Abstract
Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The study covers the creation of a microservices infrastructure, the development of a fault injection module that simulates application-level and service-level anomalies, the creation of a system monitoring dataset, and the creation and validation of the MLP model to detect anomalies. The results indicate that the MLP model effectively detects anomalies in both domains with higher accuracy, precision, recovery, and F1 score on the service-level anomaly dataset. The potential for more effective distributed system monitoring and management automation is highlighted in this study by focusing on service-level metrics such as service response times. This study provides valuable information about the effectiveness of supervised machine learning models in detecting anomalies across distributed software systems.

Abstract
Information and Communication Technologies are driving the improvement of industrial processes. According to the Industry 4.0 (I4.0) paradigm, digital systems provide real-time information to humans and machines, increasing flexibility and efficiency in production environments. Based on the I4.0 Design Principles concept, Virtual Assistants can play a vital role in processing production data and offer contextualized and real-time information to the workers in the production environment. This systematic review paper explored Virtual Assistant applications in the context of I4.0, discussing the Technical Assistance Design Principle and identifying the characteristics, services, and limitations regarding Virtual Assistant use in the production environments. The results showed that Virtual Assistants offer Physical and Virtual Assistance. Virtual Assistance provides real-time contextualized information mainly for support, while Physical Assistance is oriented toward task execution. Regarding services, the applications include integration with legacy systems and static information treatment. The limitations of the applications incorporate concerns about information security and adapting to noisy and unstable environments. It is possible to assume that the terminology of Virtual Assistants is not standardized and is mentioned as chatbots, robots, and others. Besides the worthy insights of this research, the small number of resulting papers did not allow for generalizations. Future research should focus on broadening the search scope to provide more-significant conclusions and research possibilities with new AI models and services, including the emergent Industry 5.0 concept.

Abstract
Over the years, industrial evolution has proved to be a complex process, since there are several aspects that need to be considered to achieve highly functional processes and differentiated quality products. To date, four industrial revolutions have been implemented. Thus, the paradigm of Industry 4.0 (I4.0) was born, a concept that aims to improve the efficiency, productivity, automation, and safety of industrial processes, but which also considers the operator’s relevance and centrality in these processes. Besides these four revolutions one more concept is emerging, called Industry 5.0 (I5.0). In recent years, and with the advance of scientific research, the implementation of wearables has proven to be the ideal solution to move towards the digitisation of Industrial sector. In this sense, the aim of this work is to provide a systematic review on the currently available knowledge about wearable technology and its applicability within I4.0. Through these technologies, both processes and operators can be monitored in real time, actively contributing to the identification of limitations and to the implementation of improvements. On the other hand, studies on the acceptance of these devices have shown a certain apprehension by users regarding the security and privacy of collected data. Therefore, studies should be conducted to analyse in depth these limitations, to raise users’ confidence and contribute, in a broader perspective, to the success of industrial processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Higher Education Institutions must define and monitor strategies and policies essential for decision-making in their various areas and levels, in which Business Intelligence plays a leading role. This research addresses the problem of Business Intelligence system adoption in Higher Education Institutions, with a view, in the first instance, to identify and characterise the strategic objectives that underpin decision-making, activities, processes, indicators and information in Higher Education Institutions. After a literature review, it was found that the absence of a roadmap that can serve as a reference to implement a Business Intelligence system in Higher Education Institutions may limit the adoption of this type of solution. Therefore, this research intends to present the methodology of a proposed roadmap for the implementation of Business Intelligence systems in Higher Education Institutions, that allows for increasing its capacity for analysis and evaluation of the data and information available in the various systems and platforms. © 2023 ICSBT International Conference on Smart Business Technologies. All rights reserved.

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