The topics covered in this book range from modeling and programming languages and environments, via approaches for design and verification, to issues of ethics and regulation. In terms of techniques, there are results on model-based engineering, product lines, mission specification, component-based development, simulation, testing, and proof. Applications range from manufacturing to service robots, to autonomous vehicles, and even robots than evolve in the real world. A final chapter summarizes issues on ethics and regulation based on discussions from a panel of experts. The origin of this book is a two-day event, entitled RoboSoft, that took place in November 2019, in London. Organized with the generous support of the Royal Academy of Engineering and the University of York, UK, RoboSoft brought together more than 100 scientists, engineers and practitioners from all over the world, representing 70 international institutions. The intended readership includes researchers and practitioners with all levels of experience interested in working in the area of robotics, and software engineering more generally. The chapters are all self-contained, include explanations of the core concepts, and finish with a discussion of directions for further work. Chapters 'Towards Autonomous Robot Evolution', 'Composition, Separation of Roles and Model-Driven Approaches as Enabler of a Robotics Software Ecosystem' and 'Verifiable Autonomy and Responsible Robotics' are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This textbook is about systematic problem solving and systematic reasoning using type-driven design. There are two problem solving techniques that are emphasized throughout the book: divide and conquer and iterative refinement. Divide and conquer is the process by which a large problem is broken into two or more smaller problems that are easier to solve and then the solutions for the smaller pieces are combined to create an answer to the problem. Iterative refinement is the process by which a solution to a problem is gradually made better-like the drafts of an essay. Mastering these techniques are essential to becoming a good problem solver and programmer. The book is divided in five parts. Part I focuses on the basics. It starts with how to write expressions and subsequently leads to decision making and functions as the basis for problem solving. Part II then introduces compound data of finite size, while Part III covers compound data of arbitrary size like e.g. lists, intervals, natural numbers, and binary trees. It also introduces structural recursion, a powerful data-processing strategy that uses divide and conquer to process data whose size is not fixed. Next, Part IV delves into abstraction and shows how to eliminate repetitions in solutions to problems. It also introduces generic programming which is abstraction over the type of data processed. This leads to the realization that functions are data and, perhaps more surprising, that data are functions, which in turn naturally leads to object-oriented programming. Part V introduces distributed programming, i.e., using multiple computers to solve a problem. This book promises that by the end of it readers will have designed and implemented a multiplayer video game that they can play with their friends over the internet. To achieve this, however, there is a lot about problem solving and programming that must be learned first. The game is developed using iterative refinement. The reader learns step-by-step about programming and how to apply new knowledge to develop increasingly better versions of the video game. This way, readers practice modern trends that are likely to be common throughout a professional career and beyond.

Visual languages have long been lit pursuitofeffective communication 00 tween human and machine. Today, they are suecessfully employed for e: nd user progmmming, modeliog, rapid prototypmg, and design activities by people ofmany disciplines including arehitects, artists, children, engi neers, and scientists. Furthermore. with rapid advances ofthe Internet and Web technology, human human communication through the Web or eleo tronie mobile deviees is becoming more and moreprevalent This manuscript provides a comprehensive introduetion to diagmmmatiooI visual programming languages and the technologyofautomatie genemtion ofsnch languages. It covers a broad rangeofcontents from the underlying theoryofgraph grammars to the applications in various domains. Thecon tents were ex: l: l: aeted from the papers that my Ph. D. students and I have published in the last 10 years. and are updated and organized in a coherent fashion. The manuseript gives an in. -depth treatmentof all the topic areas. Pointers to related work and further readings are also faeilitated at the end ofeverychapterexeeptChapter 9. Rather than describing how to program visually, the manuscript discusses what are visual programming languages, and how sooh languages and their underlying foundations can be usefully applied to other fields incomputer science that need graphs as the p: rimary meansofrepresentation. Assuming the basic knowledge of computer programming and compiler co: nstruetion, the manuscript can be used as a textbook for senior orgradu ate computer science classes on visual languages, or a reference book for programming language classes, practitioners, and researchers inthe related field. The manuscript cannot be completed without the helps of many people.

This book is about software product lines (SPLs) designed and developed taking UML diagrams as the primary basis, modeled according to a rigorous approach composed of an UML profile and a systematic process for variability management activities, forming the Stereotype-based Management of Variability (SMarty) approach. The book consists of five parts. Part I provides essential concepts on SPL in terms of the first development methodologies. It also introduces variability concepts and discusses SPL architectures finishing with the SMarty approach. Part II is focused on the design, verification and validation of SMarty SPLs, and Part III concentrates on the SPL architecture evolution based on ISO/IEC metrics, the SystEM-PLA method, optimization with the MOA4PLA method, and feature interaction prevention. Next, Part IV presents SMarty as a basis for SPL development, such as, the M-SPLearning SPL for mobile learning applications, the PLeTs SPL for testing tools, the PlugSPL plugin environment for supporting the SPL life cycle, the SyMPLES approach for designing embedded systems with SysML, the SMartySPEM approach for software process lines (SPrL), and re-engineering of class diagrams into an SPL. Eventually, Part V promotes controlled experimentation in UML-based SPLs, presenting essential concepts on how to plan, conduct, and document experiments, as well as showing several experiments carried out with SMarty. This book aims at lecturers, graduate students and experienced practitioners. Lecturers might use the book for graduate level courses about SPL fundamentals and tools; students will learn about the SPL engineering process, variability management, and mass customization; and practitioners will see how to plan the transition from single-product development to an SPL-based process, how to document inherent variability in a given domain, or how to apply controlled experiments to SPLs.

"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher Francois Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technology Machine learning has made remarkable progress in recent years. We've gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning-a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly from the creator of Keras, Francois Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. what's inside Deep learning from first principles Image-classification, imagine segmentation, and object detection Deep learning for natural language processing Timeseries forecasting Neural style transfer, text generation, and image generation about the reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the author Francois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques lays the foundation for the successful integration of evolutionary computation into software engineering. It surveys techniques ranging from genetic algorithms, to swarm optimization theory, to ant colony optimization, demonstrating their uses and capabilities. These techniques are applied to aspects of software engineering such as software testing, quality assessment, reliability assessment, and fault prediction models, among others, to providing researchers, scholars and students with the knowledge needed to expand this burgeoning application.

Explains the basic concepts of Python and its role in machine learning Provides comprehensive coverage of feature-engineering including real-time case studies Perceive the structural patterns with reference to data science and statistics and analytics Includes machine learning based structured exercises Appreciates different algorithmic concepts of machine learning including unsupervised, supervised and reinforcement learning

This book constitutes the refereed proceedings of the 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, held in Costa de Caparica, Portugal, in July 2021.*The 34 papers presented were carefully reviewed and selected from 92 submissions. The papers present selected results produced in engineering doctoral programs and focus on technological innovation for industry and service systems. Research results and ongoing work are presented, illustrated and discussed in the following areas: collaborative networks; smart manufacturing; cyber-physical systems and digital twins; intelligent decision making; smart energy management; communications and electronics; classification systems; smart healthcare systems; and medical devices. *The conference was held virtually. Chapters "Characteristics of Adaptable Control of Production Systems and the Role of Self-organization Towards Smart Manufacturing" and "Predictive Manufacturing: Enabling Technologies, Frameworks and Applications" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

The Lean Approach to Digital Transformation: From Customer to Code and From Code to Customer is organized into three parts that expose and develop the three capabilities that are essential for a successful digital transformation: 1. Understanding how to co-create digital services with users, whether they are customers or future customers. This ability combines observation, dialogue, and iterative experimentation. The approach proposed in this book is based on the Lean Startup approach, according to an extended vision that combines Design Thinking and Growth Hacking. Companies must become truly "customer-centric", from observation and listening to co-development. The revolution of the digital age of the 21st century is that customer orientation is more imperative -- the era of abundance, usages rate of change, complexity of experiences, and shift of power towards communities -- are easier, using digital tools and digital communities. 2. Developing an information system (IS) that is the backbone of the digital transformation - called "exponential information system" to designate an open IS (in particular on its borders), capable of interfacing and combining with external services, positioned as a player in software ecosystems and built for processing scalable and dynamic data flows. The exponential information system is constantly changing and it continuously absorbs the best of information processing technology, such as Artificial Intelligence and Machine Learning. 3. Building software "micro-factories" that produce service platforms, which are called "Lean software factories." This "software factory" concept covers the integration of agile methods, tooling and continuous integration and deployment practices, a customer-oriented product approach, and a platform approach based on modularity, as well as API-based architecture and openness to external stakeholders. This software micro-factory is the foundation that continuously produces and provides constantly evolving services. These three capabilities are not unique or specific to this book, they are linked to other concepts such as agile methods, product development according to lean principles, software production approaches such as CICD (continuous integration and deployment) or DevOps. This book weaves a common frame of reference for all these approaches to derive more value from the digital transformation and to facilitate its implementation. The title of the book refers to the "lean approach to digital transformation" because the two underlying frameworks, Lean Startup and Lean Software Factory, are directly inspired by Lean, in the sense of the Toyota Way. The Lean approach is present from the beginning to the end of this book -- it provides the framework for customer orientation and the love of a job well done, which are the conditions for the success of a digital transformation.

Based around a theme of the construction of a game engine, this textbook is for final year undergraduate and graduate students, emphasising formal methods in writing robust code quickly. This book takes an unusual, engineering-inspired approach to illuminate the creation and verification of large software systems. Where other textbooks discuss business practices through generic project management techniques or detailed rigid logic systems, this book examines the interaction between code in a physical machine and the logic applied in creating the software. These elements create an informal and rigorous study of logic, algebra, and geometry through software. Assuming prior experience with C, C++, or Java programming languages, chapters introduce UML, OCL, and Z from scratch. Extensive worked examples motivate readers to learn the languages through the technical side of software science.

Abstraction is the most basic principle of software engineering. Abstractions are provided by models. Modeling and model transformation constitute the core of model-driven development. Models can be refined and finally be transformed into a technical implementation, i.e., a software system.

The aim of this book is to give an overview of the state of the art in model-driven software development. Achievements are considered from a conceptual point of view in the first part, while the second part describes technical advances and infrastructures. Finally, the third part summarizes experiences gained in actual projects employing model-driven development.

Beydeda, Book and Gruhn put together the results from leading researchers in this area, both from industry and academia. The result is a collection of papers which gives both researchers and graduate students a comprehensive overview of current research issues and industrial forefront practice, as promoted by OMG 's MDA initiative.

Domain Modelling for Interactive Systems Design brings together in one place important contributions and up-to-date research results in this fast moving area. Domain Modelling for Interactive Systems Design serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

This book constitutes the refereed proceedings of the 20th International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2020, held in Cluj-Napoca, Romania, in October 2020 and sponsored by IFIP WG 5.4. The conference was held virtually.The 34 full papers presented were carefully reviewed and selected from 91 submissions. They are organized in the following thematic sections: computing TRIZ; education and pedagogy; sustainable development; tools and techniques of TRIZ for enhancing design; TRIZ and system engineering; TRIZ and complexity; and cross-fertilization of TRIZ for innovation management.

This book offers a practical introduction to the use of artificial intelligence (AI) techniques to improve and optimise the various phases of the software development process, from the initial project planning to the latest deployment. All chapters were written by leading experts in the field and include practical and reproducible examples. Following the introductory chapter, Chapters 2-9 respectively apply AI techniques to the classic phases of the software development process: project management, requirement engineering, analysis and design, coding, cloud deployment, unit and system testing, and maintenance. Subsequently, Chapters 10 and 11 provide foundational tutorials on the AI techniques used in the preceding chapters: metaheuristics and machine learning. Given its scope and focus, the book represents a valuable resource for researchers, practitioners and students with a basic grasp of software engineering.

Optimization in Science and Engineering is dedicated in honor of the 60th birthday of Distinguished Professor Panos M. Pardalos. Pardalos's past and ongoing work has made a significant impact on several theoretical and applied areas in modern optimization. As tribute to the diversity of Dr. Pardalos's work in Optimization, this book comprises a collection of contributions from experts in various fields of this rich and diverse area of science. Topics highlight recent developments and include: * Deterministic global optimization* Variational inequalities and equilibrium problems* Approximation and complexity in numerical optimization* Non-smooth optimization * Statistical models and data mining* Applications of optimization in medicine, energy systems, and complex network analysis This volume will be of great interest to graduate students, researchers, and practitioners, in the fields of optimization and engineering.

This is the book for Gophers who want to learn how to build distributed systems. You know the basics of Go and are eager to put your knowledge to work. Build distributed services that are highly available, resilient, and scalable. This book is just what you need to apply Go to real-world situations. Level up your engineering skills today. Take your Go skills to the next level by learning how to design, develop, and deploy a distributed service. Start from the bare essentials of storage handling, then work your way through networking a client and server, and finally to distributing server instances, deployment, and testing. All this will make coding in your day job or side projects easier, faster, and more fun. Create your own distributed services and contribute to open source projects. Build networked, secure clients and servers with gRPC. Gain insights into your systems and debug issues with observable services instrumented with metrics, logs, and traces. Operate your own Certificate Authority to authenticate internal web services with TLS. Automatically handle when nodes are added or removed to your cluster with service discovery. Coordinate distributed systems with replicated state machines powered by the Raft consensus algorithm. Lay out your applications and libraries to be modular and easy to maintain. Write CLIs to configure and run your applications. Run your distributed system locally and deploy to the cloud with Kubernetes. Test and benchmark your applications to ensure they're correct and fast. Dive into writing Go and join the hundreds of thousands who are using it to build software for the real world. What You Need: Go 1.13+ and Kubernetes 1.16+

This book constitutes the refereed post-conference proceedings of the Fourth IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2021, held virtually in November 2021. The 15 full papers presented were carefully reviewed and selected from 33 submissions. Also included is a summary of two panel sessions held at the conference. The papers are organized in the following topical sections: challenges in IoT Applications and Research, Modernizing Agricultural Practice Using IoT, Cyber-physical IoT systems in Wildfire Context, IoT for Smart Health, Security, Methods.

This book presents contemporary empirical methods in software engineering related to the plurality of research methodologies, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research. The individual chapters discuss methods that impact the current evolution of empirical software engineering and form the backbone of future research. Following an introductory chapter that outlines the background of and developments in empirical software engineering over the last 50 years and provides an overview of the subsequent contributions, the remainder of the book is divided into four parts: Study Strategies (including e.g. guidelines for surveys or design science); Data Collection, Production, and Analysis (highlighting approaches from e.g. data science, biometric measurement, and simulation-based studies); Knowledge Acquisition and Aggregation (highlighting literature research, threats to validity, and evidence aggregation); and Knowledge Transfer (discussing open science and knowledge transfer with industry). Empirical methods like experimentation have become a powerful means of advancing the field of software engineering by providing scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision-making and learning processes. Thus the book is equally suitable for academics aiming to expand the field and for industrial researchers and practitioners looking for novel ways to check the validity of their assumptions and experiences. Chapter 17 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

How to Reduce Code Complexity and Develop Software More Sustainably "Mark Seemann is well known for explaining complex concepts clearly and thoroughly. In this book he condenses his wide-ranging software development experience into a set of practical, pragmatic techniques for writing sustainable and human-friendly code. This book will be a must-read for every programmer." -- Scott Wlaschin, author of Domain Modeling Made Functional Code That Fits in Your Head offers indispensable, practical advice for writing code at a sustainable pace and controlling the complexity that causes projects to spin out of control. Reflecting decades of experience helping software teams succeed, Mark Seemann guides you from zero (no code) to deployed features and shows how to maintain a good cruising speed as you add functionality, address cross-cutting concerns, troubleshoot, and optimize. You'll find valuable ideas, practices, and processes for key issues ranging from checklists to teamwork, encapsulation to decomposition, API design to unit testing. Seemann illuminates his insights with code examples drawn from a complete sample project. Written in C#, they're designed to be clear and useful to anyone who uses any object-oriented language including Java , C++, and Python. To facilitate deeper exploration, all code and extensive commit messages are available for download. Choose mindsets and processes that work, and escape bad metaphors that don't Use checklists to liberate yourself, improving outcomes with the skills you already have Get past "analysis paralysis" by creating and deploying a vertical slice of your application Counteract forces that lead to code rot and unnecessary complexity Master better techniques for changing code behavior Discover ways to solve code problems more quickly and effectively Think more productively about performance and security If you've ever suffered through bad projects or had to cope with unmaintainable legacy code, this guide will help you make things better next time and every time. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Michael Strobel worked for several years as a software engineer and consultant in the German IT industry before joining IBM Research in Switzerland, where he developed his interest in support for negotiations in electronic markets. During his career in research, he has published several articles on this topic in major international conferences and journals and received a PhD from the University of St.Gallen, Switzerland.

Based on his experiences and contributions, the author discusses electronic negotiation technologies - key ingredients for the next generation of electronic markets - from a scientific as well as a practitioner's perspective. He reviews the state-of-the-art and then introduces novel support mechanisms and design elements, which are applied in a number of case studies. This book is geared towards technicians interested in E-Commerce application development but also offers extensive background reading for educational purposes. "

As today's most complex computing environment, the Internet confronts IT researchers, system designers, and application developers with completely new challenges and, as a fascinating new computing paradigm, agent technology has recently attracted broad interest and strong hopes for shaping the future information society. Relating both, the Internet and agents, opens up a whole new range of advanced applications in vibrant subfields of information technology such as middleware, mobile commerce, e-learning, collaborative working, and intelligent information services. Many modern advanced systems are likely to exploit Internet agents - and exploiting Internet agents mostly means dealing with coordination models and technologies of various sorts. This monograph-like anthology is the first systematic guide to models and enabling technologies for the coordination of intelligent agents on the Internet and respective applications.

What do a jilted bride, a football team and a scientist all have in common? They are all case studies that demonstrate how PRINCE2(R) can be used to manage both professional and private lives. This book can be used as a guide to the PRINCE2 framework, using everyday language and experiences, and focusing on areas such as product-based planning, project management, team structure and project flexibility. The translations and illustrations give a real-life context to the method, and provide evidence of how to use (and how not to use) it. This step-by-step guide: Explains the principles of PRINCE2 in straightforward, manageable chunks; Emphasises how to apply PRINCE2 in practice, using real-life examples; Is written by an experienced PRINCE2 practitioner and trainer, so you can be sure that the information is based upon approaches that work; Gives clear explanations and practical illustrations in each section; Explains how to effectively apply PRINCE2's principles, themes and processes to your projects and other real-world scenarios; and Has been updated for PRINCE2 2017. Susan Tuttle has 20 years' experience in project management, programme management and change management, producing exceptional results across diverse industries. She is an accredited trainer in PRINCE2. Her training style is influenced by her strong commitment to human development. She uses learner-centred theories and principles in her training and writing to help explain and communicate difficult topics.

Program debugging has always been a difficult and time-consuming task in the context of software development, where spectrum-based fault localization (SBFL) is one of the most widely studied families of techniques. While it's not particularly difficult to learn about the process and empirical performance of a particular SBFL technique from the available literature, researchers and practitioners aren't always familiar with the underlying theories. This book provides the first comprehensive guide to fundamental theories in SBFL, while also addressing some emerging challenges in this area. The theoretical framework introduced here reveals the intrinsic relations between various risk evaluation formulas, making it possible to construct a formula performance hierarchy. Further extensions of the framework provide a sufficient and necessary condition for a general maximal formula, as well as performance comparisons for hybrid SBFL methods. With regard to emerging challenges in SBFL, the book mainly covers the frequently encountered oracle problem in SBFL and introduces a metamorphic slice-based solution. In addition, it discusses the challenge of multiple-fault localization and presents cutting-edge approaches to overcoming it. SBFL is a widely studied research area with a massive amount of publications. Thus, it is essential that the software engineering community, especially those involved in program debugging, software maintenance and software quality assurance (including both newcomers and researchers who want to gain deeper insights) understand the most fundamental theories - which could also be very helpful to ensuring the healthy development of the field.

A chatbot is expected to be capable of supporting a cohesive and coherent conversation and be knowledgeable, which makes it one of the most complex intelligent systems being designed nowadays. Designers have to learn to combine intuitive, explainable language understanding and reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot construction: 1. Build a bot platform with universal NLP and ML capabilities so that a bot developer for a particular enterprise, not being an expert, can populate it with training data; 2. Accumulate a huge set of training dialogue data, feed it to a deep learning network and expect the trained chatbot to automatically learn "how to chat". Although these two approaches are reported to imitate some intelligent dialogues, both of them are unsuitable for enterprise chatbots, being unreliable and too brittle. The latter approach is based on a belief that some learning miracle will happen and a chatbot will start functioning without a thorough feature and domain engineering by an expert and interpretable dialogue management algorithms. Enterprise high-performance chatbots with extensive domain knowledge require a mix of statistical, inductive, deep machine learning and learning from the web, syntactic, semantic and discourse NLP, ontology-based reasoning and a state machine to control a dialogue. This book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning. The foci of this book are applications of discourse analysis in text relevant assessment, dialogue management and content generation, which help to overcome the limitations of platform-based and data driven-based approaches. Supplementary material and code is available at https://github.com/bgalitsky/relevance-based-on-parse-trees

This book constitutes the refereed proceedings of the 22nd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2021, held in Saint-Etienne, and virtually in November 2021. The 70 papers (15 full and 55 short) presented with 5 industrial workshop papers were carefully reviewed and selected from 189 submissions. They provide a comprehensive overview of major challenges and recent advances in various domains related to the digital transformation and collaborative networks and their applications with a strong focus on the following areas related to the main theme of the conference: sustainable collaborative networks; sustainability via digitalization; analysis and assessment of business ecosystems; human factors in collaboration 4.0; maintenance and life-cycle management; policies and new digital services; safety and collaboration management; simulation and optimization; complex collaborative systems and ontologies; value co-creation in digitally enabled ecosystems; digitalization strategy in collaborative enterprises' networks; pathways and tools for DIHs; socio-technical perspectives on smart product-service systems; knowledge transfer and accelerated innovation in FoF; interoperability of IoT and CPS for industrial CNs; sentient immersive response network; digital tools and applications for collaborative healthcare; collaborative networks and open innovation in education 4.0; collaborative learning networks with industry and academia; and industrial workshop.