HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, learning forgetting caused by streaming industrial data, and scarce communication bandwidth remain key barriers to realize practical FL. Facing the above three challenges simultaneously, this paper presents a high-performance and efficient system named HFEDMS for incorporating practical FL into Industrial Metaverse. HFEDMS reduces data heterogeneity through dynamic grouping and training mode conversion (Dynamic Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics and calibrates classifier parameters (Semantic Compression and Compensation, SCC). Finally, the network parameters of the feature extractor and classifier are synchronized in different frequencies (Layer-wiseAlternative Synchronization Protocol, LASP) to reduce communication costs. These techniques make FL more adaptable to the heterogeneous streaming data continuously generated by industrial equipment, and are also more efficient in communication than traditional methods (e.g., Federated Averaging). Extensive experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show that HFEDMS improves the classification accuracy by at least 6.4% compared with 8 benchmarks and saves both the overall runtime and transfer bytes by up to 98%, proving its superiority in precision and efficiency.Comment: This paper is submitted to IEEE Transaction on Cloud Computin

Virtual reality in theatre education and design practice - new developments and applications

The global use of Information and Communication Technologies (ICTs) has already established new approaches to theatre education and research, shifting traditional methods of knowledge delivery towards a more visually enhanced experience, which is especially important for teaching scenography. In this paper, I examine the role of multimedia within the field of theatre studies, with particular focus on the theory and practice of theatre design and education. I discuss various IT applications that have transformed the way we experience, learn and co-create our cultural heritage. I explore a suite of rapidly developing communication and computer-visualization techniques that enable reciprocal exchange between students, theatre performances and artefacts. Eventually, I analyse novel technology-mediated teaching techniques that attempt to provide a new media platform for visually enhanced information transfer. My findings indicate that the recent developments in the personalization of knowledge delivery, and also in student-centred study and e-learning, necessitate the transformation of the learners from passive consumers of digital products to active and creative participants in the learning experience

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The Consolidated Research Group MATHMODE (IT1456-22

Computational inference and control of quality in multimedia services

Quality is the degree of excellence we expect of a service or a product. It is also one of the key factors that determine its value. For multimedia services, understanding the experienced quality means understanding how the delivered delity, precision and reliability correspond to the users' expectations. Yet the quality of multimedia services is inextricably linked to the underlying technology. It is developments in video recording, compression and transport as well as display technologies that enables high quality multimedia services to become ubiquitous. The constant evolution of these technologies delivers a steady increase in performance, but also a growing level of complexity. As new technologies stack on top of each other the interactions between them and their components become more intricate and obscure. In this environment optimizing the delivered quality of multimedia services becomes increasingly challenging. The factors that aect the experienced quality, or Quality of Experience (QoE), tend to have complex non-linear relationships. The subjectively perceived QoE is hard to measure directly and continuously evolves with the user's expectations. Faced with the diculty of designing an expert system for QoE management that relies on painstaking measurements and intricate heuristics, we turn to an approach based on learning or inference. The set of solutions presented in this work rely on computational intelligence techniques that do inference over the large set of signals coming from the system to deliver QoE models based on user feedback. We furthermore present solutions for inference of optimized control in systems with no guarantees for resource availability. This approach oers the opportunity to be more accurate in assessing the perceived quality, to incorporate more factors and to adapt as technology and user expectations evolve. In a similar fashion, the inferred control strategies can uncover more intricate patterns coming from the sensors and therefore implement farther-reaching decisions. Similarly to natural systems, this continuous adaptation and learning makes these systems more robust to perturbations in the environment, longer lasting accuracy and higher eciency in dealing with increased complexity. Overcoming this increasing complexity and diversity is crucial for addressing the challenges of future multimedia system. Through experiments and simulations this work demonstrates that adopting an approach of learning can improve the sub jective and objective QoE estimation, enable the implementation of ecient and scalable QoE management as well as ecient control mechanisms

Online Learning Communities for Creative Practice

This research project proposes to model the activities and roles of a visiting Research Fellow and an Artist-in-Residence (AIR) with the intent of applying the key educational features and strategies to the online environment. Where feasible, the aim is replicate the role of a Research Fellow online by enlisting the services of well-known artists to contribute their expertise and creative input to the teaching activities of a University School of Art. The primary purpose is to support and enhance the delivery of quality learning outcomes for the Curtin BA (Art) Online degree. The project presents an opportunity to establish wider contact with audiences that have an interest in interacting with an online AIR site to access or contribute research materials and participate in creative activities.In the online environment students are empowered to learn both autonomously as well as actively explore opportunities to teach one another. This emphasis on independent learning is particularly prevalent when asynchronous discussion groups (bulletin boards) are used as an integral part of the learning experience. Students are given the incentive to explain, share, comment, critique, and develop course materials among themselves in ways rarely seen in a traditional classroom setting. The use of electronic alternatives to face-to-face dialogue often results in high quality discussions as students often refer to course materials and reflect on their answers before responding to the lecturer's questions or to classmates' comments. As a result, students have the opportunity to post well-considered comments without experiencing the immediate demands of in-class discussions.The potential of online learning communities will be examined in terms of fostering independent self-directed learning and to encourage online mentoring. Existing examples of practice in online learning will be considered with a view to devising a suitable model for application to online learning communities engaged in creative practices.Of equal importance, the project represents an example of how Curtin is able to form unique collaborations between divergent areas of interest. In this instance, the partnership combines the expertise of the Faculty of Built Environment (BEAD), the School of Art and Design, and the Learning Support Network (LSN)

Preaching to the Empty Pew: The Lived Experiences in Preaching among Seventh-day Adventist Pastors during the COVID-19 Pandemic in Singapore

Preaching is fundamental to the ministry of a pastor. It is the heart of pastoral service anywhere. Challenges in preaching and novel approaches in homiletical arts have been extensively studied. However, the COVID-19 pandemic brought a new paradigm to the field of homiletics that is worthy of investigation. This study is a pilot project investigating the experience of Seventh-day Adventist pastors preaching during the pandemic. The loss of human interaction, adaptation to technology, the change of duration, and emerging personal stress due to numerous limitations in preaching during the pandemic are the experience that participants expressed in the study. These findings highlight the need for balancing the dynamic relationship between humans and technology while providing a reminder of one the most critical aspect of homiletics, and that is the preacher themselves. Keywords: Homiletics, Pastors, COVID-19 Pandemic, Technolog