Abstract
Falls are one of the main causes of mortality and morbidity in the elderly worldwide. This had let to the research and development of electronic fall-detection systems. We propose a complete fall-detection system, that combines a wearable device (called Nyon) and a message microservice (for email and SMS) to alert caregiver every time a fall occurs. The wearable uses a simple threshold method and has the capability of search and switch between Wi-Fi and Bluetooth, using the available communication technology when a fall occurs. The results have shown that the wearable autonomy is adequate for a daily use and the server microservices are reliable and deliver a message to the caregiver every time a fall alert occurs. Several improvements are planned to increase the autonomy and range of the wearable device. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The Internet revolution in 1990, followed by the data-driven and information revolution, has transformed the world as we know it. Nowadays, what seam to be 10 to 20 years ago, a science fiction idea (i.e., machines dominating the world) is seen as possible. This revolution also brought a need for new regulatory practices where user trust and artificial Intelligence (AI) discourse has a central role. This work aims to clarify some misconceptions about user trust in AI discourse and fight the tendency to design vulnerable interactions that lead to further breaches of trust, both real and perceived. Findings illustrate the lack of clarity in understanding user trust and its effects on computer science, especially in measuring user trust characteristics. It argues for clarifying those notions to avoid possible trust gaps and misinterpretations in AI adoption and appropriation.

Abstract

Abstract
Abstract This study aims to assess the lecturers’ opinions about the use of e-learning tools to support distance and blended learning in higher education in Portugal, evidently reinforced by the COVID-19 pandemic. This research was based on a qualitative methodology, specifically, a focus group with professors from five higher education institutions from different geographical areas in Portugal. The obtained results were analysed along four main dimensions: (1) the level of knowledge of e-learning tools, (2) the reasons for using or (3) not using them, and, finally, (4) the opinion of lecturers on the student assessment process using these tools. The results showed that in addition to the concerns with smooth running classes and the appropriate delivery of the syllabus, the lecturers considered the transition to the e-learning context to have been easy. They noted a high level of literacy in the used tools, believed in the continued use of e-learning in the post-pandemic context, indicated several advantages for those involved in the e-learning context and a majority of limitations related to the time required for the adoption of more tools; and, finally, underlined the student assessment issue, which was pointed out as the most sensitive topic in the whole e-learning context. The study informed on the lecturers’ perspective on e-learning and the used tools and provided insight into their perceived usefulness and benefits for lecturers and students. An especially strong concern was verified on the part of lecturers to optimise e-learning tools to provide better knowledge delivery to students.

Abstract

Abstract
Grape varieties play an important role in wine's production chain, its identification is crucial for controlling and regulating the production. Nowadays, two techniques are widely used, ampelography and molecular analysis. However, there are problems with both of them. In this scenario, Deep Learning classifiers emerged as a tool to automatically classify grape varieties. A problem with the classification of on-field acquired images is that there is a lot of information unrelated to the target classification. In this study, the use of segmentation before classification to remove such unrelated information was analyzed. We used two grape varieties identification datasets to fine-tune a pre-trained EfficientNetV2S. Our results showed that segmentation can slightly improve classification performance if only unrelated information is removed.