Accessibility and inclusiveness of new information and communication technologies for disabled users and content creators in the Metaverse.

PURPOSE: The study critically reassesses existing Metaverse concepts and proposes a novel framework for inclusiveness of physically disabled artists. The purpose is to enable and inspire physically disabled users and content creators to participate in the evolving concept of the Metaverse. The article also highlights the need for standards and regulations governing the inclusion of people with disabilities in Metaverse projects. MATERIALS AND METHODS: The study examines current information technologies and their relevance to the inclusion of physically disabled individuals in the Metaverse. We analyse existing Metaverse concepts, exploring emerging information technologies such as Virtual and Augmented Reality, and the Internet of Things. The emerging framework in the article is based on the active involvement of disabled creatives in the development of solutions for inclusivity. RESULTS: The review reveals that despite the proliferation of Blockchain Metaverse projects, the inclusion of physically disabled individuals in the Metaverse remains distant, with limited standards and regulations in place. The article proposes a concept of the Metaverse that leverages emerging technologies, to enable greater engagement of disabled creatives. This approach is designed to enhance inclusiveness in the Metaverse landscape. CONCLUSIONS: Active involvement of physically disabled individuals in the design and development of Metaverse platforms is crucial for promoting inclusivity. The framework for accessibility and inclusiveness in decentralised Metaverses provides a basis for the meaningful participation of disabled creatives. The article emphasises the importance of addressing the mechanisms for art production by individuals with disabilities in the emerging Metaverse landscape.IMPLICATIONS FOR REHABILITATIONThis article addresses a global challenge related to helping disabled people operate in the modern society, targeting new and emerging technologies, and enabling early understanding of required actions for inclusiveness of people with disabilities in the Metaverse.The increased use of advanced technologies (e.g., AI and IoT) in the Metaverse, amplified the importance of this research being conducted.The aggregate impact from this research for science and society is a more inclusive, and unbiassed Metaverses that are compliant with regulations on anti-disability discrimination. This is followed by the secondary values, related to increased technological opportunities from a breakthrough in designing new, more inclusive, and autonomous devices.The research study presents a new framework for integrating new technologies in existing Metaverses, resulting with a stronger accessibility and inclusiveness of the Metaverse. This creates a new understanding on how new technologies can be used for disability discrimination prevention and early understanding of disability requirements. We also highlighted normative constraints and the need for further reflection and weighing to avoid dystopian futures for the physically disabled in relation to the Metaverse.

Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0.

Objective: The objective of this theoretical paper is to identify conceptual solutions for securing, predicting, and improving vaccine production and supply chains. Method: The case study, action research, and review method is used with secondary data - publicly available open access data. Results: A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event. A new conceptual framework is designed to integrate the emerging solutions in vaccine production and supply chains. The framework is constructed to improve the state-of-the-art by intersecting the previously isolated disciplines of edge computing; cyber-risk analytics; healthcare systems, and AI algorithms. Conclusion: For healthcare systems to cope better during a disease X event than during Covid-19, we need multiple highly specific AI algorithms, targeted for solving specific problems. The proposed framework would reduce production and supply chain risk and complexity in a Disease X event.

New and emerging forms of data and technologies: literature and bibliometric review.

With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). This article conducts a literature review and bibliometric analysis of existing research records on new and emerging forms of multimedia data. The literature review engages with qualitative search of the most prominent journal and conference publications on this topic. The bibliometric analysis engages with statistical software (i.e. R) analysis of Web of Science data records. The results are somewhat unexpected. Despite the special relationship between the US and the UK, there is not much evidence of collaboration in research on this topic. Similarly, despite the negative media publicity on the current relationship between the US and China (and the US sanctions on China), the research on this topic seems to be growing strong. However, it would be interesting to repeat this exercise after a few years and compare the results. It is possible that the effect of the current US sanctions on China has not taken its full effect yet.

Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2).

This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms - i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.

Super-forecasting the 'technological singularity' risks from artificial intelligence.

This article investigates cybersecurity (and risk) in the context of 'technological singularity' from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently. Supplementary Information: The online version contains supplementary material available at 10.1007/s12530-022-09431-7.

Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X.

Artificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology-that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

What Country, University, or Research Institute, Performed the Best on Covid-19 During the First Wave of the Pandemic?: Bibliometric analysis of scientific literature - analysing a 'snapshot in time' of the first wave of COVID-19.

In this article, we conduct data mining and statistical analysis on the most effective countries, universities, and companies, based on their output (e.g., produced or collaborated) on COVID-19 during the first wave of the pandemic. Hence, the focus of this article is on the first wave of the pandemic. While in later stages of the pandemic, US and UK performed best in terms of vaccine production, the focus in this article is on the initial few months of the pandemic. The article presents findings from our analysing of all available records on COVID-19 from the Web of Science Core Collection. The results are compared with all available data records on pandemics and epidemics from 1900 to 2020. This has created interesting findings that are presented in the article with visualisation tools. Supplementary information: The online version contains supplementary material available at 10.1007/s40745-022-00406-8.

Alternative mental health therapies in prolonged lockdowns: narratives from Covid-19.

Identify and review alternative (home-based) therapies for prolonged lockdowns. Interdisciplinary study using multi-method approach - case study, action research, grounded theory. Only secondary data has been used in this study. Epistemological framework based on a set of digital humanities tools. The set of tools are based on publicly available, open access technological solutions, enabling generalisability of the findings. Alternative therapies can be integrated in healthcare systems as home-based solutions operating on low-cost technologies.

The ethics of shared Covid-19 risks: an epistemological framework for ethical health technology assessment of risk in vaccine supply chain infrastructures.

This article addresses the topic of shared responsibilities in supply chains, with a specific focus on the application of the Internet of Things (IoT) in e-health environments, and Industry 4.0 issues-concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. In this article, we critically review methodologies and guidelines that have been proposed to approach these ethical aspects in digital supply chain settings. The emerging framework presents new findings on how digital technologies affect vaccine shared supply chain systems. Through epistemological analysis, the article derives new insights for transparency and accountability of supply chain cyber risk from Internet of Things systems. This research devises a framework for ethical awareness, assessment, transparency and accountability of the emerging cyber risk from integrating IoT technologies on shared Covid-19 healthcare supply chain infrastructure.

Epistemological Equation for Analysing Uncontrollable States in Complex Systems: Quantifying Cyber Risks from the Internet of Things.

The Internet-of-Things (IoT) triggers data protection questions and new types of cyber risks. Cyber risk regulations for the IoT, however, are still in their infancy. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. At present, there are no self-assessment methods for quantifying IoT cyber risk posture. It is considered that IoT represent a complex system with too many uncontrollable risk states for quantitative risk assessment. To enable quantitative risk assessment of uncontrollable risk states in complex and coupled IoT systems, a new epistemological equation is designed and tested though comparative and empirical analysis. The comparative analysis is conducted on national digital strategies, followed by an empirical analysis of cyber risk assessment approaches. The results from the analysis present the current and a target state for IoT systems, followed by a transformation roadmap, describing how IoT systems can achieve the target state with a new epistemological analysis model. The new epistemological analysis approach enables the assessment of uncontrollable risk states in complex IoT systems-which begin to resemble artificial intelligence-and can be used for a quantitative self-assessment of IoT cyber risk posture.

Artificial intelligence in cyber physical systems.

This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc.). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge.

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.

COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine.

OBJECTIVES: Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. METHOD: This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. RESULTS: Most countries' digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual's location databases, code scanning and individual's online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on social machines and connected devices. SOLUTIONS: The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (Internet of Things). CONCLUSION: Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Data mining and analysis of scientific research data records on Covid-19 mortality, immunity, and vaccine development - In the first wave of the Covid-19 pandemic.

BACKGROUND AND AIMS: Covid-19 is a global pandemic that requires a global and integrated response of all national medical and healthcare systems. Covid-19 exposed the need for timely response and data sharing on fast spreading global pandemics. In this study, we investigate the scientific research response from the early stages of the pandemic, and we review key findings on how the early warning systems developed in previous epidemics responded to contain the virus. METHODS: We conducted data mining of scientific literature records from the Web of Science Core Collection, using the topics Covid-19, mortality, immunity, and vaccine. The individual records are analysed in isolation, and the analysis is compared with records on all Covid-19 research topics combined. The data records are analysed with commutable statistical methods, including R Studio's Bibliometrix package, and the Web of Science data mining tool. RESULTS: From historical analysis of scientific data records on viruses, pandemics and mortality, we identified that Chinese universities have not been leading on these topics historically. However, during the early stages of the Covid-19 pandemic, the Chinese universities are strongly dominating the research on these topics. Despite the current political and trade disputes, we found strong collaboration in Covid-19 research between the US and China. From the analysis on Covid-19 and immunity, we wanted to identify the relationship between different risk factors discussed in the news media. We identified a few clusters, containing references to exercise, inflammation, smoking, obesity and many additional factors. From the analysis on Covid-19 and vaccine, we discovered that although the USA is leading in volume of scientific research on Covid-19 vaccine, the leading 3 research institutions (Fudan, Melbourne, Oxford) are not based in the USA. Hence, it is difficult to predict which country would be first to produce a Covid-19 vaccine. CONCLUSIONS: We analysed the conceptual structure maps with factorial analysis and multiple correspondence analysis (MCA), and identified multiple relationships between keywords, synonyms and concepts, related to Covid-19 mortality, immunity, and vaccine development. We present integrated and corelated knowledge from 276 records on Covid-19 and mortality, 71 records on Covid-19 and immunity, and 189 records on Covid-19 vaccine.

What Determines the Perception of Segmentation in Contemporary Music?

BACKGROUND: This study concerns the perception of musical segmentation during listening to live contemporary classical music. Little is known about how listeners form judgments of musical segments, particularly when typical section markers, such as cadences and fermatas, are absent [e.g., Sears et al. (2014)] or when the music is non-tonal (e.g., in much contemporary classical music). AIMS: The current study aimed to examine the listeners' segmentation decisions in a piece of contemporary music, Ligeti's "Fanfares"? METHODS: Data were gathered using a smartphone application [Practice & Research in Science & Music (PRiSM) Perception App] designed for this study by the Royal Northern College of Music (RNCM) Centre for PRiSM and the Oxford e-Research Centre. A total of 259 audience participants were asked to "tap" when they felt that a section had ended. Subjective responses were captured, as well as contextual data about the participants. RESULTS: The audience members demonstrated high levels of agreement regarding segmentation, mostly at places in the music involving breaks in the musical texture (one piano hand resting), changes in dynamic (volume), and changes in register/pitch. A sense of familiarity with contemporary repertoire did seem to influence the responses-the participants who self-reported being familiar with contemporary music used a wider range of cues to make their segmentation decisions. The self-report data analysis suggested that the listeners were not always aware of how they made decisions regarding segmentation. The factors which may influence their judgment of musical segmentation are, to some extent, similar to those identified by music analysis (Steinitz, 2011) but different in other ways. The effect of musical training was found to be quite small. CONCLUSION: Whether musically trained and/or familiar with contemporary classical music or not, the listeners demonstrate commonalities in segmentation, which they are not always aware of. This study has implications for contemporary composers, performers, and audiences and how they engage with new music in particular.

Structuring research methods and data with the research object model: genomics workflows as a case study.

BACKGROUND: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. RESULTS: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as "which particular data was input to a particular workflow to test a particular hypothesis?", and "which particular conclusions were drawn from a particular workflow?". CONCLUSIONS: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well. AVAILABILITY: The Research Object is available at http://www.myexperiment.org/packs/428 The Wf4Ever Research Object Model is available at http://wf4ever.github.io/ro.

Opening new gateways to workflows for life scientists.

The combination of highly complex biology problems and varying IT skills among life scientists poses a unique challenge in designing bioinformatics programs. The set of tools and initiatives described in this work shows new ways of making life science workflows more accessible to the community. Our aim is to help bioinformaticians help biologists. We present how to make Taverna workflows available from within Galaxy, both widely used bioinformatics platforms. Calling Galaxy tools from Taverna is also discussed. In addition, we describe a web application that allows a user to run arbitrary Taverna workflows by only using a web browser.

A semantic sensor web for environmental decision support applications.

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England.

An e-Research approach to Web-scale music analysis.

The growing quantity of digital recorded music available in large-scale resources such as the Internet archive provides an important new resource for musical analysis. An e-Research approach has been adopted in order to create a very substantive web-accessible corpus of musical analyses in a common framework for use by music scholars, students and beyond, and to establish a methodology and tooling that will enable others to add to the resource in the future. The enabling infrastructure brings together scientific workflow and Semantic Web technologies with a set of algorithms and tools for extracting features from recorded music. It has been used to deliver a prototype system, described here, that demonstrates the utility of LINKED DATA for enhancing the curation of collections of music signal data for analysis and publishing results that can be simply and readily correlated to these and other sources. This paper describes the motivation, infrastructure design and the proof-of-concept case study and reflects on emerging e-Research practice as researchers embrace the scale of the Web.

Elements of a computational infrastructure for social simulation.

Applications of simulation modelling in social science domains are varied and increasingly widespread. The effective deployment of simulation models depends on access to diverse datasets, the use of analysis capabilities, the ability to visualize model outcomes and to capture, share and re-use simulations as evidence in research and policy-making. We describe three applications of e-social science that promote social simulation modelling, data management and visualization. An example is outlined in which the three components are brought together in a transport planning context. We discuss opportunities and benefits for the combination of these and other components into an e-infrastructure for social simulation and review recent progress towards the establishment of such an infrastructure.