Tabla de Contenidos: “…Judge a Song by Its Cover: Cover Songs between Transformation and Extraction -- 2. Stir It Up: Remix and the Problem of Genre -- 3. …”

Tabla de Contenidos: “…Music Data Mining: An Introduction; 2. Audio Feature Extraction; II. Classification; 3. Auditory Sparse Coding; 4. …”

Tabla de Contenidos: “…Cover; Contents; Contributors; Preface; Acknowledgments; PART IIntroduction; 1 Overview of Visualization; 1.1 Introduction; 1.2 Scalar Algorithms; 1.3 Vector Algorithms; 1.4 Tensor Algorithms; 1.5 Modeling Algorithms; 1.6 Bibliographic Notes; PART IIScalar Field Visualization:Isosurfaces; 2 Accelerated Isosurface Extraction Approaches; 2.1 Introduction; 2.2 Isosurface Extraction Approaches; 2.3 The Span Space; 2.4 Near-Optimal Isosurface Extraction; 2.5 View-Dependent Isosurface Extraction; 2.6 Summary; 3 Time-Dependent Isosurface Extraction; 3.1 Space-Efficient Search Data Structure…”

Tabla de Contenidos: “…Extracting Features from a Text -- 2.9. Information Extraction -- 2.9.1. Terminology Extraction -- 2.9.2. …”

Tabla de Contenidos: “…Evolution of Domestic Extraction -- 2.5. The specialization of Spain’s agricultural production -- 2.6. …”

Tabla de Contenidos: “…Chapter 2: Analytical Chemistry of Plastic Debris: Sampling, Methods, and Instrumentation -- 2.1 Introduction -- 2.2 About the Analytes -- 2.3 Sampling -- 2.3.1 Aqueous Matrices -- 2.3.2 Air Samples -- 2.3.3 Sediments, Soils, and Dust -- 2.3.4 Biological Samples -- 2.3.5 Sample Preservation -- 2.4 Laboratory Processing -- 2.4.1 Sample Preparation -- 2.4.2 Chemical and Enzymatic Digestion -- 2.4.3 Physical Separation of Plastics from the Matrix: Filtration and Sieving -- 2.4.4 Density and Other Physical Separation -- 2.4.5 Solvent Extraction -- 2.5 Microplastic Detection and Instrumentation…”

Tabla de Contenidos: “…Chapter 2 Agent Modeling of Terrorist Organization Behavior2.1 Modeling Organizational Behavior; 2.2 Action Extraction from the Web; 2.2.1 Action Data Collection; 2.2.2 Raw Action Extraction; 2.2.3 Action Elimination; 2.2.4 Action Refinement; 2.3 Extracting Causal Knowledge from the Web; 2.4 Construction of Action Hierarchy; 2.5 Designing Causal Scenarios; 2.6 Case Study on Terrorist Organization; 2.7 Conclusion; References; Chapter 3 Security Story Generation for Computational Experiments; 3.1 Story Generation Systems; 3.2 System Workflow and Narrative Structure…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Plant Seed Oils and Their Potential for Biofuel Production in India -- 1.1 Introduction -- 1.2 Background -- 1.3 Non-Edible Oil as Feedstock for Biodiesel -- 1.3.1 Jatropha -- 1.3.2 Pongamia -- 1.3.3 Mahua -- 1.3.4 Nahor -- 1.3.5 Rubber -- 1.3.6 Lesser Explored Non-Edible Oils for Biodiesel Feedstock in India -- 1.4 Fuel Qualities -- 1.4.1 Cetane Number -- 1.4.2 Acid Value -- 1.4.3 Ester Content, Glycerides, and Glycerol -- 1.4.4 Phosphorus Content -- 1.4.5 Iodine Value -- 1.4.6 Oxidation Stability -- 1.4.7 Linolenic Acid Methyl Esters -- 1.4.8 Polyunsaturated (≥ 4 Double Bonds) Methyl Esters -- 1.5 Conclusion -- Author Contributions -- References -- Chapter 2 Processing of Feedstock in Context of Biodiesel Production -- 2.1 Introduction -- 2.2 Feedstock in Context of Biodiesel -- 2.3 Processing of Oilseeds -- 2.3.1 Pretreatment -- 2.3.2 Decortication -- 2.3.2.1 Characteristics of Oilseeds Required for Decortication -- 2.3.2.2 Decortication Method -- 2.4 Oil Extraction Methods -- 2.4.1 Aqueous Method -- 2.4.2 Hydraulic Press -- 2.4.3 Ghani (Animal or Power-Driven) -- 2.4.4 Solvent Extraction Method -- 2.4.5 Mechanical Extraction Method -- 2.4.6 Microwave Assisted Oil Extraction -- 2.4.7 Ultrasonic Assisted Oil Extraction -- 2.4.8 Supercritical Assisted Oil Extraction -- 2.5 Catalyst -- 2.5.1 Homogeneous Catalyst -- 2.5.2 Heterogeneous Catalyst -- 2.5.3 Biocatalyst -- 2.6 Production Process of Biodiesel -- 2.7 Techniques for Biodiesel Production -- 2.7.1 Catalytic Transesterification Technique -- 2.7.2 Pyrolysis -- 2.7.3 Microwave Assisted -- 2.7.4 Ultrasonic Assisted -- 2.7.5 Supercritical Assisted -- 2.8 Advantages &amp -- Disadvantages of Using Biodiesel -- 2.9 Current Challenges and Future Perspectives of Biodiesel -- 2.10 Summary -- References…”

Tabla de Contenidos: “…Maximum electric power extraction; 2.5. Power converters; 2.5.1. Introduction; 2.5.2. …”

Tabla de Contenidos: “…Proposed PVC Detection Method -- 2.2.1. Feature Extraction -- 2.2.2. Classification -- 3. Results -- 3.1. …”

Tabla de Contenidos: “…1.6 MAIN CONTENT OF THIS BOOK -- REFERENCES -- 2 - Lossless Information Hiding in Images on the Spatial Domain -- 2.1 OVERVIEW OF SPATIAL DOMAIN-BASED INFORMATION HIDING -- 2.2 MODULO ADDITION-BASED SCHEME -- 2.2.1 EMBEDDING PROCESS -- 2.2.2 AUTHENTICATION PROCESS -- 2.2.3 EXPLANATION OF THE MODULO ADDITION OPERATION -- 2.3 DIFFERENCE EXPANSION-BASED SCHEMES -- 2.3.1 TIAN'S SCHEME -- 2.3.2 ALATTER'S SCHEME -- 2.4 HISTOGRAM MODIFICATION-BASED SCHEMES -- 2.4.1 ORIGINAL HISTOGRAM SHIFTING-BASED SCHEME -- 2.4.2 ADJACENT PIXEL DIFFERENCE-BASED SCHEME -- 2.4.3 MULTILEVEL HISTOGRAM MODIFICATION-BASED SCHEME -- 2.4.3.1 Data Embedding -- 2.4.3.2 Data Extraction and Image Recovery -- 2.4.3.3 Examples -- 2.4.3.3.1 EL=0 -- 2.4.3.3.2 EL=2 -- 2.4.3.4 Discussion -- 2.4.3.4.1 Capacity Estimation -- 2.4.3.4.2 Overflow and Underflow Prevention -- 2.4.3.5 Experimental Results and Comparison With Other Schemes -- 2.4.4 HYBRID PREDICTION AND INTERLEAVING HISTOGRAM MODIFICATION-BASED SCHEME -- 2.4.4.1 Data Embedding -- 2.4.4.2 Data Extraction and Image Recovery -- 2.4.4.3 Experimental Results and Comparisons -- 2.5 LOSSLESS COMPRESSION-BASED SCHEMES -- 2.5.1 LOSSLESS BIT-PLANE COMPRESSION IN THE SPATIAL DOMAIN -- 2.5.2 LOSSLESS RS-DATA EMBEDDING METHOD -- 2.5.3 LOSSLESS G-LSB DATA EMBEDDING METHOD -- 2.5.3.1 G-LSB Embedding -- 2.5.3.2 Lossless G-LSB Data Embedding and Extraction -- 2.5.4 LOOK-UP TABLE-BASED SCHEME FOR ERROR DIFFUSED HALFTONE IMAGES -- 2.5.4.1 Pattern Histogram -- 2.5.4.2 Human Visual System Characteristics -- 2.5.4.3 Look-Up Table Construction -- 2.5.4.4 Data Hiding -- 2.5.4.5 Look-Up Table Embedding -- 2.5.4.6 Data Extraction -- 2.5.4.7 Experimental Results -- 2.6 REVERSIBLE SECRET SHARING-BASED SCHEMES -- 2.6.1 DATA HIDING IN REVERSIBLE SECRET SHARING -- 2.6.1.1 Related Work -- 2.6.1.2 Proposed Scheme -- 2.6.1.2.1 Encoding Stage…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About This Book -- Preface -- Chapter 1 Concepts of Text Summarization -- 1.1 Introduction -- 1.2 Need for Text Summarization -- 1.3 Approaches to Text Summarization -- 1.3.1 Extractive Summarization -- 1.3.2 Abstractive Summarization -- 1.4 Text Modeling for Extractive Summarization -- 1.4.1 Bag-of-Words Model -- 1.4.2 Vector Space Model -- 1.4.3 Topic Representation Schemes -- 1.4.4 Real-Valued Model -- 1.5 Preprocessing for Extractive Summarization -- 1.6 Emerging Techniques for Summarization -- 1.7 Scope of the Book -- References -- Sample Code -- Sample Screenshots -- Chapter 2 Large-Scale Summarization Using Machine Learning Approach -- 2.1 Scaling to Summarize Large Text -- 2.2 Machine Learning Approaches -- 2.2.1 Different Approaches for Modeling Text Summarization Problem -- 2.2.2 Classification as Text Summarization -- 2.2.2.1 Data Representation -- 2.2.2.2 Text Feature Extraction -- 2.2.2.3 Classification Techniques -- 2.2.3 Clustering as Text Summarization -- 2.2.4 Deep Learning Approach for Text Summarization -- References -- Sample Code -- Chapter 3 Sentiment Analysis Approach to Text Summarization -- 3.1 Introduction -- 3.2 Sentiment Analysis: Overview -- 3.2.1 Sentiment Extraction and Summarization -- 3.2.1.1 Sentiment Extraction from Text -- 3.2.1.2 Classification -- 3.2.1.3 Score Computation -- 3.2.1.4 Summary Generation -- 3.2.2 Sentiment Summarization: An Illustration -- Summarized Output -- 3.2.3 Methodologies for Sentiment Summarization -- 3.3 Implications of Sentiments in Text Summarization -- Cognition-Based Sentiment Analysis and Summarization -- 3.4 Summary -- Practical Examples -- Example 1 -- Example 2 -- Sample Code (Run Using GraphLab) -- Example 3 -- References -- Sample Code -- Chapter 4 Text Summarization Using Parallel Processing Approach…”

Tabla de Contenidos: “…Cover -- Contents -- About the Author -- Preface -- Acknowledgments -- Core Module Syllabus -- Unit I: Definition, Scope and Importance of Environmental Studies -- 1.1 Definition, Scope and importance of Environmental Studies -- 1.2 The Need for Public Awareness -- 1.3 Environment and its Components -- 1.3.1 Segments of the Environment -- Summary -- Essay Type Questions -- Short-answer Type Questions -- Multiple Choice Questions -- Answers -- Unit II: Natural Resources -- 2.1 Natural Resources -- 2.1.1 Natural Resources and Associated Problems -- 2.2 Forest Resources -- 2.3 Deforestation -- 2.3.1 Timber Extraction -- 2.3.2 Dams and their Effects on Forests and Tribal People -- 2.4 Water Resources -- 2.4.1 Hydrosphere-As a Source of Water on Earth -- 2.4.2 Use and over exploitation of Surface and Groundwater -- 2.4.3 Floods -- 2.4.4 Drought -- 2.4.5 Conflicts over Water -- 2.5 Mineral Resources -- 2.5.1 Mining -- 2.6 Food Resources -- 2.6.1 World Food Problems -- 2.6.2 Changes Caused by Agriculture and Overgrazing -- 2.6.3 Effects of Modern Agriculture -- 2.7 Energy Resources -- 2.7.1 Growing Energy Needs -- 2.7.2 Non-renewable Sources -- 2.7.3 Renewable Sources -- 2.7.4 Land Resources -- Summary -- Essay Type Questions -- Short-answer Type Questions -- Multiple Choice Questions -- Answers -- Unit III: Ecosystem -- 3.1 Concept of Ecosystem -- 3.2 Structure and Function of an Ecosystem -- 3.2.1 Biotic Component -- 3.2.2 Abiotic Components -- 3.3 Types of Ecosystem -- 3.4 Functional Components of an Ecosystem -- 3.4.1 Biodiversity -- 3.4.2 Productivity -- 3.4.3 Food Chains and Food Webs -- 3.4.4 Material Cycling and Energy Flow -- 3.4.5 Balance of Nature -- 3.4.6 Succession and Evolution of the Ecosystem -- 3.5 Different Ecosystems -- 3.5.1 Forest Ecosystem -- 3.5.2 Grassland Ecosystem -- 3.5.3 Desert Ecosystem -- 3.5.4 Aquatic Ecosystem…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- Chapter 1 Cybersecurity Risk Assessment in Advanced Metering Infrastructure -- 1.1 Introduction -- 1.2 Preliminaries -- 1.2.1 Advanced Metering Infrastructure -- 1.2.2 AMI Components -- 1.2.3 AMI Tiers -- 1.2.4 Information Security Risk Assessment -- 1.3 Implementation of the AMI System's Risk Assessment -- 1.3.1 Risk Identification Phase for the AMI System -- 1.3.2 AMI Vulnerabilities -- 1.3.3 Risk Profiling Phase for the AMI System -- 1.3.4 Risk Treatment Phase for the AMI System -- 1.4 Discussion and Recommendations -- 1.4.1 Recommendations -- 1.5 Conclusion -- Acknowledgment -- References -- Chapter 2 A Generative Neural Network for Improving Metamorphic Malware Detection in IoT Mobile Devices -- 2.1 Introduction -- 2.2 Background -- 2.2.1 Machine Learning -- 2.2.2 Deep Learning Malware Detection -- 2.2.3 Adversarial Machine Learning -- 2.2.4 Generative Adversarial Networks -- 2.2.5 Related Work -- 2.3 Methodology -- 2.3.1 Dataset -- 2.3.2 Dynamic Analysis -- 2.3.3 Data Preparation -- 2.3.4 Image Generation -- 2.3.5 Adversarial Samples -- 2.3.6 Convolutional Neural Network (CNN) -- 2.4 Experimental Design -- 2.4.1 Experimental Setup -- 2.4.2 Behavior Feature Extraction -- 2.4.3 Words to Images -- 2.4.4 Synthetic Images -- 2.4.5 Image Classification -- 2.5 Results and Discussion -- 2.5.1 Assessing the Evasive Effectiveness of the Generated Samples Using a CNN Classifier -- 2.5.2 Assessing the Effectiveness of the CNN Classifier with a Novel Dataset Including a Newly Generated Batch of Malicious Samples for Each Family Produced by the DCGAN -- 2.5.3 Evaluation -- 2.6 Conclusion -- Notes -- References -- Chapter 3 A Physical-Layer Approach for IoT Information Security During Interference Attacks -- 3.1 Introduction…”

Tabla de Contenidos: “…Material and methodology -- 2.1 Dataset (PhysioNet) -- 2.2 EEG preprocessing and feature extraction -- 2.2.1 Continuous wavelet transform -- 2.2.2 Features -- 2.2.2.1 Entropy features -- 2.2.2.1 Entropy features -- 2.2.2.1.1 Shannon Entropy -- 2.2.2.1.1 Shannon Entropy -- 2.2.2.1.2 Sure entropy -- 2.2.2.1.2 Sure entropy -- 2.2.2.2 Statistical features -- 2.2.2.2 Statistical features -- 2.2.2.2.1 Mean -- 2.2.2.2.1 Mean -- 2.2.2.2.2 Standard deviation -- 2.2.2.2.2 Standard deviation -- 2.2.2.2.3 Skewness -- 2.2.2.2.3 Skewness -- 2.2.2.2.4 Normalized standard deviation -- 2.2.2.2.4 Normalized standard deviation -- 2.2.2.2.5 Kurtosis -- 2.2.2.2.5 Kurtosis -- 2.2.2.2.6 Energy -- 2.2.2.2.6 Energy…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- List of Contributors -- Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision -- 1.1 Introduction -- 1.2 Deep Learning Algorithms -- 1.2.1 Convolutional Neural Networks -- 1.2.2 Restricted Boltzmann Machines -- 1.2.3 Deep Boltzmann Machines -- 1.2.4 Deep Belief Networks -- 1.2.5 Stacked (de-Noising) Auto-Encoders -- 1.2.5.1 Auto-Encoders -- 1.2.5.2 Denoising Auto Encoders -- 1.3 Comparison of the Deep Learning Algorithms -- 1.4 Challenges in Deep Learning Algorithms -- 1.5 Conclusion and Future Scope -- References -- Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach -- 2.1 Introduction -- 2.2 Applications of Object Extraction -- 2.3 Edge Detection Techniques -- 2.3.1 Roberts Edge Detection -- 2.3.2 Sobel Edge Detection -- 2.3.3 Prewitt's Operator -- 2.3.4 Laplacian Edge Detection -- 2.4 Related Work -- 2.5 Proposed Model -- 2.6 Results and Discussion -- 2.7 Conclusion -- References -- Chapter 3 Deep Learning Techniques for Image Captioning -- 3.1 Introduction to Image Captioning -- 3.1.1 How Does Image Recognition Work? …”

Tabla de Contenidos: “…2.2.13 Leave-One-Out Encoding -- 2.2.14 James-Stein Encoding -- 2.2.15 M-Estimator Encoding -- 2.2.16 Using HephAIstos to Encode Categorical Data -- 2.3 Time-Related Features Engineering -- 2.3.1 Date-Related Features -- 2.3.2 Lag Variables -- 2.3.3 Rolling Window Feature -- 2.3.4 Expending Window Feature -- 2.3.5 Understanding Time Series Data in Context -- 2.4 Handling Missing Values in Machine Learning -- 2.4.1 Row or Column Removal -- 2.4.2 Statistical Imputation: Mean, Median, and Mode -- 2.4.3 Linear Interpolation -- 2.4.4 Multivariate Imputation by Chained Equation Imputation -- 2.4.5 KNN Imputation -- 2.5 Feature Extraction and Selection -- 2.5.1 Feature Extraction -- 2.5.1.1 Principal Component Analysis -- 2.5.1.2 Independent Component Analysis -- 2.5.1.3 Linear Discriminant Analysis -- 2.5.1.4 Locally Linear Embedding -- 2.5.1.5 The t-Distributed Stochastic Neighbor Embedding Technique -- 2.5.1.6 More Manifold Learning Techniques -- 2.5.1.7 Feature Extraction with HephAIstos -- 2.5.2 Feature Selection -- 2.5.2.1 Filter Methods -- 2.5.2.2 Wrapper Methods -- 2.5.2.3 Embedded Methods -- 2.5.2.4 Feature Importance Using Graphics Processing Units (GPUs) -- 2.5.2.5 Feature Selection Using HephAIstos -- Further Reading -- Chapter 3 Machine Learning Algorithms -- 3.1 Linear Regression -- 3.1.1 The Math -- 3.1.2 Gradient Descent to Optimize the Cost Function -- 3.1.3 Implementation of Linear Regression -- 3.1.3.1 Univariate Linear Regression -- 3.1.3.2 Multiple Linear Regression: Predicting Water Temperature -- 3.2 Logistic Regression -- 3.2.1 Binary Logistic Regression -- 3.2.1.1 Cost Function -- 3.2.1.2 Gradient Descent -- 3.2.2 Multinomial Logistic Regression -- 3.2.3 Multinomial Logistic Regression Applied to Fashion MNIST -- 3.2.3.1 Logistic Regression with scikit-learn -- 3.2.3.2 Logistic Regression with Keras on TensorFlow…”

Tabla de Contenidos: “…Fridtjof Nansen -- Chapter Two: A Practical Model For Diplomacy and Negotiation: Steps 1 – 3 - The Preliminary Stage Extract: -- 2.1 Introduction to the Model -- Three Phases: -- 2.2 Steps to Success and Managing Costs -- 2.2.1 Step One: Is The Initiative Worthwhile and Feasible? …”

Tabla de Contenidos: “…Front Cover -- Deep Learning Through Sparse and Low-Rank Modeling -- Copyright -- Contents -- Contributors -- About the Editors -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 Basics of Deep Learning -- 1.2 Basics of Sparsity and Low-Rankness -- 1.3 Connecting Deep Learning to Sparsity and Low-Rankness -- 1.4 Organization -- References -- 2 Bi-Level Sparse Coding: A Hyperspectral Image Classi cation Example -- 2.1 Introduction -- 2.2 Formulation and Algorithm -- 2.2.1 Notations -- 2.2.2 Joint Feature Extraction and Classi cation -- 2.2.2.1 Sparse Coding for Feature Extraction -- 2.2.2.2 Task-Driven Functions for Classi cation -- 2.2.2.3 Spatial Laplacian Regularization -- 2.2.3 Bi-level Optimization Formulation -- 2.2.4 Algorithm -- 2.2.4.1 Stochastic Gradient Descent -- 2.2.4.2 Sparse Reconstruction -- 2.3 Experiments -- 2.3.1 Classi cation Performance on AVIRIS Indiana Pines Data -- 2.3.2 Classi cation Performance on AVIRIS Salinas Data -- 2.3.3 Classi cation Performance on University of Pavia Data -- 2.4 Conclusion -- 2.5 Appendix -- References -- 3 Deep l0 Encoders: A Model Unfolding Example -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 l0- and l1-Based Sparse Approximations -- 3.2.2 Network Implementation of l1-Approximation -- 3.3 Deep l0 Encoders -- 3.3.1 Deep l0-Regularized Encoder -- 3.3.2 Deep M-Sparse l0 Encoder -- 3.3.3 Theoretical Properties -- 3.4 Task-Driven Optimization -- 3.5 Experiment -- 3.5.1 Implementation -- 3.5.2 Simulation on l0 Sparse Approximation -- 3.5.3 Applications on Classi cation -- 3.5.4 Applications on Clustering -- 3.6 Conclusions and Discussions on Theoretical Properties -- References -- 4 Single Image Super-Resolution: From Sparse Coding to Deep Learning -- 4.1 Robust Single Image Super-Resolution via Deep Networks with Sparse Prior -- 4.1.1 Introduction -- 4.1.2 Related Work…”