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This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

The purpose of this study is to identify global trends in disruptive technological change and map the social and policy implications, particularly as they relate to the educational ecosystem and main stakeholders across all levels of education.

The authors conducted a two-stage meta-analysis of 1,155 scholarly, peer-reviewed articles. The investigation involves a systematized literature review for data identification and collation adhering to defined selection criteria, and a network analysis to scrutinize data, consolidate information and unveil correlations and patterns from the literature review to produce a set of recommendations.

The study unveiled educational trends related to disruptive technologies and delineated four principal clusters representing how these technologies are transforming the education ecosystem. Additionally, a series of transversal aspects that reveal a societal vulnerability toward future prospects in the realms of ethics, sustainability, resilience, security, and policy were identified.

The findings spotlight an enlarging chasm between industry (and society at large) and conventional education, where many transformations triggered by disruptive technologies remain absent from teaching and learning systems. The study further offers recommendations and envisions potential scenarios, urging stakeholders to respond based on their positions concerning disruptive technologies.

Expanding from the meta-analysis of pertinent literature, this paper offers four collections of curated resources, four mini case studies and four scenarios for policymakers and local communities to consider, enabling them to plot courses for their optimal futures.

This case study paper aims to explore the complexities and challenges of epidemic response and public health surveillance in Native American and Indigenous American communities in the United States and find viable solutions. This paper explores these topics through the emergence and impact of the hantavirus pulmonary syndrome (HPS) within the Navajo Nation in the United States using critical incident analysis and best practices.

This project is a case study paper based on a topical review of the literature. A topical review of the literature is a comprehensive exploration of the current body of knowledge within a particular research field. It is an important tool used by scholars and practitioners to further the development of existing knowledge as well as to identify potential directions for future research (Fourie, 2020). Such a paper can provide a useful insight into the various aspects of the process that the researcher may have overlooked, as well as highlighting potential areas of improvement (Gall et al., 2020). It can also provide a useful source of ideas and inspiration for the researcher as it can provide an overview of the various approaches used by other researchers in the field (Göpferich, 2009). Case study papers using a topical review of the literature have been used to help frame and inform research topics, problems and best practices for some time. They are typically used to explore a topic in greater depth and to provide an overview of the literature to improve the world of practice to provide a foundation for future comprehensive empirical research. Case study papers can provide research value by helping to identify gaps in the literature and by providing a general direction for further research. They can also be used to provide a starting point for research questions and hypotheses and to help identify potential areas of inquiry.

This study explores best practices in public health surveillance and epidemic response that can help strengthen public health infrastructure by informing the development of effective surveillance systems and emergency response plans, as well as improving data collection and analysis capabilities within Native American and Indigenous American communities in the United States that also have the option to include new technologies like artificial intelligence (AI) with similar outbreaks in the future.

The literature review did not include any primary data collection, so the existing available research may have limited the findings. The scope of the study was limited to published literature, which may not have reported all relevant findings. For example, unpublished studies, field studies and industry reports may have provided additional insights not included in the literature review. This research has significant value based on the limited amount of studies on how infectious diseases can severely impact Native American communities in the United States, leading to unnecessary and preventable suffering and death. As a result, research on viable best practices is needed on the best practices in public health surveillance and epidemic response in Native American and Indigenous American communities through historical events and critical incident analysis.

Research on public health surveillance and epidemic response in Native American communities can provide insights into the challenges faced by these communities and help identify potential solutions to improve their capacity to detect, respond to and prevent infectious diseases using innovative approaches and new technologies like AI.

More research on public health surveillance and epidemic response can inform policies and interventions to improve access to healthcare for Native American populations, such as increasing availability of healthcare services, providing culturally appropriate health education and improving communication between providers and patients. By providing better public health surveillance and response capacity, research can help reduce the burden of infectious diseases in Native American communities and ultimately lead to improved public health outcomes.

The purpose of this paper is to discuss changes in the roles of librarians against the backdrop of emerging technologies.

Through reflection on practice drawn from the author’s 30 years in librarianship, this paper explores the evolving role of librarians in the constantly developing technological environment and demonstrates how librarians can combine information resources, technology and research assistance in these constantly changing spaces. It also explores how technology enhances the role of the librarian, with specific reference to ChatGPT.

The author stressed the need for library courses for credit to allow for feedback, assessment and critical thinking. The need for continuing professional development for librarians was highlighted. Additionally, areas such as Scholarly Publishing, Metrics and Analytics, Academic Integrity and Intellectual Property were identified as areas of focus for the 21st century Librarian.

This study is limited to changes in roles as a result of emerging technologies which impact librarians. It is set in the Caribbean.

This paper is relevant for librarians, regardless of their geographical location, who are also required to be fluid, keep on the cutting edge, adapt and adopt to deliver service in the face of constantly advancing technology.

This paper is a reflection on an original experience from a Caribbean territory. it also explores recent developments in technology, more specifically ChatGPT, and its impact on the roles of librarians. It provides contributions to the ongoing discourse on the evolving role of the librarian from a different perspective.

This research analyses challenges faced by users at various levels in planning and designing participatory simulation models of cities. It aims to identify issues that hinder experts from maximising the effectiveness of the SUMO tool. Additionally, evaluating current methods highlights their strengths and weaknesses, facilitating the use of participatory simulation advantages to address these issues. Finally, the presented case studies illustrate the diversity of user groups and emphasise the need for further development of blueprints.

In this research, action research was used to assess and improve a step-by-step guideline. The guideline's conceptual design is based on stakeholder analysis results from those involved in developing urban logistics scenarios and feedback from potential users. A two-round process of application and refinement was conducted to evaluate and enhance the guideline's initial version.

The guidelines still demand an advanced skill level in simulation modelling, rendering them less effective for the intended audience. However, they have proven beneficial in a simulation course for students, emphasising the importance of developing accurate conceptual models and the need for careful implementation.

This paper introduces a step-by-step guideline designed to tackle challenges in modelling urban logistics scenarios using SUMO simulation software. The guideline's effectiveness was tested and enhanced through experiments involving diverse groups of students, varying in their experience with simulation modelling. This approach demonstrates the guideline's applicability and adaptability across different skill levels.

The purpose of this research is to explore how one unsanctioned community sport organization (CSO), AM Hockey, sought to acquire legitimacy in a highly institutionalized minor hockey marketplace at various points in its organizational life cycle.

This study was guided by instrumental case study methodology. Twenty (20) AM Hockey stakeholders from a variety of roles (e.g. executives, program directors and coaches) were interviewed. Document analysis was also utilized to supplement the interviewees. Internal and public documents reflective of the CSO's creation and growth were obtained.

Findings revealed that the CSO had to navigate distinct phases of evolution including the Building, Growth, Competition and Stabilization phases. Although the four life cycle phases identified in this study share similarities with the phases identified by Lester et al. (2003), findings indicated that institutional work mechanisms must be understood in their context as they can vary over the life cycle of an organization. Therefore, start-up sports organizations must approach the pursuit of legitimacy as a continual process rather than something acquired and defended through maintenance work.

Developing legitimacy remains a central challenge for CSOs that seek to deliver alternative sport programming, yet it continues to be understudied. Ultimately, the long-term viability of an unsanctioned CSO in a federated sports system relies, in part, on its ability to continually determine the actions needed to achieve legitimacy within its environment.

This chapter presents a survey of education development in Oceania, a region of diversity held together by its commonalities, shaped by the largest ocean on the planet. The chapter outlines the regional contexts of Oceania and offers a brief historical overview of formal education. Oceania, like most regions, has struggled to mediate between global agendas and national and regional aspirations for sovereignty and self-determination. The chapter recounts ongoing efforts to navigate education in the post-colonial period, efforts to negotiate some of the aspirations of the Millennium Development Goals (MDGs), Education for All (EFA), and other global agendas of the early 2000s with, more recently, the Sustainable Development Goals (SDGs). In this survey, we hope to demonstrate collective efforts to respond to global agendas, to shape and strengthen regionalism, while maintaining sovereignty in a globalized world. We also highlight the evolving identities of the region, in particular the relationships between Australia and Aotearoa New Zealand and the Pacific countries that collectively make up Oceania.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.