Abstract
This research focuses on developing a wearable device that aims to enhance problem-solving and communication abilities within the context of Industry 4.0. The wearable is being developed in the Continental Advanced Antenna, and it allows operators to notify material shortages on the manufacturing line and helps minimize workflow disturbance. The wearable gives a list of missing materials using context-aware computing, allowing operators to identify and prioritize the missing item quickly. We used the Quick and Dirty usability testing approach to ensure the device's usability and efficacy, allowing quick feedback and iterative modifications throughout the development process. Experienced consultants of project participated initial tests on the device and found that it has the potential to improve efficiency and communication in an industrial setting. However, further testing involving end users is necessary to optimize the device for the unique demands of the production environment. This paper offers valuable insights into the lessons learned from the project and proposes potential future research directions.

Abstract
Virtual assistants offer a new type of solution to handle interaction between human and machine and can be applied in various business contexts such as Industry or Education. When designing and building a virtual assistant the developers must ensure a set of parameters to achieve a good solution. Various platforms and frameworks emerged to allow developers to create virtual assistant solutions easier and faster. This paper provides a review of available platforms and frameworks used by authors to create their own solutions in different areas. Big tech companies like Google with Dialogflow, IBM with Watson Assistant and Microsoft with Bot Framework, present mature solutions to build virtual assistants that provide to the developer all components of the basic architecture to build a fast and solid solution. Open-Source solutions focus on providing to the developer the main components to build a virtual assistant, namely language understanding and response generation.

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
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.

Abstract
The main purpose for conducting this research is to analyze the core advantages and disadvantages of automatic assessment tools. With this in mind, we considered two different web sites of two distinct Portuguese Universities and compare the accessibility and usability issues found, aiming at identifying core problems through errors / warnings. This outcome will help us understand the weaknesses of such automatic tools and, thus, suggest features that could be added to improve their analysis. These findings will serve as a basis for a proposal of a new type of platform capable of making any web or mobile application accessible to all types of users, regardless of their impairments (e.g., blindness, deafness, motor or intellectual disabilities). © 2022 IEEE.

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.