Tabla de Contenidos: “…3.2 Deep learning approaches for digital signal processing -- 3.3 Optical IM/DD systems based on deep learning -- 3.3.1 ANN receiver -- 3.3.1.1 PAM transmission -- 3.3.1.2 Sliding window FFNN processing -- 3.3.2 Auto-encoders -- 3.3.2.1 Auto-encoder design based on a feed-forward neural network -- 3.3.2.2 Auto-encoder design based on a recurrent neural network -- 3.3.3 Performance -- 3.3.4 Distance-agnostic transceiver -- 3.4 Implementation on a transmission link -- 3.4.1 Conventional PAM transmission with ANN-based receiver -- 3.4.2 Auto-encoder implementation -- 3.5 Outlook -- References -- 4 Machine learning techniques for passive optical networks -- 4.1 Background -- 4.2 The validation of NN effectiveness -- 4.3 NN for nonlinear equalization -- 4.4 End to end deep learning for optimal equalization -- 4.5 FPGA implementation of NN equalizer -- 4.6 Conclusions and perspectives -- References -- 5 End-to-end learning for fiber-optic communication systems -- 5.1 Introduction -- 5.2 End-to-end learning -- 5.3 End-to-end learning for fiber-optic communication systems -- 5.3.1 Direct detection -- 5.3.2 Coherent systems -- 5.3.2.1 Nonlinear phase noise channel -- 5.3.2.2 Perturbation models (NLIN and GN) -- 5.3.2.3 Split-step Fourier method (SSFM) -- 5.4 Gradient-free end-to-end learning -- 5.5 Conclusion -- Acknowledgments -- References -- 6 Deep learning techniques for optical monitoring -- 6.1 Introduction -- 6.2 Building blocks of deep learning-based optical monitors -- 6.2.1 Digital coherent reception as a data-acquisition method -- 6.2.2 Deep learning and representation learning -- 6.2.3 Combination of digital coherent reception and deep learning -- 6.3 Deep learning-based optical monitors -- 6.3.1 Training mode of DL-based optical monitors -- 6.3.2 Advanced topics for the training mode of DL-based optical monitors…”

Tabla de Contenidos: “…7.2.3.1 Digital Arrays -- 7.2.3.2 Analog Arrays -- 7.2.3.3 Hybrid Arrays -- 7.2.3.4 A Millimeter-Wave Antenna Array System Prototype -- 7.2.4 UE Antennas -- 7.2.5 Antenna Ports and QCL -- 7.2.6 CSI Acquisition -- 7.2.6.1 Reciprocity Based -- 7.2.6.2 Feedback Based -- 7.2.7 Massive MIMO -- 7.3 Multiantenna Techniques in NR -- 7.3.1 CSI Acquisition -- 7.3.1.1 Interference Measurements -- 7.3.2 Downlink MIMO Transmission -- 7.3.3 Uplink MIMO Transmission -- 7.3.4 Beam Management -- 7.3.4.1 Beam Acquisition During Initial Access -- 7.3.4.2 Beam Management Procedures -- 7.3.4.3 Beam Measurement and Reporting -- 7.3.4.4 Beam Indication -- 7.3.4.5 Beam Recovery -- 7.3.4.6 Uplink Beam Management -- 7.4 Experimental Results -- 7.4.1 Beam-Forming Gain -- 7.4.2 Beam Tracking -- 7.4.3 System Simulations -- References -- 8 Channel Coding -- 8.1 Fundamental Limits of Forward Error Correction -- 8.1.1 The Binary AWGN Channel -- 8.1.2 Coding Schemes for the Binary-AWGN Channels -- 8.1.3 Performance Metrics -- 8.2 FEC Schemes for the Bi-AWGN Channel -- 8.2.1 Introduction -- 8.2.2 Some De nitions -- 8.2.3 LDPC Codes -- 8.2.3.1 Fundamentals of LDPC Codes -- 8.2.3.2 The LDPC-Code Solution Chosen for 5G NR -- 8.2.4 Polar Codes -- 8.2.4.1 Fundamentals of Polar Codes -- 8.2.4.2 The Polar-Code Solution Chosen for 5G NR -- Deterministic Reliability Ordering -- Parity-Check Coding -- Rate Adaptation -- 8.2.5 Other Coding Schemes for the Short-Blocklength Regime -- 8.2.5.1 Short Algebraic Linear Block Codes With Ordered-Statistics Decoding -- 8.2.5.2 Linear Block Codes With Tail-Biting Trellises -- 8.2.5.3 Nonbinary LDPC Codes -- 8.2.5.4 Performance -- 8.3 Coding Schemes for Fading Channels -- 8.3.1 The SISO Case -- 8.3.2 The MIMO Case -- References -- 9 Simulator -- 9.1 Simulator Overview -- 9.2 Functional Modules -- 9.2.1 Channel Model -- 9.2.2 Power Ampli er Model…”

Tabla de Contenidos: “…Questions -- Exercises -- Chapter 1: The Architecture of 8086 -- 1.1 Internal Block Diagram of the 8086 -- 1.2 The Execution Unit -- 1.2.1 The Scratchpad Registers -- 1.2.2 Flag Register -- 1.3 Bus Interface Unit -- 1.3.1 The Instruction Queue -- 1.3.2 Memory Segmentation -- 1.3.2.1 Segment Registers -- 1.3.2.2 The Code Segment and the Instruction Pointer -- 1.3.2.3 The Stack Segment and the Stack Pointer -- 1.3.2.4 The Data Segment and Extra Segment -- 1.4 Addressing Modes -- Key Points of this Chapter -- Questions -- Exercises -- Chapter 2: Programming Concepts - I -- 2.1 The Assembly Process -- 2.1.1 Features of Assemblers -- 2.1.2 Instructions and Directives -- 2.1.3 The Forward Reference Problem -- 2.1.4 Two Pass Assemblers -- 2.2 Assemblers for x86 -- 2.2.1 Why MASM? …”

Tabla de Contenidos: “…4.5 Typical Applications of Tensors -- 4.5.1 Scalar -- 4.5.2 Vector -- 4.5.3 Matrix -- 4.5.4 Three-Dimensional Tensor -- 4.5.5 Four-Dimensional Tensor -- 4.6 Indexing and Slicing -- 4.6.1 Indexing -- 4.6.2 Slicing -- 4.6.3 Slicing Summary -- 4.7 Dimensional Transformation -- 4.7.1 Reshape -- 4.7.2 Add and Delete Dimensions -- 4.7.3 Swap Dimensions -- 4.7.4 Copy Data -- 4.8 Broadcasting -- 4.9 Mathematical Operations -- 4.9.1 Addition, Subtraction, Multiplication and Division -- 4.9.2 Power Operations -- 4.9.3 Exponential and Logarithmic Operations -- 4.9.4 Matrix Multiplication -- 4.10 Hands-On Forward Propagation -- Chapter 5: Advanced TensorFlow -- 5.1 Merge and Split -- 5.1.1 Merge -- 5.1.2 Split -- 5.2 Common Statistics -- 5.2.1 Norm -- 5.2.2 Max, Min, Mean, and Sum -- 5.3 Tensor Comparison -- 5.4 Fill and Copy -- 5.4.1 Fill -- 5.4.2 Copy -- 5.5 Data Limiting -- 5.6 Advanced Operations -- 5.6.1 tf.gather -- 5.6.2 tf.gather_nd -- 5.6.3 tf.boolean_mask -- 5.6.4 tf.where -- 5.6.5 tf.scatter_nd -- 5.6.6 tf.meshgrid -- 5.7 Load Classic Datasets -- 5.7.1 Shuffling -- 5.7.2 Batch Training -- 5.7.3 Preprocessing -- 5.7.4 Epoch Training -- 5.8 Hands-On MNIST Dataset -- Chapter 6: Neural Networks -- 6.1 Perceptron -- 6.2 Fully Connected Layer -- 6.2.1 Tensor Mode Implementation -- 6.2.2 Layer Implementation -- 6.3 Neural Network -- 6.3.1 Tensor Mode Implementation -- 6.3.2 Layer Mode Implementation -- 6.3.3 Optimization -- 6.4 Activation function -- 6.4.1 Sigmoid -- 6.4.2 ReLU -- 6.4.3 LeakyReLU -- 6.4.4 Tanh -- 6.5 Design of Output Layer -- 6.5.1 Common Real Number Space -- 6.5.2 [0, 1] Interval -- 6.5.3 [0,1] Interval with Sum 1 -- 6.5.4 (-1, 1) Interval -- 6.6 Error Calculation -- 6.6.1 Mean Square Error Function -- 6.6.2 Cross-Entropy Error Function -- 6.7 Types of Neural Networks -- 6.7.1 Convolutional Neural Network -- 6.7.2 Recurrent Neural Network…”

Tabla de Contenidos: “…Cover -- Half Title -- Title -- Copyright -- Dedication -- Contents -- List of Figures -- List of Tables -- Preface -- Chapter 1: Introduction -- 1.1 Using This Book -- 1.2 Fundamentals of Audio Signal Processing -- 1.2.1 Acquisition of Audio Samples -- 1.3 Reconstruction of the Analog Signal -- 1.4 Numerical Representation of Audio Data -- 1.5 Analytical DSP Test Signals -- 1.5.1 DC and Step (0 Hz) -- 1.5.2 Nyquist -- 1.5.3 1⁄2 Nyquist -- 1.5.4 1⁄4 Nyquist -- 1.5.5 Impulse -- 1.6 Signal Processing Algorithms -- 1.6.1 Bookkeeping -- 1.6.2 The One-Sample Delay -- 1.6.3 Multiplication With a Scalar Value -- 1.6.4 Addition and Subtraction -- 1.6.5 Some Algorithm Examples and Difference Equations -- 1.6.5.1 Gain, Attenuation and Phase Inversion -- 1.7 1st Order Feed-Forward and Feedback Algorithms -- 1.8 Bibliography -- Chapter 2: Anatomy of an Audio Plugin -- 2.1 Plugin Packaging: Dynamic-Link Libraries (DLLs) -- 2.2 The Plugin Description: Simple Strings -- 2.2.1 The Plugin Description: Features and Options -- 2.3 Initialization: Defining the Plugin Parameter Interface -- 2.3.1 Initialization: Defining Channel I/O Support -- 2.3.2 Initialization: Sample Rate Dependency -- 2.4 Processing: Preparing for Audio Streaming -- 2.4.1 Processing: Audio Signal Processing (DSP) -- 2.5 Mixing Parameter Changes With Audio Processing -- 2.5.1 Plugin Variables and Plugin Parameters -- 2.5.2 Parameter Smoothing -- 2.5.3 Pre and Post-Processing Updates -- 2.5.4 VST3 Sample Accurate Updates -- 2.5.5 Multi-Threaded Software -- 2.6 Monolithic Plugin Objects -- 2.7 Bibliography -- Chapter 3: VST3 Programming Guide -- 3.1 Setting Up the VST3 SDK -- 3.1.1 VST3 Sample Projects -- 3.1.2 VST3 Documentation -- 3.2 VST3 Architecture and Anatomy -- 3.2.1 Single vs. …”

Tabla de Contenidos: “…-- 1.7 A Real-life Compiler-gcc -- 1.8 What Do We Mean by "Meaning"? -- Looking Forward -- Historical Notes -- Exercises -- Web Resources -- Glossary -- Chapter 2: A Simple Translator -- 2.1 A Simple Language -- 2.1.1 Grammar of simple -- 2.1.2 Target Language -- 2.1.3 Example Program in Machine Code -- 2.2 Compiler for Simple -- 2.2.1 Scanner -- 2.2.2 Parser -- 2.2.3 Intermediate Form and Semantic Phase -- 2.2.4 Code Generation -- 2.2.5 Comments on the Compiled Code -- 2.3 A Virtual Machine for Simple -- Looking Forward -- Exercises -- Web Resources -- Glossary -- Chapter 3: Lexical Analyzer -- 3.1 Scanner -- 3.1.1 Examples: RE, FSM and Implementation -- 3.2 Symbol Tables and a Scanner -- 3.3 Compiler Writing Tools -- 3.3.1 Lex - A Scanner Generator -- 3.3.2 Flex -- 3.3.3 Debugging lex and flex -- 3.4 Error Handling in a Scanner -- Exercises -- Web Resources -- Glossary…”

Tabla de Contenidos: “…16.1.1 WebSphere Edge Server -- 16.1.2 Load balancer -- 16.2 Load balancing using Media Access Control (MAC) forwarding -- 16.2.1 How it works -- 16.2.2 Overall solution design -- 16.2.3 Load balancing deployment design -- 16.3 Install WebSphere Edge Server Network Dispatcher -- 16.4 Configure Network Dispatcher for load balancing -- 16.4.1 Start Network Dispatcher -- 16.4.2 Configure Network Dispatcher with the Dispatcher GUI -- 16.4.3 Configuring the cluster IP address on the network adapter -- 16.4.4 Aliasing the cluster address on the load-balancing servers -- 16.5 Validate the load balancing environment -- 16.6 IP session affinity -- 16.7 Access the components -- Chapter 17. …”

Tabla de Contenidos: “…-- Excited about Innovation, Okay with Blurriness -- The New Tool Belt -- Using Every Tool for Impact -- Aspirational Innovators -- Being Unreasonable to Do Something Extraordinary -- Taking Risks and Failing Forward -- What Does This Mean? -- Notes -- PART TWO: Going All In -- CHAPTER 5: The "Do Something" Generation -- Get Out There and Do It -- Looking for a Real Relationship -- With Great Power Comes Great Responsibility -- Engaged Donors Are Bigger, Better Donors -- What Does This Mean? …”

Tabla de Contenidos: “…The Korean experience of sharing economy and its policy implications -- Introduction -- Sharing economy: status and prospects -- Sharing economy in Korea: an overview -- Scope and structure -- Setting the framework: concepts and components -- Previous discussions -- A narrow definition with a focus on the sharing of idle assets -- A Broad definition with a focus on ICT platform applications -- Core components -- Sharing economy in Korea: status and limits -- Status and characteristics -- Car-sharing -- Ride-sharing -- Accommodation-sharing -- Cases of failure: sharing of intangible assets and government-run sharing -- Cases of failure: government-run sharing -- Issues around sharing economy in Korea -- Conflicts with incumbent businesses -- Platform labour workers in regulatory grey area -- Government efforts towards social compromise -- A Need for alternative regulatory strategies -- What conventional regulations can do to the sharing economy -- The purpose of regulatory alternatives -- Alternative 1: Regulation-in-proportion (transaction-volume-based regulations) -- Alternative 2: Platform regulation (delegated implementation) -- Co-regulation: an ideal way forward -- Conclusion -- References…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Acknowledgments -- Preface -- Chapter 1 Basic Numerical Methods -- 1.1 Introduction -- 1.2 Ordinary Differential Equation -- 1.3 Euler Method -- 1.4 Improved Euler Method -- 1.5 Runge-Kutta Methods -- 1.5.1 Midpoint Method -- 1.5.2 Runge-Kutta Fourth Order -- 1.6 Multistep Methods -- 1.6.1 Adams-Bashforth Method -- 1.6.2 Adams-Moulton Method -- 1.7 Higher‐Order ODE -- References -- Chapter 2 Integral Transforms -- 2.1 Introduction -- 2.2 Laplace Transform -- 2.2.1 Solution of Differential Equations Using Laplace Transforms -- 2.3 Fourier Transform -- 2.3.1 Solution of Partial Differential Equations Using Fourier Transforms -- References -- Chapter 3 Weighted Residual Methods -- 3.1 Introduction -- 3.2 Collocation Method -- 3.3 Subdomain Method -- 3.4 Least‐square Method -- 3.5 Galerkin Method -- 3.6 Comparison of WRMs -- References -- Chapter 4 Boundary Characteristics Orthogonal Polynomials -- 4.1 Introduction -- 4.2 Gram-Schmidt Orthogonalization Process -- 4.3 Generation of BCOPs -- 4.4 Galerkin's Method with BCOPs -- 4.5 Rayleigh-Ritz Method with BCOPs -- References -- Chapter 5 Finite Difference Method -- 5.1 Introduction -- 5.2 Finite Difference Schemes -- 5.2.1 Finite Difference Schemes for Ordinary Differential Equations -- 5.2.1.1 Forward Difference Scheme -- 5.2.1.2 Backward Difference Scheme -- 5.2.1.3 Central Difference Scheme -- 5.2.2 Finite Difference Schemes for Partial Differential Equations -- 5.3 Explicit and Implicit Finite Difference Schemes -- 5.3.1 Explicit Finite Difference Method -- 5.3.2 Implicit Finite Difference Method -- References -- Chapter 6 Finite Element Method -- 6.1 Introduction -- 6.2 Finite Element Procedure -- 6.3 Galerkin Finite Element Method -- 6.3.1 Ordinary Differential Equation -- 6.3.2 Partial Differential Equation -- 6.4 Structural Analysis Using FEM…”

Tabla de Contenidos: “…Grasping the Essence of Character -- Employing positive character traits -- Steering clear of negative character traits -- Recognizing How to Make Character Count -- Looking at Real-Life Scenarios: Character in action -- Part 2 Going Even Further with Soft Skills -- Chapter 4 Being Clear with Interpersonal Communication -- Making all the Right Connections -- Stepping on the Communication Scale -- Getting Connected -- Listening first, talking second -- Framing the walls of disconnection -- Completing the Connection with the Three Vs -- Doing the math -- Speaking from the heart -- Becoming a Communication Champion -- Talking about body language -- Raising your voice -- Using your words -- Looking at Real-Life Scenarios: Understanding the Value of Listening -- Chapter 5 Respecting Diversity and Developing Cultural Awareness -- Defining Culture -- Delving into dominant cultural patterns -- Stepping Out of Your Cultural Comfort Zone -- Diving beneath the Diversity Iceberg -- Celebrating Diversity -- Understanding individuals' differences -- Accepting that seeing is not always believing -- Saying No to Stereotyping -- Stepping on the stereotyping scale -- Recognizing how you stereotype -- Looking at Real-Life Scenarios: The Newcomer -- Chapter 6 Making an Impression with Your Appearance and Manners -- Acing the Four-Second First Impression -- Stepping on the Appearance and Etiquette Scale -- Looking the Part: Why Appearance Matters -- Putting your best foot (and face) forward -- Dressing for success -- Acting the Part: Why Etiquette Is Important -- Shaking on it -- Speaking volumes with manners -- Sharing your positive energy -- Walking your talk -- Looking at Real-Life Scenarios: A Striking Difference -- Chapter 7 Staying on Top of Things with Time Management -- Stepping on the Time Management Scale -- Taking Back Control of Your Time…”

Tabla de Contenidos: “…-- Introduction -- Professional service firms -- Improvisation in professional service firms -- Improvisation and change -- Improvisation and knowledge -- Improvisation and innovation -- Moving forward the research of improvisation in PSFs…”

Tabla de Contenidos: “…: Exploring Residents' Interest in and Suggestions for the Development of Film Tourism in Seville, Spain -- Introduction -- Local Residents' Support for Film Tourism Development -- Local Residents' Involvement in Film Tourism Development -- Research Methods -- Results -- Conclusion -- Acknowledgments -- Notes -- References -- Part 4 Developing Media Tourism: Policy, Management and Strategies -- 14 Film Tourism in Brazil: Learning From Local Perspectives -- Introduction -- Film Tourism and Local Communities -- Mapping Film Tourism in Brazil -- Film Tourism Planning: Potential for Collaboration, Development and Sustainability -- Film Tourism Execution: Hierarchies, Tensions and Neglected Goals -- Film Tourism Aftermath: The Telenovela Effect -- Lessons From the Brazilian Experience -- Moving Forward -- Note -- References -- 15 Developing and Managing Film-Related Tourism at Film Studios With the Design of Cultural and Heritage Features: The Case of Hengdian World Studios (China) -- Introduction -- Film-related Tourism and Cultural Tourism in China -- Film-related Tourism Products and Activities at Hengdian World Studios -- Tourists' Perceptions of Film-Related Tourism With the Design of Cultural and Heritage Elements -- Conclusion -- Notes -- Bibliography -- 16 Promoting Popular Culture (And) Tourism as National Policies: Comparing "Cool Japan" and "Korean Wave" -- Abbreviations -- Introduction -- 'Cool Japan' and Tourism Policy -- Integrating Industrial, Diplomatic and Tourism Policies Through Popular Culture -- Cool Japan Policy and Tourism Promotion -- Achievements and Challenges of the Cool Japan Tourism Policy…”

Tabla de Contenidos: “…-- GPT Models in Medicine -- Radiology -- Patient Self-Care Management and Informed Decision-Making -- Public Health -- Conclusion -- Part V Summary, Looking Forward, and Additional Resources -- Epilogue: Getting Good at AI for Good -- Communication -- Setting Realistic Expectations for AI -- Confronting Technical Limitations -- Project Scoping and Implementation -- Data -- Adapting to Previously Collected Datasets -- Creating Training and Test Sets with the Application Scenario in Mind -- Modeling -- Incorporating Domain Expertise -- Model Development with Resource Constraints -- Evaluation and Metrics -- Humans in the Loop -- Impact -- Uphill Path to Deployment and Adoption -- Measuring Impact -- Conclusion -- Key Takeaways -- AI and Satellites: Critical Tools to Help Us with Planetary Emergencies -- Amazing Things in the Amazon -- Quick Help Saving Lives in Disaster Response -- Additional Resources…”

“…The book builds on the ideas put forward by the European Research Cluster and the IoT European Platform Initiative (IoT-EPI) and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in the next years. …”

“…It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. …”

Tabla de Contenidos: “…5.2 Learning a Task-Speci c Deep Architecture for Clustering -- 5.2.1 Introduction -- 5.2.2 Related Work -- 5.2.2.1 Sparse Coding for Clustering -- 5.2.2.2 Deep Learning for Clustering -- 5.2.3 Model Formulation -- 5.2.3.1 TAGnet: Task-speci c And Graph-regularized Network -- 5.2.3.2 Clustering-Oriented Loss Functions -- 5.2.3.3 Connections to Existing Models -- 5.2.4 A Deeper Look: Hierarchical Clustering by DTAGnet -- 5.2.5 Experiment Results -- 5.2.5.1 Datasets and Measurements -- 5.2.5.2 Experiment Settings -- 5.2.5.3 Comparison Experiments and Analysis -- Bene ts of the Task-speci c Deep Architecture -- Effects of Graph Regularization -- Scalability and Robustness -- 5.2.5.4 Hierarchical Clustering on CMU MultiPIE -- 5.2.6 Conclusion -- References -- 6 Signal Processing -- 6.1 Deeply Optimized Compressive Sensing -- 6.1.1 Background -- 6.1.2 An End-to-End Optimization Model of CS -- 6.1.3 DOCS: Feed-Forward and Jointly Optimized CS -- Complexity -- Related Work -- 6.1.4 Experiments -- Settings -- Simulation -- Reconstruction Error -- Ef ciency -- Experiments on Image Reconstruction -- 6.1.5 Conclusion -- 6.2 Deep Learning for Speech Denoising -- 6.2.1 Introduction -- 6.2.2 Neural Networks for Spectral Denoising -- 6.2.2.1 Network Architecture -- 6.2.2.2 Implementation Details -- Activation Function -- Cost Function -- Training Strategy -- 6.2.2.3 Extracting Denoised Signals -- 6.2.2.4 Dealing with Gain -- 6.2.3 Experimental Results -- 6.2.3.1 Experimental Setup -- 6.2.3.2 Network Structure Analysis -- 6.2.3.3 Analysis of Robustness to Variations -- 6.2.3.4 Comparison with NMF -- 6.2.4 Conclusion and Future Work -- References -- 7 Dimensionality Reduction -- 7.1 Marginalized Denoising Dictionary Learning with Locality Constraint -- 7.1.1 Introduction -- 7.1.2 Related Works -- 7.1.2.1 Dictionary Learning -- 7.1.2.2 Auto-encoder…”

Tabla de Contenidos: “…11.2.3 Day-to-Day Example -- 11.2.3.1 Optical Character Recognition (OCR) -- 11.2.3.2 Face Recognition -- 11.2.3.3 Recognition of Speech -- 11.2.3.4 Medical Findings -- 11.2.3.5 Extraction of Acquaintance -- 11.2.3.6 Compression -- 11.2.3.7 Additional Examples -- 11.2.4 Discriminant -- 11.2.5 Algorithms -- 11.3 Clustering -- 11.3.1 Data Examples Using Natural Clusters -- 11.4 Clustering (k-means) -- 11.4.1 Outline -- 11.4.2 Example -- 11.4.2.1 Problem -- 11.4.2.2 Solution -- 11.4.3 Some Methods for Initialization -- 11.4.4 Disadvantages -- 11.4.5 Use Case: Image Compression and Segmentation -- 11.4.5.1 Segmentation of Images -- 11.4.5.2 Compression of Data -- 11.5 Reduction of Dimensionality -- 11.5.1 Introduction -- 11.5.1.1 Feature Selection -- 11.5.1.2 Feature Extraction -- 11.5.1.3 Error Measures -- 11.5.2 Benefits of Reducing Dimensionality -- 11.5.3 Subset Selection -- 11.5.3.1 Selecting Forward -- 11.5.3.2 Remarks -- 11.5.3.3 Selection in Reverse -- 11.6 The Ensemble Method -- 11.6.1 Random Forest -- 11.6.2 Algorithm -- 11.6.3 Benefits and Drawbacks -- 11.6.3.1 Benefits -- 11.6.3.2 Drawbacks -- 11.6.4 Deep Learning and Neural Networks -- 11.6.4.1 Definition -- 11.6.4.2 Remarks -- 11.6.5 Applications -- 11.6.6 Artificial Neural Network -- 11.6.6.1 Biological Motivation -- 11.7 Transfer of Learning -- 11.8 Learning Through Reinforcement -- 11.9 Processing of Natural Languages -- 11.10 Word Embeddings -- 11.11 Conclusion -- References -- Chapter 12 Recognition Attendance System Ensuring COVID-19 Security -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Software Requirements -- 12.3.1 Operating System - Windows 7 and Above -- 12.3.2 IDE-Visual Studio Code -- 12.3.3 Programming Languages: Python, HTML, CSS, JS, and PHP -- 12.4 Hardware Requirements -- 12.4.1 Three Processors and Above -- 12.4.2 RAM - 2GB (Minimum Capacity)…”

Tabla de Contenidos: “…DFSMShsm control data sets -- 2.1 Types of DFSMShsm CDSs -- 2.1.1 Migration control data set (MCDS) -- 2.1.2 Backup control data set (BCDS) -- 2.1.3 Offline control data set (OCDS) -- 2.2 Multi-cluster control data sets -- 2.3 CDS allocation parameters -- 2.4 Primer record -- 2.5 CDS backup -- 2.5.1 Concurrent copy -- 2.5.2 SnapShot -- 2.6 Reorganizing control data sets -- 2.7 CDS forward recovery -- 2.7.1 Enhanced CDS recovery function -- Chapter 3. …”

Tabla de Contenidos: “…Meta Integration of DB2 Cube Views within the enterprise toolset -- 9.1 Meta Integration Technology products overview -- 9.1.1 Meta Integration Works (MIW) -- 9.1.2 Meta Integration Repository (MIR) -- 9.1.3 Meta Integration Model Bridge (MIMB) -- 9.2 Architecture and components involved -- 9.3 Metadata flow scenarios -- 9.4 Metadata mapping and limitations considerations -- 9.4.1 Forward engineering from a relational model to a cube model -- 9.4.2 Reverse engineering of a cube model into a relational model -- 9.5 Implementation steps scenario by scenario -- 9.5.1 Metadata integration of DB2 Cube Views with ERwin v4.x -- 9.5.2 Metadata integration of DB2 Cube Views with ERwin v3.x -- 9.5.3 Metadata integration of DB2 Cube Views with PowerDesigner -- 9.5.4 Metadata integration of DB2 Cube Views with IBM Rational Rose -- 9.5.5 Metadata integration of DB2 Cube Views with CWM and XMI -- 9.5.6 Metadata integration of DB2 Cube Views with DB2 Warehouse Manager -- 9.5.7 Metadata integration of DB2 Cube Views with Informatica -- 9.6 Refresh considerations -- 9.7 Conclusion: benefits -- Chapter 10. …”