Computers

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.

In today’s rapidly evolving digital landscape, traditional security frameworks struggle to keep pace with increasingly sophisticated cyber threats. As cybercrime and espionage surge, the need for innovative, predictive solutions has never been more urgent. Machine learning offers a proactive approach to threat detection, responsive security measures, and access management, supporting global peace and justice in alignment with the United Nations’ Sustainable Development Goal 16 (SDG 16).

Security Intelligence and SDG16: Applying Machine Learning for Peaceful and Inclusive Societies presents a timely exploration of how machine learning can enhance security intelligence practices. This edited collection brings together insights from experts worldwide, covering core topics such as advanced threat detection algorithms, predictive risk analytics, and automated access control. Beyond technical applications, the volume delves into the ethical, legal, and social dimensions of AI-driven security, providing a comprehensive framework that aligns security intelligence advancements with the SDG 16 objectives of fostering peaceful, inclusive societies. Addressing the needs of policymakers, cybersecurity professionals, and academic researchers, this collection provides the theoretical knowledge and practical insights essential for building ethical and effective security systems.

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.