School of Computing and Innovative Technologies

Optimizing Evacuations Using Agent-Based Modeling

School of Computing and Innovative Technologies

Edge Artificial Intelligence: Foundations, Techniques, and Applications

Secure your expertise in the next wave of computing with this essential book, which provides a comprehensive guide to Edge AI, detailing its foundational concepts, deployment strategies, and real-world applications for revolutionizing performance and privacy across various industries. Edge AI has the potential to bring the computational power of AI algorithms closer to where data is generated, processed, and utilized. Traditionally, AI models are deployed in centralized cloud environments, leading to latency issues, bandwidth constraints, and privacy concerns. Edge AI addresses these limitations by enabling AI inference and decision-making directly on edge devices, such as smartphones, IoT sensors, and edge servers. Despite its challenges, edge AI presents numerous opportunities across various domains. From real-time health monitoring and predictive maintenance in industrial IoT to personalized recommendations in retail and immersive experiences in augmented reality, edge AI has the potential to revolutionize how we interact with technology. This book aims to provide a comprehensive exploration of edge AI, covering its foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, emerging trends, and real-world applications. This guide is essential for anyone pushing the boundaries to leverage edge computing for enhanced performance and efficiency.

School of Communications & Creative Industries

False Memory at the Movies: Experimentally Inducing False Visual Memories Through Sound Design in Two Cuts of a Creative Narrative Fiction Film

In film, some important narrative events are presented by sound alone, leading some audience members to remember seeing events that were not presented visually. The ability of off-screen sound to evoke false visual memories of unseen narrative events has never been empirically tested in an ecologically valid setting. In a unique collaboration between cognitive psychologists and professional filmmakers, we created two experimentally manipulated versions of a narrative short film in which different events were depicted on screen or by sound alone. Adult participants from the broader university community were invited to view a film in a cinema (Stage 1, N = 59, age: M = 32.4, SD = 10.7) or online (Stage 2, N = 68, age: M = 50.3, SD = 11.7). Recognition for visual content was tested immediately after the film screening (Stage 1), or after a delay of at least 1 hr (Stage 2), by presenting short scenes from the film in which some of the previously unseen visual content was presented. In general, people did not report higher confidence for previously unseen visual content presented by sound alone; however, we identified a subset of test events that were susceptible to this effect. In an iterative preregistered design, the test events identified in Stage 1 of the study were preregistered as targets for analysis in Stage 2, where we replicated the false visual recognition memory effect. Confidence for seen/unseen events also differed according to the narrative salience of different events, and audience empathy for the character played a role. These findings are the first to suggest the presence of psychologically real sound-evoked false visual memory in film audiences.

School of Communications & Creative Industries

The Cinematographer’s Canvas Aspect Ratios and Framing the Film

School of Business

Multivariate trait evolution: models for the evolution of the quantitative genetic G-matrix on phylogenies

Genetic covariance matrices (G-matrices) are a key focus for research and predictions from quantitative genetic evolutionary models of multiple traits. There is a consensus among quantitative geneticists that the G-matrix can evolve through deep time. Yet, quantitative genetic models for the evolution of the G-matrix are lacking. In contrast, the field of macroevolution has several stochastic models for univariate traits evolving on phylogenies. However, analytical models of how multivariate trait matrices might evolve on phylogenies have not been considered. Here, we show how three analytical models for matrix evolution can be combined to unify quantitative genetics and macroevolutionary theory in a coherent mathematical framework. The models provide a basis for understanding how G-matrices might evolve on phylogenies. We fit models to data via simulation using Approximate Bayesian Computation. Such models can be used to generate and test hypotheses about the evolution of genetic variances and covariances, together with the evolution of the traits themselves, and how these might vary across a phylogeny. This unification of macroevolutionary theory and quantitative genetics is an advance in the study of phenotypes, allowing for the construction of a synthetic quantitative theory of the evolution of species and multivariate traits over deep time.

School of Business

Global Business Transformation – Innovation, Technology, and Sustainability

Many contemporary business models are now completely based on the idea of a circular economy or sustainability, where they extensively use technology to save resources, become more efficient, and leave a smaller carbon footprint on the planet. Thus, this book aims to bring the discussion of global business transformation, which is a need of the hour, to the forefront and highlight the use of modern technology in actively aiding businesses to become more sustainable. Global Business Transformation: Innovation, Technology, and Sustainability showcases the emerging economy context where innovation and technology are extensively used for business transformation to achieve the Sustainable Development Goals (SDGs). It serves as a comprehensive resource to study the different dimensions of technology, such as AI, data mining, and machine learning from businesses that utilize disruptive technology to achieve sustainability. The book addresses a variety of challenges in the pursuit of global business transformation, which policymakers and experts at all levels of society need to understand well. It also provides an outline of the most pertinent issues and effects that Industry 4.0 is expected to bring to global organizations in the near future and highlights the role of government in streamlining the alignment between SDGs and technology during strategic business transformations globally. It further analyses the possible implications for international business practice and theory and examines the wider repercussions on employment, development, and ethics. Apart from being a valuable resource for researchers, students, and professionals involved in the corporate business, manufacturing, and industrial engineering sectors, this book will also be of interest to those in fields related to economics, psychology, management, strategy, political science, government bodies, sociology, NGOs, and other industrial organizations.

School of Hospitality and Tourism

Platforming Cancel Culture, Digital Media, Identity and Cultural Intersections: Manufacturing Anger: Exploring Discursive Constructions of Cancel Culture on X in India

Platforming Cancel Culture: Digital Media, Identity and Cultural Intersections delves into one of the most polarizing phenomena of the digital age. Bringing together global, intersectional, and interdisciplinary perspectives, this edited collection unpacks the evolving dynamics of cancel culture, examining its practices and implications across diverse political and cultural landscapes. While some hail cancel culture as a tool for social justice, amplifying marginalized voices and calling out systemic inequalities, others critique it as performative virtue signalling or a form of censorship. This book navigates these tensions by analysing the complex interplay of digital platforms and governance mechanisms that shape cancel culture. It explores how platform architectures enable or resist cancel practices, how narratives and media discourses surrounding cancel culture are constructed and contested, and how these dynamics differ across national and cultural contexts. The contributors engage with cutting-edge research and offer localized insights from a range of contexts—including India, South Africa, China, Southeast Europe, the United States, and Russia—to challenge the universalizing assumptions often made about cancel culture. Methodologically diverse, the book employs sentiment and corpus analysis, digital ethnography, interviews, case studies, and critical cultural studies to provide a multifaceted examination of this volatile site of politics and cultural expression. By weaving together perspectives from the humanities, social sciences, and cultural studies, Platforming Cancel Culture presents a nuanced understanding of how cancel culture functions as a driver of accountability and a locus of contested power. This collection is an essential resource for scholars, students, and anyone seeking to critically engage with the intersections of digital media, culture, and identity in the 21st century.

School of Computing and Innovative Technologies

Towards a strengths-based PEER framework: how peer learning behaviours help develop ICT competencies in older adults

As populations age and digital technologies become increasingly embedded in everyday life, fostering ICT competencies among older adults is essential for promoting independence, social inclusion, and well-being. While peer learning has shown promise in supporting digital engagement, existing research often adopts a deficit perspective that emphasises limitations and barriers. This study takes a strengths-based approach, focusing on older adults’ capabilities and agency in peer-led ICT learning environments. Drawing on qualitative data from observations and interviews with learners and peer tutors at an older adult learning organisation, we identify four key strengths-based behaviours – Pioneering, Experiencing, Enabling, and Responding – that support ICT skill development. These behaviours are mapped to corresponding collaborative mindsets, forming the PEER framework. Our findings demonstrate how peer learning can empower older adults through mutual respect, practical engagement, emotional support, and responsiveness to individual needs. The study offers theoretical and practical contributions to the design of ICT programmes that build on older adults’ strengths.

School of Business

An explorative study on intrinsic factors influencing decision-making and turnover intention with gender as moderating factor among call center employees in Malaysia

This study investigates intrinsic factors affecting employee turnover intention in Malaysian call centers, focusing on managerial empowerment, career growth, organizational commitment, and rewards management, with gender as a moderating factor. Drawing on Daniel Pink's motivation theory, which emphasizes autonomy, mastery, and purpose, the study explores how these intrinsic motivators impact employees’ decisions. A quantitative, cross-sectional approach was employed, gathering data via a web-based questionnaire distributed to 320 executive and management-level respondents using convenience sampling. Smart PLS 4 was used to analyze the relationships between variables, yielding key findings. Managerial Behavioral Empowerment (H1) significantly reduced turnover intention (β = -0.426, t = 4.151, p = 0.047), indicating that empowered employees are less inclined to leave, aligning with the importance of autonomy and purpose in retention. Contrary to expectations, Career Growth Opportunities (H2) did not significantly influence turnover intention (β = 0.079, t = 0.673, p = 0.001), raising questions about career development’s perceived value in call centers. Organizational Commitment (H3) negatively impacted turnover intention (β = -0.382, t = 2.222, p = 0.025), emphasizing that committed employees are less likely to leave, consistent with Pink’s purpose-driven motivation. Rewards Management (H4) also had a significant negative effect (β = -0.382, t = 2.311, p = 0.046), highlighting the importance of fair compensation in reducing turnover. Gender (H5) moderated these relationships (β = -0.094, t = 2.587, p = 0.042), indicating gender differences in responses to organizational policies. Findings highlight the roles of managerial empowerment, organizational commitment, and rewards management in reducing turnover, suggesting that organizations should prioritize these areas while also considering gender-specific retention strategies. Additionally, the non-significant impact of career growth suggests a need to better align development opportunities with employee expectations. These insights contribute to turnover theory and offer practical guidance for designing targeted, intrinsic-motivator-based retention interventions.

School of Business

Reducing cross-validation variance through seed blocking in hyperparameter tuning

Hyperparameter tuning plays a crucial role in optimizing the performance of predictive learners. Cross-validation (CV) is a widely adopted technique for estimating the error of different hyperparameter settings. Repeated cross-validation (RCV) is commonly employed to reduce the variability of CV errors. This study investigates the efficacy of blocking cross-validation partitions and algorithm initialization seeds during hyperparameter tuning. The proposed approach, termed Controlled Cross-Validation (CCV), reduces variability in error estimates, enabling fairer and more reliable comparisons of predictive model performance. We provide both theoretical and empirical evidence to demonstrate that this blocking approach lowers the variance of the estimates compared to RCV. Our experiments indicate that the algorithm’s internal random behavior often does not significantly affect CV error variability. We present extensive examples using real-world datasets to compare the effectiveness and efficiency of blocking the CV partitions when tuning the hyperparameters of different supervised predictive learning algorithms.