Artificial intelligence in cyber physical systems

This article conducts a literature review of current and future challenges in the use of artifcial 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 justifcations of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework

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

Review of algorithms for artificial intelligence on low memory devices

The aim of the article is to conceptualise a more compact and efficient version of algorithms for artificial intelligence (AI). The core objective is to construct the design for a self-optimising and self-adapting autonomous artificial intelligence (AutoAI) that can be applied for edge analytics using real-time data. The methodology is based on synthesising existing knowledge on AI (i.e., knowledge modelling, symbolic reasoning, modal logic), with novel concepts from neuromorphic engineering in combination with deep learning algorithms (i.e., reinforcement learning, neural networks, evolutionary algorithms) and data science (i.e., statistics, linear regression, Bayesian methods). Far-reaching implications are expected from the unique integration of approaches in neuromorphic engineering and edge analytics

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

Objective: Identify and review alternative (home-based) therapies for prolonged lockdowns. Method: Interdisciplinary study using multi-method approach – case study, action research, grounded theory. Only secondary data has been used in this study. Results: 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. Conclusions: Alternative therapies can be integrated in healthcare systems as home-based solutions operating on low-cost technologies.</p

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.</p

Dance Sport Movement Therapy in the Metaverse: A New Frontier for Alternative Mental Health Therapies

This paper delves into the innovative integration of Dance Movement Therapy (DMT) within extended reality (XR) environments, exploring its potential as a non-pharmacological intervention for mental health conditions. The study employed a blend of qualitative evidence synthesis and meta-analyses of primary quantitative data, focusing on the therapeutic implications of dance in virtual, augmented, and mixed realities. Utilising wearables and sensors, real-time data on participants&apos; movements, physiological responses, and emotional feedback were collected and analysed using AI/ML algorithms, including Random Forest, SVM, CNNs, and RNNs. The research highlighted the importance of data privacy and ethical considerations, emphasising the need for securely storing metadata to ensure user trust and legal compliance. Findings underscored the potential of XR environments like the Metaverse in transforming mental health practices, offering efficient, engaging, and effective therapeutic interventions. The study also introduced the novel concept of Physical Intensity Matching and the significance of personalised exercise selection. Despite its ground-breaking insights, the research acknowledged potential biases introduced by wearables and the challenges of ensuring data accuracy. This paper is a foundational exploration into the convergence of DMT, XR, and AI, paving the way for future interdisciplinary research in mental health and technology.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

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

Cyber-attacks on Public Key Cryptography

Data records on scientific publication on Cyber-attacks on Public Key CryptographyTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV