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• PART I - INTELLIGENT TRANSPORT SYSTEMS AND INFRASTRUCTURE
• CHAPTER 1: An Overview on Intelligent Transportation Systems in the Context of Internet of Vehicles
• CHAPTER 2: Intelligent Transportation Systems and its Necessity in Various Traffic Conditions in Indian Scenarios PART II - CONNECTED VEHICLES
• CHAPTER 3: Autonomous Driving Cars: Decision-Making
• CHAPTER 4: IEEE 802.11ah for Internet of Vehicles: Design Issues and Challenges
• CHAPTER 5: Medium access control in connected vehicles: Advances and Limitations PART III - ADVANCE DRIVER ASSISTED SYSTEM
• CHAPTER 6: An Overview of ADAS in Internet of Vehicles
• CHAPTER 7: iDriveAR: in Vehicle Driver Awareness and Drowsiness Framework based on Facial Tracking and Augmented reality
• CHAPTER 8: The Concept of Fusion for Clear Vision of Hazy Roads in ADAS PART VI - EVALUATION METHODS AND SECURITY ISSUES
• CHAPTER 9: Blockchain-enabled Security and Privacy for Internet of Vehicles
• CHAPTER 10: Approximation Algorithm and Linear Congruence: A state-of-art Approach in Information Security issues towards Internet of Vehicles
• CHAPTER 11: E-powertrain error detection using AI Techniques.
• (source: Nielsen Book Data)

This book provides an insight on the importance that Internet of Vehicles (IoV) solutions can have in taking care of vehicular safety through internetworking and automation. Key features of the book are the inclusion and elaboration of recent and emerging developments in various specializations of intelligent transportation systems and their solutions by incorporating IoT (Internet of Things) and IoV. This book presents to its readers useful IoV applications and architectures that cater to their improved driving requirements and lead towards autonomous driving. The application domains have a large range in which vehicular networking, communication technology, sensor devices, computing materials and devices, IoT communication, vehicular and on-road safety, data security and other topics are included. .
(source: Nielsen Book Data)

• Introduction
• Monitoring of Weld Pool Surface with Active Vision
• Visual Sensing of 3D Weld Pool Geometry with Passive Vision Image
• Penetration prediction with machine learning models
• Penetration Control for Bead-on plate welding
• Penetration Detection of narrow U-groove welding
• Lack of fusion detection inside narrow U-groove
• Conclustions and recommendations.

This book describes the application of vision-sensing technologies in welding processes, one of the key technologies in intelligent welding manufacturing. Gas tungsten arc welding (GTAW) is one of the main welding techniques and has a wide range of applications in the manufacturing industry. As such, the book also explores the application of AI technologies, such as vision sensing and machine learning, in GTAW process sensing and feature extraction and monitoring, and presents the state-of-the-art in computer vision, image processing and machine learning to detect welding defects using non-destructive methods in order to improve welding productivity. Featuring the latest research from ORNL (Oak Ridge National Laboratory) using digital image correlation technology, this book will appeal to researchers, scientists and engineers in the field of advanced manufacturing.
(source: Nielsen Book Data)

"The first book that provides a systematic and rigorous logical theory of causality"-- Provided by publisher.

• Part I: Natural Language Processing.- A Hybrid Feature Selection Approach Based on LSI for Classification of Urdu Text.- Automated Document Categorization Model.- Automated Categorization & Mining Tweets for Disaster Management.- Sentiment Analysis in Airline Data: Customer Rating Based Recommendation Prediction using WEKA.- Part II: Computer Vision.- Image Inpainting for Irregular Holes using Extreme Learning Machine.- OCR using Computer Vision and Machine Learning.- Few Shot Learning for Medical Imaging.- Hyperspectral Remote Sensing Image Classification using Active Learning.- A Smart Document Converter: Conversion of Handwritten Text Document to Computerized Text Document.- GRNN Based An Intelligent Technique for Image Inpainting.- Part III: Data Analysis and Prediction.- Content-Based Airline Recommendation Prediction using Machine Learning Techniques.- Meta-heuristic Based Approach for Slope Stability Analysis to Design an Optimal Soil Slope.- An Application of Operational Analytics: for Predicting Sales Revenue of Restaurant.- Application of Machine Learning Algorithm for Anomaly Detection for Industrial Pumps.- Part IV: Decision Making System.- Fast Accessing Non-volatile, High Performance-High Density, Optimized Array for Machine Learning Processor.- Long Term Evolution for Secured Smart Railway Communications using Internet of Things.- Application of the Flower Pollination Algorithm to Locate Critical Failure Surface for Slope Stability Analysis.
• (source: Nielsen Book Data)

This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics. Accordingly, there is a pressing need for novel and innovative algorithms to help us find effective solutions in industrial application areas such as media, healthcare, travel, finance, and retail. In all of these areas, data is the crucial parameter, and the main key to unlocking the value of industry. The book presents a range of intelligent algorithms that can be used to filter useful information in the above-mentioned application areas and efficiently solve particular problems. Its main objective is to raise awareness for this important field among students, researchers, and industrial practitioners. .
(source: Nielsen Book Data)

• Table of Contents Getting Started with TensorFlow 2.x The TensorFlow Way Keras Linear Regression Boosted Trees Neural Networks Predicting with Tabular Data Convolutional Neural Networks Recurrent Neural Networks Transformers Reinforcement Learning with TensorFlow and TF-Agents Taking TensorFlow to Production.
• (source: Nielsen Book Data)

Master TensorFlow to create powerful machine learning algorithms, with valuable insights on Keras, Boosted Trees, Tabular Data, Transformers, Reinforcement Learning and more Key Features Work with the latest code and examples for TensorFlow 2 Get to grips with the fundamentals including variables, matrices, and data sources Learn advanced deep learning techniques to make your algorithms faster and more accurate Book DescriptionThe independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. You will work through recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google's machine learning library, TensorFlow. This cookbook begins by introducing you to the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You'll then take a deep dive into some real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and for regression to provide a baseline for tabular data problems. As you progress, you'll explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be applied to computer vision and natural language processing (NLP) problems. Once you are familiar with the TensorFlow ecosystem, the final chapter will teach you how to take a project to production. By the end of this machine learning book, you will be proficient in using TensorFlow 2. You'll also understand deep learning from the fundamentals and be able to implement machine learning algorithms in real-world scenarios. What you will learn Grasp linear regression techniques with TensorFlow Use Estimators to train linear models and boosted trees for classification or regression Execute neural networks and improve predictions on tabular data Master convolutional neural networks and recurrent neural networks through practical recipes Apply reinforcement learning algorithms using the TF-Agents framework Implement and fine-tune Transformer models for various NLP tasks Take TensorFlow into production Who this book is forIf you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.
(source: Nielsen Book Data)

"A collection of critical essays dealing with the social and ethical impacts of AI including issues of trust, reliability, and bias"-- Provided by publisher.

This book intends to bring together researchers and developers from industry, the education field, and the academic world to report on the latest scientific research, technical advances, and methodologies. The 10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning is hosted by the University of L'Aquila and is going to be held in L'Aquila (Italy). Initially planned on the 17th to the 19th of June 2020, it was postponed to the 7th to the 9th of October 2020, due to the COVID-19 outbreak. The 10th edition of this conference and its related workshops expand the topics of the evidence-based TEL workshops series in order to provide an open forum for discussing intelligent systems for TEL, their roots in novel learning theories, empirical methodologies for their design or evaluation, stand-alone solutions, or web-based ones. This bridge has been realized also thanks to the sponsor of this edition of MIS4TEL: the Armundia Group https://www.armundia.com, the support from national associations (AEPIA, APPIA, CINI, and EurAI), and organizers (UNIVAQ, UNIROMA1, UNIBZ, UCV, UFSC, USAL, AIR institute, UNC, and UNIBA).
(source: Nielsen Book Data)
This book features papers from workshops at the 10th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, which was hosted by the University of L'Aquila (Italy) from 17th to 19th June 2020. The workshops provided participants with the opportunity to present and discuss novel research ideas on emerging topics complementing the main conference. They particularly focused on multi-disciplinary and transversal aspects such as TEL in nursing education programs, social and personal computing for web-supported learning communities, interactive environments and emerging technologies for eLearning, and TEL for future citizens.
(source: Nielsen Book Data)

• Introduction
• Kinematic Geometry of the PnP Robots
• Differential Kinematics of the Robots
• Kinematic Performance Criteria
• Elastostatics and Stiffness
• Rigid Multibody Dynamics
• Robot Elastodynamics
• Design optimization of parallel PnP Robots.

This book discusses the parametric modeling, performance evaluation, design optimization and comparative study of the high-speed, parallel pick-and-place robots. It collects the modeling methodology, evaluation criteria and design guidelines for parallel PnP robots to provide a systematic analysis method for robotic developers. Furthermore, it gathers the research results previously scattered in many prestigious international journals and conference proceedings and methodically edits them and presents them in a unified form. The book is of interest to researchers, R&D engineers and graduate students in industrial parallel robotics who wish to learn the core principles, methods, algorithms, and applications.
(source: Nielsen Book Data)

• Ability Study of Proximity Measure for Big Data Mining Context on Clustering.- Not Yet Decided.- An Approach to Implement MapReduce and aggregation pipeline utilizing NoSql Technologies.- Energy Hole Alleviation in Wireless sensor.
• (source: Nielsen Book Data)

This book offers a collection of high-quality peer-reviewed research papers presented at the Second International Conference on Communication and Computational Technologies (ICCCT 2019), held at Rajasthan Institute of Engineering and Technology, Jaipur, Rajasthan, India, on 30-31 August 2019. In contributions prepared by researchers from academia and industry alike, the book discusses a wide variety of industrial, engineering and scientific applications of emerging techniques.
(source: Nielsen Book Data)

• Development of an Industry 4.0 demonstrator using Sequence Planner and ROS2.- ROS2 for ROS1 users.- Multi-Robot SLAM framework for ROS with Efficient Information Sharing.- Agile experimentation of robot swarms in large scale.- Lessons learned building a self-driving car on top of ROS.- Landing a UAV on a moving platform using a front facing camera.- Integrating the Functional Mock-up Interface with ROS and Gazebo.- An ARVA sensor simulator.- ROS Implementation for Untethered Microrobot Manipulation.- ClegS: A package to develop C-legged robots.- Video frames selection method for 3D Reconstruction depending on ROS-based monocular SLAM.- ROS Rescue: Fault Tolerance System for ROS.
• (source: Nielsen Book Data)

This book is the fifth volume in the successful book series Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage on the Robot Operating System (ROS), which is currently considered to be the primary development framework for robotics applications, and the latest trends and contributing systems. The content is divided into six parts. Pat I presents for the first time the emerging ROS 2.0 framework, while Part II focuses on multi-robot systems, namely on SLAM and Swarm coordination. Part III provides two chapters on autonomous systems, namely self-driving cars and unmanned aerial systems. In turn, Part IV addresses the contributions of simulation frameworks for ROS. In Part V, two chapters explore robotic manipulators and legged robots. Finally, Part VI presents emerging topics in monocular SLAM and a chapter on fault tolerance systems for ROS. Given its scope, the book will offer a valuable companion for ROS users and developers, helping them deepen their knowledge of ROS capabilities and features. .
(source: Nielsen Book Data)

• Intro
• Preface
• Introduction
• Contents
• 1 Safety the Basis for Future Mobility
• 1.1 Safety is Much More Than Safety Engineering
• 1.2 Safety as a Social Right
• 1.3 Legal Basis for "Auto-Mobility"
• 1.3.1 The German Road Traffic Act (StVG)
• 1.3.2 Origin of the German Road Traffic Act
• 1.3.3 Adaptation of the Traffic Right for the Globalisation Trend
• 1.3.4 Activities in Germany for Future Mobility Solutions
• 1.3.5 Genevan and Viennese Convention
• 1.4 European Union (EU) and Legislation
• 1.4.1 EU Directives Related to Road Traffic
• 1.4.2 European Vehicle Class

• 1.4.3 EU Directive for Future Mobility
• 1.5 Licensing Act
• 1.6 US Road Traffic Regulations
• 1.7 UNECE
• 1.8 Legal View on Future Mobility
• 1.8.1 New Licensing Approach for Automated Vehicles
• 1.8.2 ITS Law
• 1.8.3 ETSI-European Telecommunications Standards Institute
• 1.8.4 OECD, Organisation for Economic Co-operation and Development
• 1.8.5 ETSC European Transport Safety Council
• 1.8.6 IT Security Act
• 1.9 Product Liability
• 1.10 Common Law and Civil Law
• 1.11 Legal Regulation in China
• 1.12 Insurance
• 2 Risk Management
• 2.1 Risk Management Cycle

• 2.1.1 Development of the Context
• 2.1.2 Identification of Risk
• 2.1.3 Assessment of the Risk in the Target Context
• 2.1.4 Development of a Strategy and Treatment of Potential Risk
• 2.1.5 Development and Defining of Activities and Their Objectives
• 2.1.6 Development of an Implementation and Realisation Strategy
• 2.1.7 Review and Evaluation of the Plan
• 2.2 Technical Risk
• 2.3 Risk in Road Traffic
• 2.3.1 Causes of Technical Risk
• 2.3.2 Control of Technical Risk
• 2.4 Standardisation of Safety for Road Vehicles
• 2.4.1 Risk and Integrity Definition from the IEC 61508

• 2.4.2 Risk According to ISO 26262
• 2.5 Critical Infrastructure
• 2.5.1 Organisation for Critical Infrastructure
• 2.5.2 Cloud Computing
• 2.5.3 Transport and Traffic and Critical Infrastructure
• 3 Automation in Mobility
• 3.1 Human Driving: a Closed-Loop Control System
• 3.2 Human Driving
• 3.2.1 Driver's HMI
• 3.2.2 Driver in the Loop
• 3.2.3 Human Driving Journey
• 3.3 Human Control Mechanism
• 3.4 Human Behaviour and Context
• 3.4.1 Observer of the Observer
• 3.4.2 Industry Automation
• 3.4.3 Multiple Contexts for Different Layers of Abstraction

• 3.5 Communication and Interfaces
• 3.6 Human Driver as a Control System
• 3.6.1 Human Communication
• 3.6.2 Human Perception
• 3.6.3 Technical Control System Comparison
• 3.6.4 Control System Architecture in Comparison with the Human Control System
• 3.7 Cyclic Data Processing Versus Distributed Event-Driven Data Processing
• 3.7.1 Distributed Control
• 3.7.2 Configuration of Human Control System
• 3.8 Cybernetics and Control
• 3.8.1 Digital Twin
• 3.8.2 Cybernetics About Behaviour and Control
• 3.8.3 Cybernetics and Sufficiently Perceiving the Environment

The book provides background information about technical solutions, processes and methodology to develop future automated mobility solutions. Beginning from the legal requirements as the minimum tolerable risk level of the society, the book provides state-of-the-art risk-management methodologies. The system engineering approach based on todays engineering best practices enhanced by principles derived from cybernetics. The approach derived from the typical behaviour of a human driver in public road traffic to a cybernetical based system engineering approach. Beyond the system engineering approach, a common behaviour model for the operational domain will show aspects how to extend the system engineering model with principles of cybernetics. The role and the human factors of road traffic participants and drivers of motor vehicles are identified and several viewpoints for different observers show how such mixed traffic scenarios could be assessed and optimised. The influence of the changing mobility demands of the society and the resulting changes to the origination of producer, owner, driver and supplier show aspects for future liability and risk share option for new supply chains. Examples from various industries provide some well-proven engineering principles how to adapt those for the future mobility for the benefit of the users. The aim of the book is to raise awareness that the safety provided by a product, a means of transport or a system up to an entire traffic system depends on the capabilities of the various actors. In addition to the driver and passengers, there are also other road users, maintenance personnel and service providers, who must have certain abilities to act safely in traffic. These are also the capabilities of the organisation, not only the organisation that develops or brings the product to market, but also the organisation that is responsible for the operation and the whole lifecycle of the products. The book is for people who want to get invo lved in the mobility of the future. People, that have ideas to become a player who want to help shape the future mobility of society and who want to bring responsible solutions for users into the market.

• Chapter 1. An Integrated Design of Fuzzy C-Means and NCA based Multi-Properties Features Reduction for Brain Tumor Recognition.-
• Chapter 2. Hybrid Image Processing based Examination of 2D Brain MRI Slices to Detect Brain Tumour/Stroke Section - A Study.-
• Chapter 3. Edge Enhancing Coherence Diffusion Filter for Level Set Segmentation and Asymmetry Analysis using Curvelets in Breast Thermograms.-
• Chapter 4. Lung Cancer Diagnosis Based on Image Fusion and prediction using CT and PET image.-
• Chapter 5. Segmentation and Validation of Infrared Breast Images using Weighted Level Set and Phase Congruency Edge Map Framework.-
• Chapter 6. Analysis of Material Profile for Polymer Based Mechanical Microgripper for Thin Plate Holding.-
• Chapter 7. Design and Testing of Elbow Actuated Wearable Robotic Arm for Muscular Disorders.-
• Chapter 8. A Comprehensive Study of Image Fusion Techniques and Their Applications.-
• Chapter 9. Multilevel Mammogram Image Analysis for Identifying Outliers, Misclassification using Machine Learning.-
• Chapter 10. A Review on Automatic Detection of Retinal Lesions in Fundus Images for Diabetic Retinopathy.-
• Chapter 11. Medical Image Watermarking: A Review on Wavelet Based Methods.-
• Chapter 12. EEG Signal Extraction Analysis Techniques.-
• Chapter 13. Classification of sEMG Signal based Arm Action using Convolutional Neural Network.-
• Chapter 14. An Automated Approach for the Identification of TB Images Enhanced by Non-uniform Illumination Correction.
• (source: Nielsen Book Data)

This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.
(source: Nielsen Book Data)

• Introduction.- A Brief Review of Socio-Inspired Metaheuristics.- Multi Cohort Intelligence Algorithm.- Optimization of Electric Discharge Machining (EDM).- Optimization of Abrasive Water Jet Machining (AWJM).- Optimization of Micro Milling Process.- Optimization of Micro Drilling Process.- Optimization of Cutting Forces in Micro Drilling of CFRP Composites for Aerospace Applications.- Optimization of Micro Turning Process.- Optimization of Machining Process Parameters of Titanium Alloy Under (MQL) Environment.
• (source: Nielsen Book Data)

This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods. .
(source: Nielsen Book Data)

• Intro
• Preface
• Invited Speakers
• DSS, Classifications, Trends and Enabling Modern Information and Communication Technologies
• Distorted Statistics based on Choquet Calculus
• Overview of QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
• Nature-Inspired Optimization of Type-2 Fuzzy Logic Controllers
• Seeing Is Believing
• Contents
• Short CVs of Guest Editors
• I Soft Computing and Conventional Techniques in Power Engineering Methods and Applications in Electrical Engineering I

• Daily Load Curve Forecasting. Comparative Analysis: Conventional vs. Unconventional Methods
• Abstract
• 1 Introduction
• 2 Developed Algorithms
• 2.1 Artificial Neural Networks (ANN)
• 2.2 Multiple Linear Regression (MLR)
• 2.3 Curve Fitting (CF)
• 2.4 Decision Trees (DT)
• 3 Numerical Results. Discussions
• 3.1 Forecast Performed for 2017 Year
• 3.2 Forecast Performed for 2018 Year
• 3.3 Comparative Analysis for Several Forecasts
• 4 Conclusion
• Acknowledgment
• References
• LoRaWAN Based Airport Runway Lights Monitoring System
• Abstract
• 1 Introduction
• 2 LoRa Technology

• 3 Case Study and Results
• 4 Conclusions
• Acknowledgment
• References
• The Enhanced Crow Search Algorithm for Fuel-Cost Function Parameters Assessment of the Cogeneration Units from Thermal Power Plants
• Abstract
• 1 Introduction
• 2 Estimating the Fuel-Cost Function Parameters
• 3 The Crow Search Algorithm (CS)
• 4 The Enhanced CS Algorithm (ECS)
• 5 Case Studies and Results
• 6 Conclusions
• References
• Modelling a Photovoltaic Power Station
• Abstract
• 1 Introduction
• 2 Simulation of the Photovoltaic Panels Using MatLAB Software

• 2.1 Simulink: Modelling of a PV Solar Cell
• 2.2 The Intensity of the Induced Electrical Current by the Sun
• 2.3 Dependence on the Temperature
• 3 The Simulation of a PV Power Station
• 4 Conclusions
• References
• Identification of Fault Locations on Tree Structure Systems
• 1 Introduction
• 2 Description of the Troubleshooting System Currently Used by the Power Suppliers
• 3 Mathematical Background of the Proposed Method
• 4 Fault Probability Data Bank
• 5 SGFI Algorithm
• 5.1 Localization of the Fault Zone (Procedure 1)

• 5.2 Fault Position Localization in the Fault Zone (Procedure 2)
• 5.3 Result of the Proposed Algorithm
• 5.4 Simulation Analysis of the SGFI Algorithm
• 6 Conclusions
• References
• Probability Distribution Functions for Short-Term Wind Power Forecasting
• 1 Introduction
• 2 Wind Probability Distribution Functions
• 2.1 Weibull Distribution
• 2.2 Lindley Distribution for Random Variables
• 2.3 Joint Probability Distribution Function for Wind Forecasting
• 3 Short-Term Wind Power Forecasting Using SVR and ANN
• 3.1 Wind Power Forecasting Using Support Vector Regressors
• 3.2 Wind Power Forecasting Using ANN.

This book presents the proceedings of the 8th International Workshop on Soft Computing Applications, SOFA 2018, held on 13-15 September 2018 in Arad, Romania. The workshop was organized by Aurel Vlaicu University of Arad, in conjunction with the Institute of Computer Science, Iasi Branch of the Romanian Academy, IEEE Romanian Section, Romanian Society of Control Engineering and Technical Informatics - Arad Section, General Association of Engineers in Romania - Arad Section and BTM Resources Arad. The papers included in these proceedings, published post-conference, cover the research including Knowledge-Based Technologies for Web Applications, Cloud Computing, Security Algorithms and Computer Networks, Business Process Management, Computational Intelligence in Education and Modelling and Applications in Textiles and many other areas related to the Soft Computing. The book is directed to professors, researchers, and graduate students in area of soft computing techniques and applications.
(source: Nielsen Book Data)

• Table of Contents Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let Your Data Do the Talking: Story, Questions, and Answers Detecting Customer Emotions to Make Predictions Analyzing Fake News with Transformers Appendix: Answers to the Questions.
• (source: Nielsen Book Data)

Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models Key Features Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine Learn training tips and alternative language understanding methods to illustrate important key concepts Book DescriptionThe transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learn Use the latest pretrained transformer models Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models Create language understanding Python programs using concepts that outperform classical deep learning models Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more Measure the productivity of key transformers to define their scope, potential, and limits in production Who this book is forSince the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.
(source: Nielsen Book Data)

• CHAPTER 1 Introduction
• CHAPTER 2 Accelerated Algorithms for Unconstrained Convex Optimization
• 1. Preliminaries
• 2. Accelerated Gradient Method for smooth optimization
• 3. Extension to the Composite Optimization
• 3.1. Nesterov's First Scheme
• 3.2. Nesterov's Second Scheme
• 3.2.1. A Primal Dual Perspective
• 3.3. Nesterov's Third Scheme
• 4. Inexact Proximal and Gradient Computing
• 4.1. Inexact Accelerated Gradient Descent
• 4.2. Inexact Accelerated Proximal Point Method
• 5. Restart
• 6. Smoothing for Nonsmooth Optimization
• 7. Higher Order Accelerated Method
• 8. Explanation: An Variational Perspective
• 8.1. Discretization
• CHAPTER 3 Accelerated Algorithms for Constrained Convex Optimization
• 1. Preliminaries
• 1.1. Case Study: Linear Equality Constraint
• 2. Accelerated Penalty Method
• 2.1. Non-strongly Convex Objectives
• 2.2. Strong Convex Objectives
• 3. Accelerated Lagrange Multiplier Method
• 3.1. Recovering the Primal Solution
• 3.2. Accelerated Augmented Lagrange Multiplier Method
• 4. Accelerated Alternating Direction Method of Multipliers
• 4.1. Non-strongly Convex and Non-smooth
• 4.2. Strongly Convex and Non-smooth
• 4.3. Non-strongly Convex and Smooth
• 4.4. Strongly Convex and Smooth
• 4.5. Non-ergodic Convergence Rate
• 4.5.1. Original ADMM
• 4.5.2. ADMM with Extrapolation and Increasing Penalty Parameter
• 5. Accelerated Primal Dual Method
• 5.1. Case 1
• 5.2. Case 2
• 5.3. Case 3
• 5.4. Case 4
• CHAPTER 4 Accelerated Algorithms for Nonconvex Optimization
• 1. Proximal Gradient with Momentum
• 1.1. Basic Assumptions
• 1.2. Convergence Theorem
• 1.3. Another Method: Monotone APG
• 2. AGD Achieves the Critical Points Quickly
• 2.1. AGD as a Convexity Monitor
• 2.2. Negative Curvature
• 2.3. Accelerating Nonconvex Optimization
• 3. AGD Escapes the Saddle Points Quickly
• 3.1. Almost Convex
• 3.2. Negative Curvature Descent
• 3.3. AGD for Non-Convex Problem
• 3.3.1. Locally Almost Convex! Globally Almost Convex
• 3.3.2. Outer Iterations
• 3.3.3. Inner Iterations
• CHAPTER 5 Accelerated Stochastic Algorithms
• 1. The Individual Convexity Case
• 1.1. Accelerated Stochastic Coordinate Descent
• 1.2. Background for Variance Reduction Methods
• 1.3. Accelerated Stochastic Variance Reduction Method
• 1.4. Black-Box Acceleration
• 2. The Individual Non-convexity Case
• 2.1. Individual Non-convex but Integrally Convex
• 3. The Non-Convexity Case
• 3.1. SPIDER
• 3.2. Momentum Acceleration
• 4. Constrained Problem
• 5. Infinity Case
• CHAPTER 6 Paralleling Algorithms
• 1. Accelerated Asynchronous Algorithms
• 1.1. Asynchronous Accelerated Gradient Descent
• 1.2. Asynchronous Accelerated Stochastic Coordinate Descent
• 2. Accelerated Distributed Algorithms
• 2.1. Centralized Topology
• 2.1.1. Large Mini-batch Algorithms
• 2.1.2. Dual Communication-Efficient Methods
• 2.2. Decentralized Topology
• CHAPTER 7 Conclusions
• APPENDIX Mathematical Preliminaries.
• (source: Nielsen Book Data)

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
(source: Nielsen Book Data)

• Part I: Basics and Theory
• Photometry
• Geometry
• Sensors
• Light Sources
• Part II: Algorithms Photometric stereo
• Structured light
• Other shape reconstruction techniques
• Photometric estimation Part III: Applications
• Visualization, Augmented Reality, Virtual Reality, and Mixed Reality Systems
• Biomedical Applications
• E-Heritage
• Robot Vision, Autonomous Vehicles, and Humans.
• (source: Nielsen Book Data)

This book describes active illumination techniques in computer vision. We can classify computer vision techniques into two classes: passive and active techniques. Passive techniques observe the scene statically and analyse it as is. Active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene. Traditional passive techniques have a fundamental limitation. The external world surrounding us is three-dimensional; the image projected on a retina or an imaging device is two-dimensional. That is, reduction of one dimension has occurred. Active illumination techniques compensate for the dimensional reduction by actively controlling the illumination. The demand for reliable vision sensors is rapidly increasing in many application areas, such as robotics and medical image analysis. This book explains this new endeavour to explore the augmentation of reduced dimensions in computer vision. This book consists of three parts: basic concepts, techniques, and applications. The first part explains the basic concepts for understanding active illumination techniques. In particular, the basic concepts of optics are explained so that researchers and engineers outside the field can understand the later chapters. The second part explains currently available active illumination techniques, covering many techniques developed by the authors. The final part shows how such active illumination techniques can be applied to various domains, describing the issue to be overcome by active illumination techniques and the advantages of using these techniques. This book is primarily aimed at 4th year undergraduate and 1st year graduate students, and will also help engineers from fields beyond computer vision to use active illumination techniques. Additionally, the book is suitable as course material for technical seminars.
(source: Nielsen Book Data)

• Designing and Developing Adaptive Instructional Systems.- Sensor-based Adaptive Instructional Systems in Live Simulation Training.- An Ambient & Pervasive Personalized Learning Ecosystem: "Smart Learning" in the Age of the Internet of Things.- Bridging Conceptual Models and Architectural Interchange for Adaptive Instructional Systems.- Dewey's Ethics of Moral Principles and Deliberation: Extending IEEE's Ethics Initiative for Adaptive Instructional Systems.- Realistic and Relevant Role-Players for Experiential Learning.- Learning Traces, Measurement and Assessment Templates for AIS Interoperability.- Supporting different Roles and Responsibilities in Developing and Using Context-based Adaptive Personalized Collaboration Environments Compliant to the Law.- Experiential Instruction of Metacognitive Strategies.- Falling Forward: Lessons Learned from Real-Life Implementation of Adaptive Learning Solutions.- Usability Dimensions of Simulated Trainers for Buried Explosives.- Toward Zero Authoring: Considering How to Maximize Courseware Quality and Affordability Simultaneously.- Agent-Based Methods in Support of Adaptive Instructional Decisions.- Representing Functional Relationships of Adaptive Instructional Systems in a Conceptual Model.- Knowledge-to-Information Translation Training (KITT): An Adaptive Approach to Explainable Artificial Intelligence.- User Rights and Adaptive A/IS - From Passive Interaction to Real Empowerment.- Supporting Metacognitive Learning Strategies through an Adaptive Application.- Towards Iteration by Design: An Interaction Design Concept for Safety Critical Systems.- Learner Modelling and Methods of Adaptation.- Bayesian Student Modelling in the AC&NL Tutor.- Nature at Your Service - Nature Inspired Representations Combined with Eye-gaze Features to Infer User Attention and Provide Contextualized Support.- Adapting Instruction by Measuring Engagement with Machine Learning in Virtual Reality Training.- Realizing the Promise of AI-Powered, Adaptive, Automated, Instant Feedback on Writing for Students in Grade 3-8 with an IEP.- Declarative Knowledge Extraction in the AC&NL Tutor.- On the Importance of Adaptive Operator Training in Human-Swarm Interaction.- The Mental Machine: Classifying Mental Workload State from Unobtrusive Heart Rate- measures Using Machine Learning.- Production Implementation of Recurrent Neural Networks in Adaptive Instructional Systems.- Pilot State Monitoring for Cursus Recommendation.- Experimental Evaluation of Heart-based Workload Measures as Related to Their Suitability for Real-time Applications.- EEG Covariance-based Estimation of Cooperative States in Teammates.- Adapting the Zone of Proximal Development to the Wicked Environments of Professional Practice.- An Adaptive Instructional System for the Retention of Complex Skills.- Learner Modeling in the Context of Caring Assessments.- Evaluating the Effectiveness of Adaptive Instructional Systems.- The Evolving Assessment Landscape and Adaptive Instructional Systems: Moving Beyond Good Intentions.- Contextual Barriers to Validity in Adaptive Instruction and Assessment.- Does Time Matter in Learning? A Computer Simulation of Carroll's Model of Learning.- Competency Development through Experiential Training: Mapping Scenarios with Assessments.- Does Gamification Work? Analyzing Effects of Game Features on Learning in an Adaptive Scenario-Based Trainer.- From "Knowing What" to "Knowing When": Exploring a Concept of Situation Awareness Synchrony for Evaluating SA Dynamics in Teams.- Exploring Video Engagement in an Intelligent Tutoring System.- Using a Non-Player Character to Improve Training Outcomes for Submarine Electronic Warfare Operators.- The Impact of Adaptive Activities in Acrobatiq Courseware: Investigating the Efficacy of Formative Adaptive Activities on Learning Estimates and Summative Assessment Scores.- A Mastery Approach to Flashcard-based Adaptive Training. .
• (source: Nielsen Book Data)

This volume constitutes the refereed proceedings of the Second International Conference on Adaptive Instructional Systems, AIS 2020, which was due to be held in July 2020 as part of HCI International 2020 in Copenhagen, Denmark. The conference was held virtually due to the COVID-19 pandemic. A total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings from a total of 6326 submissions. The 41 papers presented in this volume were organized in topical sections as follows: designing and developing adaptive instructional systems; learner modelling and methods of adaptation; evaluating the effectiveness of adaptive instructional systems. Chapter "Exploring Video Engagement in an Intelligent Tutoring System" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(source: Nielsen Book Data)

• 1st Workshop on Intelligent Data - From Data to Knowledge (DOING 2020).- 2nd Workshop on Modern Approaches in Data Engineering and Information System Design (MADEISD 2020).- 1st Workshop on Scientic Knowledge Graphs (SKG 2020).- 2nd Workshop of BI & Big Data Applications (BBIGAP 2020).- International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2020).- The 1st International Workshop on Assessing Impact and Merit in Science (AIMinScience 2020).- Doctoral Consortium.
• (source: Nielsen Book Data)

This book constitutes thoroughly reviewed and selected papers presented at Workshops and Doctoral Consortium of the 24th East-European Conference on Advances in Databases and Information Systems, ADBIS 2020, the 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, and the 16th Workshop on Business Intelligence and Big Data, EDA 2020, held in August 2020. Due to the COVID-19 the joint conference and satellite events were held online. The 26 full papers and 5 short papers were carefully reviewed and selected from 56 submissions. This volume presents the papers that have been accepted for the following satellite events: Workshop on Intelligent Data - From Data to Knowledge, DOING 2020; Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2020; Workshop on Scientic Knowledge Graphs, SKG 2020; Workshop of BI & Big Data Applications, BBIGAP 2020; International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2020; International Workshop on Assessing Impact and Merit in Science, AIMinScience 2020; Doctoral Consortium.
(source: Nielsen Book Data)

• Exploring Artificial Jabbering For Automatic Text Comprehension Question Generation.- Digital Value-Adding Chains in Vocational Education: Automatic Keyword Extraction from Learning Videos to Provide Learning Resource Recommendations.- Human-centered design of a dashboard on students' revisions during writing.- An operational Framework for Evaluating the Performance of Learning Record Stores.- Does an e-mail reminder intervention with learning analytics reduce procrastination in a blended university course.- Designing an Online Self-Assessment for Informed Study Decisions: The User Perspective.- What Teachers Need for Orchestrating Robotic Classrooms.- Assessing Teacher's Discourse Effect on Students' Learning: A Keyword Centrality Approach.- For learners, with learners: Identifying indicators for an academic advising dashboard for students.- Living with learning difficulties: Two case studies exploring the relationship between emotion and performance in students with learning difficulties.- Learnersourcing Quality Assessment of Explanations for Peer Instruction.- Using Diffusion Network Analytics to Examine and Support Knowledge Construction in CSCL Settings.- Supporting Second Language Learners' Development of Affective Self-Regulated Learning Skills through the Use and Design of Mobile Technology.- We Know What You Did Last Semester: Learners' Perspectives on Screen Recordings as a Long-Term Data Source for Learning Analytics.- Teaching Simulation Literacy with Evacuations: Concept, Technology, and Material for a Novel Approach.- Design of conversational agents for CSCL: comparing two types of agent intervention strategies in a university classroom.- Exploring Human-AI Control over Dynamic Transitions between Individual and Collaborative Learning.- Exploring Student-Controlled Social Comparison.- New Measures for Offline Evaluation of Learning Path Recommenders.- Assessing the Impact of the combination of Self-directed learning, Immediate feedback and Visualizations on Student Engagement in Online Learning.- CGVis: A Visualization-Based Learning Platform for Computational Geometry Algorithms.- How to Design Effective Learning Analytics Indicators? A Human-Centered Design Approach.- Emergency Remote Teaching: Capturing Teacher Experiences in Spain with SELFIE.- Utilising Learnersourcing to Inform Design Loop Adaptivity.- Fooling it - Student Attacks on Automatic Short Answer Grading.- Beyond indicators: A scoping review of the academic literature related to SDG4 and educational technology.- Pedagogical underpinnings of open science, citizen science and open innovation activities: a state-of-the-art analysis.- Knowledge-Driven Wikipedia Article Recommendation for Electronic Textbooks.- InfoBiTS: A mobile application to foster digital competencies of senior citizens.- Student Awareness and Privacy Perception of Learning Analytics in Higher Education.- User Assistance for Serious Games Using Hidden Markov Model.- Guiding Socio-technical Reflection of Ethical Principles in TEL Software Development: The SREP Framework.- Git4School: a Dashboard for Supporting Teacher Interventions in Software Engineering Courses.- Exploring the design and impact of online exercises for teacher training about dynamic models in mathematics.- Interactive Concept Cartoons: Exploring an Instrument for Developing Scientific Literacy.- Quality Evaluation of Open Educational Resources.- Designing Digital Activities to Screen Locomotor Skills in Developing Children.- Towards Adaptive Social Comparison for Education.- Simulation Based Assessment of Epistemological Beliefs about Science.- An Approach to Support Interactive Activities in Live Stream Lectures.- Educational Escape Games for Mixed Reality.- Measuring Learning Progress for serving Immediate Feedback needs: Learning Process Quantification Framework (LPQF).- Data-driven Game Design: The Case of Difficulty in Educational Games.- Extracting Topics from Open Educational Resources.- Supporting Gamification with an Interactive Gamification Analytics Tool (IGAT).- OpenLAIR an Open Learning Analytics Indicator Repository Dashboard.- CasualLearn: A smart application to learn History of Art.- Applying Instructional Design Principles on Augmented Reality Cards for Computer Science Education.- Extending Patient Education with CLAIRE: An Interactive Virtual Reality and Voice User Interface Application.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, held in Heidelberg, Germany, in September 2020. The 24 research papers and 20 demo and 5 poster papers presented in this volume were carefully reviewed and selected from 91 submissions. The European Conference on Technology-Enhance Learning, which celebrates its 15th anniversary this year, is committed to address global challenges and quality education. The papers deal with the Sustainable Development Goals, particularly SDG 4 and SDG 10, to help to reduce the existing gaps and inequalities between countries and regions from around the world in terms of inclusiveness, equity, access, and quality of education. The chapters: "Designing an Online Self-Assessment for Informed Study Decisions: The User Perspective"; "Living with Learning Difficulties: Two Case Studies Exploring the Relationship Between Emotion and Performance in Students With Learning Difficulties"; "Applying Instructional Design Principles on Augmented Reality Cards for Computer Science Education"; and "Teaching Simulation Literacy With Evacuations - Concept, Technology, and Material for a Novel Approach" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. Due to the Corona pandemic EC-TEL 2020 was held as an virtual event.
(source: Nielsen Book Data)