrinsic functions to perform SIMD arithmetic operations using floating-point arrays andmatrices.Basic Floating-Point Arithmetic Addition, subtraction, etc. (Ch03_01)Compares (Ch03_02)Conversions (Ch03_03)Floating-Point ArraysArray mean and standard deviation (Ch03_04, Ch03_05)Array square roots and compares (Ch03_06, Ch03_07)Floating-Point MatricesMatrix column means (Ch03_08, Ch03_09)Chapter 4 – AVX2 C++ Programming - Part 1Chapter 4 describes AVX2 integer programming using C++ intrinsic functions. This chapter also highlights the coding of moresophisticated image processing functions using the AVX2 instruction set.Basic Integer ArithmeticAddition and subtraction (Ch04_01) Pack and unpack operations (Ch04_02)Size promotions (Ch04_03)Image Processing AlgorithmsPixel clipping (Ch04_04)RGB to grayscale (Ch04_05)Modern X86 SIMD Programming – Outline Page 3 of 7D. Kusswurm – F:\ModX86SIMD\Outline\ModernX86SIMD_Outline (v1).docxThresholding (Ch04_06)Pixel conversions (Ch04_07)Chapter 5 – AVX2 C++ Programming - Part 2Chapter 5 explains how to accelerate the performance of commonly used floating-point algorithms using C++ intrinsicfunctions and the AVX2 instruction set. The source code examples in this chapter also demonstrate use of FMA (fusedmultiply-add) arithmetic.Floating-Point ArraysLeast squares with FMA (Ch05_01)Floating-Point MatricesMatrix multiplication (Ch05_02, Ch05_03)Matrix (4x4) multiplication (Ch05_04, Ch05_05)Matrix (4x4) vector multiplication (Ch05_06)Matrix inversion (Ch05_07, Ch05_08)Chapter 6 – AVX2 C++ Programming - Part 3Chapter 6 is a continuation of the previous chapter. It focuses on more advanced algorithms and SIMD programmingtechniques.Signal ProcessingBrief overview of convolution arithmetic1D ConvolutionsVariable and fixed width kernels (Ch06_01, Ch06_02)2D ConvolutionsNon-separable kernel (Ch06_03)Separable kernel (Ch06_04)Chapter 7 – AVX-512 C++ Programming - Part 1Chapter 7 explains AVX-512 integer arithmetic and other operations using C++ intrinsic functions. It also discusses how tocode a few basic image processing algorithms using the AVX-512 instruction set.Integer ArithmeticAddition and subtraction (Ch07_01)Masked arithmetic (Ch07_02)Image ProcessingRGB to grayscale (Ch07_03)Image thresholding (Ch07_04)Image statistics (Ch07_05)Chapter 8 – AVX-512 C++ Programming - Part 2Chapter 8 describes how to code common and advanced floating-point algorithms using C++ intrinsic functions and the AVX512 instruction set.Floating-Point ArithmeticAddition, subtraction, etc. (Ch08_01)Masked operations (Ch08_02)Floating-Point ArraysArray mean and standard deviation (Ch08_03)Modern X86 SIMD Programming – Outline Page 4 of 7D. Kusswurm – F:\ModX86SIMD\Outline\ModernX86SIMD_Outline (v1).docxFloating-Point MatricesCovariance matrix (Ch08_04)Matrix multiplication (Ch08_05, Ch08_06)Matrix (4x4) vector multiplication (Ch08_07)Signal Processing1D convolution using variable and fixed width kernels (Ch08_08)2D convolutions using separable kernel (Ch08_09)Chapter 9 – Supplemental C++ SIMD ProgrammingChapter 9 examines supplemental x86 SIMD programming topics including instruction set detection, how to use SIMD mathlibrary functions, and SIMD operations using text strings.Instruction set detection (Ch09_01)SIMD Math Library FunctionsRectangular to polar coordinate conversions (Ch09_02)Body surface area calculations (Ch09_03)SIMD String OperationsString length (Ch09_04)Chapter 10 – X86 Processor ArchitectureChapter 10 explains x86 processor architecture including data types, register sets, memory addressing modes, and conditioncodes. Knowledge of this material is necessary for the reader to successfully understand the subsequent x86 assemblylanguage programming chapters.Data typesFundamental data typesNumerical data types SIMD data typesStringsInternal architectureGeneral-purpose registersRFLAGS registerMXCSR registerScalar FP and SIMD registersMemory addressingCondition codesChapter 11 – Core Assembly Language Programming – Part 1Chapter 11 teaches fundamental x86-64 assembly language programming and basic instruction use. Understanding of thismaterial is required to comprehend the source code examples in subsequent chapters.Integer ArithmeticAddition and subtraction (Ch11_01)Multiplication (Ch11_02)Division (Ch11_03)Mixed integer types and stack arguments (Ch11_04)Integer OperationsMemory addressing modes (Ch11_05)Simple for-loops (Ch11_06)Modern X86 SIMD Programming – Outline Page 5 of 7D. Kusswurm – F:\ModX86SIMD\Outline\ModernX86SIMD_Outline (v1).docxCompares (Ch11_07)Text StringsString instructions (Ch11_08)Chapter 12 – Core Assembly Language Programming – Part 2Chapter 12 is a continuation of the previous chapter. Topics discussed include scalar floating-point arithmetic, floating-pointarrays, and function calling conventions.Scalar Floating-Point ArithmeticSingle-precision arithmetic (Ch12_01)Double-precision arithmetic (Ch12_02)Compares (Ch12_03)Conversions (Ch12_04)Scalar Floating-Point Arrays Mean, SD (Ch12_05)Function Calling ConventionStack frames (Ch12_06)Using non-volatile general-purpose registers (Ch12_07)Using non-volatile SIMD registers (Ch12_08)Macros for function prologues and epilogues (Ch12_09)Chapter 13 – AVX Assembly Language Programming – Part 1Chapter 13 explains AVX integer arithmetic and other operations using x86-64 assembly language. It also describes how tocode a few simple image processing algorithms using assembly language.Integer ArithmeticAddition and subtraction (Ch13_01)Multiplication (Ch13_02)Common Integer Operations Bitwise logical operations (Ch13_03)Arithmetic and logical shifts (Ch13_04)Image Processing AlgorithmsPixel minimum and maximum (Ch13_05)Pixel mean (Ch13_06)Chapter 14 – AVX Assembly Language Programming – Part 2Chapter 14 is similar to the previous chapter but uses floating-point instead of integer values. This chapter also illustrateshow to employ x86-64 assembly language to perform SIMD arithmetic operations using arrays and matrices.Basic Floating-Point ArithmeticAddition and subtraction, etc. (Ch14_01)Compares and size conversions (Ch14_02)Floating-Point ArraysArray mean and standard deviation (Ch14_03)Array square roots and compares (Ch14_04)Floating-Point MatricesMatrix column means (Ch14_05)Modern X86 SIMD Programming – Outline Page 6 of 7D. Kusswurm – F:\ModX86SIMD\Outline\ModernX86SIMD_Outline (v1).docxChapter 15 – AVX2 Assembly Language Programming – Part 1Chapter 15 describes AVX2 integer programming using x86-64 assembly language. This chapter also highlights the coding ofmore sophisticated image processing functions using the AVX2 instruction set.Integer ArithmeticAddition and subtraction (Ch15_01)Image ProcessingPixel clipping (Ch15_02)RGB to grayscale (Ch15_03)Thresholding (Ch15_04)Pixel conversions (Ch15_05)Chapter 16 – AVX2 Assembly Language Programming – Part 2Chapter 16 explains how to enhance the performance of frequently used floating-point algorithms using x86-64 assemblylanguage and the AVX2 instruction set.Floating-Point ArraysLeast squares with FMA (Ch16_01)Floating-Point MatricesMatrix multiplication (Ch16_02)Matrix (4x4) multiplication (Ch16_03)Matrix (4x4) vector multiplication (Ch16_04)Signal Processing1D convolutions using fixed and variable width kernels (Ch16_05)Chapter 17 – AVX-512 Assembly Language Programming – Part 1Chapter 17 highlights AVX-512 integer arithmetic and other operations using x86-64 assembly language. It also discusseshow to code a few simple image processing algorithms using the AVX-512 instruction set.Integer ArithmeticAddition and subtraction (Ch17_01)Compares, merge masking, and zero-masking (Ch17_02)Image ProcessingPixel clipping (Ch17_03)Image statistics (Ch17_04)Chapter 18 – AVX-512 Assembly Language Programming – Part 2Chapter 18 explains how to code common and advanced floating-point algorithms using x86-64 assembly language and theand the AVX-512 instruction set.Floating-Point ArraysCorrelation coefficient (Ch18_01)Merge and zero masking (Ch18_02)Embedded rounding and broadcasts (Ch18_03)Floating-Point MatricesMatrix (4x4) vector multiplication (Ch18_04)Signal Processing 1D convolutions using fixed and variable width kernels (Ch18_05)Modern X86 SIMD Programming – Outline Page 7 of 7D. Kusswurm – F:\ModX86SIMD\Outline\ModernX86SIMD_Outline (v1).docxAppendix A – Source Code and Development ToolsAppendix A describes how to download, install, and execute the source code. It also includes some brief usage notesregarding Visual Studio and the GNU C++ compiler.Source Code Download InformationSoftware Development ToolsMicrosoft Visual StudioGNU C++ compilerAppendix B – References and Additional ResourcesAppendix B contains a list of references that were consulted during the writing of this book. It also lists supplementalresources that the reader can consult for additional x86 SIMD programming information.X86 SIMD Programming ReferencesAlgorithm ReferencesC++ ReferencesAdditional Resources
Raspberry Pi Retail Applications
Increase productivity and lower the operating cost of your business by automating crucial business processes with the Raspberry Pi. After completing this book's projects you'll be able to determine the best way to quickly apply automation to existing systems and processes in your retail outlet with Raspberry Pi.You’ll start by composing ideas to transform your business, and then gain practical, accessible methods for executing them. Find real-world ways to implement tech solutions to accelerate the growth of your business, and navigate the ever-changing landscape of retail automation with this book. Then see what automation can and cannot do at the current level of technical progress for retail businesses. By comparing the efficiency of machines with manual labor, you’ll be able to assess how open-source hardware performs in lowering operating costs and identify business components that can be improved with automation.Raspberry Pi Retail Applications features projects that are easy to dive into and will function readily in your day-to-day business right now.WHAT YOU'LL LEARN* Identify business components that can be improved with automation* Combine the existing array of Raspberry Pi hardware options to build customized solutions* Implement tech ideas in a practical retail environment to reduce cost and streamline your business processes WHO THIS BOOK IS FORSmall and medium business owners or technology officers looking for solutions to increase efficiency, lower operating costs, and drive up profits for their retail companies with automation. Familiarity with open-source hardware and programming skills is helpful, but not necessary.ELAINE WU specializes in business partnerships and marketing in various tech industries, from software to embedded hardware. She is currently the marketing and partnership manager at Seeed (an open-source AIoT hardware platform) where she focuses on the global IoT solution ecosystem, making technology accessible for all. At Seeed, by aligning with partners and best hardware, she believes and strives on the path of the most reliable hardware platform, empowering everyone to achieve their digital transformation goals. She was also leading community partnerships, content marketing, new products' go-to-market strategies at Seeed before 2021. Elaine is also an active article contributor on a variety of industries topics, including but not limited to SBCs, microcontrollers, ML/AI, robotics, and SLAM.DMITRY MASLOV works professionally in applied machine learning and robotics. He’s spearheaded a variety of machine learning projects, both for previous employers and as a freelancer. Proficient in Python and C/C++, Dmitry has an excellent knowledge of ROS and ROS-i. He speaks four languages with professional fluency in three. Dmitry is the owner of Hardware.ai, a YouTube channel where he publishes videos on creating intelligent systems with machine learning and ROS on single-board computers.Chapter 1 Understanding Automation Applications in RetailWhat automation can and cannot do at the current levelCompare the efficiency of machines with manual laborFind the best application scenarios for automation and also where it might not be the best choice.Chapter 2 People Counting for Resource ManagementCreate a system using computer vision to perform analysis of retail outlet occupancy at different times.Use the data to improve allocation of the company resources (e.g. personnel, electricity).Chapter 3 Vending MachineUse the Raspberry Pi compute module to process orders and control the hardware in a vending machine.Understand how to interface a Raspberry Pi compute module with a customized carrier board for specific needs (e.g., vending machine control modules)Chapter 4 Interactive Touch Screen Directory/ Customer Service RobotConnect a Raspberry Pi with a high resolution touch screen display and transform it into an interactive directory to help customers in the mall or other retail environment.Instead of a large touch screen display, a mobile platform such as a robot can also help customers with directions.Chapter 5 Voice Interaction Drive-Through Self-Service StationPrototype a replacement for a traditional drive-through station by utilizing Raspberry Pi.Speech recognition to help customers with their order.Chapter 6 Employee Management SystemBuild a system to optimize time tracking and the punch in/punch out process.Use collected data to drive human resource management decisions.Chapter 7 Advertisement DisplayCreate a device to display advertising promotion content for a real estate screen.Remotely schedule and control the content via a network interface.Chapter 8 Check-Out Counter with Barcode ScannerLower equipment cost by replacing a traditional check-out computer system with a single Raspberry Pi 400 all-in-one keyboard.Chapter 9 Server/Server Cluster for HostingOptimize your small business hosting costs by moving away from renting public cloud serversHosting your website/application/chatbot customer service on a Raspberry Pi/Raspberry Pi clusterChapter 10 Summary and Tips on Practical ImplementationSummaryIdeas and practical advice on the best ways to quickly replace or upgrade current systems with automation by Raspberry Pi
Allgemeinbildung Digitalisierung für Dummies
"Die Digitalisierung geht nicht mehr weg." - Ein grundlegendes Verständnis der Prinzipien der Digitalisierung und ihrer wichtigsten Anwendungen ist deshalb die Voraussetzung, um im Beruf und als Privatperson informierte Entscheidungen treffen zu können - ob es nun um Kryptowährungen, New Work oder den Schutz der eigenen Daten in sozialen Medien geht. In diesem Buch wird das Thema Digitalisierung anschaulich und unterhaltsam aufbereitet. Der Fokus liegt auf der fundierten und leicht verdaulichen Vermittlung der Grundlagen, die es Ihnen ermöglicht, nach der Lektüre eigenständig auf dem Laufenden zu bleiben und neue Entwicklungen mit ihren Konsequenzen zu verstehen und einzuordnen. Christina Czeschik ist Ärztin und Medizininformatikerin, freie Autorin und Beraterin zu den Themen "Digitalisierung im Gesundheitswesen" und "Informationssicherheit". Bei Wiley-VCH ist erschienen "Gut gerüstet gegen Überwachung im Web". Sie lebt im Ruhrgebiet.Über die Autorin 9Einführung 19Törichte Annahmen über die Leser 20Wie dieses Buch aufgebaut ist 20Teil I: Was heißt Digitalisierung? 20Teil II: Daten und Algorithmen – die Welt als Einsen und Nullen 20Teil III: Digitalisierung zum Anfassen – Schnittstellen zur physischen Realität 21Teil IV: Digitalisierung in Aktion – Anwendungsbereiche 21Teil V: Digitalisierung und wir – gesellschaftliche Auswirkungen 21Teil VI: Der Top-Ten- Teil 22Symbole, die in diesem Buch verwendet werden 22Wie es weitergeht 22TEIL I: WAS HEIẞT DIGITALISIERUNG? 23KAPITEL 1: DIGITALE WELT – WAS BRINGT UNS DAS? 25Die reale Welt in Zahlen abbilden 27Digitale Abbilder sind unvollständig 28… dürfen es aber auch sein 29Informationen (fast) umsonst übermitteln und vervielfältigen 30Informationen intelligent verarbeiten 30TEIL II: DATEN UND ALGORITHMEN – DIE WELT ALS EINSEN UND NULLEN 33KAPITEL 2: WO WOHNT INFORMATION? 35Information existiert nur auf einem Träger 36Hebel, Walzen, Rechenschieber 36Mechanische Träger sind groß und langsam 40Vom Rauchzeichen zum Telegrafen 40Eine gute Idee: Das Relais 41Logisch: 1 ist NICHT 0 43Ein Saal voller Flipflops 45Hier können Sie Ihre Bits registrieren 47Daten auf der Flucht 50Ein Netzwerk für (fast) alles: Das Internet 52Vom Internet zum World Wide Web 58KAPITEL 3:ALGORITHMEN: MIT DATEN DINGE TUN59Kochrezepte für den Computer 60Englisch: Weltsprache auch für Computer 62In kleinen Schritten zum Erfolg 65Das Problem kenne ich irgendwoher … 66Große Datenmengen: Mehr ist manchmal einfach mehr 67So lernen Maschinen 73Supervised Learning: Die Maschine an die Hand nehmen 75kNN-Algorithmus: In guter Nachbarschaft 76Unsupervised Learning: Auf sich allein gestellt 79k-Means Clustering: Ballungsräume finden 80Das Hirn nachbauen: Künstliche neuronale Netze 82Das neuronale Netz in Aktion 86Schicht um Schicht: Deep Learning 87Starke und schwache künstliche Intelligenz 87Meine Geheimnisse gehören mir: Kryptografie 90Die Blockchain: Revolution oder Betrugsmasche? 92Unkopierbares Geld 94Betrug ist teuer 96Mining: So wird Geld gemacht 99Nützlicher als Bitcoin 99Automatisierung mit Smart Contracts 102Blockchain ohne Geld 106KAPITEL 4: COMPUTER MAL ANDERS: QUANTEN, DNA UND ANDERE109Ternäre Logik: Flip, Flap, Flop 109Biologische Computer 110Quantencomputer und die spukhafte Fernwirkung 113Die Antwort ist 42: Deep Thought 116TEIL III: DIGITALISIERUNG ZUM ANFASSEN – SCHNITTSTELLEN ZUR PHYSISCHEN REALITÄT 119KAPITEL 5: VIRTUAL UND AUGMENTED REALITY 121Virtuelle Realität: Eine Dimension mehr 123Vom Pixel zum Voxel 1233D-Modellierung und –Rendering 125Räumlich sehen, ohne VR-krank zu werden 128Augmented Reality: Die bessere Realität 130KAPITEL 6: INTERNET OF THINGS UND INDUSTRIE 4.0137Sensoren und Aktoren: Wir regeln das 138Kybernetik: Alles ist ein System 139Das hilfreiche Zuhause 140Cyber-Physical Systems: Maschinen im Internet 143KAPITEL 7: ROBOTIK145Wie sehe ich aus? Design eines Roboters 149Die Gesetze der Robotik 151KAPITEL 8: NANOTECHNOLOGIE155Viel Spielraum nach unten 155Maschinen auf Kohlenstoffbasis 158TEIL IV: DIGITALISIERUNG IN AKTION – ANWENDUNGSBEREICHE 161KAPITEL 9: SOZIALE MEDIEN UND NETZWERKE163Smileys, Emoticons, Emojis 164Die Geburt des Blogs 167Vom Onlinetagebuch zum (We)Blog 168Der Aufstieg von Facebook 169Feeds, Likes und Dopamin 171Nichts verpassen: Soziale Netzwerke unterwegs 172Alte und neue soziale Netzwerke 173Triff mich im Livestream 173Werbung in sozialen Netzwerken 176Soziale Netzwerke und Datenschutz 177Dezentrale Netzwerke 179KAPITEL 10: DIGITALES ARBEITEN UND NEW WORK 183Homeoffice und mobiles Arbeiten 183Digitale Nomaden 186New Work oder gar kein Work? 186Decentralized Autonomous Organizations (DAOs) 188Geld, kontrolliert von Algorithmen 189KAPITEL 11: E-COMMERCE, DIGITALE ZAHLUNGSMITTEL UND KRYPTOWÄHRUNGEN191Der Siegeszug von Amazon und eBay 192PayPal und die Kontensperrung 194Kryptowährungen: Ohne Netz und doppelten Boden 194Micropayments: Kleinvieh macht mehr Mist 197KAPITEL 12: DIGITALE GESUNDHEIT203Wertvolles Gut: Gesundheitsdaten 203Telematikinfrastruktur 206Quantified Self: Wer bin ich – und wie kann ich das messen? 210Wir sind Cyborgs 212Schluss mit Wartezimmern? 214KAPITEL 13: SMARTE MOBILITÄT UND AUTONOMES FAHREN217Wissen, wo es langgeht 217Autonome Automobile 220TEIL V: DIGITALISIERUNG UND WIR – GESELLSCHAFTLICHE AUSWIRKUNGEN 227KAPITEL 14: INFORMATIONELLE SELBSTBESTIMMUNG UND ÜBERWACHUNG229Vom Volkszählungsgesetz zur Verfassungsbeschwerde 230Bürger unter Beobachtung 231Post Privacy: Nichts zu verbergen? 232Bitte vergiss mich 233KAPITEL 15: NUDGING UND BEVORMUNDUNG235Nicht schubsen! 235Unter uns Denkfaulen 237Lieber nichts verlieren als etwas gewinnen 238Soziale Einflüsse und Normen 238Falsche Einschätzung von Wahrscheinlichkeiten 239So geht Nudging 240KAPITEL 16: DIGITALE BOHÈME UND DIGITALES PREKARIAT243Selbst und ständig 243Der Mensch als Automat 245Selbstkontrolle, Selbstökonomisierung, Selbstrationalisierung 246KAPITEL 17: INFORMATIONSFLUT UND STÄNDIGE ERREICHBARKEIT 249Ein Online-Brain für die digitale Welt 250Nie wieder Langeweile? 251Langeweile macht kreativ 252Transaktionsgedächtnis: Wissen, wo was steht 253Ihr Smartphone: Risiken und Nebenwirkungen 254KAPITEL 18: DIGITALE HELFER UND VERLUST ZWISCHENMENSCHLICHER KONTAKTE257TEIL VI: DER TOP-TEN- TEIL. 261KAPITEL 19: ZEHN HÖRENSWERTE VORTRÄGE ZUR DIGITALISIERUNG263Hirne hacken: Menschliche Faktoren in der IT-Sicherheit 264Ich sehe, also bin ich … Du 264Embracing Post Privacy 265Bias in Algorithmen 265Digitale Entmündigung 266Hold Steering Wheel! Autopilots and Autonomous Driving 266Quantum Computing: Are we there yet? 266Virtual Reality für Arme 267Computer, Kunst und Kuriositäten 267What the cyberoptimists got wrong – and what to do about it 268Abbildungsverzeichnis 269Stichwortverzeichnis 273
Windows 11 für Senioren
- Texte schreiben, Dateien Speichern, Im Internet surfen, E-Mails versenden, Fotos verwalten.- Schritt für Schritt erklärt, leicht nachvolziehbar, mit viele Bildern und Beispielen.Lernen Sie von Anfang an den sicheren Umgang mit Ihrem PC, Laptop oder Tablet! Mit diesem Handbuch gelingt nicht nur Senioren, sondern allen Computerneulingen der mühelose Einstieg in Windows 11, auch ganz ohne Vorkenntnisse. Anhand leicht nachvollziehbarer Schritt-für-Schritt-Anleitungen sowie mit vielen Beispielen und Bildern erklären die beiden erfahrenen Autorinnen alle notwendigen Techniken, Funktionen und noch vieles mehr. Schon nach kurzer Zeit schreiben und speichern Sie z. B. Briefe, tauschen E-Mails aus, verwalten und bearbeiten Ihre Fotos und surfen im Internet. Legen Sie das Buch mit seinem praktischen Querformat zum Üben vor Ihre Tastatur und freuen Sie sich über schnelle Lernerfolge!Aus dem Inhalt:- Einstellungen und erster Start von Windows 11- Die verschiedenen Apps öffnen und beenden- Einen Brief gestalten und drucken- Dateien speichern und Ordnung halten- Im Internet surfen und E-Mails versenden- Fotos betrachten, bearbeiten und verwalten- Termine eintragen – Erinnerungen erhalten- Bildschirmanzeige individuell anpassen & vergrößern- Glossar mit ausführlichen BegriffserklärungenInge Baumeister und Anja Schmid sind Dozentinnen in der Erwachsenenbildung und haben bereits unzähligen Teilnehmern jeden Alters dabei geholfen, sicher in der Bedienung ihres Computers zu werden. Durch ihre langjährige Erfahrung kennen sie die Fragen und Probleme von Einsteigern und wissen, wie komplexe Sachverhalte einfach zu erklären sind. Die beiden haben bereits zahlreiche Bücher im BILDNER Verlag veröffentlicht.
Adaptive Machine Learning Algorithms with Python
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use.Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth.Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment.WHAT YOU WILL LEARN* Apply adaptive algorithms to practical applications and examples* Understand the relevant data representation features and computational models for time-varying multi-dimensional data* Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data* Speed up your algorithms and put them to use on real-world stationary and non-stationary data* Master the applications of adaptive algorithms on critical edge device computation applicationsWHO THIS BOOK IS FORMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.CHANCHAL CHATTERJEE, PH.D, has held several leadership roles in machine learning, deep learning and real-time analytics. He is currently leading Machine Learning and Artificial Intelligence at Google Cloud Platform, California, USA. Previously, he was the Chief Architect of EMC CTO Office where he led end-to-end deep learning and machine learning solutions for data centers, smart buildings, and smart manufacturing for leading customers. Chanchal received several awards including an Outstanding paper award from IEEE Neural Network Council for adaptive learning algorithms recommended by MIT professor Marvin Minsky. Chanchal founded two tech startups between 2008-2013. Chanchal has 29 granted or pending patents, and over 30 publications. Chanchal received M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University.Chapter 1. Introducing Data Representation FeaturesSet the context for the reader with important data representation features, present the need for adaptive algorithms to compute them and demonstrate how these algorithms are important in multiple disciplines. Additionally, discuss a common methodology adopted to derive all our algorithms.Sub-topics:1. Data representation features2. Computational models for time-varying multi-dimensional data3. Multi-disciplinary origin of adaptive algorithms4. Common Methodology for Derivations of Algorithms5. Outline of The BookChapter 2. General Theories and NotationsIntroduce the reader to types of data in real-world streaming applications, discuss practical use cases and derive adaptive algorithms for mean, normalized mean, median, and covariances. Support the results with experiments on real data.Sub-topics:1. Introduction2. Stationary and Non-Stationary Sequences3. Use Cases for Algorithms Covered in this Chapter4. Adaptive Mean and Covariance of Nonstationary Sequences5. Adaptive Covariance and Inverses6. Adaptive Normalized Mean Algorithm7. Adaptive Median Algorithm8. Experimental ResultsChapter 3. Square Root and Inverse Square RootIntroduce readers to practical applications of square roots and inverse square roots of streaming data matrices, then present algorithms to compute them. Support the algorithms with real data.Sub-topics:1. Introduction and Use Cases2. Adaptive Square Root Algorithms3. Adaptive Inverse Square Root Algorithms4. Experimental ResultsChapter 4. First Principal EigenvectorIntroduce the reader to adaptive computation of first principal component of streaming data, discuss the use cases with examples, derive ten algorithms with the common methodology adopted here. Demonstrate the algorithms with real-world non-stationary streaming data examples.Sub-topics:1. Introduction and Use Cases2. Algorithms and Objective Functions3. OJA Algorithm4. RQ, OJAN, and LUO Algorithms5. IT and XU Algorithms6. Penalty Function Algorithm7. Augmented Lagrangian Algorithms8. Summary of Algorithms9. Experimental ResultsChapter 5. Principal and Minor EigenvectorsIntroduce the reader to adaptive computation of all principal components, discuss powerful use cases with examples, derive 21 adaptive algorithms and demonstrate the algorithms on real-world time-varying data.Sub-topics:1. Introduction and Use Cases2. Algorithms and Objective Functions3. OJA Algorithms4. XU Algorithms5. PF Algorithms6. AL1 Algorithms7. AL2 Algorithms8. IT Algorithms9. RQ Algorithms10. Summary of Adaptive Eigenvector Algorithms11. Experimental ResultsChapter 6. Accelerated Computation eigenvectorsIntroduce the reader to methods to speed up the adaptive algorithms presented in this book. Help the reader speed up a few algorithms and demonstrate their usefulness and acceleration on real-world stationery and non-stationary data.Sub-topics:1. Introduction2. Gradient Descent Algorithm3. Steepest Descent Algorithm4. Conjugate Direction Algorithm5. Newton-Raphson Algorithm6. Experimental ResultsChapter 7. Generalized EigenvectorsIntroduce the reader to the adaptive computation of generalized eigenvectors of streaming data matrices in real-time applications. Discuss use cases and algorithms and show experimental results on real data.Sub-topics:1. Introduction and Use Cases2. Algorithms and Objective Functions3. OJA GEVD Algorithms4. XU GEVD Algorithms5. PF GEVD Algorithms6. AL1 GEVD Algorithms7. AL2 GEVD Algorithms8. IT GEVD Algorithms9. RQ GEVD Algorithms10. Experimental ResultsChapter 8. Real–World Applications Linear AlgorithmsHelp the reader understand real-world applications of the adaptive algorithms. Demonstrate five important applications of adaptive algorithms on critical edge device computation applications.Sub-topics:1. Detecting Feature Drift2. Adapt to Incoming Data Drift3. Compress High Volume Data4. Detecting Feature Anomalies
Pro Serverless Data Handling with Microsoft Azure
Design and build architectures on the Microsoft Azure platform specifically for data-driven and ETL applications. Modern cloud architectures rely on serverless components more than ever, and this book helps you identify those components of data-driven or ETL applications that can be tackled using the technologies available on the Azure platform. The book shows you which Azure components are best suited to form a strong foundation for data-driven applications in the Microsoft Azure Cloud.If you are a solution architect or a decision maker, the conceptual aspects of this book will help you gain a deeper understanding of the underlying technology and its capabilities. You will understand how to develop using Azure Functions, Azure Data Factory, Logic Apps, and to employ serverless databases in your application to achieve the best scalability and design. If you are a developer, you will benefit from the hands-on approach used throughout this book. Many practical examples and architectures applied in real-world projects will be valuable to you on your path to serverless success.WHAT YOU WILL LEARN* Know what services are available in Microsoft Azure that can deal with large amounts of data* Design modern data applications based on serverless technology in the cloud* Transform and present data without the use of infrastructure* Employ proven design patterns for rapid implementation of serverless data applications* Choose the correct set of development tools for the services you are using* Understand the term "serverless" and how it can be a benefit* Identify scenarios in which serverless is not the best option availableWHO THIS BOOK IS FORArchitects and decision makers who want to understand how modern architectures are designed and how to modernize their applications. The book is aimed at the developer who needs a steppingstone to quickly implement a serverless data application. And the book is for any IT professional who seeks a head start to serverless computing for data-heavy applications on the Azure platform.DR. BENJAMIN KETTNER is co-founder and CTO of ML!PA Consulting GmbH. Since 2020, he has been a Microsoft Data Platform MVP and a Friend of Red Gate. He received his doctorate in applied mathematics at the Free University of Berlin in 2012. At the time of his doctorate, he was a member of the DFG Research Center Matheon-Mathematics for Key Technologies, and a member of the Computational Nano Optics group at the Zuse Institute Berlin.FRANK GEISLER is owner and CEO of GDS Business Intelligence GmbH, a Microsoft Gold Partner in five categories. He is located in Lüdinghausen, in the lovely Münsterland. He is a Data Platform MVP, MCT, MCSE-Business Intelligence, MCSE-Data Platform, MCSE-Azure Solutions Architect, and DevOps Engineer Expert. In his job he is building business intelligence systems based on Microsoft technologies, mainly on SQL Server and Microsoft Azure. He has also a strong focus on Database DevOps.PART I. THE BASICS1. Azure Basics2. Serverless Computing3. Data Driven ApplicationsPART II. HANDS-ON4. Azure Functions5. Logic Apps6. Azure Data Factory7. Database and Storage Options8. IoT Hub, Event Hub, and Streaming Data9. Power BIPART III. DESIGN PRACTICES10. Achieving Resiliency11. Queues, Messages, and Commands12. Processing Streams of Data13. Monitoring Serverless ApplicationsPART IV. PUTTING IT ALL TOGETHER14. Tools and Helpers15. Data Loading Patterns16. Data Storage Patterns17. Architecture for a Modern Data Driven Application
Python for MATLAB Development
MATLAB can run Python code!Python for MATLAB Development shows you how to enhance MATLAB with Python solutions to a vast array of computational problems in science, engineering, optimization, statistics, finance, and simulation. It is three books in one:* A thorough Python tutorial that leverages your existing MATLAB knowledge with a comprehensive collection of MATLAB/Python equivalent expressions* A reference guide to setting up and managing a Python environment that integrates cleanly with MATLAB* A collection of recipes that demonstrate Python solutions invoked directly from MATLABThis book shows how to call Python functions to enhance MATLAB's capabilities. Specifically, you'll see how Python helps MATLAB:* Run faster with numba* Distribute work to a compute cluster with dask* Find symbolic solutions to integrals, derivatives, and series summations with SymPy* Overlay data on maps with Cartopy* Solve mixed-integer linear programming problems with PuLP* Interact with Redis via pyredis, PostgreSQL via psycopg2, and MongoDB via pymongo* Read and write file formats that are not natively understood by MATLAB, such as SQLite, YAML, and iniWHO THIS BOOK IS FORMATLAB developers who are new to Python and other developers with some prior experience with MATLAB, R, IDL, or Mathematica.ALBERT DANIAL is an aerospace engineer with 30 years of experience, currently working for Northrop Grumman near Los Angeles. Before Northrop Grumman, he was a member of the NASTRAN Numerical Methods team at MSC Software and a systems analyst at SPARTA. He has a Bachelor of Aerospace Engineering degree from the Georgia Institute of Technology, and Masters and Ph.D. degrees in Aeronautics and Astronautics from Purdue University. He is the author of cloc, the open source code counter.Al has used MATLAB since 1990 and Python since 2006 for algorithm prototyping, earth science data processing, spacecraft mission planning, optimization, visualization, and countless utilities that simplify daily engineering work. Chapter 1: IntroductionGoal: Describe the book’s goals, what to expect, what benefit to gain.• Learn Python through MATLAB Equivalents• Is Python really free?• What About Toolboxes?• I already know Python. How do I call Python functions in MATLAB?• What you won’t find in this book• Beyond MATLABPart I – Learning Python through MATLAB comparisonsChapter 2: InstallationGoal: Create a working Python installation on the computer with MATLAB• Downloads• Post-Install Checkout• ipython, IDE’s• Python and MATLAB Versions Used in This BookChapter 3: Language BasicsGoal: Learn the basic mechanics of Python• Assignment• Printing• Indentation• Indexing• `for` Loops• `while` Loops• `if` Statements• Functions• Comments• Line Continuation• Exceptions• Modules and PackagesChapter 4: Data ContainersGoal: Learn about lists, dictionaries, etc, and how these compare to MATLAB matrices and cell arrays• NumPy Arrays• Strings• Python Lists and MATLAB Cell Arrays• Python Tuples • Python Sets and MATLAB Set Operations• Python Dictionaries and MATLAB Maps• Structured Data• Tables• Caveat: ```=`'' copies a reference for non-scalars!Chapter 5: Date and TimeGoal: Learn about measuring, storing, and converting temporal values.• Time• Dates• Timezones• Time Conversions to and from `datetime` ObjectsChapter 6: Input and OutputGoal: Learn about reading and writing data, with emphasis on numeric data and scientific file formats like HDF and NetCDF.• Reading and Writing Text Files• Reading and Writing Binary Files• Reading and Writing Pickle Files• Reading and Writing `.mat` files• Command Line Input • Interactive Input• Receiving and Sending over a Network• Interacting with DatabasesChapter 7: Interacting with the File SystemGoal: Show how Python manages file system operations.• Reading Directory Contents• Finding Files• Deleting Files• Creating Directories• Deleting Directories• Walking Directory TreesChapter 8: Interacting with the Operating System and External ExecutablesGoal: Show how to make system calls in Python and how these differ from MATLAB.• Reading, setting environment variables• Calling External Executables• Inspecting the Process Table and Process ResourcesPart II – MATLAB with PythonChapter 9: MATLAB/Python IntegrationGoal: Show how to make system calls in Python and how these differ from MATLAB.• MATLAB’s `py` Module• System calls and File I/O• TCP/IP ExchangeChapter 10: Object Oriented ProgrammingGoal: Demonstrate Python’s OO semantics compared to MATLAB• Classes• Custom Exceptions• Performance ImplicationsChapter 11: NumPy and SciPyGoal: Introduce Python’s numeric and scientific computing capability. This is by far the largest chapter in the book.• NumPy Arrays• Linear Algebra• Sparse Matrices• Interpolation • Curve Fitting• Statistics• Finding Roots• Optimization • Differential Equations• Symbolic Mathematics• Unit SystemsChapter 12: PlottingGoal: Demonstrate how publication-quality plots are produced in Python alongside MATLAB equivalents• Point and Line Plots• Area Plots• Animations• Plotting on Maps• 3D Plots• Making plots in batch modeChapter 13: Tables and DataframesGoal: Show Pandas dataframes in comparison to MATLAB tables (and how the former pre-dates the latter by five years)• Loading tables from files• Table summaries• Cleaning data• Creating tables programmatically• Sorting rows• Table subsets• Iterating over rows• Pivot tables• Adding columns• Deleting columns• Joins across tablesChapter 14: High Performance ComputingGoal: Demonstrate techniques for profiling Python code and making computationally intensive Python code run faster. Significant performance advantages over MATLAB are shown.• Paths to faster Python code• Reference Problems• Reference Hardware and OS• Baseline performance• Profiling Python Code• Vectorization• Cython• Pythran• Numba• Linking to C, C++, Fortran• Distributed memory parallel processingChapter 15: `py` Module ExamplesGoal: A collection of examples that show how Python enables the core MATLAB product to perform tasks that would either require a Toolbox or less-vetted code from the MathWorks’ user contributed FileExchange.• Read a YAML File• Write a YAML File• Compute Laplace Transforms• Interact with Redis• Units• Propagate a satellite’s orbit• Controls• Plotting on mapsChapter 16: Language WartsGoal: Identify MATLAB and Python language ‘features’ that often cause beginners grief.• Dangerous language features• MATLAB• Python• Common Errors
Multimedia Security 1
Today, more than 80% of the data transmitted over networks and archived on our computers, tablets, cell phones or clouds is multimedia data - images, videos, audio, 3D data. The applications of this data range from video games to healthcare, and include computer-aided design, video surveillance and biometrics.It is becoming increasingly urgent to secure this data, not only during transmission and archiving, but also during its retrieval and use. Indeed, in today’s "all-digital" world, it is becoming ever-easier to copy data, view it unrightfully, steal it or falsify it.Multimedia Security 1 analyzes the issues of the authentication of multimedia data, code and the embedding of hidden data, both from the point of view of defense and attack. Regarding the embedding of hidden data, it also covers invisibility, color, tracing and 3D data, as well as the detection of hidden messages in an image by steganalysis. WILLIAM PUECH is Professor of Computer Science at Université de Montpellier, France. His research focuses on image processing and multimedia security in particular, from its theories to its applications.Foreword by Gildas Avoine xiForeword by Cédric Richard xiiiPreface xvilliam PUECHCHAPTER 1 HOW TO RECONSTRUCT THE HISTORY OF A DIGITAL IMAGE, AND OF ITS ALTERATIONS 1Quentin BAMMEY, Miguel COLOM, Thibaud EHRET, Marina GARDELLA, Rafael GROMPONE, Jean-Michel MOREL, Tina NIKOUKHAH and Denis PERRAUD1.1 Introduction 21.1.1 General context 21.1.2 Criminal background 31.1.3 Issues for law enforcement 41.1.4 Current methods and tools of law enforcement 51.1.5 Outline of this chapter 51.2 Describing the image processing chain 81.2.1 Raw image acquisition 81.2.2 Demosaicing 81.2.3 Color correction 101.2.4 JPEG compression 111.3 Traces left on noise by image manipulation 111.3.1 Non-parametric estimation of noise in images 111.3.2 Transformation of noise in the processing chain 131.3.3 Forgery detection through noise analysis 151.4 Demosaicing and its traces 181.4.1 Forgery detection through demosaicing analysis 191.4.2 Detecting the position of the Bayer matrix 201.4.3 Limits of detection demosaicing 231.5 JPEG compression, its traces and the detection of its alterations 231.5.1 The JPEG compression algorithm 231.5.2 Grid detection 251.5.3 Detecting the quantization matrix 271.5.4 Beyond indicators, making decisions with a statistical model 281.6 Internal similarities and manipulations 311.7 Direct detection of image manipulation 331.8 Conclusion 341.9 References 35CHAPTER 2 DEEP NEURAL NETWORK ATTACKS AND DEFENSE: THE CASE OF IMAGE CLASSIFICATION 41Hanwei ZHANG, Teddy FURON, Laurent AMSALEG and Yannis AVRITHIS2.1 Introduction 412.1.1 A bit of history and vocabulary 422.1.2 Machine learning 442.1.3 The classification of images by deep neural networks 462.1.4 Deep Dreams 482.2 Adversarial images: definition 492.3 Attacks: making adversarial images 512.3.1 About white box 522.3.2 Black or gray box 622.4 Defenses 642.4.1 Reactive defenses 642.4.2 Proactive defenses 662.4.3 Obfuscation technique 672.4.4 Defenses: conclusion 682.5 Conclusion 682.6 References 69CHAPTER 3 CODES AND WATERMARKS 77Pascal LEFEVRE, Philippe CARRE and Philippe GABORIT3.1 Introduction 773.2 Study framework: robust watermarking 783.3 Index modulation 813.3.1 LQIM: insertion 813.3.2 LQIM: detection 823.4 Error-correcting codes approach 823.4.1 Generalities 843.4.2 Codes by concatenation 863.4.3 Hamming codes 883.4.4 BCH codes 903.4.5 RS codes 933.5 Contradictory objectives of watermarking: the impact of codes 963.6 Latest developments in the use of correction codes for watermarking 983.7 Illustration of the influence of the type of code, according to the attacks 1023.7.1 JPEG compression 1033.7.2 Additive Gaussian noise 1063.7.3 Saturation 1063.8 Using the rank metric 1083.8.1 Rank metric correcting codes 1093.8.2 Code by rank metric: a robust watermarking method for image cropping 1133.9 Conclusion 1213.10 References 121CHAPTER 4 INVISIBILITY 129Pascal LEFEVRE, Philippe CARRE and David ALLEYSSON4.1 Introduction 1294.2 Color watermarking: an approach history? 1314.2.1 Vector quantization in the RGB space 1324.2.2 Choosing a color direction 1334.3 Quaternionic context for watermarking color images 1354.3.1 Quaternions and color images 1354.3.2 Quaternionic Fourier transforms 1374.4 Psychovisual approach to color watermarking 1394.4.1 Neurogeometry and perception 1394.4.2 Photoreceptor model and trichromatic vision 1414.4.3 Model approximation 1444.4.4 Parameters of the model 1454.4.5 Application to watermarking color images 1464.4.6 Conversions 1474.4.7 Psychovisual algorithm for color images 1484.4.8 Experimental validation of the psychovisual approach for color watermarking 1514.5 Conclusion 1554.6 References 157CHAPTER 5 STEGANOGRAPHY: EMBEDDING DATA INTO MULTIMEDIA CONTENT 161Patrick BAS, Remi COGRANNE and Marc CHAUMONT5.1 Introduction and theoretical foundations 1625.2 Fundamental principles 1635.2.1 Maximization of the size of the embedded message 1635.2.2 Message encoding 1655.2.3 Detectability minimization 1665.3 Digital image steganography: basic methods 1685.3.1 LSB substitution and matching 1685.3.2 Adaptive embedding methods 1695.4 Advanced principles in steganography 1725.4.1 Synchronization of modifications 1735.4.2 Batch steganography 1755.4.3 Steganography of color images 1775.4.4 Use of side information 1785.4.5 Steganography mimicking a statistical model 1805.4.6 Adversarial steganography 1825.5 Conclusion 1865.6 References 186CHAPTER 6 TRAITOR TRACING 189Teddy FURON6.1 Introduction 1896.1.1 The contribution of the cryptography community 1906.1.2 Multimedia content 1916.1.3 Error probabilities 1926.1.4 Collusion strategy 1926.2 The original Tardos code 1946.2.1 Constructing the code 1956.2.2 The collusion strategy and its impact on the pirated series 1956.2.3 Accusation with a simple decoder 1976.2.4 Study of the Tardos code-Škori´c original 1996.2.5 Advantages 2026.2.6 The problems 2046.3 Tardos and his successors 2056.3.1 Length of the code 2056.3.2 Other criteria 2056.3.3 Extensions 2076.4 Research of better score functions 2086.4.1 The optimal score function 2086.4.2 The theory of the compound communication channel 2096.4.3 Adaptive score functions 2116.4.4 Comparison 2136.5 How to find a better threshold 2136.6 Conclusion 2156.7 References 216CHAPTER 7 3D WATERMARKING 219Sebastien BEUGNON, Vincent ITIER and William PUECH7.1 Introduction 2207.2 Preliminaries 2217.2.1 Digital watermarking 2217.2.2 3D objects 2227.3 Synchronization 2247.3.1 Traversal scheduling 2247.3.2 Patch scheduling 2247.3.3 Scheduling based on graphs 2257.4 3D data hiding 2307.4.1 Transformed domains 2317.4.2 Spatial domain 2317.4.3 Other domains 2327.5 Presentation of a high-capacity data hiding method 2337.5.1 Embedding of the message 2347.5.2 Causality issue 2357.6 Improvements 2367.6.1 Error-correcting codes 2367.6.2 Statistical arithmetic coding 2367.6.3 Partitioning and acceleration structures 2377.7 Experimental results 2387.8 Trends in high-capacity 3D data hiding 2407.8.1 Steganalysis 2407.8.2 Security analysis 2417.8.3 3D printing 2427.9 Conclusion 2427.10 References 243CHAPTER 8 STEGANALYSIS: DETECTION OF HIDDEN DATA IN MULTIMEDIA CONTENT 247Remi COGRANNE, Marc CHAUMONT and Patrick BAS8.1 Introduction, challenges and constraints 2478.1.1 The different aims of steganalysis 2488.1.2 Different methods to carry out steganalysis 2498.2 Incompatible signature detection 2508.3 Detection using statistical methods 2528.3.1 Statistical test of χ2 2528.3.2 Likelihood-ratio test 2568.3.3 LSB match detection 2618.4 Supervised learning detection 2638.4.1 Extraction of characteristics in the spatial domain 2648.4.2 Learning how to detect with features 2698.5 Detection by deep neural networks 2708.5.1 Foundation of a deep neural network 2718.5.2 The preprocessing module 2728.6 Current avenues of research 2798.6.1 The problem of Cover-Source mismatch 2798.6.2 The problem with steganalysis in real life 2798.6.3 Reliable steganalysis 2808.6.4 Steganalysis of color images 2808.6.5 Taking into account the adaptivity of steganography 2818.6.6 Grouped steganalysis (batch steganalysis) 2818.6.7 Universal steganalysis 2828.7 Conclusion 2838.8 References 283List of Authors 289Index 293
Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage.WHAT YOU'LL LEARN* Review data structures in NumPy and Pandas * Demonstrate machine learning techniques and algorithm* Understand supervised learning and unsupervised learning * Examine convolutional neural networks and Recurrent neural networks* Get acquainted with scikit-learn and PyTorch* Predict sequences in recurrent neural networks and long short term memoryWHO THIS BOOK IS FORData scientists, machine learning engineers, and software professionals with basic skills in Python programming.Ashwin Pajankar holds a Master of Technology from IIIT Hyderabad, and has over 25 years of programming experience. He started his journey in programming and electronics with BASIC programming language and is now proficient in Assembly programming, C, C++, Java, Shell Scripting, and Python. Other technical experience includes single board computers such as Raspberry Pi and Banana Pro, and Arduino. He is currently a freelance online instructor teaching programming bootcamps to more than 60,000 students from tech companies and colleges. His Youtube channel has an audience of 10000 subscribers and he has published more than 15 books on programming and electronics with many international publications.Aditya Joshi has worked in data science and machine learning engineering roles since the completion of his MS (By Research) from IIIT Hyderabad. He has conducted tutorials, workshops, invited lectures, and full courses for students and professionals who want to move to the field of data science. His past academic research publications include works on natural language processing, specifically fine grain sentiment analysis and code mixed text. He has been the organizing committee member and program committee member of academic conferences on data science and natural language processing.Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter NotebookChapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics:1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypesChapter 3 : Introduction to Data VisualizationChapter goal – In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics:1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizationsChapter 4 : Introduction to PandasChapter goal – Get started with Pandas data structuresNo of pages: 10Sub - Topics:1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) ClipboardChapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal – Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasetsChapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and imagesChapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision treesChapter 8: Tuning Supervised LearnersChapter Goal: Analyzing and improving the performance of supervised learning modelsNo of Pages: 201. Training methodology, evaluation methodology2. Hyperparameter tuning3. Regularization in linear regression4. Regularization in logistic regression5. Regularization in decision trees6. Crossvalidation, K-fold cross validation7. ROC CurveChapter 9: Supervised Learning Methods - 2Chapter Goal: Learn more algorithmsNo of Pages: 151. Naive bayes2. Support vector machines3. Visualization of decision boundariesChapter 10: Ensemble Learning MethodsChapter Goal: Learn the in-depth background of ensemble learning methodsNo of Pages: 101. Bagging vs boosting2. Random forest3. Adaboost4. Gradient boostingChapter 11: Unsupervised Learning MethodsChapter Goal: Detailed theory and practically oriented introduction to dimensionality reduction and clustering algorithmsNo of Pages: 201. Dimensionality reduction2. Principle components analysis3. Clustering4. K-Means method5. Density-based methodChapter 12: Neural Networks and Pytorch BasicsChapter Goal: Understand the basics of neural networks, deep learning, and PytorchNo of Pages: 101. Introduction to Pytorch, tensors2. Tensor operations3. ExercisesChapter 13: Feedforward Neural NetworksChapter Goal: In-depth introduction to basic dense neural networks along with necessary mathematical background and implementation. (chapter might split into two while writing)No of Pages: 201. Perceptron model2. Neural network and activation functions3. Multiclass classification4. Cost functions and gradient descent5. Backpropagation6. Pytorch gradients7. Linear regression with PyTorch8. Basic dense network with PyTorch for regression9. Basic dense network with Pytorch for classificationChapter 14: Convolutional Neural NetworkChapter Goal: Explore details behind CNNs and implement two solutions for image classificationNo of Pages: 201. Dense network for digits classification2. Image filters and kernels3. Convolutional layers4. Pooling layers5. CNN for digits classification6. CNN for image classificationChapter 15: Recurrent Neural NetworkChapter Goal: Understand sequence networks and implement them for forecasting values (or text classification)No of Pages: 151. Introduction to recurrent neural networks2. Vanishing gradient problem3. LSTM4. RNN batches, LSTM5. Text classification Problem (or forecasting problem)Chapter 16: Bringing It All TogetherChapter Goal: Discuss, conceptualize, design, and develop end to endNo of Pages: 201. Project 12. Project 2
Wireshark Fundamentals
Understand the fundamentals of the Wireshark tool that is key for network engineers and network security analysts. This book explains how the Wireshark tool can be used to analyze network traffic and teaches you network protocols and features.Author Vinit Jain walks you through the use of Wireshark to analyze network traffic by expanding each section of a header and examining its value. Performing packet capture and analyzing network traffic can be a complex, time-consuming, and tedious task. With the help of this book, you will use the Wireshark tool to its full potential. You will be able to build a strong foundation and know how Layer 2, 3, and 4 traffic behave, how various routing protocols and the Overlay Protocol function, and you will become familiar with their packet structure.Troubleshooting engineers will learn how to analyze traffic and identify issues in the network related to packet loss, bursty traffic, voice quality issues, etc. The book will help you understand the challenges faced in any network environment and how packet capture tools can be used to identify and isolate those issues.This hands-on guide teaches you how to perform various lab tasks. By the end of the book, you will have in-depth knowledge of the Wireshark tool and its features, including filtering and traffic analysis through graphs. You will know how to analyze traffic, find patterns of offending traffic, and secure your network.WHAT YOU WILL LEARN* Understand the architecture of Wireshark on different operating systems* Analyze Layer 2 and 3 traffic frames* Analyze routing protocol traffic* Troubleshoot using Wireshark GraphsWHO THIS BOOK IS FORNetwork engineers, security specialists, technical support engineers, consultants, and cyber security engineersVINIT JAIN, CCIE No. 22854 (R&S, SP, Security & DC), is a Sr. Technical Leader for Network Engineering at Cisco focusing on architecting network infrastructure for edge computing solutions. Prior to that, he worked as a Network Development Engineer at Amazon as part of Amazon’s backbone network operations team and as a technical leader at Cisco Technical Assistance Center (TAC), providing escalation support in enterprise, service provider, and data center technologies.Vinit is a speaker at various networking forums, including Cisco Live events, NANOG, and CHINOG. He has co-authored several Cisco Press books and video courses with Cisco Press. Vinit holds a Bachelor of Arts degree in Mathematics from Delhi University and also holds a Master of Science in Information Technology. Apart from CCIE, he also holds multiple certifications in programming, database, and system administration and is also a Certified Ethical Hacker. Vinit can be found on twitter @vinugenie.Chapter 1: Introduction to WiresharkCHAPTER GOAL: THE GOAL OF THE CHAPTER IS TO HELP THE READERS UNDERSTAND THE NEED FOR WIRESHARK TOOL AND WHAT ARE THE VARIOUS WAYS TO INSTALL THE TOOL ON DIFFERENT OPERATING SYSTEMS.NO OF PAGES 20-30SUB -TOPICS1. Introduction to Network Traffic Analysisa. Network Sniffing2. Wiresharka. Installing Wireshark3. Setting up Port Mirroringa. SPAN on Cisco IOS/IOS-XEb. SPAN on Cisco Nexusc. Enabling Port Mirroring on Arista EOSd. Enabling Port Mirroring on JunOSChapter 2: Getting Familiar with WiresharkCHAPTER GOAL: THE GOAL OF THIS CHAPTER IS TO FAMILIARIZE THE READERS WITH THE WIRESHARK TOOLS, ITS CAPABILITIES AND HOW IT CAN BE USED IN DIFFERENT SCENARIOS.NO OF PAGES: 40-50Sub - Topics1. Overview of Wireshark Toola. Wireshark Preferences2. Performing Packet Capturea. Dissectorsb. Configuration Profilesc. Filtering with Wireshark3. Wireshark Capture Filesa. PCAP vs. PCAPngb. Splitting Packet Captures into multiple filesc. Merging multiple capture files4. Analyzing packets in Wiresharka. OSI Modelb. Analyzing packetsChapter 3: Analyzing Layer-2 and Layer-3 TrafficCHAPTER GOAL: THE GOAL OF THIS CHAPTER IS TO FAMILIARIZE THE READERS HOW TO ANALYZE LAYER-2 AND LAYER-3 TRAFFIC AND THE VARIOUS FIELDS THAT ONE NEEDS TO LOOK AT WHEN ANALYZING NETWORK TRAFFIC.NO OF PAGES: 60-70SUB - TOPICS1. Layer-2 Framesa. Ethernet Frames2. Layer-3 Packetsa. Address Resolution Protocolb. IPv4 Packetsc. IPv6 Packets3. Analyzing QoS MarkingsChapter 4: Analyzing Layer-4 TrafficCHAPTER GOAL: GOAL OF THIS CHAPTER IS TO HELP THE READERS HOW TO ANALYZE TCP AND UDP TRAFFIC STREAMS AND HOW TO IDENTIFY PACKET LOSS ISSUESNO OF PAGES : 40-50SUB - TOPICS:1. Understanding TCP/IP Modela. Problem of Ownership2. Transmission Control Protocola. TCP Flagsb. TCP 3-way Handshakec. Port Scanningd. Investigating Packet Losse. Troubleshooting with Wireshark Graphsf. TCP Expert3. User Datagram ProtocolChapter 5: Analyzing Routing Protocol TrafficCHAPTER GOAL: GOAL OF THIS CHAPTER IS TO HELP THE READERS GET FAMILIAR WITH VARIOUS ROUTING PROTOCOL PACKET FORMATS AND TO IDENTIFY ANY POSSIBLE ISSUES WITH THOSE PROTOCOLSNO OF PAGES : 40-50SUB - TOPICS:1. Routing Protocols1. OSPF2. EIGRP3. BGP4. PIM2. Analyzing Overlay Traffic1. GRE2. IPSEC3. LISP4. VXLAN
Snowflake Access Control
Understand the different access control paradigms available in the Snowflake Data Cloud and learn how to implement access control in support of data privacy and compliance with regulations such as GDPR, APPI, CCPA, and SOX. The information in this book will help you and your organization adhere to privacy requirements that are important to consumers and becoming codified in the law. You will learn to protect your valuable data from those who should not see it while making it accessible to the analysts whom you trust to mine the data and create business value for your organization.Snowflake is increasingly the choice for companies looking to move to a data warehousing solution, and security is an increasing concern due to recent high-profile attacks. This book shows how to use Snowflake's wide range of features that support access control, making it easier to protect data access from the data origination point all the way to the presentation and visualization layer. Reading this book helps you embrace the benefits of securing data and provide valuable support for data analysis while also protecting the rights and privacy of the consumers and customers with whom you do business.WHAT YOU WILL LEARN* Identify data that is sensitive and should be restricted* Implement access control in the Snowflake Data Cloud* Choose the right access control paradigm for your organization* Comply with CCPA, GDPR, SOX, APPI, and similar privacy regulations* Take advantage of recognized best practices for role-based access control* Prevent upstream and downstream services from subverting your access control* Benefit from access control features unique to the Snowflake Data CloudWHO THIS BOOK IS FORData engineers, database administrators, and engineering managers who want to improve their access control model; those whose access control model is not meeting privacy and regulatory requirements; those new to Snowflake who want to benefit from access control features that are unique to the platform; technology leaders in organizations that have just gone public and are now required to conform to SOX reporting requirementsJESSICA MEGAN LARSON was born and raised in a small town across the Puget Sound from Seattle, but now calls Oakland, California home. She studied cognitive science with a minor in computer science at University of California Berkeley. She thrives on mentorship, solving data puzzles, and equipping colleagues with new technical skills. Jessica is passionate about helping women and non-binary people find their place in the technology industry. She was the first engineer within the Enterprise Data Warehouse team at Pinterest, and additionally helps to develop fantastic women through Built By Girls. Previously, she wrangled data at Eaze and Flexport. Outside of work, Jessica spends her time soaking up the California sun playing volleyball on the beach or at the park. PART I. BACKGROUND1. What is Access Control?2. Data Types Requiring Access Control3. Data Privacy Laws and Regulatory Drivers4. Permission typesPART II. CREATING ROLES5. Functional Roles - What A Person Does6. Team Roles - Who A Person Is7. Assuming A Primary Role8. Secondary RolesPART III. GRANTING PERMISSIONS TO ROLES9. Role Inheritance10. Account and Database Level Privileges11. Schema-Level Privileges12. Table and View Level Privileges13. Row-Level Permissioning and Fine-Grained Access Control14. Column-Level Permissioning and Data MaskingPART IV. OPERATIONALLY MANAGING ACCESS CONTROL15. Secure Data Sharing16. Separating Production from Development17. Upstream & Downstream Services18. Managing Access Requests
Artificial Intelligent Techniques for Wireless Communication and Networking
ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKINGTHE 20 CHAPTERS ADDRESS AI PRINCIPLES AND TECHNIQUES USED IN WIRELESS COMMUNICATION AND NETWORKING AND OUTLINE THEIR BENEFIT, FUNCTION, AND FUTURE ROLE IN THE FIELD. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. AUDIENCEResearchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies. R. KANTHAVEL, PhD is a Professor in the Department of Computer Engineering, King Khalid University Abha, Kingdom of Saudi Arabia. He has published more than 150 research articles in reputed journals and international conferences as well as published 10 engineering books. He specializes in communication systems engineering and information and communication engineering.K. ANANTHAJOTHI, PhD is an assistant professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. He has published a book on "Theory of Computation and Python Programming" and holds 2 patents.S. BALAMURUGAN, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.R. KARTHIK GANESH, PhD is an associate professor in the Department of Computer Science and Engineering, SCAD College of Engineering and Technology, Cheranmahadevi, Tamilnadu, India. His research interests are in wireless communication, video and audio compression, image classification, and ontology techniques.Preface xvii1 COMPREHENSIVE AND SELF-CONTAINED INTRODUCTION TO DEEP REINFORCEMENT LEARNING 1P. Anbalagan, S. Saravanan and R. Saminathan1.1 Introduction 21.2 Comprehensive Study 31.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 71.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 91.5 Conclusion 122 IMPACT OF AI IN 5G WIRELESS TECHNOLOGIES AND COMMUNICATION SYSTEMS 15A. Sivasundari and K. Ananthajothi2.1 Introduction 162.2 Integrated Services of AI in 5G and 5G in AI 182.3 Artificial Intelligence and 5G in the Industrial Space 232.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks 252.5 Conclusion 283 ARTIFICIAL INTELLIGENCE REVOLUTION IN LOGISTICS AND SUPPLY CHAIN MANAGEMENT 31P.J. Sathish Kumar, Ratna Kamala Petla, K. Elangovan and P.G. Kuppusamy3.1 Introduction 323.2 Theory--AI in Logistics and Supply Chain Market 353.3 Factors to Propel Business Into the Future Harnessing Automation 403.4 Conclusion 434 AN EMPIRICAL STUDY OF CROP YIELD PREDICTION USING REINFORCEMENT LEARNING 47M. P. Vaishnnave and R. Manivannan4.1 Introduction 474.2 An Overview of Reinforcement Learning in Agriculture 494.3 Reinforcement Learning Startups for Crop Prediction 524.4 Conclusion 575 COST OPTIMIZATION FOR INVENTORY MANAGEMENT IN BLOCKCHAIN AND CLOUD 59C. Govindasamy, A. Antonidoss and A. Pandiaraj5.1 Introduction 605.2 Blockchain: The Future of Inventory Management 625.3 Cost Optimization for Blockchain Inventory Management in Cloud 665.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud 715.5 Conclusion 726 REVIEW OF DEEP LEARNING ARCHITECTURES USED FOR IDENTIFICATION AND CLASSIFICATION OF PLANT LEAF DISEASES 75G. Gangadevi and C. Jayakumar6.1 Introduction 756.2 Literature Review 766.3 Proposed Idea 826.4 Reference Gap 866.5 Conclusion 877 GENERATING ART AND MUSIC USING DEEP NEURAL NETWORKS 91A. Pandiaraj, S. Lakshmana Prakash, R. Gopal and P. Rajesh Kanna7.1 Introduction 917.2 Related Works 927.3 System Architecture 947.4 System Development 967.5 Algorithm-LSTM 1007.6 Result 1007.7 Conclusions 1018 DEEP LEARNING ERA FOR FUTURE 6G WIRELESS COMMUNICATIONS--THEORY, APPLICATIONS, AND CHALLENGES 105S.K.B. Sangeetha and R. Dhaya8.1 Introduction 1068.2 Study of Wireless Technology 1088.3 Deep Learning Enabled 6G Wireless Communication 1138.4 Applications and Future Research Directions 1179 ROBUST COOPERATIVE SPECTRUM SENSING TECHNIQUES FOR A PRACTICAL FRAMEWORK EMPLOYING COGNITIVE RADIOS IN 5G NETWORKS 121J. Banumathi, S.K.B. Sangeetha and R. Dhaya9.1 Introduction 1229.2 Spectrum Sensing in Cognitive Radio Networks 1229.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments 1249.4 Cooperative Sensing Among Cognitive Radios 1259.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems 1289.6 Spectrum Agile Radios: Utilization and Sensing Architectures 1289.7 Some Fundamental Limits on Cognitive Radio 1309.8 Cooperative Strategies and Capacity Theorems for Relay Networks 1319.9 Research Challenges in Cooperative Communication 1339.10 Conclusion 13510 NATURAL LANGUAGE PROCESSING 139S. Meera and S. Geerthik10.1 Introduction 13910.2 Conclusions 152References 15211 CLASS LEVEL MULTI-FEATURE SEMANTIC SIMILARITY-BASED EFFICIENT MULTIMEDIA BIG DATA RETRIEVAL 155D. Sujatha, M. Subramaniam and A. Kathirvel11.1 Introduction 15611.2 Literature Review 15811.3 Class Level Semantic Similarity-Based Retrieval 15911.4 Results and Discussion 16412 SUPERVISED LEARNING APPROACHES FOR UNDERWATER SCALAR SENSORY DATA MODELING WITH DIURNAL CHANGES 175J.V. Anand, T.R. Ganesh Babu, R. Praveena and K. Vidhya12.1 Introduction 17612.2 Literature Survey 17612.3 Proposed Work 17712.4 Results 18012.5 Conclusion and Future Work 19013 MULTI-LAYER UAV AD HOC NETWORK ARCHITECTURE, PROTOCOL AND SIMULATION 193Kamlesh Lakhwani, Tejpreet Singh and Orchu Aruna13.1 Introduction 19413.2 Background 19613.3 Issues and Gap Identified 19713.4 Main Focus of the Chapter 19813.5 Mobility 19913.6 Routing Protocol 20113.7 High Altitude Platforms (HAPs) 20213.8 Connectivity Graph Metrics 20413.9 Aerial Vehicle Network Simulator (AVENs) 20613.10 Conclusion 20714 ARTIFICIAL INTELLIGENCE IN LOGISTICS AND SUPPLY CHAIN 211Jeyaraju Jayaprakash14.1 Introduction to Logistics and Supply Chain 21214.2 Recent Research Avenues in Supply Chain 21714.3 Importance and Impact of AI 22214.4 Research Gap of AI-Based Supply Chain 22415 HEREDITARY FACTOR-BASED MULTI-FEATURED ALGORITHM FOR EARLY DIABETES DETECTION USING MACHINE LEARNING 235S. Deepajothi, R. Juliana, S.K. Aruna and R. Thiagarajan15.1 Introduction 23615.2 Literature Review 23715.3 Objectives of the Proposed System 24415.4 Proposed System 24515.5 HIVE and R as Evaluation Tools 24615.6 Decision Trees 24715.7 Results and Discussions 25015.8 Conclusion 25216 ADAPTIVE AND INTELLIGENT OPPORTUNISTIC ROUTING USING ENHANCED FEEDBACK MECHANISM 255V. Sharmila, K. Mandal, Shankar Shalani and P. Ezhumalai16.1 Introduction 25516.2 Related Study 25816.3 System Model 25916.4 Experiments and Results 26416.5 Conclusion 26717 ENABLING ARTIFICIAL INTELLIGENCE AND CYBER SECURITY IN SMART MANUFACTURING 269R. Satheesh Kumar, G. Keerthana, L. Murali, S. Chidambaranathan, C.D. Premkumarand R. Mahaveerakannan17.1 Introduction 27017.2 New Development of Artificial Intelligence 27117.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing 27117.4 Current Status and Problems of Green Manufacturing 27217.5 Artificial Intelligence for Green Manufacturing 27617.6 Detailed Description of Common Encryption Algorithms 28017.7 Current and Future Works 28217.8 Conclusion 28318 DEEP LEARNING IN 5G NETWORKS 287G. Kavitha, P. Rupa Ezhil Arasi and G. Kalaimani18.1 5G Networks 28718.2 Artificial Intelligence and 5G Networks 29118.3 Deep Learning in 5G Networks 29319 EIDR UMPIRING SECURITY MODELS FOR WIRELESS SENSOR NETWORKS 299A. Kathirvel, S. Navaneethan and M. Subramaniam19.1 Introduction 29919.2 A Review of Various Routing Protocols 30219.3 Scope of Chapter 30719.4 Conclusions and Future Work 31120 ARTIFICIAL INTELLIGENCE IN WIRELESS COMMUNICATION 317Prashant Hemrajani, Vijaypal Singh Dhaka, Manoj Kumar Bohra and Amisha Kirti Gupta20.1 Introduction 31820.2 Artificial Intelligence: A Grand Jewel Mine 31820.3 Wireless Communication: An Overview 32020.4 Wireless Revolution 32020.5 The Present Times 32120.6 Artificial Intelligence in Wireless Communication 32120.7 Artificial Neural Network 32420.8 The Deployment of 5G 32620.9 Looking Into the Features of 5G 32720.10 AI and the Internet of Things (IoT) 32820.11 Artificial Intelligence in Software-Defined Networks (SDN) 32920.12 Artificial Intelligence in Network Function Virtualization 33120.13 Conclusion 332References 332Index 335
Praxiswissen Joomla! 4 - Das Kompendium
Das bewährte Standardwerk zu Joomla! jetzt aktualisiert auf Version 4Sie möchten Schritt für Schritt und ohne langwieriges Ausprobieren eine eigene Website mit Joomla! aufsetzen? Dann ist dieser praxisorientierte Leitfaden zur Joomla!-Version 4 genau das Richtige für Sie. Tim Schürmann führt Sie anhand eines Beispielprojekts in den Aufbau und die Pflege eines Webauftritts ein und behandelt dabei das komplette Joomla!-Themenspektrum von den Grundlagen bis hin zum Profiwissen. Sie erfahren, wie Sie Joomla! installieren, Bilder und Texte verwalten, Ihrer Webpräsenz mit Templates ein unverwechselbares Look-and-feel geben und Ihre Website um zusätzliche Funktionen wie einen Kalender, Kommentarmöglichkeiten oder eine eigene Erweiterung ergänzen. Kapitel zu Suchmaschinenoptimierung, Barrierefreiheit und Datenbankpflege runden das Handbuch ab. Es deckt umfassend die in Joomla! enthaltenen Funktionen ab und eignet sich daher sowohl als Einstieg als auch als Nachschlagewerk.Zielgruppe: Webentwickler*innenalle, die mit Joomla! eine eigene Webpräsenz aufbauen möchtenAutor: Tim Schürmann ist selbständiger Diplom-Informatiker und derzeit hauptsächlich als freier Autor unterwegs. Seine zahlreichen Artikel erscheinen in führenden Zeitschriften und wurden in mehrere Sprachen übersetzt. Er hat bereits einige erfolgreiche Bücher geschrieben, darunter mehrere Auflagen von Praxiswissen Joomla! oder WordPress komplett – Das Kompendium für Websites und Blogs (O’Reilly Verlag). Die Entwicklung von Joomla! verfolgt er nicht nur seit dessen Anfängen, er folterte das Content-Management-System selbstverständlich auch schon in der Praxis mit schwer verdaulichen Inhalten. Seine Steckenpferde sind die Programmierung, Algorithmen, freie Software, Computergeschichte, Schokoladeneis und der ganz alltägliche Wahnsinn.
Patterns of Software Construction
Master how to implement a repeatable software construction system. This book closely examines how a system is designed to tie a series of activities together that are needed when building software-intensive systems.Software construction and operations don't get enough attention as a repeatable system. The world is stuck in agile backlog grooming sessions, and quality is not increasing. Companies' budgets are shrinking, and teams need a way to get more done with less, consistently. This topic is very relevant to our current economic conditions and continuing globalization trends. A reason we constantly need more hands-on-the-keyboards is because of all the waste created in development cycles. We need more literature on how to "do software" not just write software.These goals are accomplished using the concept of evolutions, much like the Navy SEALS train their team members. For LIFT, the evolutions are: Plan, Build, Test, Release, Operate and Manage. The entire purpose of the book is instructing professionals how to use these distinct evolutions while remaining agile. And then, inside of each evolution, to explicitly break down the inputs to the evolution, outputs and series of activities taking place. Patterns of Software Construction clearly outlines how together this becomes the system.WHAT YOU WILL LEARN* Optimize each evolution of a software delivery cycle* Review best practices of planning, highest return in the build cycle, and ignored practices in test, release, and operate * Apply the highest return techniques during the software build evolutionWHO THIS BOOK IS FORManagers, developers, tech lead, team lead, aspiring engineer, department leaders in corporations, executives, small business owner, IT DirectorStephen Rylander is currently SVP, Global Head of Engineering Company at Donnelley Financial Solutions. He is a software engineer turned technical executive who has seen a variety of industries from music, to ecommerce, to finance and more. He is invested in improving the practice of software delivery, operational platforms and all the people involved in making this happen. He has worked on platforms handling millions of daily transactions and developed digital transformation programs driving financial platforms. He has also had the opportunity to construct platforms with digital investing advice engines and has a history of dealing with scale and delivering results leading local and distributed teams.For fun he used to also run the API Craft Chicago Meetup, help organize Morningstar Tech Talks and has been a member mentor at 1871 - Chicago's Technology & Entrepreneurship Center.Chapter 1: Not a Processo 1.1 Systemo 1.2 The Problemo 1.3 Realityo 1.4 The Solutiono 1.5.The EvolutionsChapter 2 LIFT System EvolutionsChapter 3 Plano 3.1 Plano 3.1 Targeto 3.1 Map it outo 3.1 Development StrategyChapter 4 Buildo 4.1 Anatomy of a Sprinto 4.2 Most Software Looks like this.o 4.3 Non-functional Requirements Pay the Billso 4.4 …Chapter 5 TestChapter 6 ReleaseChapter 7 OperateChapter 8 ManageChapter 9 The Long GameChapter 10 - Summary
Introducing Software Verification with Dafny Language
Get introduced to software verification and proving correctness using the Microsoft Research-backed programming language, Dafny. While some other books on this topic are quite mathematically rigorous, this book will use as little mathematical symbols and rigor as possible, and explain every concept using plain English. It's the perfect primer for software programmers and developers with C# and other programming language skills.Writing correct software can be hard, so you'll learn the concept of computation and software verification. Then, apply these concepts and techniques to confidently write bug-free code that is easy to understand. Source code will be available throughout the book and freely available via GitHub.After reading and using this book you'll be able write correct, big free software source code applicable no matter which platform and programming language you use.WHAT YOU WILL LEARN* Discover the Microsoft Research-backed Dafny programming language* Explore Hoare logic, imperative and functional programs* Work with pre- and post-conditions* Use data types, pattern matching, and classes* Dive into verification examples for potential re-use for your own projectsWHO THIS BOOK IS FORSoftware developers and programmers with at least prior, basic programming experience. No specific language needed. It is also for those with very basic mathematical experience (function, variables).BORO SITNIKOVSKI has over ten years of experience working professionally as a software engineer. He started programming with assembly on an Intel x86 at the age of ten. While in high school, he won several prizes in competitive programming, varying from 4th, 3rd, and 1st place. He is an informatics graduate - his bachelor’s thesis was titled “Programming in Haskell using algebraic data structures”, and his master’s thesis was titled “Formal verification of Instruction Sets in Virtual Machines”. He has also published a few papers on software verification. Other research interests of his include programming languages, mathematics, logic, algorithms, and writing correct software. He is a strong believer in the open-source philosophy and contributes to various open-source projects. In his spare time, he enjoys some time off with his family.Introduction: Languages and SystemsChapter 1: Our First ProgramChapter 2: LogicChapter 3: ComputationChapter 4: Mathematical FoundationsChapter 5: ProofsChapter 6: SpecificationsChapter 7: Mathematical InductionChapter 8: Verification ExercisesChapter 9: Implementing a Formal SystemConclusionBibliographyAppendix A: Gödel’s Theorems
Modellselektion
Die Modellselektion ist der Bereich der Statistik, welcher Wissenschaftlern eine Möglichkeit bietet ein Modell für die Analyse von Rohdaten zu geben. Dabei ist die Wahl eins geeigneten Modells entscheidend, da mit der Wahl eines geeigneten Modells die jeweilige Theorie einer wissenschaftlichen Forschung unterstützt werden kann. In der wissenschaftlichen Praxis stehen hierfür diverse Ansätze zur Verfügung. Die Modellselektion bietet, mit diversen Ansätzen, einen Anhaltspunkt, wie Modelle selektiert werden können, um die vorhandenen Daten zu analysieren und in der Folge die Theorie zu verifizieren bzw. falsifizieren.Hierbei stehen Wissenschaftlern diverse Ansätze und Selektionskriterien zur Verfügung, welche die Wissenschaftler dabei unterstützen können, ein geeignetes Modell für die Analyse der Daten zu selektieren. Die Selektion kann dabei mittels Tests und der Richtung der Modellselektion, mittels diversen mittels Shrinkageansätzen oder auf Basis eines Informationskriteriums erfolgen. Die Wahl eines Informationskriteriums findet in der Folge Anwendung in einer Regressionsanalyse. Dabei stehen dem Wissenschaftler diverse univariate und multivariate Regressionsmodelle zur Verfügung. Falls die Daten von Kollinearität gekennzeichnet sind, sollten Verfahren, wie die Ridge Regression oder die LASSO Regression den linearen Regressionsmodellen bevorzugt werden.
Mastering Snowflake Solutions
Design for large-scale, high-performance queries using Snowflake’s query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis.MASTERING SNOWFLAKE SOLUTIONS starts with a refresher on Snowflake’s unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater operational agility in responding to the needs of modern enterprises, for example in supporting agile development techniques via database cloning. The practical examples and in-depth background on theory in this book help you unleash the power of Snowflake in building a high-performance system with little to no administrative overhead. Your result from reading will be a deep understanding of Snowflake that enables taking full advantage of Snowflake’s architecture to deliver value analytics insight to your business.WHAT YOU WILL LEARN* Optimize performance and costs associated with your use of the Snowflake data platform* Enable data security to help in complying with consumer privacy regulations such as CCPA and GDPR* Share data securely both inside your organization and with external partners* Gain visibility to each interaction with your customers using continuous data feeds from Snowpipe* Break down data silos to gain complete visibility your business-critical processes* Transform customer experience and product quality through real-time analyticsWHO THIS BOOK IS FORData engineers, scientists, and architects who have had some exposure to the Snowflake data platform or bring some experience from working with another relational database. This book is for those beginning to struggle with new challenges as their Snowflake environment begins to mature, becoming more complex with ever increasing amounts of data, users, and requirements. New problems require a new approach and this book aims to arm you with the practical knowledge required to take advantage of Snowflake’s unique architecture to get the results you need.ADAM MORTON is a senior data and analytics professional with almost two decades of experience. He has architected, designed, and led the implementation of numerous data warehouse and business intelligence solutions. Adam has extensive experience and certifications across several data analytics platforms ranging from Microsoft SQL Server, Teradata, and Hortonworks, to modern cloud-based tools such as AWS Redshift, Google Big Query, and Snowflake.Having successfully combined his experience with traditional technologies with his knowledge of modern platforms, Adam has accumulated substantial practical expertise in data warehousing and analytics in Snowflake, which he has captured and distilled into this book. Today, Adam runs his own data and analytics consultancy which focuses on helping companies solve problems with data, along with designing and executing modern data strategies to deliver tangible business value. Adam currently lives in Sydney, Australia and is the proud recipient of a Global Talent Visa. 1. Snowflake Architecture2. Data Movement3. Cloning4. Managing Security and User Access Control5. Protecting Data in Snowflake6. Business Continuity and Disaster Recovery7. Data Sharing and the Data Cloud8. Programming9. Advanced Performance Tuning10. Developing Applications in Snowflake
Analytics Optimization with Columnstore Indexes in Microsoft SQL Server
Meet the challenge of storing and accessing analytic data in SQL Server in a fast and performant manner. This book illustrates how columnstore indexes can provide an ideal solution for storing analytic data that leads to faster performing analytic queries and the ability to ask and answer business intelligence questions with alacrity. The book provides a complete walk through of columnstore indexing that encompasses an introduction, best practices, hands-on demonstrations, explanations of common mistakes, and presents a detailed architecture that is suitable for professionals of all skill levels.With little or no knowledge of columnstore indexing you can become proficient with columnstore indexes as used in SQL Server, and apply that knowledge in development, test, and production environments. This book serves as a comprehensive guide to the use of columnstore indexes and provides definitive guidelines. You will learn when columnstore indexes should be used, and the performance gains that you can expect. You will also become familiar with best practices around architecture, implementation, and maintenance. Finally, you will know the limitations and common pitfalls to be aware of and avoid.As analytic data can become quite large, the expense to manage it or migrate it can be high. This book shows that columnstore indexing represents an effective storage solution that saves time, money, and improves performance for any applications that use it. You will see that columnstore indexes are an effective performance solution that is included in all versions of SQL Server, with no additional costs or licensing required.WHAT YOU WILL LEARN* Implement columnstore indexes in SQL Server* Know best practices for the use and maintenance of analytic data in SQL Server* Use metadata to fully understand the size and shape of data stored in columnstore indexes* Employ optimal ways to load, maintain, and delete data from large analytic tables* Know how columnstore compression saves storage, memory, and time* Understand when a columnstore index should be used instead of a rowstore index* Be familiar with advanced features and analyticsWHO THIS BOOK IS FORDatabase developers, administrators, and architects who are responsible for analytic data, especially for those working with very large data sets who are looking for new ways to achieve high performance in their queries, and those with immediate or future challenges to analytic data and query performance who want a methodical and effective solutionEdward Pollack has over 20 years of experience in database and systems administration, architecture, and development, becoming an advocate for designing efficient data structures that can withstand the test of time. He has spoken at many events, such as SQL Saturdays, PASS Community Summit, Dativerse, and at many user groups and is the organizer of SQL Saturday Albany. Edward has authored many articles, as well as the book Dynamic SQL: Applications, Performance, and Security, and a chapter in Expert T-SQL Window Functions in SQL Server.In his free time, Ed enjoys video games, sci-fi & fantasy, traveling and baking. He lives in the sometimes-frozen icescape of Albany, NY with his wife Theresa and sons Nolan and Oliver, and a mountain of (his) video game plushies that help break the fall when tripping on (their) kids’ toys.1. Introduction to Analytic Data in a Transactional Database2. Transactional vs. Analytic Workloads3. What are Columnstore Indexes?4. Columnstore Index Architecture5. Columnstore Compression6. Columnstore Metadata7. Batch Execution8. Bulk Loading Data9. Delete and Update Operations10. Segment and Rowgroup Elimination11. Partitioning12. Non-Clustered Columnstore Indexes on Rowstore Tables13. Non-Clustered Rowstore Indexes on Columnstore Tables14. Columnstore Index Maintenance15. Columnstore Index Performance
Machine Learning for Auditors
Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings.MACHINE LEARNING FOR AUDITORS provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization.WHAT YOU WILL LEARN* Understand the role of auditors as trusted advisors* Perform exploratory data analysis to gain a deeper understanding of your organization* Build machine learning predictive models that detect fraudulent vendor payments and expenses* Integrate data analytics with existing and new technologies* Leverage storytelling to communicate and validate your findings effectively* Apply practical implementation use cases within your organizationWHO THIS BOOK IS FORAI AUDITING is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes.MARIS SEKAR is a professional computer engineer, Certified Information Systems Auditor (ISACA), and Senior Data Scientist (Data Science Council of America). He has a passion for using storytelling to communicate on high-risk items within an organization to enable better decision making and drive operational efficiencies. He has cross-functional work experience in various domains such as risk management, data analysis and strategy, and has functioned as a subject matter expert in organizations such as PricewaterhouseCoopers LLP, Shell Canada Ltd., and TC Energy. Maris’ love for data has motivated him to win awards, write LinkedIn articles, and publish two papers with IEEE on applied machine learning and data science.PART I. TRUSTED ADVISORS1. Three Lines of Defense2. Common Audit Challenges3. Existing Solutions4. Data Analytics5. Analytics Structure & EnvironmentPART II. UNDERSTANDING ARTIFICIAL INTELLIGENCE6. Introduction to AI, Data Science, and Machine Learning7. Myths and Misconceptions8. Trust, but Verify9. Machine Learning Fundamentals10. Data Lakes11. Leveraging the Cloud12. SCADA and Operational TechnologyPART III. STORYTELLING13. What is Storytelling?14. Why Storytelling?15. When to Use Storytelling16. Types of Visualizations17. Effective Stories18. Storytelling Tools19. Storytelling in AuditingPART IV. IMPLEMENTATION RECIPES20. How to Use the Recipes21. Fraud and Anomaly Detection22. Access Management23. Project Management24. Data Exploration25. Vendor Duplicate Payments26. CAATs 2.027. Log Analysis28. Concluding Remarks
Data Science
Dieses Buch entstand aus der Motivation heraus, eines der ersten deutschsprachigen Nachschlagewerke zu entwickeln, in welchem relativ simple Quellcode-Beispiele enthalten sind, um so Lösungsansätze für die (wiederkehrenden) Programmierprobleme in der Datenanalyse weiterzugeben. Dabei ist dieses Werk nicht uneigennützig verfasst worden. Es enthält Lösungswege für immer wiederkehrende Problemstellungen die ich über meinen täglichen Umgang entwickelt habe Zweifellos gehört das Nachschlagen von Lösungsansätzen in Büchern oder im Internet zur normalen Arbeit eines Programmierers. Allerdings ist diese Suche in der Regel ein unstrukturierter und damit, zumindest teilweise, ein zeitaufwendiger Prozess.Unabhängig davon, ob Sie das Buch als Student, Mitarbeiter oder Gründer lesen, hoffe ich, dass Ihnen dieses Nachschlagewerk ein wertvoller Helfer für die ersten Anfänge sein wird. Ich gehe davon aus, dass jede Person die Grundlagen der Datenanalyse mit Hilfe moderner Programmiersprachen erlernen kann.Seit März 2018 forscht und promoviert Herr BENJAMIN M. ABDEL-KARIM im Bereich der künstlichen Intelligenz im Kontext der Wissensextraktion. Das spezielle Augenmerk seiner Forschung sind künstliche neuronale Netze, beispielsweise zur Modellierung komplexer Finanzmarktstrukturen. Zuvor hat er eine klassische Bankausbildung sowie ein Bachelor- und Masterstudium in der Wirtschaftsinformatik absolviert. Seit März 2021 bringt Herr Benjamin M. Abdel-Karim als Berater sein Fachwissen aus Forschung und Entwicklung bei der Unternehmungsberatung Capgemini im Bereich Financial Services mit ein.Data Science - Datenanalyse - Python - Quellcode-Beispiele - Datenauswertung - Datentypen - Datenstrukturen - Kontrollstrukturen - Funktionen -Anwendungsbeispiele Data Science.
Pro ASP.NET Core 6
Professional developers will produce leaner applications for the ASP.NET Core platform using the guidance in this best-selling book, now in its 9th edition and updated for ASP.NET Core for .NET 6. It contains detailed explanations of the ASP.NET Core platform and the application frameworks it supports. This cornerstone guide puts ASP.NET Core for .NET 6 into context and dives deep into the tools and techniques required to build modern, extensible web applications. New features and capabilities such as MVC, Razor Pages, Blazor Server, and Blazor WebAssembly are covered, along with demonstrations of how they are applied.ASP.NET Core for .NET 6 is the latest evolution of Microsoft’s ASP.NET web platform and provides a "host-agnostic" framework and a high-productivity programming model that promotes cleaner code architecture, test-driven development, and powerful extensibility.Author Adam Freeman has thoroughly revised this market-leading book and explains how to get the most from ASP.NET Core for .NET 6. He starts with the nuts-and-bolts topics, teaching you about middleware components, built-in services, request model binding, and more. As you gain knowledge and confidence, he introduces increasingly more complex topics and advanced features, including endpoint routing and dependency injection. He goes in depth to give you the knowledge you need.This book follows the same format and style as the popular previous editions but brings everything up to date for the new ASP.NET Core for .NET 6 release and broadens the focus to include all of the ASP.NET Core platform. You will appreciate the fully worked case study of a functioning ASP.NET Core application that you can use as a template for your own projects.Source code for this book can be found at https://github.com/Apress/pro-asp.net-core-6.WHAT YOU WILL LEARN* Explore the entire ASP.NET Core platform* Apply the new ASP.NET Core for .NET 6 features in your developer environment* See how to create RESTful web services, web applications, and client-side applications* Build on your existing knowledge to get up and running with new programming models quickly and effectivelyWHO THIS BOOK IS FORWeb developers with a basic knowledge of web development and C# who want to incorporate the latest improvements and functionality in ASP.NET Core for .NET 6 into their own projects.ADAM FREEMAN is an experienced IT professional who has held senior positions in a range of companies, most recently serving as chief technology officer and chief operating officer of a global bank. Now retired, he spends his time writing and long-distance running.Part 11. Putting ASP.NET Core into Context2. Getting Started3. Your First ASP.NET Core Application4. Using the Development Tools5. Essential C# Features6. Unit Testing ASP.NET Core Applications7. SportsStore8. SportsStore: Navigation & Cart9. SportsStore: Completing the Cart10. SportsStore: Adminstration11. SportsStore: Security & DeploymentPart 212. Understanding the ASP.NET Core Platform13. Using URL Routing14. Using Dependency Injection15. Using the Platform Features, Part 116. Using the Platform Features, Part 217. Working with DataPart 318. Creating the Example Project19. Creating RESTFul Web Services20. Advanced Web Service Features21. Using Controllers with Views22. Using Controllers with Views, Part 223. Using Razor Pages24. Using View Components25. Using Tag Helpers26. Using the Built-In Tag Helpers27. Using the Forms Tag Helpers28. Using Model Binding29. Using Model Validation30. Using Filters31. Creating Form ApplicationsPart 432. Creating the Example Application33. Using Blazor Server, Part 134. Using Blazor Server Part 235. Advanced Blazor Features36. Blazor Forms and Data37. Blazor Web Assembly38. Using ASP.NET Core Identity39. Applying ASP.NET Core Identity
Azure Virtual Desktop Specialist
Enhance your knowledge and become certified with the Azure Virtual Desktop technology. This book provides the theory, lab exercises, and knowledge checks you need to prepare for the AZ-140 exam.The book starts with an introduction to Azure Virtual Desktop and AZ-140 exam objectives. You will learn the architecture behind Azure Virtual Desktop, including compute, identity, and storage. And you will learn how to implement all of the services that make up the Azure Virtual Desktop platform. Each chapter includes exam and practice questions. The book takes you through the access and security of Azure Virtual Desktop along with its user environment and application. And it teaches you how to monitor and maintain an Azure Virtual Desktop infrastructure.After reading this book, you will be prepared to take the AZ-140 exam.WHAT YOU WILL LEARN* Plan an Azure Virtual Desktop architecture* Install and configure apps on a session host* Plan and implement business continuity and disaster recovery* Understand user environment and applications in Azure Virtual DesktopWHO THIS BOOK IS FORAzure administrators who wish to increase their knowledge and become certified with the Azure Virtual Desktop technologySHABAZ DARR has more than 15 years of experience in the IT industry and more than eight years working with cloud technologies. Currently, he is working as a Senior Infrastructure Specialist for Netcompany. He is a certified Microsoft MVP in Enterprise Mobility, a certified Microsoft trainer with certifications in Azure Virtual Desktop Administrator, Office 365 Identity and Services, Modern Workplace Administrator Associate, and Azure Administrator Associate.CHAPTER 1: EXAM OVERVIEW & INTRODUCTION TO AZURE VIRTUAL DESKTOPCHAPTER GOAL: Introduce Microsoft Certification exams and Azure Virtual DesktopNO OF PAGES: 15SUB -TOPICS1. Prepare for your Microsoft exam and AZ-140 objectives2. Introduction to Azure Virtual DesktopCHAPTER 2: PLAN AN AZURE VIRTUAL DESKTOP ARCHITECTURECHAPTER GOAL: Outline the architecture behind Azure Virtual Desktop, including compute, identity and storage.NO OF PAGES: 35SUB - TOPICS1. Design the Azure Virtual Desktop architecture2. Design for User identities and profiles3. Knowledge CheckCHAPTER 3: IMPLEMENT AN AZURE VIRTUAL DESKTOP INFRASTRUCTURECHAPTER GOAL: Learn how to implement all services that make up the Azure Virtual Desktop platformNO OF PAGES : 45SUB - TOPICS:1. Implement and manage networking for Azure Virtual Desktop2. Implement and manage storage for Azure Virtual Desktop3. Create and configure host pools and session hosts.4. Create and manage session host images5. Knowledge CheckCHAPTER 4: MANAGE ACCESS AND SECURITY TO AZURE VIRTUAL DESKTOPCHAPTER GOAL: Learn how to secure user access and implement additional security within Azure for AVDNO OF PAGES: 35SUB - TOPICS:1. Manage Access to Azure Virtual Desktop2. Manage Security for Azure Virtual Desktop3. Knowledge CheckCHAPTER 5: MANAGE USER ENVIRONMENT AND APPLICATIONS FOR AZURE VIRTUAL DESKTOPCHAPTER GOAL: Learn how to implement and Manage the user experience and deploy applications within Azure Virtual Desktop.NO OF PAGES: 40SUB-TOPICS:1. Implement and manage FSLogix2. Configure user experience settings3. Install and configure apps on a session host4. Knowledge CheckCHAPTER 6: MONITOR AND MAINTAIN AN AZURE VIRTUAL DESKTOP INFRASTRUCTURECHAPTER GOALS: Learn how to monitor and keep an Azure Virtual Desktop Infrastructure fully up-to-dateNO OF PAGES: 45SUB-TOPICS:1. Plan and implement business continuity and disaster recovery2. Automate Azure Virtual desktop management tasks3. Monitor and manage performance tasks4. Knowledge check
Linux System Administration for the 2020s
Build and manage large estates, and use the latest OpenSource management tools to breakdown a problems. This book is divided into 4 parts all focusing on the distinct aspects of Linux system administration.The book begins by reviewing the foundational blocks of Linux and can be used as a brief summary for new users to Linux and the OpenSource world. Moving on to Part 2 you'll start by delving into how practices have changed and how management tooling has evolved over the last decade. You’ll explore new tools to improve the administration experience, estate management and its tools, along with automation and containers of Linux.Part 3 explains how to keep your platform healthy through monitoring, logging, and security. You'll also review advanced tooling and techniques designed to resolve technical issues. The final part explains troubleshooting and advanced administration techniques, and less known methods for resolving stubborn problems.With Linux System Administration for the 2020s you'll learn how to spend less time doing sysadmin work and more time on tasks that push the boundaries of your knowledge.WHAT YOU'LL LEARN* Explore a shift in culture and redeploy rather than fix* Improve administration skills by adopting modern tooling* Avoid bad practices and rethink troubleshooting* Create a platform that requires less human interventionWHO THIS BOOK IS FOREveryone from sysadmins, consultants, architects or hobbyists.Ken Hitchcock currently is a Principal Consultant working for Red Hat, with over twenty years of experience in IT. He has spent the last eleven years predominately focused on Red Hat products, certificating himself as a Red Hat Architect along the way. The last eleven years have been paramount in his understanding of how large Linux estates should be managed and in the spirit of openness, was inspired to share his knowledge and experiences in this book. Originally from Durban South Africa, he now lives in the south of England where he hopes to not only continue inspiring all he meets but also to continue improving himself and the industry he works in.PART ONE: Laying the foundation.- CHAPTER 1: Linux at a Glance.- PART TWO : Strengthening core skills.- CHAPTER 2: New tools to improve the administration experience.- CHAPTER 3: Estate management.- CHAPTER 4: Estate Management Tools.- CHAPTER 5: Automation.- CHAPTER 6: Containers.-PART THREE: Day two practices and keeping the lights on.-CHAPTER 7: Monitoring.-CHAPTER 8: Logging.-CHAPTER 9: Security.-CHAPTER 10: Maintenance tasks and planning.- PART FOUR: See, analyze and act.-CHAPTER 11: Troubleshooting.-CHAPTER 12: Advanced Administration