Abstract

Abstract
Assistive environments is a well established research area, particularly, the Ambient Assisted Living (AAL) paradigm, which relies in data and artificial intelligence to infer the user's actions and status, thus enabling assistive actions. In a very distinct environment - the manufacturing industry, we propose a twin concept - the Ambient Assisted Working (AAW), to provide assistance to the workers on the factory floor, using solutions with design principles similar to AAL systems. The Industry 4.0 (I4.0) Technical Assistance design principle, introduces the assistance concept in I4.0 solutions and the AAW builds on top of that design principle.

Abstract
In industrial environments, the measurement and monitoring of filling levels (FL) in refilling stations (RS) are critical for quality control processes. Traditional methods used for this purpose, such as manual inspection and sensor-based techniques, have proven to be costly and time-consuming. As an alternative, this paper proposes a novel approach that leverages computer vision (CV) and advanced image processing techniques. This approach provides a more efficient and accurate method for monitoring filling levels in refilling stations, thereby reducing operational costs. The system operates through a comprehensive five-stage pipeline, including pre-processing, perspective transformation, thresholding and edge detection, post-processing and filling level calculation. The performance evaluation of this approach demonstrated promising results in accurately determining filling levels in most scenarios. However, we also identified challenges such as overlapping columns and occlusions in the camera’s field of view that require further improvements. By addressing these challenges, our research aims to develop a streamlined and automated method for filling level measurement in refilling stations, thereby enhancing productivity in industrial environments. Ultimately, this proposed approach holds potential to significantly improve the efficiency of refilling stations across multiple sectors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Industry 4.0 was publicly introduced in Germany in 2011 and is known as the fourth industrial revolution, whose goal is to improve manufacturing processes and increase the competitiveness of the manufacturing industry. Industry 4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity in industry. This paper aims to explore the use of context-aware applications in Industry 4.0 in order to assist workers in decision making and thus improve the performance of factory production lines. This literature review is part of the project “Continental AA’s Factory of the Future” (Continental FoF) and will integrate a context-aware system in Industry 4.0 of the mentioned company, which is a manufacturer of radio frequency devices for the automotive industry. This systematic literature review identifies, from the researched solutions, the concept of context and context-awareness, the main technologies used in context-aware systems, how context management is performed, as well as the most used integration and communication protocols. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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 systems can be useful tools to understand complex agroecological systems. Farmers and extensionists in the Global South may not have access to such information systems due limited resources, skills and opportunities. End-user development (EUD) has the potential to best suit local needs and conditions. This article summarises and furthers a research and development effort targeting communities as well as agroecological extension professionals and organisations in the Serra da Capivara Territory, Piaui, Brazil. In a retrospective ethnographic study we observed information abundance, topdown IS bias, informational competence and digital infrastructure limitations. A set of requirements was identified, with Portuguese syntax and semantics being crucial for an EUD solution. Based on these requirements a multiparadigm controlled natural language is specified and described as well as a prototype implementation and evaluation method. This language should provide a language that enables the end-user to develop IS suitable to their needs and conditions.