Computers

The ebook edition of this title is Open Access and freely available to read online.

In this first part of the INFINITECH book series, which is a series of three books, the principles of the modern economy that make the modern financial sector and FinTech the most disruptive areas in today’s global economy are discussed. INFINITECH envision many opportunities emerging for activating new channels of innovation on the local and global scale while at the same time catapulting opportunities for more disruptive user-centric services. At the same time, INFINITECH is the result of a shared vision from a representative global group of experts, providing a common vision and identifying impacts in the financial and insurance sectors.

The ebook edition of this title is Open Access and freely available to read online.

In this second part of the INFINITECH book series, which is a series of three books, the basic concepts for Fintech referring to the diversity in the use of technology to underpin the delivery of financial services are reviewed. The demand and the supply side in the financial sector are demonstrated, and further discussed is why Fintech is the focus of industry nowadays and the meaning for waves of digitization. Financial technology (FinTech) and insurance technology (InsuranceTech) are rapidly transforming the financial and insurance services industry. An overview of Reference Architecture (RA) for BigData, IoT and AI applications in the financial and insurance sectors (INFINITECH-RA) is also provided. Moreover, the book reviews the concept of innovation and its application in INFINITECH, and innovative technologies provided by the project for financial sector practical examples.

The ebook edition of this title is Open Access and freely available to read online.

Healthcare systems around the world are grappling with several major challenges such as the growing prevalence of chronic diseases, rising healthcare costs, and a shortage of healthcare workers. Without action, the Organisation for Economic Co-operation and Development has estimated that the average public spending on healthcare costs will increase to 10% of GDP. The shortage of healthcare workers has been a long-standing issue in Europe, even before the COVID-19 pandemic. According to a 2018 report by the European Commission's Joint Research Centre, there was a shortage of over 1 million healthcare workers across the European Union. Such shortages have resulted in longer waiting times for patients, increased workload and stress for healthcare workers, and lower quality of care for patients. The pandemic has further underscored the importance of addressing this issue and ensuring that healthcare systems have adequate capacity to meet the needs of patients, both now and in the future.

It is clear that the healthcare sector needs to undergo radical changes to ensure that future generations have easy access to quality care that is also affordable. One way to achieve this is by leveraging the increasing use of digitization in the sector. According to a 2019 report by the European Commission, the volume of healthcare data in the EU could reach 2.8 zettabytes by 2020, with an annual growth rate of 36%. The question then is, how can we leverage the insights that lie within this vast amount of data to transform the healthcare sector and address its most pressing issues, namely cost, quality, and accessibility of care.

This book captures the learnings from the BigMedilytics project, funded by the EC from 2018 to 2021. The project aimed to transform Europe’s healthcare sector by using state-of-the-art Big Data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes, and delivering better access to healthcare facilities, covering the entire Healthcare Continuum. The project executed 12 real-life, in-hospital, big data pilots across three different themes: (i) Population health and chronic disease management, (ii) Oncology, and (iii) Industrialization of healthcare. The pilots spanned 8 European countries, the health data of 11 million patients, involved 35 consortium partners, and incorporated diverse data sets originating from the public health sector, insurance companies, IoT devices, pharmaceutical industry, and public data sets.

The ebook edition of this title is Open Access and freely available to read online.

This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning.

The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning.

A defining feature of this book is that it succinctly relates the “story” of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow.

This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python.

The ebook edition of this title is Open Access and freely available to read online.

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection.

The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions.

The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies.

The ebook edition of this title is Open Access and freely available to read online.

Differential Privacy in Artificial Intelligence: From Theory to Practice is a comprehensive resource designed to review the principles and applications of differential privacy in a world increasingly driven by data. This book delves into the theoretical underpinnings of differential privacy, its use in machine learning systems, practical implementation details, and its broader social and legal ramifications. Intended as a primer and a deep dive, it lays a solid foundation by introducing essential concepts and mechanisms critical to understanding differential privacy.

From theoretical foundations to practical application, the book is organized into five distinct parts. Part I reviews the foundational notions of differential privacy in the central and local models, delving into composition and privacy amplification. The discussion extends to practical strategies for data release and the creation of synthetic data, which is essential for real-world applications. Part II focuses on the application of differential privacy in optimization and learning, examining the integration of privacy measures in machine learning, including private optimization methods and private federated learning.

Beyond technical applications, the book highlights the use of differential privacy in critical sectors such as healthcare and energy, and discusses its implications in image and video analysis in Part III. Part IV provides a thorough look at the tools and challenges in deploying privacy-preserving models, including insights into programming frameworks and machine learning tools. Finally, Part V addresses the societal impact of differential privacy, discussing its intersection with public policy, law, fairness, and bias.

Targeted at researchers, practitioners, and policymakers; Differential Privacy in Artificial Intelligence: From Theory to Practice aims to be an essential guide for anyone committed to advancing privacy in the digital age, providing the knowledge needed to develop and deploy effective and ethical privacy solutions across various domains.

The ebook edition of this title is Open Access and freely available to read online.

Artificial intelligence (AI) depends on data. In sensitive domains – such as healthcare, security, finance, and many more – there is therefore tension between unleashing the power of AI and maintaining the confidentiality and security of the relevant data.

This book – intended for researchers in academia and R&D engineers in industry – explains how advances in three areas—AI, privacy-preserving techniques, and acceleration—allow us to achieve the dream of high performance privacy-preserving AI. It also discusses applications enabled by this emerging interplay.

The book covers techniques, specifically secure multi-party computation and homomorphic encryption, that provide complexity theoretic security guarantees even with a single data point. These techniques have traditionally been too slow for real-world usage, and the challenge is heightened with the large sizes of today's state-of-the-art neural networks, including large language models (LLMs). This book does not cover techniques like differential privacy that only concern statistical anonymization of data points.

The ebook edition of this title is Open Access and freely available to read online.

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well.

To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks.

This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities.

The ebook edition of this title is Open Access and freely available to read online.

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains.

This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability.

In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot.

The ebook edition of this title is Open Access and freely available to read online.

The Internet of Things (IoT) is rapidly transforming our world, connecting billions of devices and enabling unprecedented levels of automation, data exchange, and convenience. From smart homes and wearable fitness trackers to industrial sensors and connected cars, IoT devices are permeating every aspect of our lives. However, this widespread adoption has also introduced significant security challenges that must be addressed to ensure the safety and privacy of individuals and organizations.

The ERATOSTHENES project, funded by the European Commission, rises to meet these challenges by developing a novel Trust and Identity Management Framework for IoT devices. This framework takes a holistic approach to IoT security, considering the entire lifecycle of devices from initial deployment to decommissioning. Designed to be distributed, automated, auditable, and privacy-respectful, the ERATOSTHENES framework empowers users with control over their data and strengthens trust and resilience within the IoT ecosystem. By leveraging cutting-edge technologies like blockchain, advanced cryptography, and machine learning, ERATOSTHENES provides a comprehensive suite of security mechanisms to address the evolving threats and vulnerabilities in the IoT landscape.

This book dives into the territory of the ERATOSTHENES project, providing a detailed exploration of its technical architecture, implementation, and real-world applications. It examines the key challenges addressed by the project, including the heterogeneity of IoT devices, the management of device identities, and the protection of user privacy. The book also highlights the innovative solutions developed within ERATOSTHENES, such as the Trust Manager & Broker, the Self-Sovereign Identity system, and the Advanced Data Protector. Furthermore, it showcases the practical implementation of these and other research solutions and outcomes through pilot projects in diverse domains, including connected vehicles, remote patient monitoring, and industrial network security.

This book serves as a valuable resource for researchers, practitioners, and policymakers seeking to understand and address the complex security challenges of the IoT. It offers insights into the latest advancements in IoT security technologies and provides a roadmap for building a more secure and trustworthy IoT ecosystem.

The ebook edition of this title is Open Access and freely available to read online.

This third and final part of the INFINITECH book series begins by providing a definition for Fintech, namely: the use of technology to underpin the delivery of financial services. The book further discusses why Fintech is the focus of industry nowadays as the waves of digitization and the way financial technology (FinTech) and insurance technology (InsuranceTech) are rapidly transforming the financial and insurance services industry.

The book also introduces technology assets that follow the Reference Architecture (RA) for BigData, IoT and AI applications. Moreover, the series of assets includes the domain area where applications from the INFINITECH innovation project and the concept of innovation for the financial sector are described. Further described is the INFINITECH Marketplace and its components including details of available assets, as well as a description of solutions developed in INFINITECH.

The ebook edition of this title is Open Access and freely available to read online.

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians.

Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles.

CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions.

The ebook edition of this title is Open Access and freely available to read online.

ENACT is a research project funded by the European Commission under its H2020 program. The project consortium consists of twelve industry and research member organisations spread across the whole EU.

The overall goal of the ENACT project was to provide a novel set of solutions to enable DevOps in the realm of trustworthy Smart IoT Systems. Smart IoT Systems (SIS) are complex systems involving not only sensors but also actuators with control loops distributed all across the IoT, Edge and Cloud infrastructure. Since smart IoT systems typically operate in a changing and often unpredictable environment, the ability of these systems to continuously evolve and adapt to their new environment is decisive to ensure and increase their trustworthiness, quality and user experience. DevOps has established itself as a software development life-cycle model that encourages developers to continuously bring new features to the system under operation without sacrificing quality.

This book reports on the ENACT work to empower the development and operation as well as the continuous and agile evolution of SIS, which is necessary to adapt the system to changes in its environment, such as newly appearing trustworthiness threats.

The ebook edition of this title is Open Access and freely available to read online.

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics.

The ebook edition of this title is Open Access and freely available to read online.

Information theory deals with mathematical laws that govern the flow, representation and transmission of information. The most significant achievement of the field is the invention of digital communication which forms the basis of our daily-life digital products such as smart phones, laptops and any IoT devices. Recently it has also found important roles in a spotlight field that has been revolutionized during the past decades: data science.

This book aims at demonstrating modern roles of information theory in a widening array of data science applications. The first and second parts of the book covers the core concepts of information theory: basic concepts on several key notions; and celebrated source and channel coding theorems which concern the fundamental limits of communication. The last part focuses on applications that arise in data science, including social networks, ranking, and machine learning.

The book is written as a text for senior undergraduate and graduate students working on Information Theory and Communications, and it should also prove to be a valuable reference for professionals and engineers from these fields.

The ebook edition of this title is Open Access and freely available to read online.

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI).

This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed.

The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors.

Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well.

Complex cognitive systems, such as social networks, robotic swarms, or biological networks, are composed of individual entities (the agents) whose actions typically arise from some sophisticated form of “social” interaction with other agents. For example, consider the way humans form their individual opinions about a certain phenomenon. The opinions take shape via repeated interactions with other individuals, whether through physical contact or virtually. A diffusion mechanism emerges through which opinions, information, or even fake news propagate.

Social learning also arises over man-made systems in the form of decision-making strategies by multiple agents interacting over a network. Consider a robotic swarm deployed over a hazardous area, where some robots operating under disadvantageous conditions (e.g., with limited visibility or partial information) would only be able to perform their task (such as saving a life during a rescue operation) by leveraging significant cooperation from other robots that have better access to critical information. Nature itself provides many other excellent examples of cooperative learning in the form of biological networks.

The main topic of this book relates to mechanisms for information diffusion and decision-making over graphs, and the study of how agents’ decisions evolve dynamically through interactions with neighbors and the environment.

The ebook edition of this title is Open Access and freely available to read online.

The Internet's exponential growth is driven by the development and emergence of advanced technologies, attended by a flood of new services. The maturity of these technologies is a decisive factor in the progress of the entire telecom industry, leading to a profound transformation of computer networks from traditional, hardware-centric functions to flexible, software-based solutions. Network function virtualization (NFV), together with software-defined networking (SDN) and cloud computing, are not just key enablers, but they are the basis of more programmable networks that are able to adapt to the evolving requirements of diverse customers, ensuring a promising future for the Internet. This future implies the efficient collaboration of different software components, with the aim of improving flexibility, scalability, and the overall agility of the network. Numerous researchers and professionals today focus their research on finding solutions for the resource, service, security, and other orchestration types and further automation of the processes.

The monograph focuses on the following contents:

• Shortcomings in traditional networks and the primary motives and benefits of NFV implementation in network infrastructure
• Fundamentals and the key components of the ETSI NFV framework
• The virtualization techniques and their application in modern computing environments, with an accent on the characteristics and functionality of virtual entities (virtual machines and containers)
• Containerization technology - creation, characteristics of various containers, and their orchestration and usage of the Kubernetes platform for the management of containerized workloads and services
• The basic principles of NFV-based network design and the path for migration from traditional to NFV-based architecture
• SDN technology, including architecture, protocols, use cases, and integration with NFV in modern networks
• The role of virtualization in the clouds' architectures, types, and delivery models
• Key service chaining strategies and standardized architecture for SFC deployment and SDN/NFV-based architecture for SFC deployment
• The key principles of network slicing in networks with implemented NFV and SDN

The monograph offers a practical blend of theoretical considerations and practical NFV implementation in modern computer environments. It is a valuable resource for students, engineers, and researchers looking for a comprehensive guide to further network softwarization and building a highly programmable, scalable, and software-based network infrastructure.

The ebook edition of this title is Open Access and freely available to read online.

This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms. Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to the statistical approach to the analysis of complex networks.

In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition “à la carte”. Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7. As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding.

The ebook edition of this title is Open Access and freely available to read online.

20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come.

AI and Business: Mapping the Present for Harnessing the Future addresses the growing need for businesses to not only understand but effectively implement artificial intelligence (AI) as a core component of their operational strategies. AI has quickly evolved from an experimental technology to a transformative tool reshaping the business landscape. While many organisations recognise its potential, numerous others struggle to integrate AI into their existing frameworks and align it with their strategic goals. This contributed work offers a comprehensive, practical guide for businesses seeking to implement AI across various functions, including marketing, finance, operations, and human resources, while highlighting the critical steps to bridge the gap between technology and effective business use.

Through a blend of fundamental AI concepts such as machine learning, deep learning, and neural networks, alongside real-world case studies from industry leaders like Amazon, Google, IBM, and BOSH, the authors offer insights into how AI has catalysed transformation within these companies. By providing a balanced view of both the opportunities and challenges AI presents, it equips business professionals, leaders, and students with the tools and knowledge necessary to leverage AI for tangible, future-driven success.

HR 5.0 Integration: Unleashing the Potential of the Digital Twin Revolution encapsulates the transformation of Human Resources (HR) through Digital Twin technology. The book provides numerous practical strategies and case studies for implementing Digital Twin technology to revolutionize organizational efficiency, agility, and competitiveness in the digital age.

Digital Twins, or virtual replicas of physical assets or processes, is a technology which could revolutionize HR practices through real-time monitoring, analysis, and simulation of workforce dynamics, to model employees and their interactions within the organisation. By leveraging data analytics and machine learning, HR 5.0 optimizes talent acquisition, enhances employee engagement, predicts future trends, fosters personalised employee experiences, facilitates skill development, and creates a culture of continuous improvement.

Covering the vast array of technological advancements, from ethical concerns such as data privacy, to case studies of organisational success, the book bridges theory and practice to drive HR innovation into the age of 5.0. Various HR functions such as recruitment, onboarding, learning and development, performance management, and workforce planning can all be rationalised by the creation and implementation of Digital Twins in HR, including AI, Internet of Things, data analytics, and privacy considerations.

AI and Deep Learning Enabled Surveillance System Using Image Processing is an essential resource for anyone interested in the convergence of advanced technologies to enhance surveillance capabilities. This book explores in detail how artificial intelligence and deep learning can be applied to image processing, offering a transformative approach to modern surveillance systems. It covers foundational concepts of AI and deep learning, explaining how these technologies are reshaping the way we understand and utilize image data. The book emphasizes practical implementation, guiding readers through the integration of AI algorithms with image processing techniques to create sophisticated, real-time surveillance solutions.

Through in-depth discussions and real-world case studies, the book highlights the applications of AI in various surveillance contexts, such as public safety, traffic monitoring, and access control. Readers will learn about the latest advancements in neural networks, object detection, and anomaly detection, gaining insights into developing and deploying intelligent surveillance systems. Additionally, the book addresses critical ethical and privacy considerations, ensuring that readers are aware of the balance between enhanced security and individual privacy rights. This comprehensive guide is an invaluable tool for professionals and researchers aiming to harness the power of AI and deep learning in the field of surveillance.

AI and Deep Learning Enabled Surveillance System Using Image Processing is an essential resource for anyone interested in the convergence of advanced technologies to enhance surveillance capabilities. This book explores in detail how artificial intelligence and deep learning can be applied to image processing, offering a transformative approach to modern surveillance systems. It covers foundational concepts of AI and deep learning, explaining how these technologies are reshaping the way we understand and utilize image data. The book emphasizes practical implementation, guiding readers through the integration of AI algorithms with image processing techniques to create sophisticated, real-time surveillance solutions.

Through in-depth discussions and real-world case studies, the book highlights the applications of AI in various surveillance contexts, such as public safety, traffic monitoring, and access control. Readers will learn about the latest advancements in neural networks, object detection, and anomaly detection, gaining insights into developing and deploying intelligent surveillance systems. Additionally, the book addresses critical ethical and privacy considerations, ensuring that readers are aware of the balance between enhanced security and individual privacy rights. This comprehensive guide is an invaluable tool for professionals and researchers aiming to harness the power of AI and deep learning in the field of surveillance.

Harnessing the Potential of Artificial Intelligence to Promote Environmental Sustainability provides a comprehensive examination of how AI is transforming environmental conservation and sustainability efforts. This scholarly work delves into cutting-edge AI technologies—such as automation, data analytics, and machine learning—and explores their potential to address pressing environmental challenges. Through in-depth analysis and practical case studies, the book demonstrates how AI can enhance resource management, support sustainable practices, and improve environmental monitoring.

Beyond technical solutions, the authors engage with the ethical considerations and potential risks of deploying AI in environmental contexts. Issues such as fairness, transparency, and accountability in AI systems are explored, along with the need for regulatory frameworks to mitigate negative impacts. An essential resource for researchers, professionals, policymakers, and anyone interested in the intersection of technology and environmental stewardship, this contributed work offers a roadmap for leveraging AI to foster a sustainable future, emphasising the crucial role of advanced technologies in addressing global environmental challenges.

In this era of transition from traditional, people-centric Human Resources (HR) to data-driven and unbiased approaches, this collection not only highlights the revolutionary potential of Artificial Intelligence (AI) but also addresses the challenges and ethical considerations of AI deployment in the workplace.

Impact of Artificial Intelligence on Data-Driven Decision Making in HR for Revolutionizing Organizational Growth explores the transformative role of artificial intelligence in reshaping HR, recruitment, and organisational culture. As AI becomes increasingly embedded in organisational systems, it offers unprecedented opportunities to enhance HR operations—from advanced analytics that predict employee behaviours and trends, to optimising structures that foster innovation and growth.

By providing a comprehensive overview of AI’s impact on HR, this insightful volume offers HR leaders, C-suite executives, organisational strategists, and academics the tools to harness data-driven decision-making for organisational growth in a rapidly changing world.

The series, Contemporary Perspectives in Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. The series is targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether there are associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

The series, Contemporary Perspectives in Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. The series is targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether there are associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

Discover how the internet’s next evolution is reshaping the world of business. The first of two volumes, Web 3.0 Unleashed: Transforming Experiences with AR, AI, and Immersive Technologies explores the groundbreaking technologies that define Web 3.0—blockchain, decentralized finance (DeFi), augmented reality, and artificial intelligence—and their profound impact on the way businesses innovate, grow, and connect with customers.

Through insightful analysis and real-world examples, this contributed work provides a comprehensive guide to harnessing Web 3.0’s potential. From revolutionising supply chains to reimagining customer engagement, every aspect of business is poised for transformation. Whether you’re a technologist, entrepreneur, executive, academic, or student, this book equips you with the tools, strategies, and knowledge to thrive in the digital economy.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.

Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.

Data mining applications are in business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.

Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.

Data mining applications are in business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

With the rapid advancement of artificial intelligence (AI), new technology is reshaping business landscapes and creating unprecedented opportunities for innovation and growth. Professors Babu George and Ontario Wooden explore the transformative journey of African American entrepreneurs as they navigate the evolving AI era.

Beginning with a conceptual analysis based on firsthand experiences with African American business leaders and supported by the existing literature, George and Wooden trace the historical trajectory of African American entrepreneurship, highlighting the resilience and ingenuity that have long defined this community. They examine critical aspects such as policy frameworks, financial strategies, educational initiatives, and the importance of networking within the AI ecosystem, underscoring the need for inclusive policies and accessible resources to ensure African American entrepreneurs can fully participate in and benefit from the AI revolution.

At the heart of the discussion is AI’s role in bridging socioeconomic divides, equipping African American entrepreneurs with powerful tools to overcome systemic barriers and advance toward economic empowerment. Through micro-case studies, innovative insights, and actionable strategies, the book brings home the hope that digital divides and systemic inequities can be overcome successfully.

AI Empowered concludes with a forward-looking perspective, envisioning a future where African American entrepreneurs lead AI-driven innovation. It calls for a collective effort to support these trailblazers and foster a diverse, dynamic entrepreneurial landscape that reflects the promise of the digital age.

Joining a thriving field of new media, this collective volume authored by global academics features important research by thought leaders within computer-mediated communication (CMC) and social media. Featuring 40 comprehensive chapters of new research that focuses on what is new, relevant, and cutting edge in the areas of CMC and social media, authors critically explore topics ranging from social media theories to civil rights. Divided into three parts, the handbook begins with theory and methods, which sets the foundation for the text and then moves into the applicability of strategy, tactics, and measurement. The final focus is toward the future of CMC and social media and its impact on the study and practice of communication.

Uniquely relating social media communication research to its computer-mediated communication foundation, as well as digital and emerging media trends, this handbook is an indispensable resource whether you're a graduate student or a seasoned practitioner.

The long-standing cultural imperative of augmenting human intellect continues to move ever closer to its full manifestation, described by Marshall McLuhan as an extension of the human nervous system. The escalating blending of immersive technologies with advanced computation has created an emerging domain which increasingly allows socio-technical system makers to produce not only human-computer interactions, but advanced, multi-minded human+computer (H+C) systems. The critical shift toward user immersion within systems of digital information and simulation makes the scale of immersive media's potential impact on human life, culture and well-being unlike that of any previous medium.

In Designing XR, Peter (Zak) Zakrzewski presents H+C immersion as a multi-dimensional design problem - a Research Through Design (RTD) zone which addresses the question of: How can transformative design-thinking-based knowledge system complement the existing human-computer interaction (HCI) invention model to contribute to the creation of more participatory, socially viable, and humane immersive media environments?

The book lays out a proposal for ushering the creation of ecologically sound augmented mind based on two essential tasks. The first involves a framework for the design, implementation, and iteration of purposeful, multi-minded, participatory immersive H+C systems. The second focuses on the extended reality experience (XRX) design practice that rhetorically invites users to actively engage with immersive systems while fully exercising their autonomy and agency based on informed choice.

This book contains an Open Access chapter

The COVID-19 pandemic disrupted economies and society globally. In addressing the crisis, the Internet proved incredibly important in enabling many to shift physical work, education, and social activities online and facilitating the tracking of the progress of the pandemic.

Beyond the Pandemic? is the first edited collection to concentrate on the dynamic and complex relationship between the Internet and the role it played in responding to the pandemic. Covid-19 accelerated the digital economy transformation, changing the way work, education, and social engagement is organized, potentially permanently. The collection of international scholars who contributed to this volume offer insightful perspectives on how the Internet ecosystem responded and was changed as a consequence of Covid-19; the sectoral consequences of shifting activity online that the Internet enabled for many, but not for all; and the implications for regulatory policies.

Given how central digital technologies are to all aspects of business, society, and government, Beyond the Pandemic? is integral to the exploration of the sectoral consequences of the Internet for business managers, policymakers and researchers engaged in planning and study for the digital economy future and planning for future pandemics.

BIM for Project Managers is a concise practical guide which shows how cutting-edge BIM related technologies can facilitate the successful management of construction and infrastructure projects. Focused specifically on core project management topic areas, the book shows how BIM can act as a catalyst for key project management functions in construction and lead to improved collaboration, communication and co-ordination. It also describes new skills and competencies which project managers need to acquire to work effectively on BIM projects, and important legal and contractual issues including dispute avoidance, copyright, liability and insurance. Covering the full project lifecycle from briefing, inception and pre-construction through to project closure or disposal, the book enables project managers to facilitate the use of BIM across the entire lifecycle.

BIM for Project Managers is a core reference that empowers project managers to play a more proactive role in BIM implementation at both project and organisational levels. Students seeking to understand how to integrate the new technology and methodologies of BIM with established principles of planning and project management will also find this an indispensable reference.

Digital twin technology has made great strides in the engineering and manufacturing sector over the past decade, however, there remains confusion around what a digital twin is and how they can be utilised successfully at a city scale. Digital Twins for Smart Cities provides conceptual clarity and practical guidance for supporting city-scale digital twin (CDT) development, outlining challenges, showcasing real-life CDT practices globally, and offering socio-technical frameworks to compare CDT projects. Providing theoretical and practical guidance on digital twin development, this book covers

• the purpose, trust and function of CDTs
• case studies on city, district and subterranean assets of cities
• comparisons of CDT cases
• recommendations for putting CDTs into practice.

Digital Twins for Smart Cities is a much-needed reference for urban designers and planners, built environment professionals, and digital-twin-related technology providers.

Managing Projects for Success is a 'how to do it' book of considerable value to practitioners and students alike. It provides the challenge of theory application through a series of exercises and is comprehensively illustrated.

Managing Projects for Success equips the reader with specialist skills that can be immediately applied in practice and is written in three interrelated parts:

• Part 1: The framework
• Part 2: Planning for control
• Part 3: The tool kit - to promote clarity of understanding and study

The framework sets out the bigger picture and a body of knowledge that describes the profession of project management. Planning and controlling are of paramount importance in the successful outcome of projects and part three describes and works through management processes.

Each section includes an extensive list of references, enabling the reader to explore the topics covered in further detail, if required. The text is written in an easy readable style, making it accessible to all levels of reader while maintaining its standing as a work of considerable merit. A comprehensive index ensures that this book will be used as an excellent reference tool by practitioners and students alike. There is also an additional list of references at the end of the book. This is both an excellent reference book for existing project managers and a useful textbook for students new to the profession".

Albert Hamilton's years of research, academic and practice experience gives this definitive book a unique approach to project management, that readers can directly draw on as an effective practical work tool.

Delving into ChatGPT’s architecture and ability to generate human-like text, chapters go beyond technical explanations, exploring the factors contributing to ChatGPT's widespread adoption and its significance in society and industry. The work showcases how ChatGPT has transformed conversational experiences, revolutionizing customer service, streamlining business processes, and enhancing user engagement across various domains. Authors address the potential pitfalls and ethical concerns associated with ChatGPT. They delve into bias, misinformation, and the potential amplification of harmful content that may arise, as well as revealing insights into responsible AI practices, transparency, and accountability, emphasizing the importance of mitigating these risks and ensuring fairness in AI-driven conversations. Thoroughly examining the impact of ChatGPT on the workforce and the potential for job displacement, the need for upskilling and reskilling, and the importance of balanced integration of human oversight in chatbot interactions, the concept of collaborative human-AI systems is explored, where ChatGPT works alongside humans to enhance conversation quality and foster trust.

The ChatGPT Revolution is essential reading for AI researchers and academics interested in the technical intricacies of ChatGPT, industry professionals in AI and NLP seeking practical insights for real-world applications, and ethics and responsible AI practitioners focusing on mitigating ethical concerns.

The book provides best practices from online educators who are engaged in online teaching and program development in Christian higher education. It also explores the distinct aspects of teaching and developing online courses and programs from a Christian perspective and within Christian higher education institutions. As such it is can serve as a ready resource for academic administrators and professors, novices and veterans at online program development and instruction.

There are many myths about Artificial Intelligence (AI) relating to what it is and what it can and cannot do. The people making decisions on AI projects are often not technologically savvy and unable to find easy answers. The spending on and the returns from AI projects are not necessarily straightforward. Part of the reason for this is the lack of understanding of the impact of critical decision criteria. AI touches on many ethical concepts - data privacy, validity, and, more importantly, its potential misuse. AI often replaces human decision-making, as managers do not clearly understand the implications of those choices. This book provides an easy and accessible guide for practitioners without a technological background to understand AI. It guides the reader through the fundamental issues confronting decision-makers. It offers advice on ‘how to ask relevant questions’ using the 15 decision scales. There is currently no comparable book on the market that acts as a pocketbook management reference guide for the AI layman.

There are many myths about Artificial Intelligence (AI) relating to what it is and what it can and cannot do. The people making decisions on AI projects are often not technologically savvy and unable to find easy answers. The spending on and the returns from AI projects are not necessarily straightforward. Part of the reason for this is the lack of understanding of the impact of critical decision criteria. AI touches on many ethical concepts - data privacy, validity, and, more importantly, its potential misuse. AI often replaces human decision-making, as managers do not clearly understand the implications of those choices. This book provides an easy and accessible guide for practitioners without a technological background to understand AI. It guides the reader through the fundamental issues confronting decision-makers. It offers advice on ‘how to ask relevant questions’ using the 15 decision scales. There is currently no comparable book on the market that acts as a pocketbook management reference guide for the AI layman.

The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased.

Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers’ engagement in testing.

In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased.

Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers’ engagement in testing.

In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

The book provides best practices from online educators who are engaged in online teaching and program development in Christian higher education. It also explores the distinct aspects of teaching and developing online courses and programs from a Christian perspective and within Christian higher education institutions. As such it is can serve as a ready resource for academic administrators and professors, novices and veterans at online program development and instruction.

Crossing the Bridge of the Digital Divide: A Walk with Global Leaders explores the combined effect of the rapid growth of information as an increasingly fragmented information base, a large component of which is available only to people with money and/or acceptable institutional affiliations. In the recent past, the outcome of these challenges has been characterized as the 'digital divide' between the information 'haves' and 'have nots' along racial and socio economic lines that seem to widen as time passes. To address the issues of digital equity and digital inequality in an effort to bridge the digital divide, educational scholars, researchers and practitioners are in positions to ensure equitable opportunities are made available for people of all ages, races, ability, sexual orientation, and ethnicity in support of social justice for bridging the digital divide. The digital divide addresses issues concerning equal opportunity, equity and access that have an effect on the development of marginalized and otherwise disenfranchised populations within and across systems nationally and internationally.

The contributing authors- representing Unites States, Canada, South Africa, New Zealand, and the UK - posit that education institutions can serve as the bridge to close the digital divide for students who do not have access to information technology in their homes. At a time when more computers are made available in schools than ever before, the digital divide continues to widen and fewer people in the lowest SES groups are given the opportunity to join the world of computer technology and the internet. As a result, the influence of leadership activity on institutional racism, gender discrimination, inequality of opportunity, inequity of educational processes, digital exclusion, and justice have gained currency and attention.

The contributing national and international authors examine the digital divide in terms of social justice leadership, equity and access. It is within this context that the authors offer discussions from a lens of their choice, i.e. conceptual, review of literature, epistemological, etc. By adopting an educational approach to bridging the digital divide, researchers and practitioners can connect and extend long established lines of conceptual and empirical inquiry aimed at improving organizational practices and thereby gain insights that might be otherwise overlooked, or assumed. This holds great promise for generating, refining, and testing theories of leadership for equity and access, and helps strengthen already vibrant lines of inquiry on social justice.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.

Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted form this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.

Data mining applications are seen in finance (banking, brokerage, insurance), marketing (customer relationships, retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and law enforcement.

Feeling guilty about not networking enough? Stop! You can learn how to network effectively without attending networking events, collecting business cards, or getting a lot of LinkedIn followers. How? By learning how to Connect the Dots.

Written by Inga Carboni, Ph.D., Connect the Dots is a fun, fast-paced, and fact-based book for working professionals seeking to take the next step in their careers. The truth is that networking is not about managing impressions or projecting your personal brand. Effective networkers build, nurture, and leverage relationships, real relationships built on genuine connection. When done correctly, networking isn’t sleazy or manipulative. Instead, it’s empowering, for you, for all the people you know, and for all the people they know.

Connect the Dots: How to Build, Nurture, and Leverage Your Network to Achieve Your Personal and Professional Goals offers a combination of personal stories, business anecdotes, self-assessments, exercises, and concrete guidelines grounded in the latest scientific research. Connect the Dots focuses on developing your personal power and leadership skills by creating effective networks and networking effectively. This book is designed to benefit everyone, from young professionals to senior managers to human resource professionals to C-suite executives.

Unleash the power of your network by learning how to connect the dots to make your network work for you.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.

Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted form this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.

Data mining applications are seen in finance (banking, brokerage, insurance), marketing (customer relationships, retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and law enforcement.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.

Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.

Data mining applications are in finance (banking, brokerage, and insurance), marketing (customer relationships, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security, and law enforcement.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.

Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.

Data mining applications are in finance (banking, brokerage, and insurance), marketing (customer relationships, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security, and law enforcement.

Feeling guilty about not networking enough? Stop! You can learn how to network effectively without attending networking events, collecting business cards, or getting a lot of LinkedIn followers. How? By learning how to Connect the Dots.

Written by Inga Carboni, Ph.D., Connect the Dots is a fun, fast-paced, and fact-based book for working professionals seeking to take the next step in their careers. The truth is that networking is not about managing impressions or projecting your personal brand. Effective networkers build, nurture, and leverage relationships, real relationships built on genuine connection. When done correctly, networking isn’t sleazy or manipulative. Instead, it’s empowering, for you, for all the people you know, and for all the people they know.

Connect the Dots: How to Build, Nurture, and Leverage Your Network to Achieve Your Personal and Professional Goals offers a combination of personal stories, business anecdotes, self-assessments, exercises, and concrete guidelines grounded in the latest scientific research. Connect the Dots focuses on developing your personal power and leadership skills by creating effective networks and networking effectively. This book is designed to benefit everyone, from young professionals to senior managers to human resource professionals to C-suite executives.

Unleash the power of your network by learning how to connect the dots to make your network work for you.

Crossing the Bridge of the Digital Divide: A Walk with Global Leaders explores the combined effect of the rapid growth of information as an increasingly fragmented information base, a large component of which is available only to people with money and/or acceptable institutional affiliations. In the recent past, the outcome of these challenges has been characterized as the 'digital divide' between the information 'haves' and 'have nots' along racial and socio economic lines that seem to widen as time passes. To address the issues of digital equity and digital inequality in an effort to bridge the digital divide, educational scholars, researchers and practitioners are in positions to ensure equitable opportunities are made available for people of all ages, races, ability, sexual orientation, and ethnicity in support of social justice for bridging the digital divide. The digital divide addresses issues concerning equal opportunity, equity and access that have an effect on the development of marginalized and otherwise disenfranchised populations within and across systems nationally and internationally.

The contributing authors- representing Unites States, Canada, South Africa, New Zealand, and the UK - posit that education institutions can serve as the bridge to close the digital divide for students who do not have access to information technology in their homes. At a time when more computers are made available in schools than ever before, the digital divide continues to widen and fewer people in the lowest SES groups are given the opportunity to join the world of computer technology and the internet. As a result, the influence of leadership activity on institutional racism, gender discrimination, inequality of opportunity, inequity of educational processes, digital exclusion, and justice have gained currency and attention.

The contributing national and international authors examine the digital divide in terms of social justice leadership, equity and access. It is within this context that the authors offer discussions from a lens of their choice, i.e. conceptual, review of literature, epistemological, etc. By adopting an educational approach to bridging the digital divide, researchers and practitioners can connect and extend long established lines of conceptual and empirical inquiry aimed at improving organizational practices and thereby gain insights that might be otherwise overlooked, or assumed. This holds great promise for generating, refining, and testing theories of leadership for equity and access, and helps strengthen already vibrant lines of inquiry on social justice.

Virtual Worlds are being increasingly used in business and education. With each day more people are venturing into computer generated online persistent worlds such as Second Life for increasingly diverse reasons such as commerce, education, research, and entertainment. This book explores the emerging ethical issues associated with these novel environments for human interaction and cutting-edge approaches to these new ethical problems. This volume’s goal is to put forward a number of these virtual world ethical issues of which research is only commencing. The developing literature specifically regarding virtual world ethics is a recent phenomenon. Research based on the phenomenon of virtual world life has only been developing in the past four years. This volume introduces pathbreaking work in a field which is only just beginning to take shape. It is ideal as both as a library reference and a supplementary text in upper-division courses focused on the issues of applied ethics and new media. It is unique in being one of the first volumes specifically addressed to ethical problems of the “metaverse”.

This volume includes articles from authors from around the world exploring topics such as: employing rationalist and casuistic approaches to the controversial topic of “virtual rape” yield an increased understanding of how virtual worlds ought to be designed, the relationship between the ethical and legal dimensions of virtual world users’ participation in “paratexts”, utilitarian consideration of harm and freedom in the case of virtual pedophilia, norms of research ethics in virtual worlds, the ethical implications of employing virtual worlds as tools for medical education and experimenting with healthcare services, the ethics of the collective action of virtual world communities, consideration of the virtue and potential of cosmopolitanism in virtual worlds, Deleuzian ethical approaches to the experience of the disabled in virtual worlds, the ethics of virtual world design, and the ethical implications of the “illusion of reality” presented by virtual worlds.

Virtual Worlds are being increasingly used in business and education. With each day more people are venturing into computer generated online persistent worlds such as Second Life for increasingly diverse reasons such as commerce, education, research, and entertainment. This book explores the emerging ethical issues associated with these novel environments for human interaction and cutting-edge approaches to these new ethical problems. This volume’s goal is to put forward a number of these virtual world ethical issues of which research is only commencing. The developing literature specifically regarding virtual world ethics is a recent phenomenon. Research based on the phenomenon of virtual world life has only been developing in the past four years. This volume introduces pathbreaking work in a field which is only just beginning to take shape. It is ideal as both as a library reference and a supplementary text in upper-division courses focused on the issues of applied ethics and new media. It is unique in being one of the first volumes specifically addressed to ethical problems of the “metaverse”.

This volume includes articles from authors from around the world exploring topics such as: employing rationalist and casuistic approaches to the controversial topic of “virtual rape” yield an increased understanding of how virtual worlds ought to be designed, the relationship between the ethical and legal dimensions of virtual world users’ participation in “paratexts”, utilitarian consideration of harm and freedom in the case of virtual pedophilia, norms of research ethics in virtual worlds, the ethical implications of employing virtual worlds as tools for medical education and experimenting with healthcare services, the ethics of the collective action of virtual world communities, consideration of the virtue and potential of cosmopolitanism in virtual worlds, Deleuzian ethical approaches to the experience of the disabled in virtual worlds, the ethics of virtual world design, and the ethical implications of the “illusion of reality” presented by virtual worlds.

The general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included.

Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach.

In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.

The Association officially endorses this definition of Instructional Technology which has been developed over three years by the Committee on Definition and Terminology. The Association recognizes that other theoretical frameworks exist and that these are valid, but believes that these are part of the more inclusive theoretical framework of Instructional Technology used in this definition. In making this definition and the document explaining it available, we hope to help other organizations clarify their relationship to the broad field of Instructional Technology. Although the Association offers this definition as its current position, it is committed to a continuous reevaluation of the definition and to revising and publishing it so that it reflects changing concepts and terminology.

A document of this magnitude can only be produced as the result of the dedication and effort of the persons who formed the committee and of its chairperson, Barbara Seels and her collaborator, Rita C. Richey. Without their energies, skill, perseverance, and willingness to risk stating their perceptions in this format we could not have offered this document. Whether or not we agree with the statements presented here, they will provide a benchmark and a point of dialogue for further development of a profession which seeks to provide conditions for effective learning.

The general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included.

Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach.

In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.

As sensors spread across almost every industry, the internet of things is going to trigger a massive influx of big data. We delve into where IoT will have the biggest impact and what it means for the future of big data analytics. Internet of Things is changing the face of different sectors such as manufacturing, health-care, business, education etc. by completely redefining the way people, devices, and apps connect and interact with each other in the eco system. From personal fitness and wellness sensors, implantable devices to surgical robots – IoT is bringing in new tools and efficiencies in the ecosystem resulting in more integrated healthcare. Application of computational intelligence techniques is today considered as a key success factor to solve the growing scale and complexity of problems in the field of health care systems, agriculture, e-commerce etc.

The convergence of Computational intelligence, Big Data and IoT provides new opportunities and revolutionize business in huge way. This book will support industry and governmental agencies to facilitate and make sense of myriad connected devices in coming decade. This book offers the recent advancements in Computational Intelligence, IoT and Big Data Analytics.

• Development of models and algorithms for employing IoT based facilities in healthcare, industry, agriculture, e- commerce, manufacturing, business etc.
• Methods for collection, management retrieval and processing of Big Data in various domains.
• Provides taxonomy of challenges, issues and research directions in applications of computational intelligence techniques in different domains

As sensors spread across almost every industry, the internet of things is going to trigger a massive influx of big data. We delve into where IoT will have the biggest impact and what it means for the future of big data analytics. Internet of Things is changing the face of different sectors such as manufacturing, health-care, business, education etc. by completely redefining the way people, devices, and apps connect and interact with each other in the eco system. From personal fitness and wellness sensors, implantable devices to surgical robots – IoT is bringing in new tools and efficiencies in the ecosystem resulting in more integrated healthcare. Application of computational intelligence techniques is today considered as a key success factor to solve the growing scale and complexity of problems in the field of health care systems, agriculture, e-commerce etc.

The convergence of Computational intelligence, Big Data and IoT provides new opportunities and revolutionize business in huge way. This book will support industry and governmental agencies to facilitate and make sense of myriad connected devices in coming decade. This book offers the recent advancements in Computational Intelligence, IoT and Big Data Analytics.

• Development of models and algorithms for employing IoT based facilities in healthcare, industry, agriculture, e- commerce, manufacturing, business etc.
• Methods for collection, management retrieval and processing of Big Data in various domains.
• Provides taxonomy of challenges, issues and research directions in applications of computational intelligence techniques in different domains

With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructured data source may bring new perspectives to better understand the assessment products or accuracy and the process how an item product was attained. The analysis of the conventional and non-conventional assessment data calls for more methodology other than the latent trait modeling.

Natural language processing (NLP) methods and machine learning algorithms have been successfully applied in automated scoring. It has been explored in providing diagnostic feedback to test-takers in writing assessment. Recently, machine learning algorithms have been explored for cheating detection and cognitive diagnosis. When the measurement field promote the use of assessment data to provide feedback to improve teaching and learning, it is the right time to explore new methodology and explore the value added from other data sources. This book presents the use cases of machine learning and NLP in improving the assessment theory and practices in high-stakes summative assessment, learning, and instruction. More specifically, experts from the field addressed the topics related to automated item generations, automated scoring, automated feedback in writing, explainability of automated scoring, equating, cheating and alarming response detection, adaptive testing, and applications in science assessment. This book demonstrates the utility of machine learning and NLP in assessment design and psychometric analysis.

With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructured data source may bring new perspectives to better understand the assessment products or accuracy and the process how an item product was attained. The analysis of the conventional and non-conventional assessment data calls for more methodology other than the latent trait modeling.

Natural language processing (NLP) methods and machine learning algorithms have been successfully applied in automated scoring. It has been explored in providing diagnostic feedback to test-takers in writing assessment. Recently, machine learning algorithms have been explored for cheating detection and cognitive diagnosis. When the measurement field promote the use of assessment data to provide feedback to improve teaching and learning, it is the right time to explore new methodology and explore the value added from other data sources. This book presents the use cases of machine learning and NLP in improving the assessment theory and practices in high-stakes summative assessment, learning, and instruction. More specifically, experts from the field addressed the topics related to automated item generations, automated scoring, automated feedback in writing, explainability of automated scoring, equating, cheating and alarming response detection, adaptive testing, and applications in science assessment. This book demonstrates the utility of machine learning and NLP in assessment design and psychometric analysis.

Computer science has emerged as a key driver of innovation in the 21st century. Yet preparing teachers to teach computer science or integrate computer science content into K-12 curricula remains an enormous challenge. Recent policy reports have suggested the need to prepare future teachers to teach computer science through pre-service teacher education programs. In order to prepare a generation of teachers who are capable of delivering computer science to students, however, the field must identify research-based examples, pedagogical strategies, and policies that can facilitate changes in teacher knowledge and practices.

The purpose of this book is to provide examples that could help guide the design and delivery of effective teacher preparation on the teaching of computer science.

This book identifies promising pathways, pedagogical strategies, and policies that will help teacher education faculty and pre-service teachers infuse computer science content into their curricula as well as teach stand-alone computing courses. Specifically, the book focuses on pedagogical practices for developing and assessing pre-service teacher knowledge of computer science, course design models for pre-service teachers, and discussion of policies that can support the teaching of computer science. The primary audience of the book is students and faculty in educational technology, educational or cognitive psychology, learning theory, teacher education, curriculum and instruction, computer science, instructional systems, and learning sciences.

Computer science is increasingly becoming an essential 21st century skill. As school systems around the world recognize the importance of computer science, demand for teachers who have the knowledge and skills to deliver computer science instruction is rapidly growing. Yet a number of recent studies indicate that teachers report low confidence and limited understanding of computer science, frequently confusing basic computer literacy skills with computer science. This is true for both teachers at the K-8 level as well as secondary education teachers who frequently transition to computer science from other content areas, such as mathematics. As computer science is not yet included in most teacher preparation programs, professional development is a critical step in efforts to prepare in-service teachers to deliver high-quality computer science instruction. To date, however, research on best practices in computer science professional development has been severely lacking in the literature, making it difficult for researchers and practitioners alike to examine effective in-service preparation models.

This book provide examples of professional development approaches that help teachers integrate aspects of computing in existing curricula at the K-8 level or deliver stand-alone computer science courses at the secondary school level. Further, this book identifies computational competencies for teachers, promising pedagogical strategies that advance teacher learning, as well as alternative pathways for ongoing learning including microcredentials. The primary audience of the book is graduate students and faculty in educational technology, educational or cognitive psychology, learning theory, curriculum and instruction, computer science, instructional systems and learning sciences. Additionally, the book will serve as a valuable addition to education practitioners and curriculum developers as well as policy makers looking to increase the number of teachers who are prepared to deliver computing education.

This book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the “new normal” for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.

Computer science is increasingly becoming an essential 21st century skill. As school systems around the world recognize the importance of computer science, demand for teachers who have the knowledge and skills to deliver computer science instruction is rapidly growing. Yet a number of recent studies indicate that teachers report low confidence and limited understanding of computer science, frequently confusing basic computer literacy skills with computer science. This is true for both teachers at the K-8 level as well as secondary education teachers who frequently transition to computer science from other content areas, such as mathematics. As computer science is not yet included in most teacher preparation programs, professional development is a critical step in efforts to prepare in-service teachers to deliver high-quality computer science instruction. To date, however, research on best practices in computer science professional development has been severely lacking in the literature, making it difficult for researchers and practitioners alike to examine effective in-service preparation models.

This book provide examples of professional development approaches that help teachers integrate aspects of computing in existing curricula at the K-8 level or deliver stand-alone computer science courses at the secondary school level. Further, this book identifies computational competencies for teachers, promising pedagogical strategies that advance teacher learning, as well as alternative pathways for ongoing learning including microcredentials. The primary audience of the book is graduate students and faculty in educational technology, educational or cognitive psychology, learning theory, curriculum and instruction, computer science, instructional systems and learning sciences. Additionally, the book will serve as a valuable addition to education practitioners and curriculum developers as well as policy makers looking to increase the number of teachers who are prepared to deliver computing education.

This book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the “new normal” for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.

Computer science has emerged as a key driver of innovation in the 21st century. Yet preparing teachers to teach computer science or integrate computer science content into K-12 curricula remains an enormous challenge. Recent policy reports have suggested the need to prepare future teachers to teach computer science through pre-service teacher education programs. In order to prepare a generation of teachers who are capable of delivering computer science to students, however, the field must identify research-based examples, pedagogical strategies, and policies that can facilitate changes in teacher knowledge and practices.

The purpose of this book is to provide examples that could help guide the design and delivery of effective teacher preparation on the teaching of computer science.

This book identifies promising pathways, pedagogical strategies, and policies that will help teacher education faculty and pre-service teachers infuse computer science content into their curricula as well as teach stand-alone computing courses. Specifically, the book focuses on pedagogical practices for developing and assessing pre-service teacher knowledge of computer science, course design models for pre-service teachers, and discussion of policies that can support the teaching of computer science. The primary audience of the book is students and faculty in educational technology, educational or cognitive psychology, learning theory, teacher education, curriculum and instruction, computer science, instructional systems, and learning sciences.