Industry 5.0 improves human-machine collaboration by integrating Artificial Intelligence (AI) and Extended Reality (XR) into industrial environments. This paper presents a structured Reference Architecture for a system that addresses industrial environments within the Industry 5.0 paradigm, iteratively refined based on evolving business and technical requirements. Designed for scalability, interoperability, and flexibility, it enables seamless interaction between XR applications, AI-powered decision support tools, and industrial systems. The architecture incorporates an enterprise architecture perspective that addresses industry-driven use cases and user scenarios from six pilot applications. Key components, including XR applications, AI recognition models, digital twins (DTs), and an orchestration hub, are outlined. The business layer is built on user stories that reflect real-world industrial needs from various sectors, and future directions for extending the architecture within Industry 5.0 ecosystems are discussed.

Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users' posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data. The latter highlights the emerging need for the implementation and utilization of domain-agnostic and multilingual solutions. To investigate a portion of these challenges this research work performs a comparative analysis of multilingual approaches for classifying both the sentiment and the text of an examined multilingual corpus. In this context, four multilingual BERT-based classifiers and a zero-shot classification approach are utilized and compared in terms of their accuracy and applicability in the classification of multilingual data. Their comparison has unveiled insightful outcomes and has a twofold interpretation. Multilingual BERT-based classifiers achieve high performances and transfer inference when trained and fine-tuned on multilingual data. While also the zero-shot approach presents a novel technique for creating multilingual solutions in a faster, more efficient, and scalable way. It can easily be fitted to new languages and new tasks while achieving relatively good results across many languages. However, when efficiency and scalability are less important than accuracy, it seems that this model, and zero-shot models in general, can not be compared to fine-tuned and trained multilingual BERT-based classifiers.

Edge computing has become a prominent solution when it comes to mobile applications and data management, due to its ability to considerably reduce data transmission costs, and to analyze data requiring fewer computing resources, since the analysis occurs at lower data volumes, without having to relocate data to centralized infrastructures. One major challenge, indicates the optimal data placement regarding data-intensive applications and, in general, applications requiring vast transmission of large amount of data. In this paper, we propose a novel user mobility-based data placement strategy, considering a trade-off between latency and data migration, which has not been investigated before. We classify the users into three mobility classes; namely static, local or mobile, via the use of a causal-aware Deep Learning network. This information is then exploited in order to optimize the data placement through specific data placement and retrieval algorithms for each mobility class. We evaluate the performance of the proposed solution using simulations, and prove that our solution manages to reduce the average data accessing cost by 60% for static or local users and 10% for mobile users, while the average path length is reduced by 50% for static and local users, and by 12% for mobile users.

The extraction of useful knowledge from collected data has always been the holy grail for enterprises and researchers, supporting efficient decision making, provided service's optimization and profit maximization. However, this task is easier said than done, since it presupposes the application of complex mathematical models/algorithms. Data Analysis has prospered due to the continuous demand to simplify and optimize the knowledge extraction process. Several mechanisms in different domains have been developed, consisting of various techniques to analyze specific data. The need for such mechanisms is even greater in healthcare, since there exist data of different complexity that may provide high-valuable knowledge, if properly analyzed. Considering these challenges, this paper proposes a mechanism for performing Data Analysis in diverse scenarios' healthcare data to extract valuable insights. The mechanism can collect data and apply several Machine Learning algorithms to ensure the best result about the prediction of certain features of the provided data.

The era of big data is surrounded by plenty of challenges, concerning aspects related to data quality, data management, and data analysis. Plenty of these challenges are met in several domains, such as the healthcare domain, where the corresponding healthcare platforms not only have to deal with managing and/or analyzing a tremendous quantity of health data, but also have to accomplish these actions in the most efficient and secure way possible. Towards this direction, medical institutions are paying attention to the replacement of traditional approaches such as the Monolithic and Service Oriented Architecture (SOA), which deal with many difficulties for handling the increasing amount of healthcare data. This paper presents a platform for overcoming these issues, by adopting the Microservice Architecture (MSA), being able to efficiently manage and analyze these vast amounts of data. More specifically, the proposed platform, namely beHEALTHIER, offers the ability to construct health policies out of data of collective knowledge, by utilizing a newly proposed kind of electronic health records (i.e., eXtended Health Records (XHRs)) and their corresponding networks, through the efficient analysis and management of ingested healthcare data. In order to achieve that, beHEALTHIER is architected based upon four (4) discrete and interacting pillars, namely the Data, the Information, the Knowledge and the Actions pillars. Since the proposed platform is based on MSA, it fully utilizes MSA's benefits, achieving fast response times and efficient mechanisms for healthcare data collection, processing, and analysis.

Health Information Exchange (HIE) allows healthcare providers and citizens, to access and securely share healthcare information electronically, improving the speed, quality, safety, and cost of patient care. Exchange of this information can be achieved through a wired or wireless way, at close or long distances, achieving different goals in terms of transmission speed, exchange reliability and data transfer security. While HIE cannot replace provider-patient communication, it can greatly improve the completeness of patients' records. Most of the current research is devoted on exchanging health information among healthcare organizations, without giving the ability to the citizens on exchanging healthcare data with healthcare organizations and be able to manipulate this data, mainly due to lack of standardization and security guarantees. Towards the goal of HIE, and the ability of citizens to have access to their healthcare data, in this paper two different wireless communication protocols (Remote-to-Device (R2D) and Device-to-Device (D2D)) are specified that can be used by software applications. The goal of the R2D protocol is to facilitate the acquirement of healthcare data of a citizen from an Electronic Health Record (EHR) through internet connection, while the D2D protocol aims on facilitating the exchange of this health data among citizens and healthcare professionals, on top of Bluetooth.

The healthcare sector faces multiple challenges in implementing, maintaining and upgrading its systems, including technical, security and interoperability difficulties. Ontologies are the key to achieve and improve healthcare interoperability, in the form of a permanent artefact of the specification of each of the medical concepts. However, since different ontologies may have contradicting or overlapping parts, the need for the creation of ontology matching techniques for identifying similarities between them is rising. In the meantime, a large number of ontologies, vocabularies and taxonomies, as well as medical standards are constructed and globally adopted (e.g. HL7 FHIR), without promising the existence of a single global ontology, any time soon. Thus, a substantial overhaul of methodology is required to address the real complexity of health. This can be achieved through the FHIR Ontology Mapper (FOM) technique that is presented in this paper, which aims at constructing healthcare ontologies from any data source, and then identifying both the structural and semantic similarities between the latter and the HL7 FHIR resources ontologies. The final matchings derive from the mean of the latter, aiming at translating healthcare data of any type, into the widely adopted HL7 FHIR standard, thus enabling interoperability and improving the quality of patient care and research.

Interoperability between healthcare information systems is a challenge for the identification of important health-related events (through correlation of information across systems) and as a result for improving the patient care quality. Most of these systems are far from being interoperable and operate independently, whilst the value emerging from their exploitation is limited. While several techniques to address this challenge are based on medical standards and technologies, these techniques are not applicable to different scenarios, thus a holistic solution is needed. This paper focuses on the semantic interoperability of multiple electronic health records' (EHRs) - and their standards, proposing a multi-step generic semantic architecture that can be implemented simultaneously to different medical standards, for efficiently managing heterogeneous EHRs' data. The proposed architecture combines a mechanism for extracting Domain Specific information from classified EHRs' datasets, transforming them into a Common Health Language (CHL) through Ontologies, whereas unknown datasets are mapped and translated into CHL, using Ontology-Mapping techniques.