This book offers readers a comprehensive guide to the evolution of the database field from its earliest stages up to the present-and from classical relational database management systems to the current Big Data metaphor. In particular, it gathers the most significant research from the Italian database community that had relevant intersections with international projects. Big Data technology is currently dominating both the market and research. The book provides readers with a broad overview of key research efforts in modelling, querying and analysing data, which, over the last few decades, have became massive and heterogeneous areas.

This book features research related to computational intelligence and energy and thermal aware management of computing resources. The authors publish original and timely research in current areas of power, energy, temperature, and environmental engineering as and advances in computational intelligence that are benefiting the fields. Topics include signal processing architectures, algorithms, and applications; biomedical informatics and computation; artificial intelligence and machine learning; green technologies in information; and more. The book includes contributions from a wide range of researchers, academicians, and industry professionals. The book is made up both of extended papers presented at the International Conference on Intelligent Computing and Sustainable System (ICICSS 2018), September 20-21, 2018, and other accepted papers on R&D and original research work related to the practice and theory of technologies to enable and support Intelligent Computing applications.

This book studies how autonomous aerial robots physically interact with the surrounding environment. Intended to promote the advancement of aerial physical interaction, it analyzes a particular class of aerial robots: tethered aerial vehicles. By examining specific systems, while still considering the challenges of the general problem, it will help readers acquire the knowledge and expertise needed for the subsequent development of more general methods applicable to aerial physical interaction. The formal analysis covers topics ranging from control, state estimation, and motion planning, to experimental validation. Addressing both theoretical and technical aspects, the book is intended for a broad academic and industrial readership, including undergraduate students, researchers and engineers. It can be used as a teaching reference, or as the basis for product development.

This volume comprises the proceedings of ICITCS 2020. It aims to provide a snapshot of the latest issues encountered in IT convergence and security. The book explores how IT convergence and security is core to most current research, industrial and commercial activities. Topics covered in this volume include machine learning & deep learning, communication and signal processing, computer vision and applications, future network technology, artificial intelligence and robotics, software engineering and knowledge engineering, intelligent vehicular networking and applications, healthcare and wellness, web technology and applications, internet of things, and security & privacy. Through this volume, readers will gain an understanding of the current state-of-the-art information strategies and technologies in IT convergence and security. The book will be of use to researchers in academia, industry and other research institutes focusing on IT convergence and security.

Making a Machine That Sees Like Us explains why and how our visual perceptions can provide us with an accurate representation of the external world. Along the way, it tells the story of a machine (a computational model) built by the authors that solves the computationally difficult problem of seeing the way humans do. This accomplishment required a radical paradigm shift - one that challenged preconceptions about visual perception and tested the limits of human behavior-modeling for practical application.
The text balances scientific sophistication and compelling storytelling, making it accessible to both technical and general readers. Online demonstrations and references to the authors' previously published papers detail how the machine was developed and what drove the ideas needed to make it work. The authors contextualize their new theory of shape perception by highlighting criticisms and opposing theories, offering readers a fascinating account not only of their revolutionary results, but of the scientific process that guided the way.

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often "messy" and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

The primary aim of this volume is to provide researchers and engineers from both academia and industry with up-to-date coverage of recent advances in the fields of robotic welding, intelligent systems and automation. It gathers selected papers from the 2018 International Conference on Robotic Welding, Intelligence and Automation (RWIA 2018), held Oct 20-22, 2018 in Guangzhou, China. The contributions reveal how intelligentized welding manufacturing (IWM) is becoming an inescapable trend, just as intelligentized robotic welding is becoming a key technology. The volume is divided into four main parts: Intelligent Techniques for Robotic Welding, Sensing in Arc Welding Processing, Modeling and Intelligent Control of Welding Processing, and Intelligent Control and its Applications in Engineering.

These proceedings present the latest information on intelligent- transportation technologies and their applications in real-world cases. The Second International Conference on Intelligent Transportation was held in Chengdu, China on November 25-27, 2015, to present the latest research in the field, including intelligent-transportation management, intelligent vehicles, rail transportation systems, traffic transportation networks, as well as road traffic element simulations and their industrial development. The aim of conference was to bring together academics, researchers, engineers and students from across the world to discuss state-of-the-art technologies related to intelligent transportation.

This book presents a comprehensive review for Knowledge Engineering tools and techniques that can be used in Artificial Intelligence Planning and Scheduling. KE tools can be used to aid in the acquisition of knowledge and in the construction of domain models, which this book will illustrate. AI planning engines require a domain model which captures knowledge about how a particular domain works - e.g. the objects it contains and the available actions that can be used. However, encoding a planning domain model is not a straightforward task - a domain expert may be needed for their insight into the domain but this information must then be encoded in a suitable representation language. The development of such domain models is both time-consuming and error-prone. Due to these challenges, researchers have developed a number of automated tools and techniques to aid in the capture and representation of knowledge. This book targets researchers and professionals working in knowledge engineering, artificial intelligence and software engineering. Advanced-level students studying AI will also be interested in this book.

This book features selected papers presented at the 14th International Conference on Electromechanics and Robotics 'Zavalishin's Readings' - ER(ZR) 2019, held in Kursk, Russia, on April 17-20, 2019. The contributions, written by professionals, researchers and students, cover topics in the field of automatic control systems, electromechanics, electric power engineering and electrical engineering, mechatronics, robotics, automation and vibration technologies. The Zavalishin's Readings conference was established as a tribute to the memory of Dmitry Aleksandrovich Zavalishin (1900-1968) - a Russian scientist, corresponding member of the USSR Academy of Sciences, and founder of the school of valve energy converters based on electric machines and valve converters energy. The first conference was organized by the Institute of Innovative Technologies in Electromechanics and Robotics at the Saint Petersburg State University of Aerospace Instrumentation in 2006. The 2019 conference was held with the XIII International Scientific and Technical Conference "Vibration 2019", and was organized by Saint Petersburg State University of Aerospace Instrumentation (SUAI), Saint Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS) and the Southwest State University (SWSU) in with cooperation Russian Foundation for Basic Research (project No. 19-08-20021).

Now, for the first time, publication of the landmark work in backpropagation Scientists, engineers, statisticians, operations researchers, and other investigators involved in neural networks have long sought direct access to Paul Werbos's groundbreaking, much-cited 1974 Harvard doctoral thesis, The Roots of Backpropagation, which laid the foundation of backpropagation. Now, with the publication of its full text, these practitioners can go straight to the original material and gain a deeper, practical understanding of this unique mathematical approach to social studies and related fields. In addition, Werbos has provided three more recent research papers, which were inspired by his original work, and a new guide to the field. Originally written for readers who lacked any knowledge of neural nets, The Roots of Backpropagation firmly established both its historical and continuing significance as it:
* Demonstrates the ongoing value and new potential of backpropagation
* Creates a wealth of sound mathematical tools useful across disciplines
* Sets the stage for the emerging area of fast automatic differentiation
* Describes new designs for forecasting and control which exploit backpropagation
* Unifies concepts from Freud, Jung, biologists, and others into a new mathematical picture of the human mind and how it works
* Certifies the viability of Deutsch's model of nationalism as a predictive tool--as well as the utility of extensions of this central paradigm
"What a delight it was to see Paul Werbos rediscover Freud's version of 'back-propagation.' Freud was adamant (in The Project for a Scientific Psychology) that selective learning could only take place if the presynaptic neuron was as influenced as is the postsynaptic neuron during excitation. Such activation of both sides of the contact barrier (Freud's name for the synapse) was accomplished by reducing synaptic resistance by the absorption of 'energy' at the synaptic membranes. Not bad for 1895 But Werbos 1993 is even better." --Karl H. Pribram Professor Emeritus, Stanford University

Brain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process. Since the introduction of BSO algorithms in 2011, many studies on them have been conducted. They not only offer an optimization method, but could also be viewed as a framework of optimization techniques. The process employed in the algorithms could be simplified as a framework with two basic operations: the converging operation and the diverging operation. A "good enough" optimum could be obtained through recursive solution divergence and convergence. The resulting optimization algorithm would naturally have the capability of both convergence and divergence. This book is primarily intended for researchers, engineers, and graduate students with an interest in BSO algorithms and their applications. The chapters cover various aspects of BSO algorithms, and collectively provide broad insights into what these algorithms have to offer. The book is ideally suited as a graduate-level textbook, whereby students may be tasked with the study of the rich variants of BSO algorithms that involves a hands-on implementation to demonstrate the utility and applicability of BSO algorithms in solving optimization problems.

This book presents a selection of highly relevant works concerning the Internet of Things, including: IoT Architectures, Standardization, Smart Cities, Smart Health, the communication of Smart Things, and outstanding IoT use cases. Some of these works present important future lines for the evolution of IoT systems. Thanks to its unique structure, the book allows authors to describe their works at an unprecedented level of detail. The special chapters contain extended versions of prominent and/or high-impact IoT projects that have laid the foundations of many subsequent IoT systems due to their level of innovation, and reflect highly relevant and innovative trends in the development of a new generation of IoT Systems.

This book presents a guide to navigating the complicated issues of quality and process improvement in enterprise software implementation, and the effect these have on the software development life cycle (SDLC). Offering an integrated approach that includes important management and decision practices, the text explains how to create successful automated solutions that fit user and customer needs, by mixing different SDLC methodologies. With an emphasis on the realities of practice, the book offers essential advice on defining business requirements, and managing change. This revised and expanded second edition includes new content on such areas as cybersecurity, big data, and digital transformation. Features: presents examples, case studies, and chapter-ending problems and exercises; concentrates on the skills needed to distinguish successful software implementations; considers the political and cultural realities in organizations; suggests many alternatives for how to manage and model a system.

Music recommendation systems are becoming more and more popular. The increasing amount of personal data left by users on social media contributes to more accurate inference of the user's musical preferences and the same to quality of personalized systems. Health recommendation systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy concerns. Medical doctors deal with various kinds of diseases in which the music therapy helps to improve symptoms. Listening to music may improve heart rate, respiratory rate, and blood pressure in people with heart disease. Sound healing therapy uses aspects of music to improve physical and emotional health and well-being. The book presents a variety of approaches useful to create recommendation systems in healthcare, music, and in music therapy.

This book presents original research articles addressing various aspects of economics, management and optimization. The topics discussed include economics, finance, marketing, resource allocation strategies, fuzzy logic, and network-based techniques for the analysis of economics, management and mathematical optimization. Combining the input of contributing professors and researchers from various Spanish, Italian and Latin American universities, the book will be of interest to students, researchers and practitioners, as well as members of the general public interested in the world of Economics and Management.

This book focuses on innovative strategies to manage and build software systems for generating new knowledge from large archaeological data sets The book also reports on two case studies carried out in real-world scenarios within the Cultural Heritage setting. The book presents an original conceptual framework for developing software solutions to assist the knowledge generation process in connection with large archaeological data sets and related cultural heritage information- a context in which the inputs are mainly textual sources written in freestyle, i.e. without a predetermined, standard structure. Following an in-depth exploration of recent works on the knowledge generation process in the above-mentioned context and IT-based options for facilitating it, the book proposes specific new techniques capable of capturing the structure and semantics implicit in such textual sources, and argues for using this information in the knowledge generation process. The main result is the development of a conceptual framework that can accommodate textual sources and integrate the information included in them into a software engineering framework. The said framework is meant to assist cultural heritage professionals in general, and archaeologists in particular, in both knowledge extraction and the subsequent decision-making process.

This book summarizes the new research results presented at the 12th Joint Conference on Knowledge-Based Software Engineering (JCKBSE 2018), which took place on August 27-30, 2018 on the island of Corfu, Greece. The JCKBSE is a well-established international biennial conference that focuses on the applications of Artificial Intelligence in Software Engineering. The JCKBSE 2018 was organized by the Department of Informatics of the University of Piraeus, the Department of Computer and Information Engineering of Nippon Institute of Technology, and the Department of Informatics of Ionian University. The book will benefit not only experts and researchers in the field of (Knowledge-Based) Software Engineering, but also general readers in the fields of Artificial Intelligence, Computational Intelligence and Computer Science who wish to learn more about the field of (Knowledge-Based) Software Engineering and its applications. An extensive list of bibliographic references at the end of each paper encourages readers to probe further into the application areas that interest them most.

This book focuses on the systematic modeling of complex situations characterized by escalating disruptions, and on cycles of dynamic collaboration for the best handling of disruptions. What can we do about disruptive events and their cascading effects? Thanks to the evolution of intelligent technologies for interaction, communication, sharing, and collaboration, cyberspace is a rapidly expanding world. Our systems of machines, software services, and human organizations have become increasingly interdependent, in other words - networked. As a result, disruptions that initially affect only a small part of any network tend to escalate. At the same time, cyber solutions can support first responders and emergency handlers, enhancing their responsiveness and ability to collaborate with one another in controlling disruptions and preventing their escalation. In this book, we are chiefly interested in how effectively these collaborations can be supported and how we can further optimize such support. Solution guidelines for optimizing collaborations are illustrated with examples in various application domains: agricultural robotics, civil cyber-physical infrastructure, visual analytics, manufacturing automation, and supply chains. Open-source simulation tools are also provided to supplement the main content.

Modern life is increasingly relying on digital technology, which in turn runs on mathematics. However, this underlying math is hidden from us. That is mostly a good thing since we do not want to be solving equations and calculating fractions just to get things done in our everyday business. But the mathematical details do matter for anyone who wants to understand how stuff works, or wishes to create something new in the jungle of apps and algorithms. This book takes a look at the mathematical models behind weather forecasting, climate change prediction, artificial intelligence, medical imaging and computer graphics. The reader is expected to have only a curious mind; technical math skills are not needed for enjoying this text.

This book provides a deep analysis and wide coverage of the very strong trend in computer vision and visual indexing and retrieval, covering such topics as incorporation of models of Human Visual attention into analysis and retrieval tasks. It makes the bridge between psycho-visual modelling of Human Visual System and the classical and most recent models in visual content indexing and retrieval. The large spectrum of visual tasks, such as recognition of textures in static images, of actions in video content, image retrieval, different methods of visualization of images and multimedia content based on visual saliency are presented by the authors. Furthermore, the interest in visual content is modelled with the means of the latest classification models such as Deep Neural Networks is also covered in this book. This book is an exceptional resource as a secondary text for researchers and advanced level students, who are involved in the very wide research in computer vision, visual information indexing and retrieval. Professionals working in this field will also be interested in this book as a reference.

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.

This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.