Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and “Other NNs”, which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.

The complexity inherent to software development and maintenance - not only in technical terms, but also from a human perspective - entails challenges that can be addressed as learning problems. Machine learning techniques may be employed as tools to gain insight about strategies that can lead to the improvement of the quality of software processes and products. Defect proneness prediction, in particular, may be identified as an active research field. As stated by DevOps guidelines, the possibility of obtaining quick feedback allows teams to operate in an agile mode in which communication, decision taking and problem solving are expeditious, allowing companies to boost business value. This paper describes ongoing research for applying machine learning techniques to improve the quality of processes and products inside the DevOps pipeline of the La Redoute's IT department.

Nowadays, the Portuguese population is aging at a fast pace. The situation is more severe in the interior regions of the country, where the rural areas have few people and have been constantly losing population; these are mostly elderly who, in some cases, live socially isolated. They are also often deprived of some types of social, health and technological services. One of the current challenges with respect to the elderly is that of improving the quality of life for those who still have some autonomy and live in their own residences so that they may continue living autonomously, while receiving the assistance of some exterior monitoring and supporting services. The Internet of Things (IoT) paradigm demonstrates great potential for creating technological solutions in this area as it aims to seamlessly integrate information technology with the daily lives of people. In this context, it is necessary to develop services that monitor the activity and health of the elderly in real time and alert caregivers or other family members in the case of an unusual event or behaviour. It is crucial that the technological system is able to collect data in a nonintrusive manner and without requiring much interaction with the elderly. Smartband devices are very good candidates for this purpose and, therefore, this work proposes assessing the level of acceptance of the usage of a smartbands by senior users in their daily activities. By using the definition of an architecture and the development of a prototype, it was possible to test the level of acceptance of smartbands by a sample of the elderly population—with surprising results from both the elderly and the caregivers—which constitutes an important contribution to the research field of Ambient Assisted Living (AAL). The evaluation showed that most users did not feel that the smartband was intrusive to their daily tasks and even considered using it in the future, while caregivers considered that the platform was very intuitive.

Portugal has recently become one the few European countries to fully acknowledge Traditional Chinese Medicine (TCM); this substantial paradigm shift calls for novel tools for TCM practitioners, students and patients alike. This paper describes an Expert System for supporting the TCM consultation process – both in terms of gathering and managing the patients’ personal and symptomatic data, and of obtaining accurate diagnoses and treatments under regulated and reviewed protocols. The proposed tool was designed and is being developed with the support of two TCM therapists, which act as experts and provide aid to the processes of building the knowledge base and the automatic diagnosis system. In terms of architecture, the current version of the framework includes a mobile client application for the Android platform, integrated with an online spreadsheet. A survey was conducted in order to assess some of the needs of the community of TCM practitioners, and allowed gathering information on their needs