Artificial intelligence (AI) Books

Book SynopsisWork through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learning Key Features Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi Pico Work with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge Impulse Explore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPU Book DescriptionThis book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.What you will learn Understand the relevant microcontroller programming fundamentals Work with real-world sensors such as the microphone, camera, and accelerometer Run on-device machine learning with TensorFlow Lite for Microcontrollers Implement an app that responds to human voice with Edge Impulse Leverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE Sense Create a gesture-recognition app with Raspberry Pi Pico Design a CIFAR-10 model for memory-constrained microcontrollers Run an image classifier on a virtual Arm Ethos-U55 microNPU with microTVM Who this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.Table of ContentsTable of Contents Getting Started with TinyML Prototyping with Microcontrollers Building a Weather Station with TensorFlow Lite for Microcontrollers Voice Controlling LEDs with Edge Impulse Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano Building a Gesture-Based Interface for YouTube Playback Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS Toward the Next TinyML Generation with microNPU

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Book SynopsisLearn and implement game AI in Unity to build smart environments and enemies with A* pathfinding, finite state machines, behavior trees, and the NavMeshKey Features Explore the latest Unity features to make AI implementation in your game easier Build richer and more dynamic games using AI concepts such as behavior trees and navigation meshes Implement character behaviors and simulations using the Unity Machine Learning toolkit Book DescriptionDeveloping artificial intelligence (AI) for game characters in Unity has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from basic techniques to cutting-edge machine learning-powered agents. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating game worlds and characters. The updated fifth edition of Unity Artificial Intelligence Programming starts by breaking down AI into simple concepts. Using a variety of examples, the book then takes those concepts and walks you through actual implementations designed to highlight key concepts and features related to game AI in Unity. As you progress, you'll learn how to implement a finite state machine (FSM) to determine how your AI behaves, apply probability and randomness to make games less predictable, and implement a basic sensory system. Later, you'll understand how to set up a game map with a navigation mesh, incorporate movement through techniques such as A* pathfinding, and provide characters with decision-making abilities using behavior trees. By the end of this Unity book, you'll have the skills you need to bring together all the concepts and practical lessons you've learned to build an impressive vehicle battle game.What you will learn Understand the basics of AI in game design Create smarter game worlds and characters with C# programming Apply automated character movement using pathfinding algorithm behaviors Implement character decision-making algorithms using behavior trees Build believable and highly efficient artificial flocks and crowds Create sensory systems for your AI world Become well-versed with the basics of procedural content generation Explore the application of machine learning in Unity Who this book is forThis Unity artificial intelligence book is for Unity developers with a basic understanding of C# and the Unity Editor who want to expand their knowledge of AI Unity game development.Table of ContentsTable of Contents Introduction to AI Finite State Machines Randomness and Probability Implementing Sensors Flocking Path Following and Steering Behaviors A* Pathfinding Navigation Mesh Behavior Trees Procedural Content Generation Machine Learning in Unity Putting It All Together

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Book SynopsisVisualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with easeKey Features Understand 3D data processing with rendering, PyTorch optimization, and heterogeneous batching Implement differentiable rendering concepts with practical examples Discover how you can ease your work with the latest 3D deep learning techniques using PyTorch3D Book DescriptionWith this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time.Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You’ll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you’ll realize how coding for these deep learning models becomes easier using the PyTorch3D library.By the end of this deep learning book, you’ll be ready to implement your own 3D deep learning models confidently.What you will learn Develop 3D computer vision models for interacting with the environment Get to grips with 3D data handling with point clouds, meshes, ply, and obj file format Work with 3D geometry, camera models, and coordination and convert between them Understand concepts of rendering, shading, and more with ease Implement differential rendering for many 3D deep learning models Advanced state-of-the-art 3D deep learning models like Nerf, synsin, mesh RCNN Who this book is forThis book is for beginner to intermediate-level machine learning practitioners, data scientists, ML engineers, and DL engineers who are looking to become well-versed with computer vision techniques using 3D data.Table of ContentsTable of Contents 3D data file formats - ply and obj, 3D coordination systems, camera models Basic rendering concepts, basic PyTorch optimization, heterogeneous batching Fitting using deformable mesh models Differentiable rendering basic concepts Differentiable volume rendering NeRF - Neural Radiance Fields GIRAFFE Human body 3D fitting using SMPL models Synsin - end-to-end view synthesis from a single image Mesh RCNN

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Book SynopsisThwart hackers by preventing, detecting, and misdirecting access before they can plant malware, obtain credentials, engage in fraud, modify data, poison models, corrupt users, eavesdrop, and otherwise ruin your day Key Features Discover how hackers rely on misdirection and deep fakes to fool even the best security systems Retain the usefulness of your data by detecting unwanted and invalid modifications Develop application code to meet the security requirements related to machine learning Book DescriptionBusinesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning. As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references. The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies. This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks. By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.What you will learn Explore methods to detect and prevent illegal access to your system Implement detection techniques when access does occur Employ machine learning techniques to determine motivations Mitigate hacker access once security is breached Perform statistical measurement and behavior analysis Repair damage to your data and applications Use ethical data collection methods to reduce security risks Who this book is forWhether you’re a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.Table of ContentsTable of Contents Defining Machine Learning Security Mitigating Risk at Training by Validating and Maintaining Datasets Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks Considering the Threat Environment Keeping Your Network Clean Detecting and Analyzing Anomalies Dealing with Malware Locating Potential Fraud Defending against Hackers Considering the Ramifications of Deepfakes Leveraging Machine Learning against Hacking Embracing and Incorporating Ethical Behavior

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Book SynopsisArtificial intelligence is everywhere-powering news feeds, curating search results and invisibly steering our lives. We talk to it and, increasingly, it talks back. And sometimes its answers seem eerily smart.... Until they don''t.Billions of dollars have been poured into AI yet it keeps surprising us with its epic fails-confidently wrong chatbots, inadvertently racist photo apps, well-meaning autonomous cars that fail to recognize traffic cones.Industry insider Emmanuel Maggiori cuts through the hype, revealing the deceptively simple mechanisms behind AI''s impressive results-and its spectacular blunders.Learn the dark secret of the AI industry-how unreasonable expectations, shady practices and outright lying have inflated a bubble of monumental proportions.Read Smart Until It''s Dumb to discover how AI really works, why it''s not always so smart, and why the AI bubble is about to burst.***Emmanuel Maggiori, PhD, is a 10-year AI industry insider, specialized in machine learning and scientific computing. He helps companies build complex software. He has developed AI for a wide variety of applications, from extracting objects from satellite images to packaging holiday deals for millions of travelers every day.

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Book SynopsisGet hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practicesKey Features Understand how large-scale state-of-the-art RL algorithms and approaches work Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more Explore tips and best practices from experts that will enable you to overcome real-world RL challenges Book DescriptionReinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.What you will learn Model and solve complex sequential decision-making problems using RL Develop a solid understanding of how state-of-the-art RL methods work Use Python and TensorFlow to code RL algorithms from scratch Parallelize and scale up your RL implementations using Ray's RLlib package Get in-depth knowledge of a wide variety of RL topics Understand the trade-offs between different RL approaches Discover and address the challenges of implementing RL in the real world Who this book is forThis book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.Table of ContentsTable of Contents Introduction to Reinforcement Learning Multi-armed Bandits Contextual Bandits Makings of the Markov Decision Process Solving the Reinforcement Learning Problem Deep Q-Learning at Scale Policy Based Methods Model-Based Methods Multi-Agent Reinforcement Learning Machine Teaching Generalization and Domain Randomization Meta-reinforcement learning Other Advanced Topics Autonomous Systems Supply Chain Management Marketing, Personalization and Finance Smart City and Cybersecurity Challenges and Future Directions in Reinforcement Learning

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Book SynopsisImplement machine learning and deep learning techniques to perform predictive analytics on real-time IoT dataKey Features Discover quick solutions to common problems that you'll face while building smart IoT applications Implement advanced techniques such as computer vision, NLP, and embedded machine learning Build, maintain, and deploy machine learning systems to extract key insights from IoT data Book DescriptionArtificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease. By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.What you will learn Explore various AI techniques to build smart IoT solutions from scratch Use machine learning and deep learning techniques to build smart voice recognition and facial detection systems Gain insights into IoT data using algorithms and implement them in projects Perform anomaly detection for time series data and other types of IoT data Implement embedded systems learning techniques for machine learning on small devices Apply pre-trained machine learning models to an edge device Deploy machine learning models to web apps and mobile using TensorFlow.js and Java Who this book is forIf you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.Table of ContentsTable of Contents Setting up the IoT and AI Environment Handling Data Machine Learning for IoT Deep Learning for Predictive Maintenance Anomaly Detection Computer Vision NLP and Bots for Self-Ordering Kiosk Optimizing with Microcontrollers and Pipelines Deploying to the Edge

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Book SynopsisNew edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.Key Features Completely updated and revised to Python 3.x New chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineering Learn more about deep learning algorithms, machine learning data pipelines, and chatbots Book DescriptionArtificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data.Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.What you will learn Understand what artificial intelligence, machine learning, and data science are Explore the most common artificial intelligence use cases Learn how to build a machine learning pipeline Assimilate the basics of feature selection and feature engineering Identify the differences between supervised and unsupervised learning Discover the most recent advances and tools offered for AI development in the cloud Develop automatic speech recognition systems and chatbots Apply AI algorithms to time series data Who this book is forThe intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.Table of ContentsTable of Contents Introduction to Artificial Intelligence Fundamental Use Cases for Artificial Intelligence Machine Learning Pipelines Feature Selection and Feature Engineering Classification and Regression Using Supervised Learning Predictive Analytics with Ensemble Learning Detecting Patterns with Unsupervised Learning Building Recommender Systems Logic Programming Heuristic Search Techniques Genetic Algorithms and Genetic Programming Artificial Intelligence on the Cloud Building Games with Artificial Intelligence Building a Speech Recognizer Natural Language Processing Chatbots Sequential Data and Time Series Analysis Image Recognition Neural Networks Deep Learning with Convolutional Neural Networks Recurrent Neural Networks and Other Deep Learning Models Creating Intelligent Agents with Reinforcement Learning Artificial Intelligence and Big Data

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Book SynopsisIncreased use of artificial intelligence (AI) is being deployed in many hospitals and healthcare settings to help improve health care service delivery. Machine learning (ML) and deep learning (DL) tools can help guide physicians with tasks such as diagnosis and detection of diseases and assisting with medical decision making. This edited book outlines novel applications of AI in e-healthcare. It includes various real-time/offline applications and case studies in the field of e-Healthcare, such as image recognition tools for assisting with tuberculosis diagnosis from x-ray data, ML tools for cancer disease prediction, and visualisation techniques for predicting the outbreak and spread of Covid-19. Heterogenous recurrent convolution neural networks for risk prediction in electronic healthcare record datasets are also reviewed. Suitable for an audience of computer scientists and healthcare engineers, the main objective of this book is to demonstrate effective use of AI in healthcare by describing and promoting innovative case studies and finding the scope for improvement across healthcare services.Table of Contents Chapter 1: Introduction to AI in E-healthcare Chapter 2: The scope and future outlook of artificial intelligence in healthcare systems Chapter 3: Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays Chapter 4: Drug discovery clinical trial exploratory process and bioactivity analysis optimizer using deep convolutional neural network for E-prosperity Chapter 5: An automated NLP methodology to predict ICU mortality CLINICAL dataset using multiclass grouping with LSTM RNN approach Chapter 6: Applying machine learning techniques to build a hybrid machine learning model for cancer prediction Chapter 7: AI in healthcare: challenges and opportunities Chapter 8: Impression of artificial intelligence in e-healthcare medical applications Chapter 9: Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset Chapter 10: A narrative review and impacts on trust for data in the healthcare industry using artificial intelligence Chapter 11: Analysis of COVID-19 outbreak using data visualization techniques: a review Chapter 12: Artificial intelligence-based electronic health records for healthcare Chapter 13: Automatic structuring on Chinese ultrasound report of Covid-19 diseases via natural language processing

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Book SynopsisThe classic text on robot manipulators now covers visual control, motion planning and mobile robots too!Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. It has been expanded to include coverage of mobile robots, visual control and motion planning. A variety of problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained.The text includes coverage of fundamental topics like kinematics, and trajectory planning and related technological aspects including actuators and sensors.To impart practical skill, examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, end-of-chapter exercises are proposed, and the book is accompanied by an electronic solutions manual containing the MATLAB® code for computer problems; this is available free of charge to those adopting this volume as a textbook for courses.Trade ReviewRobotics: Modelling, Planning and Control is a book that comprehensively covers all aspects of robotic fundamentals. It is particularly an excellent text for graduate educators, as it covers the fundamentals of the field with a rigorous formalism that is well blended with the technological aspects of robotics. The text covers in detail the theory of manipulators and wheeled robots starting with kinematics, dynamics and motion control, as well interaction with the environment through perception - force and vision sensors. The book is written by technical authorities in the field, and will be in invaluable addition to graduate education as well as a useful guide for industrial practitioners. Alexander Zelinsky, CSIRO, Australia Robotics is a diverse field bringing together disparate areas from computer science, electrical engineering and mechanical engineering. This book is an integrative but rigorous treatment of all the relevant concepts, with an eye toward modern, practical applications making it an excellent choice for a first year graduate course in robotics. Vijay Kumar, University of Pennsylvania This book provides rock-solid foundations for the study of classical mechanics and control of robots, with the authoritative character of a reference where you can surely find the correct expression and the rigorous derivation of the results you need. On top of this, new chapters on motion planning, visual servoing, and mobile robot control provide support to teaching wider and more interdisciplinary aspects of robotics, and open up vistas that will certainly inspire a new generation of scholars to embrace this incredibly rich and fertile research field. Antonio Bicchi, University of Pisa, Italy This book offers a well-balanced and intellectually satisfying treatment of robot mechanics, planning, and control – from the choice and sequence of topics, to the level of detail in the analysis, and the clear connections made between the latest technologies and the theoretical foundations of robotics, this book is an essential element in the library of every aspiring young robotics researcher. Frank Chongwoo Park, Seoul National University Robotics: Modeling, Planning and Control is a historiography from the materialistic view of robotics. Authors clearly explain physical and mathematical foundation to understand the most up-to-date robotics, so faithfully to bibliography and terminology in robotics. Unquestionably, the best textbook for senior students and graduate students and the closest reference book for engineers and scientists! Yoshihiko Nakamura, University of Tokyo Exceptional! A text with such a span of robotics fundamentals and advanced research in both manipulation and mobility, and a treatment that creatively balances mathematical depth and physical intuition – a fresh and certainly unique reference for researchers and engineers in the field of robotics. Oussama Khatib, Stanford University Certainly because of its youth, robotics is not always considered as a discipline as such. It is often introduced as a technological "area" integrating various aspects of mechanics, automatic control and computer science. Such a dispersed view is prejudicial for students. The book by Siciliano et al. achieves the introduction of the basic concepts in a coherent, self-contained and didactic way. In that sense, when reading Robotics: Modelling, Planning and Control the reader – from the undergraduate student to the researcher – understands that a new discipline is born, with its own foundations. Jean-Paul Laumond, LAAS-CNRSTable of ContentsKinematics.- Differential Kinematics and Statics.- Trajectory Planning.- Actuators and Sensors.- Control Architecture.- Dynamics.- Motion Control.- Force Control.- Visual Servoing.- Mobile Robots.- Motion Planning.

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Book SynopsisAt the core of many engineering problems is the solution of sets of equa­ tions and inequalities, and the optimization of cost functions. Unfortunately, except in special cases, such as when a set of equations is linear in its un­ knowns or when a convex cost function has to be minimized under convex constraints, the results obtained by conventional numerical methods are only local and cannot be guaranteed. This means, for example, that the actual global minimum of a cost function may not be reached, or that some global minimizers of this cost function may escape detection. By contrast, interval analysis makes it possible to obtain guaranteed approximations of the set of all the actual solutions of the problem being considered. This, together with the lack of books presenting interval techniques in such a way that they could become part of any engineering numerical tool kit, motivated the writing of this book. The adventure started in 1991 with the preparation by Luc Jaulin of his PhD thesis, under Eric Walter's supervision. It continued with their joint supervision of Olivier Didrit's and Michel Kieffer's PhD theses. More than two years ago, when we presented our book project to Springer, we naively thought that redaction would be a simple matter, given what had already been achieved . . .Trade ReviewFrom the reviews:"Applied Interval Analysis is the right book at the right time to move computing with intervals into the mainstream of engineering, financial, and scientific computing."G. William Walster, Interval Technology Engineering Manager, Sun Microsystems and Member of the Editorial Board of Reliable ComputingTable of ContentsI. Introduction.- 1. Introduction.- 1.1 What Are the Key Concepts?.- 1.2 How Did the Story Start?.- 1.3 What About Complexity?.- 1.4 How is the Book Organized?.- II. Tools.- 2. Interval Analysis.- 2.1 Introduction.- 2.2 Operations on Sets.- 2.2.1 Purely set-theoretic operations.- 2.2.2 Extended operations.- 2.2.3 Properties of set operators.- 2.2.4 Wrappers.- 2.3 Interval Analysis.- 2.3.1 Intervals.- 2.3.2 Interval computation.- 2.3.3 Closed intervals.- 2.3.4 Interval vectors.- 2.3.5 Interval matrices.- 2.4 Inclusion Functions.- 2.4.1 Definitions.- 2.4.2 Natural inclusion functions.- 2.4.3 Centred inclusion functions.- 2.4.4 Mixed centred inclusion functions.- 2.4.5 Taylor inclusion functions.- 2.4.6 Comparison.- 2.5 Inclusion Tests.- 2.5.1 Interval Booleans.- 2.5.2 Tests.- 2.5.3 Inclusion tests for sets.- 2.6 Conclusions.- 3. Subpavings.- 3.1 Introduction.- 3.2 Set Topology.- 3.2.1 Distances between compact sets.- 3.2.2 Enclosure of compact sets between subpavings.- 3.3 Regular Subpavings.- 3.3.1 Pavings and subpavings.- 3.3.2 Representing a regular subpaving as a binary tree.- 3.3.3 Basic operations on regular subpavings.- 3.4 Implementation of Set Computation.- 3.4.1 Set inversion.- 3.4.2 Image evaluation.- 3.5 Conclusions.- 4. Contractors.- 4.1 Introduction.- 4.2 Basic Contractors.- 4.2.1 Finite subsolvers.- 4.2.2 Intervalization of finite subsolvers.- 4.2.3 Fixed-point methods.- 4.2.4 Forward—backward propagation.- 4.2.5 Linear programming approach.- 4.3 External Approximation.- 4.3.1 Principle.- 4.3.2 Preconditioning.- 4.3.3 Newton contractor.- 4.3.4 Parallel linearization.- 4.3.5 Using formal transformations.- 4.4 Collaboration Between Contractors.- 4.4.1 Principle.- 4.4.2 Contractors and inclusion functions.- 4.5 Contractors for Sets.- 4.5.1 Definitions.- 4.5.2 Sets defined by equality and inequality constraints.- 4.5.3 Improving contractors using local search.- 4.6 Conclusions.- 5. Solvers.- 5.1 Introduction.- 5.2 Solving Square Systems of Non-linear Equations.- 5.3 Characterizing Sets Defined by Inequalities.- 5.4 Interval Hull of a Set Defined by Inequalities.- 5.4.1 First approach.- 5.4.2 Second approach.- 5.5 Global Optimization.- 5.5.1 The Moore—Skelboe algorithm.- 5.5.2 Hansen’s algorithm.- 5.5.3 Using interval constraint propagation.- 5.6 Minimax Optimization.- 5.6.1 Unconstrained case.- 5.6.2 Constrained case.- 5.6.3 Dealing with quantifiers.- 5.7 Cost Contours.- 5.8 Conclusions.- III. Applications.- 6. Estimation.- 6.1 Introduction.- 6.2 Parameter Estimation Via Optimization.- 6.2.1 Least-square parameter estimation in compartmental modelling.- 6.2.2 Minimax parameter estimation.- 6.3 Parameter Bounding.- 6.3.1 Introduction.- 6.3.2 The values of the independent variables are known.- 6.3.3 Robustification against outliers.- 6.3.4 The values of the independent variables are uncertain.- 6.3.5 Computation of the interval hull of the posterior feasible set.- 6.4 State Bounding.- 6.4.1 Introduction.- 6.4.2 Bounding the initial state.- 6.4.3 Bounding all variables.- 6.4.4 Bounding by constraint propagation.- 6.5 Conclusions.- 7. Robust Control.- 7.1 Introduction.- 7.2 Stability of Deterministic Linear Systems.- 7.2.1 Characteristic polynomial.- 7.2.2 Routh criterion.- 7.2.3 Stability degree.- 7.3 Basic Tests for Robust Stability.- 7.3.1 Interval polynomials.- 7.3.2 Polytope polynomials.- 7.3.3 Image-set polynomials.- 7.3.4 Conclusion.- 7.4 Robust Stability Analysis.- 7.4.1 Stability domains.- 7.4.2 Stability degree.- 7.4.3 Value-set approach.- 7.4.4 Robust stability margins.- 7.4.5 Stability radius.- 7.5 Controller Design.- 7.6 Conclusions.- 8. Robotics.- 8.1 Introduction.- 8.2 Forward Kinematics Problem for Stewart—Gough Platforms.- 8.2.1 Stewart—Gough platforms.- 8.2.2 From the frame of the mobile plate to that of the base.- 8.2.3 Equations to be solved.- 8.2.4 Solution.- 8.3 Path Planning.- 8.3.1 Graph discretization of configuration space.- 8.3.2 Algorithms for finding a feasible path.- 8.3.3 Test case.- 8.4 Localization and Tracking of a Mobile Robot.- 8.4.1 Formulation of the static localization problem.- 8.4.2 Model of the measurement process.- 8.4.3 Set inversion.- 8.4.4 Dealing with outliers.- 8.4.5 Static localization example.- 8.4.6 Tracking.- 8.4.7 Example.- 8.5 Conclusions.- IV. Implementation.- 9. Automatic Differentiation.- 9.1 Introduction.- 9.2 Forward and Backward Differentiations.- 9.2.1 Forward differentiation.- 9.2.2 Backward differentiation.- 9.3 Differentiation of Algorithms.- 9.3.1 First assumption.- 9.3.2 Second assumption.- 9.3.3 Third assumption.- 9.4 Examples.- 9.4.1 Example 1.- 9.4.2 Example 2.- 9.5 Conclusions.- 10. Guaranteed Computation with Floating-point Numbers.- 10.1 Introduction.- 10.2 Floating-point Numbers and IEEE 754.- 10.2.1 Representation.- 10.2.2 Rounding.- 10.2.3 Special quantities.- 10.3 Intervals and IEEE 754.- 10.3.1 Machine intervals.- 10.3.2 Closed interval arithmetic.- 10.3.3 Handling elementary functions.- 10.3.4 Improvements.- 10.4 Interval Resources.- 10.5 Conclusions.- 11. Do It Yourself.- 11.1 Introduction.- 11.2 Notions of C++.- 11.2.1 Program structure.- 11.2.2 Standard types.- 11.2.3 Pointers.- 11.2.4 Passing parameters to a function.- 11.3 INTERVAL Class.- 11.3.1 Constructors and destructor.- 11.3.2 Other member functions.- 11.3.3 Mathematical functions.- 11.4 Intervals with PROFIL/BIAS.- 11.4.1 BIAS.- 11.4.2 PROFIL.- 11.4.3 Getting started.- 11.5 Exercises on Intervals.- 11.6 Interval Vectors.- 11.6.1 INTERVAL_VECTOR class.- 11.6.2 Constructors, assignment and function call operators.- 11.6.3 Friend functions.- 11.6.4 Utilities.- 11.7 Vectors with PROFIL/BIAS.- 11.8 Exercises on Interval Vectors.- 11.9 Interval Matrices.- 11.10 Matrices with PROFIL/BIAS.- 11.11 Exercises on Interval Matrices.- 11.12 Regular Subpavings with PROFIL/BIAS.- 11.12.1 NODE class.- 11.12.2 Set inversion with subpavings.- 11.12.3 Image evaluation with subpavings.- 11.12.4 System simulation and state estimation with subpavings.- 11.13 Error Handling.- 11.13.1 Using exit.- 11.13.2 Exception handling.- 11.13.3 Mathematical errors.- References.

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Book SynopsisMobile Robotics: A Practical Introduction (2nd edition) is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots. A fascinating, cutting-edge, research topic, autonomous mobile robotics is now taught in more and more universities. In this book you are introduced to the fundamental concepts of this complex field via twelve detailed case studies that show how to build and program real working robots. Topics covered in clued learning, autonomous navigation in unmodified, noisy and unpredictable environments, and high fidelity robot simulation. This new edition has been updated to include a new chapter on novelty detection, and provides a very practical introduction to mobile robotics for a general scientific audience. It is essential reading for 2nd and 3rd year undergraduate students and postgraduate students studying robotics, artificial intelligence, cognitive science and robot engineering. The update and overview of core concepts in mobile robotics will assist and encourage practitioners of the field and set challenges to explore new avenues of research in this exiting field. The author is Senior Lecturer at the Department of Computer Science at the University of Essex. "A very fine overview over the relevant problems to be solved in the attempt to bring intelligence to a moving vehicle." Professor Dr. Ewald von Puttkamer, University of Kaiserslautern "Case studies show ways of achieving an impressive repertoire of kinds of learned behaviour, navigation and map-building. The book is an admirable introduction to this modern approach to mobile robotics and certainly gives a great deal of food for thought. This is an important and though-provoking book." Alex M. Andrew in Kybernetes Vol 29 No 4 and Robotica Vol 18Trade ReviewFrom the reviews of the second edition: "The book is an admirable introduction to its modern approach to mobile robotics and certainly gives a great deal of food for thought. … Like its first edition, this is an important and thought-provoking book." (Alex M. Andrew, Robotica, Vol. 22 (347), 2004) "Mobile Robotics: A Practical Introduction (2nd edition) is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots. … provides a very practical introduction to mobile robotics for a general scientific audience. It is essential reading for 2nd and 3rd year undergraduate students and postgraduate students studying robotics, artificial intelligence, cognitive science and robot engineering. … is an admirable introduction to this modern approach to mobile robotics and certainly gives a great deal of food for thought." (Alex M. Andrew, Artificial Life & Robotics, Vol. 7 (4), 2004) Table of ContentsIntroduction Foundations Robot Hardware Robot Learning: Making Sense of Raw Sensor Data Navigation Novelty Detection Simulation: Modelling Robot-Environment Interaction Analysis of Robot Behaviour Outlook

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Book Synopsis'Artificial intelligence is going to have a massive impact on everyone’s lives... an accessible and sensible read that helps demystify AI' Deborah Meaden, entrepreneur and star of Dragon's Den'Nigel Toon is a visionary leader in the field of artificial intelligence... a must-read' Marc Tremblay, Distinguished Engineer, MicrosoftThose who understand how AI thinks are about to win big.We are used to thinking of computers as being a step up from calculators - very good at storing information, and maybe even at playing a logical game like chess. But up to now they haven't been able to think in ways that are intuitive, or respond to questions as a human might. All that has changed, dramatically, in the past few years.Our search engines are becoming answer engines. Artificial intelligence is already revolutionising sectors from education to healthcare to the creative arts. But how does an AI understand sentiment or context? How does it play and win games that have an almost infinite number of moves? And how can we work with AI to produce insights and innovations that are beyond human capacity, from writing code in an instant to unfolding the elaborate 3D puzzles of proteins?We stand at the brink of a historic change that will disrupt society and at the same time create enormous opportunities for those who understand how AI thinks. Nigel Toon shows how we train AI to train itself, so that it can paint images that have never existed before or converse in any language. In doing so he reveals the strange and fascinating ways that humans think, too, as we learn how to live in a world shared by machine intelligences of our own creation.Trade ReviewFew books are more timely than How AI Thinks, an accessible guide that walks the reader through the technology’s developmental history right back to the days before the computer... This is a fascinating read. -- Simon Hunt * Evening Standard *I believe that AI is going to have a massive impact on everyone’s lives; it’s such a hugely important topic that we can’t just leave it to technologists and governments to think about. Business people, teachers, students and parents - everyone needs to learn more about it. In How AI Thinks, Nigel Toon provides us with an accessible and sensible read that helps demystify AI and lets us all understand more about this incredibly powerful tool. -- Deborah Meaden, entrepreneur and star of Dragon's DenNigel Toon is not only a visionary leader in the field of artificial intelligence, but also a captivating storyteller who takes us on a journey through his own fascinating history and the evolution of our young industry. He has a gift for explaining complex concepts in simple terms, making this book accessible and engaging for anyone interested in AI. He also offers a prescriptive and optimistic view of the future of AI, showing how it can transform our lives and society for the better. This book is a must-read for anyone who wants to understand the past, present and future of artificial intelligence. -- Marc Tremblay PhD, Distinguished Engineer, MicrosoftAn insightful, informative, inspiring book which takes the reader on a journey of discovery, it ultimately paints a hopeful and reasoned vision of how humanity can move on from a position of fear and trepidation, and embrace AI, deriving profound benefit from all it makes possible. Nigel has a skill in taking highly technical content and making AI not just comprehensible, but also engaging. -- Professor Evelyn Welch, Vice-Chancellor and President, University of BristolAs a business leader, it was great to have all the strands that have created AI pulled together. Nigel Toon synthesizes everything so clearly, simply and in such an inspiring way. How AI Thinks delivers the perspective that leaders and politicians need so that they can regulate AI well. -- Sir Andrew MacKenzie, Chairman of Shell

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Book SynopsisOver the next twenty years, the presence of robots will dramatically increase in our daily lives. Robots will serve as maids, gardeners, companions, waiters, security guards, nurses, teachers, playmates, receptionists, chauffeurs and prostitutes – to name only a few roles they will assume. These robots will be intelligent, autonomous, communicative, emotional, and continually progressing in their abilities.This book provides an in-depth look at how American consumers will react to the significant social, economic and marketplace changes that will be brought about by the robot revolution. Our insights come from national surveys of over 2,700 Americans, as well as a thorough review of existing academic research and expert predictions. We provide suggestions for publically-acceptable robot roles, robot design and the optimal marketplace approaches for successful human-robot interactions. Ready or not, it’s coming. And sooner than you might think.

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Book SynopsisAs its title says, it's the hundred-page machine learning book. It was written by an expert in machine learning holding a Ph.D. in Artificial Intelligence with almost two decades of industry experience in computer science and hands-on machine learning.This is a unique book in many aspects. It is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.The book contains only those parts of the huge body of material on machine learning developed since the 1960s that have proven to have a significant practical value. A beginner in machine learning will find in this book just enough details to get a comfortable level of understanding of the field and start asking the right questions. Practitioners with experience will use this book as a collection of pointers to the directions of further self-improvement.The book also comes in handy when brainstorming at the beginning of a project, when you try to answer the question whether a given technical or business problem is 'machine-learnable' and, if yes, which techniques you should try to solve it.The book comes with a wiki which contains pages that extend some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources. Thanks to the continuously updated wiki this book like a good wine keeps getting better after you buy it.

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Book SynopsisThe book is based on interdisciplinary research on various aspects and dynamics of human multimodal signal exchanges. It discusses realistic application scenarios where human interaction is the focus, in order to identify new methods for data processing and data flow coordination through synchronization, and optimization of new encoding features combining contextually enacted communicative signals, and develop shared digital data repositories and annotation standards for benchmarking the algorithmic feasibility and successive implementation of believable human–computer interaction (HCI) systems. This book is a valuable resource for a. the research community, PhD students, early stage researchersc. schools, hospitals, and rehabilitation and assisted-living centerse. the ICT market, and representatives from multimedia industriesTable of ContentsA Human-Centered Behavioral Informatics.- Wearable Devices for Self-Enhancement and Improvement of Plasticity: Effects on Neurocognitive Efficiency.- Age and Culture Effects on The Ability to Decode Affective Bursts.- N200 ERP Component in Olfactory and Haptic Crossmodal Perception.- Handwriting and Drawing Features for Detecting Negative Moods.- Oressinergic System: Network Between Sympathetic System and Exercise.- Experimental Analysis of In-Air Trajectories at Long Distances in Online Handwriting.- Kendon Model-Based Gesture Recognition Using Hidden Markov Model and Learning Vector Quantization.- A Neural Network to Identify Driving Habits and Compute Car-Sharing Users' Reputation.- Unsupervised Gene Identification in Colorectal Cancer.- Supervised Gene Identification In Colorectal Cancer.- Intelligent Quality Assessment of Geometrical Features for 3D Face Recognition.- A Novel Deep Learning Approach in Hematology for Classification of Leucocytes.

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Book SynopsisThe International Workshop on Human-Friendly Robotics (HFR) is an annual meeting that brings together academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects related to the introduction of robots into everyday life. HFR collects contributions on current developments of a new generation of human-friendly robots, i.e., safe and dependable machines, operating in the close vicinity to humans or directly interacting with them in a wide range of domains. The papers contained in the book describe the newest and most original achievements in the field of human-robot-interaction coming from the work and ideas of young researchers. The contributions cover a wide range of topics related to human-robot interaction, both physical and cognitive, including theories, methodologies, technologies, empirical and experimental studies.Table of ContentsRobot Control and Manipulation.- Natural Human-Robot Interaction.- Human robot collaboration.- Robot Learning.

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Book SynopsisThis handbook offers a comprehensive treatise on Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm that employs a function to map binary strings into higher-level structures such as programs. GE's simplicity and modular nature make it a very flexible tool. Since its introduction almost twenty years ago, researchers have applied it to a vast range of problem domains, including financial modelling, parallel programming and genetics. Similarly, much work has been conducted to exploit and understand the nature of its mapping scheme, triggering additional research on everything from different grammars to alternative mappers to initialization. The book first introduces GE to the novice, providing a thorough description of GE along with historical key advances. Two sections follow, each composed of chapters from international leading researchers in the field. The first section concentrates on analysis of GE and its operation, giving valuable insight into set up and deployment. The second section consists of seven chapters describing radically different applications of GE. The contributions in this volume are beneficial to both novices and experts alike, as they detail the results and researcher experiences of applying GE to large scale and difficult problems. Topics include: • Grammar design • Bias in GE • Mapping in GE • Theory of disruption in GE · Structured GE · Geometric semantic GE · GE and semantics · Multi- and Many-core heterogeneous parallel GE · Comparing methods to creating constants in GE · Financial modelling with GE · Synthesis of parallel programs on multi-cores · Design, architecture and engineering with GE · Computational creativity and GE · GE in the prediction of glucose for diabetes · GE approaches to bioinformatics and system genomics · GE with coevolutionary algorithms in cybersecurity · Evolving behaviour trees with GE for platform games · Business analytics and GE for the prediction of patient recruitment in multicentre clinical trialsTable of Contents1 Introduction to GE.- 2 Understanding Grammatical Evolution: Grammar Design.- 3 Bias in Grammatical Evolution.- 4 Mapping in Grammatical Evolution.- 5 Theory of Disruption in GE.- 6 Structured Grammatical Evolution.- 7 Geometric Semantic Grammatical Evolution.- 8 GE and Semantics.- 9 Multi- and Many-core Heterogeneous Parallel Grammatical Evolution.- 10 Grammatical Evolution.- 11 Financial Modelling with Grammatical Evolution.- 12 Synthesis of Parallel Programs on Multi-cores.- 13 Design, Architecture and Engineering with Grammatical Evolution.- 14 Computational Creativity and Grammatical Evolution.- 15 Grammatical Evolution in the Prediction of Glucose for Diabetes.- 16 Genomics.- 17 Grammatical Evolution with Coevolutionary Algorithms in Cybersecurity.- 18 Evolving Behavior Trees with Grammatical Evolution for Platform Games.- 19 Patient Recruitment in Multicentre Clinical Trials.

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Book SynopsisThis book is devoted to embedded systems (ESs), which can now be found in practically all fields of human activity. Embedded systems are essentially a special class of computing systems designed for monitoring and controlling objects of the physical world. The book begins by discussing the distinctive features of ESs, above all their cybernetic-physical character, and how they can be designed to deliver the required performance with a minimum amount of hardware. In turn, it presents a range of design methodologies. Considerable attention is paid to the hardware implementation of computational algorithms. It is shown that different parts of complex ESs could be implemented using models of finite state machines (FSMs). Also, field-programmable gate arrays (FPGAs) are very often used to implement different hardware accelerators in ESs. The book pays considerable attention to design methods for FPGA-based FSMs, before the closing section turns to programmable logic controllers widely used in industry. This book will be interesting and useful for students and postgraduates in the area of Computer Science, as well as for designers of embedded systems. In addition, it offers a good point of departure for creating embedded systems for various spheres of human activity. Table of ContentsIntroduction into embedded systems.- Design of embedded systems.- Implementation of computational algorithms in embedded systems.- Field programmable gate arrays.- Implementing Control Algorithms with FPGAs.- Programmable Logic Controllers.- Conclusion.

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Book SynopsisThis book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection. Trade Review“The book discusses mathematical theories of machine learning. … The book is very technically written and it is addressed to professionals in the field.” (Smaranda Belciug, zbMATH 1422.68003, 2019)Table of ContentsChapter 1. Introduction.- Chapter 2. General Framework of Mathematics.- Chapter 3. Problem Formulation.- Chapter 4. Development of Novel Techniques of CoCoSSC Method.- Chapter 5. Further Discussions of the Proposed Method.- Chapter 6. Related Work on Geometry of Non-Convex Programs.- Chapter 7. Gradient Descent Converges to Minimizers.- Chapter 8. A Conservation Law Method Based on Optimization.- Chapter 9. Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations.- Chapter 10. Online Discovery for Stable and Grouping Causalities in Multi-Variate Time Series.- Chapter 11. Conclusion.

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Book SynopsisIn this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens. Trade Review“The book claims to be suitable for undergraduate students, and interested and concerned researchers, practitioners, and citizens. It will serve these audiences well but would be of equal benefit and importance to policymakers and those making decisions about adopting AI technology who might otherwise be focused on a narrower functional perspective. … Highly recommended for all.” (Nicolas E. Gold, Genetic Programming and Evolvable Machines, Vol. 22, 2021)“Responsible Artificial Intelligence is a valuable contribution to the debate about AI … at the level of building principled, responsible AI systems, and the use of these systems. The further reading which the book suggests complements this technical monograph with accessible contributions about the nature and future of AI. … If you want to know how a vision for responsible AI systems in the European fashion can be built from values, read this book.” (Neil Yorke-Smith, Prometheus, September, 2020)Table of ContentsIntroduction.- What Is Artificial Intelligence?.- Ethical Decision-Making.- Taking Responsibility.- Can AI Systems Be Ethical?.- Ensuring Responsible AI in Practice.- Looking Further.

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Book SynopsisThis book constitutes the proceedings of the 21st International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2020. The 21 papers presented in this volume were carefully reviewed from 44 submissions. VMCAI provides a forum for researchers from the communities of verification, model checking, and abstract Interpretation, facilitating interaction, cross-fertilization, and advancement of hybrid methods that combine these and related areas. Table of ContentsWitnessing Secure Compilation.- BackFlow: Backward Context-sensitive Flow Reconstruction of Taint Analysis Results.- Fixing Code That Explodes Under Symbolic Evaluation.- The Correctness of a Code Generator for a Functional Language.- Leveraging Compiler Intermediate Representation for Multi- and Cross-Language Verification.- Putting the Squeeze on Array Programs: Loop Verification via Inductive Rank Reduction.- A Systematic Approach to Abstract Interpretation of Program Transformations.- Sharing ghost variables in a collection of abstract domains.- Harnessing Static Analysis to Help Learn Pseudo-Inverses of String Manipulating Procedures for Automatic Test Generation.- Synthesizing Environment Invariants for Modular Hardware Verification.- Systematic Classi cation of Attackers via Bounded Model Checking.- Cheap CTL Compassion in NuSMV.- A Cooperative Parallelization Approach for Property-Directed k-Induction.- Generalized Property-Directed Reachability for Hybrid Systems.- Language Inclusion for Finite Prime Event Structures.- Promptness and Bounded Fairness in Concurrent and Parameterized Systems.- Solving LIA* Using Approximations.- Formalizing and checking Multilevel Consistency.- Practical Abstractions for Automated Veri cation of Shared-Memory Concurrency.- How to Win First-Order Safety Games.- Improving Parity Game Solvers with Justifications.

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Book SynopsisThis book constitutes the refereed proceedings of the First Multidisciplinary International Symposium, MISDOOM 2019, held in Hamburg, Germany, in February/March 2019. The 14 revised full papers were carefully reviewed and selected from 21 submissions. The papers are organized in topical sections named: human computer interaction and disinformation, automation and disinformation, media and disinformation.Table of ContentsHuman Computer Interaction and Disinformation.- Human and Algorithmic Contributions to Misinformation Online -Identifying the Culprit.- Between Overload and Indifference. Detection of Fake Accounts and Social Bots by Community Managers.- Use and Assessment of Sources in Conspiracy Theorists' Communities.-Credibility Development with Knowledge Graphs.- Automated Detection of Nostalgic Text in the Context of Societal Pessimism.- What is Abusive Language? Integrating Different Views on Abusive Language for Machine Learning.- Automation and Disinformation.- Detecting Malicious Social Bots: Story of a Never-Ending Clash.- The Markets of Manipulation: The Trading of Social Bots on Clearnet and Darknet Markets.- Inside the Tool Set of Automation: Free Social Bot Code Revisited.- Analysis of Automation in Account Engagement for Onsetting Twitter Message Cascades.- How Facebook and Google accidentally created a perfect ecosystem for targeted disinformation.- Between Mainstream and Alternative.- Populist Alternative News Media.- Maintaining Journalistic Authority.- State propaganda on Twitter - How Iranian propaganda accounts tried to negatively in uence the discourse on Saudi Arabia.

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Book Synopsis This book systematically introduces readers to the development of simulation models as well as the implementation and evaluation of simulation experiments with Tecnomatix Plant Simulation. Intended for all Plant Simulation users whose work involves complex tasks, it also offers an easy start for newcomers. Particular attention has been paid to introducing the simulation flow language SimTalk and its use in various aspects of simulation. In over 200 examples, the author demonstrates how to combine the blocks for simulation models and how to employ SimTalk in complex control and analysis tasks. The content ranges from a description of the basic functions of the material flow blocks to more advanced topics such as the implementation of database-supported warehouse control by using the SQLite interface, and the exchange of data using XML, ActiveX, COM or DDE. Table of ContentsBasics.- SimTalk and Dialogs.- Modeling of Production Processes.- Information Flow, Controls.- Working with random values.- Simulation of transport processes.- Simulation of Robots and Handling Equipment.- Warehousing and Procurement.- Simulation of Workers.- The Fluids Library.- 2D and 3D Visualization.- Integrate Energy Consumption and Costs.- Statistics.- Data Exchange and Interfaces.

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Book SynopsisThis book provides innovative ideas on achieving sustainable development and using green technologies to conserve our ecosystem. Innovation is the successful exploitation of a new idea. Through innovation, we can achieve MORE while using LESS. Innovations in science & technology will not only help mankind as a whole, but also contribute to the economic growth of individual countries. It is essential that the global problem of environmental degradation be addressed immediately, and thus, we need to rethink the concept of sustainable development. Indeed, new environmentally friendly technologies are fundamental to attaining sustainable development. The book shares a wealth of innovative green technological ideas on how to preserve and improve the quality of the environment, and how to establish a more resource-efficient and sustainable society. The book provides an interdisciplinary approach to addressing various technical issues and capitalizing on advances in computing & optimization for scientific & technological development, smart information, communication, bio-monitoring, smart cities, food quality assessment, waste management, environmental aspects, alternative energies, sustainable infrastructure development, etc. In short, it offers valuable information and insights for budding engineers, researchers, upcoming young minds and industry professionals, promoting awareness for recent advances in the various fields mentioned above. Table of ContentsChapter 1: A Treatise on Distributed Generation Control.- Chapter 2: Approach for the prevention of Audio Piracy.- Chapter 3: Quality assessment of kaji lemon (Citrus limon) juice under ultrasound and pasteurization processing conditions.- Chapter 4: Optimal Parameter Extraction of PEM Fuel Cell using an Effective Approach.- Chapter 5: A Survey on Feature Extraction Methods for EEG based Emotion Recognition.- Chapter 6: Block Steganography Based Secure Key Encryption To Improve Data Security.- Chapter 7: Texture analysis for rice grain classification using wavelet decomposition and back propagation neural network.- Chapter 8: An analytics-based envelope neural net approach to forecasting memory leakage in an enterprise applications environment.

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Book Synopsis This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.Table of ContentsChapter 1. Introduction Chapter 2. Pairs Trading – Background and Related Work Chapter 3. Proposed Pairs Selection Framework Chapter 4. Proposed Trading Model Chapter 5. Implementation Chapter 6. Results Chapter 7. Conclusions and Future Work

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Book SynopsisThis book highlights recent research on soft computing, pattern recognition and biologically inspired computing. It presents 24 selected papers from the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) and 5 papers from the 11th World Congress on Nature and Biologically Inspired Computing (NaBIC 2019), held at Vardhaman College of Engineering, Hyderabad, India, on December 13–15, 2019. SoCPaR–NaBIC is a premier conference and brings together researchers, engineers and practitioners whose work involves soft computing and bio-inspired computing, as well as their industrial and real-world applications. Including contributions by authors from 15 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.Table of Contents​Generalized Fuzzy Rough Sets Based on New Fuzzy Similarity Relation.- SV-NET: A Deep Learning Approach To Video Based Human Activity Recognition.- Machine Learning based Framework for Recognizing Traffic Signs on Road Surfaces.- Cursor Control Using Face Gestures.- A Smart Discussion Forum Website.- Certificate Management System Using Blockchain.- Reality Check in Virtual Space for Privacy Behavior of Indian Users of Social Networking Sites.- Application of artificial electric field algorithm for economic load dispatch problem.- Intelligent Data Compression Policy for Hadoop Performance Optimization.

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Book SynopsisThis research-oriented book presents key contributions on architecting the digital transformation. It includes the following main sections covering 20 chapters: · Digital Transformation · Digital Business · Digital Architecture · Decision Support · Digital Applications Focusing on digital architectures for smart digital products and services, it is a valuable resource for researchers, doctoral students, postgraduates, graduates, undergraduates, academics and practitioners interested in digital transformation.Table of ContentsTOC available in MS.

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Book SynopsisThis book is of interest to researchers wanting to know more about the latest topics and methods in the fields of the kinematics, control and design of robotic systems. The papers cover the full range of robotic systems, including serial, parallel and cable-driven manipulators. The systems range from being less than fully mobile, to kinematically redundant, to over-constrained. The book brings together 43 peer-reviewed papers. They report on the latest scientific and applied achievements. The main theme that connects them is the movement of robots in the most diverse areas of application.Table of ContentsAdvances in Robot Kinematics Facts and Thoughts.- Inverse Kinematics Using a Converging Paths Algorithm.- Design parameters influence on the static workspace and the stiffness range of a tensegrity mechanism.- Bennett Based Balanced Butterfly Linkage, Deployable Linkage with Inherent Balance.- A Compliant Linkage for Cooperative Object Manipulation through a Heterogeneous Mobile Multi-Robot System.- Modeling and Control of a Redundant Tensegrity-based Manipulator.

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Book SynopsisIn this book the author discusses synergies between computers and thought, related to the field of Artificial Intelligence; between people and thought, leading to questions of consciousness and our existence as humans; and between computers and people, leading to the recent remarkable advances in the field of humanoid robots. He then looks toward the implications of intelligent 'conscious' humanoid robots with superior intellects, able to operate in our human environments. After presenting the basic engineering components and supporting logic of computer systems, and giving an overview of the contributions of pioneering scientists in the domains of computing, logic, and robotics, in the core of the book the author examines the meaning of thought and intelligence in the context of specific tasks and successful AI approaches. In the final part of the book he introduces related societal and ethical implications.The book will be a useful accompanying text in courses on artificial intelligence, robotics, intelligent systems, games, and evolutionary computing. It will also be valuable for general readers and historians of technology.Table of ContentsIntroduction.- Part I: The Components.- Computers.- People.- Thought.- Part II: The Synergies.- Computers and People.- People and Thought.- Computers and Thought.- Computers, People, and Thought.- Part III: The Wider Picture.- Ethical, Societal, Philosophical, and Spiritual Issues.- Jobs and Education.- Superintelligence.- Lethal Autonomous Weapons Systems.- The Right to Privacy as a Cornerstone of Human Individuality and Freedom?.- The Future of Society: Choices, Decisions, and Consequences.- App. A: What Is the Most Efficient Number Base?.- App. B: Ternary State Machine Design.- References.

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Book SynopsisThis textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail.The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified.The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.Table of ContentsPart I: Introduction to Visual Analytics in Data Science.- 1. Introduction to Visual Analytics by an Example.- 2. General Concepts.- 3. Principles of Interactive Visualisation.- 4. Computational Techniques in Visual Analytics.- Part II: Visual Analytics along the Data Science Workflow.- 5. Visual Analytics for Investigating and Processing Data.- 6. Visual Analytics for Understanding Multiple Attributes.- 7. Visual Analytics for Understanding Relationships between Entities.- 8. Visual Analytics for Understanding Temporal Distributions and Variations.- 9. Visual Analytics for Understanding Spatial Distributions and Spatial Variation.- 10. Visual Analytics for Understanding Phenomena in Space and Time.- 11. Visual Analytics for Understanding Texts.- 12. Visual Analytics for Understanding Images and Video.- 13. Computational Modelling with Visual Analytics.- 14. Conclusion.

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Book SynopsisThis book is the very first book-length study devoted to the advances in technological development and systems research in cooperative economics. The chapters provide, first of all, a coherent framework for understanding and applying the concepts and approaches of complexity and systems science for the advanced study of cooperative networks and particular cooperative enterprises and communities. Second, the book serves as a unique source of reliable information on the frontier information technologies available for the production, consumer, credit, and agricultural cooperative enterprises, discussing predominant strategies, potential drivers of change, and responses to complex problems. Given the diverse range of backgrounds and advanced research results, researchers, decision-makers, and stakeholders from all fields of cooperative economics in any country of the world will undoubtedly benefit from this book.Table of ContentsThe Role of Credit Cooperatives in Financing the Real Sector of the Economy.- Innovative Marketing Technologies in the Development of a New Product: Methodological Solutions in the Context of Economic Integration and Cooperation.- Cooperative Platform in the Modern Economy.- Education at a Cooperative University in the Digital Economy.- The Development of Cooperation in the Digital Economy Based on Scientific Research by A. V. Chayanov.- Involvement of the World’s Largest Cooperatives in Sustainable Development Processes.- Collaborations in the Modern Economy.

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Book SynopsisThis open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility. Table of ContentsHaptic Science.- The EmojiGrid as a Rating Tool for the Affective Appraisal of Touch.- A 2-DoF Skin Stretch Display on Palm: Effect of Stimulation Shape, Speed and Intensity.- User-Defined Mid-Air Haptic Sensations for Interacting with an AR Menu Environment.- Surface Roughness Judgment during Finger Exploration is Changeable by Visual Oscillations.- Identifying tactors locations on the proximal phalanx of the finger for navigation.- Tactile Perception of Objects by the User's Palm for the Development of Multi-contact Wearable Tactile Displays.- From Hate to Love: How Learning Can Change Affective Responses to Touched Materials.- Switching between objects improves precision in haptic perception of softness.- Discriminating between Intensities and Velocities of Mid-Air Haptic Patterns.- Density estimation is influenced more by mass when objects are denser.- Haptic feedback in a teleoperated Box & Blocks task.- Systematic Adaptation of Exploration Force to Exploration Duration in Softness Discrimination.- Perception of vibratory direction on the back.- Comparing Lateral Modulation and Amplitude Modulation in Phantom Sensation.- Context Matters: The Effect of Textual Tone on the Evaluation of Mediated Social Touch.- Influence of roughness on contact force estimation during active touch.- Green Fingers: Plant Thigmo Responses as an Unexplored Area for Haptics Research.- The impact of control-display gain in kinesthetic search.- The arm’s blind line: anisotropic distortion in perceived orientation of stimuli on the arm.- Evaluation of Changes in Perceived Intensity and Threshold of Moisture Sensation of Clothes Associated with Skin Moisture.- The Effects of Simultaneous Multi-Point Vibratory Stimulation on Kinesthetic Illusion.- Isometric force matching asymmetries depend on the position of the left hand regardless of handedness.- Computational Model of a Pacinian Corpuscle for an Electrical Stimulus: Spike-Rate and Threshold Characteristics.- Haptic Technology.- SwitchPaD: Active Lateral Force Feedback over a Large Area Based on Switching Resonant Modes.- Visuo-Haptic Display by Embedding Imperceptible Spatial Haptic Information into Projected Images.- Manipulating the Perceived Directions of Wind by Visuo-audio-haptic Cross-modal Effects.- A 6-DoF Zero-order Dynamic Deformable Tool for Haptic Interactions of Deformable and Dynamic Objects.- Evaluating Ultrasonic Tactile Feedback Stimuli.- WeATaViX: WEarable Actuated TAngibles for VIrtual reality eXperiences.- Noncontact Thermal and Vibrotactile Display Using Focused Airborne Ultrasound.- KATIB: Haptic-visual Guidance for Handwriting.- ThermalTex: A two-modal tactile display for delivering surface texture and thermal information.- Can Stiffness Sensations be Rendered in Virtual Reality Using Mid-air Ultrasound Haptic Technologies?.- Midair Haptic Presentation Using Concave Reflector.- Movement-Free Virtual Reality Interface using Kinesthetic Illusion Induced by Tendon Vibration.- Haptic Display Using Fishing Rod.- Confinement of Vibrotactile Stimuli in Periodically Supported Plates.- 2MoTac: Simulation of button click by superposition of two ultrasonic plate waves.- A Proposal and Investigation of Displaying Method by Passive Touch with Electrostatic Tactile Display.- Sensing Ultrasonic Mid-Air Haptics with a Biomimetic Tactile Fingertip.- Soft-wearable device for the estimation of shoulder orientation and gesture.- Wearable Vibrotactile Interface Using Phantom Tactile Sensation for Human-Robot Interaction.- A Parallel Elastic Haptic Thimble for Wide Bandwidth Cutaneous Feedback.- Instrumenting Hand-held Surgical Drills With a Pneumatic Sensing Cover for Haptic Feedback.- Rendering Ultrasound Pressure Distribution on Hand Surface in Real-Time.- Energy Analysis of Lateral vs. Normal Vibration Modes for Ultrasonic Surface Haptic Devices.- Midair Tactile Reproduction of Real Objects.- LinkRing: A Wearable Haptic Display for Delivering Multi-contact and Multi-modal Stimuli at the Finger Pads.- ElectroAR: Distributed Electro-tactile Stimulation for Tactile Transfer.- Haptic Applications.- Identification Rate of Simple and Complex Tactile Alerts in MUM-T Setup.- Attention-based Robot Learning of Haptic Interaction.- Motion Guidance using Translational Force and Torque Feedback by Induced Pulling Illusion.- Perceptually Compressive Communication of Interactive Telehaptic Signal.- Sound Image Icon with Aerial Haptic Feedback.- Stiffness Discrimination by Two Fingers with Stochastic Resonance.- Interest Arousal by Haptic Feedback During a Storytelling for Kindergarten Children.- Investigating the influence of haptic feedback in rover navigation with communication delay.- Shared haptic perception for human-robot collaboration.- Two-Point Haptic Pattern Recognition with the Inverse Filter Method.- Adaptive Fuzzy Sliding Mode Controller Design for a New Hand Rehabilitation Robot.

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Book SynopsisThis book constitutes the refereed post-conference proceedings of 10 workshops held at the 35th International ISC High Performance 2020 Conference, in Frankfurt, Germany, in June 2020: First Workshop on Compiler-assisted Correctness Checking and Performance Optimization for HPC (C3PO); First International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics Simulations and Analysis (CFDML); HPC I/O in the Data Center Workshop (HPC-IODC); First Workshop \Machine Learning on HPC Systems" (MLHPCS); First International Workshop on Monitoring and Data Analytics (MODA); 15th Workshop on Virtualization in High-Performance Cloud Computing (VHPC). The 25 full papers included in this volume were carefully reviewed and selected. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include high-performance computing (HPC), computer architecture and hardware, programming models, system software, performance analysis and modeling, compiler analysis and optimization techniques, software sustainability, scientific applications, deep learning.Table of ContentsChecking and Performance Optimization for HPC (C3PO'20).- Compiler-assisted type-safe checkpointing.- Static analysis to enhance programmability and performance in OmpSs-2 21 Automatic detection of MPI assertions.- Automatic Code Motion to Extend MPI Nonblocking Overlap Window.- First International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics Simulations and Analysis (CFDML) .-Complete Deep Computer-Vision Methodology for Investigating Hydrodynamic Instabilities.- Prediction of Acoustic Fields using a Lattice-Boltzmann Method and Deep Learning.- Unsupervised Learning of Particle Image Velocimetry.- Reduced order modeling of dynamical systems using arti cial neural networks applied to water circulation.- Parameter Identification of RANS turbulence model using Physics-embedded neural network.- Investigating the Overhead of the REST Protocol when Using Cloud Services for HPC Storage.- Characterizing I/O Optimization E ect Through Holistic Log Data Analysis of Parallel File Systems and Interconnects.- The Importance of Temporal Behavior when Classifying Job IO Patterns Using Machine Learning Techniques.- GOPHER, an HPC framework for large scale graph exploration and inference.- Ensembles of Networks Produced from Neural Architecture Search.- SmartPred: Unsupervised Hard Disk Failure Detection.- Application IO analysis with Lustre Monitoring using LASSi for ARCHER.- Characterizing HPC Performance Variation with Monitoring and Unsupervised Learning.- Service Function Chaining Based on Segment Routing Using P4 and SR-IOV (P4-SFC) .- Seamlessly managing HPC workloads through Kubernetes.- Interference-aware Orchestration in Kubernetes.- RustyHermit: A Scalable, Rust-based Virtual Execution Environment.- Rootless Containers with Podman for HPC.- Bioinformatics application with Kube ow for batch processing in clouds.- Converging HPC, Big Data and Cloud technologies for precision agriculture data analytics on supercomputers.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Similarity Search and Applications, SISAP 2020, held in Copenhagen, Denmark, in September/October 2020. The conference was held virtually due to the COVID-19 pandemic.The 19 full papers presented together with 12 short and 2 doctoral symposium papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections named: scalable similarity search; similarity measures, search, and indexing; high-dimensional data and intrinsic dimensionality; clustering; artificial intelligence and similarity; demo and position papers; and doctoral symposium.Table of ContentsScalable Similarity Search.- Accelerating Metric Filtering by Improving Bounds on Estimated Distances.- Differentially Private Sketches for Jaccard Similarity Estimation.- Pivot Selection for Narrow Sketches by Optimization Algorithms.- mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data.- Parallelizing Filter-Verification based Exact Set Similarity Joins on Multicores.- Similarity Search with Tensor Core Units.- On the Problem of p1 in Locality-Sensitive Hashing.- Similarity Measures, Search, and Indexing.- Confirmation Sampling for Exact Nearest Neighbor Search.- Optimal Metric Search Is Equivalent to the Minimum Dominating Set Problem.- Metrics and Ambits and Sprawls, Oh My: Another Tutorial on Metric Indexing.- Some branches may bear rotten fruits: Diversity browsing VP-Trees.- Continuous Similarity Search for Evolving Database.- Taking advantage of highly-correlated attributes in similarity queries with missing values.- Similarity Between Points in Metric Measure Spaces.- High-dimensional Data and Intrinsic Dimensionality.- GTT: Guiding the Tensor Train Decomposition.- Noise Adaptive Tensor Train Decomposition for Low-Rank Embedding of Noisy Data.- ABID: Angle Based Intrinsic Dimensionality.- Sampled Angles in High-Dimensional Spaces.- Local Intrinsic Dimensionality III: Density and Similarity.- Analysing Indexability of Intrinsically High-dimensional Data using TriGen.- Reverse k-Nearest Neighbors Centrality Measures and Local Intrinsic Dimension.- Clustering.- BETULA: Numerically Stable CF-Trees for BIRCH Clustering.- Using a Set of Triangle Inequalities to Accelerate K-means Clustering.- Angle-Based Clustering.- Artificial Intelligence and Similarity.- Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors.- SIR: Similar Image Retrieval for Product Search in E-Commerce.- Cross-Resolution deep features based Image Search.- Learning Distance Estimators from Pivoted Embeddings of Metric Objects.- Demo and Position Papers.- Visualizer of Dataset Similarity using Knowledge Graph.- vitrivr-explore: Guided Multimedia Collection Exploration for Ad-hoc Video Search.- Running experiments with confidence and sanity.- Doctoral Symposium.- Temporal Similarity of Trajectories in Graphs.- Relational Visual-Textual Information Retrieval.

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