Browsing by Author "Ribeiro, José"
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- An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testingPublication . Ribeiro, José; Zenha-Rela, Mário Alberto; Vega, Francisco Fernández deThis paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.
- A Conversational Agent for Promoting Physical Activity Among COPD PatientsPublication . Rodrigues, Ricardo; Caceiro, Ruben; Brites, Marcelo; Flora, Sofia; Cruz, Joana; Silva, Fernando; Ribeiro, JoséChronic Obstructive Pulmonary Disease (COPD) is one of the most prevalent diseases in the world, affecting respiratory performance of many people, limiting the airflow and is not fully reversible. It is a clinical syndrome characterized by chronic respiratory symptoms, structural pulmonary abnormalities or impairment of lung function. In order to help people with this disease, we propose an innovative personalized mHealth coaching platform that will address patient preferences and contextual factors – the OnTRACK platform. This platform is composed of a mobile application for patients, a web platform for healthcare professionals – and a conversational agent (or chatbot), named “Hígia”, which acts as an alternative interface between patients and the platform. This conversational agent includes several of the main functionalities already available in OnTRACK’s smartphone app, complementing and extending it. It allows consulting prescription information in a multitude of ways, getting and setting all personal data, inserting physical activity measurements, and obtaining historical data on physical activity and prescriptions, among others. The evaluation of the conversational agent yielded encouraging results, with users reporting being happier, more motivated, dedicated and confident when interacting with the systems using their voice, while allowing the development team to identify topics for improvement.
- Motivation and physical activity in COPD: An exploratory studyPublication . Pimenta, Sara; Flora, Sofia; Silva, Cândida G.; Oliveira, Ana; Morais, Nuno; Ribeiro, José; Silva, Fernando; Caceiro, Rúben; Carreira, Bruno P.; Januário, Filipa; Andrade, Lília; Rodrigues, Fátima; Brooks, Dina; Burtin, Chris; Marques, Alda; Cruz, JoanaA key factor for the adoption of an active lifestyle is self-determined motivation; however, it is often overlooked in COPD. Understanding the motives underlying patients’ decision to be (or not) physically active will provide insight into future interventions. This study assessed the motives for patients with COPD to engage in physical activity (PA) and their association with PA behaviour. A cross-sectional study was conducted in stable patients with COPD. Motivation was assessed with the Exercise Motivation Inventory-2 (EMI-2; score 0[Not at all true for me]–5[Very true for me]; 5 dimensions) and PA with accelerometry [ActiGraph-GT3X+, 7 days; moderate to vigorous PA (MVPA), steps/day]. Spearman’s correlations (ρ) were used to assess their relationship. 60 participants were enrolled (67.2±7.7 years; 76.7% men; FEV1 49.5±19.7pp). Patients’ motives to be physically active were mostly Health, Fitness and Psychological. Correlations with PA were weak and non-significant (p>0.05) (Table 1). Patients with COPD value Health, Fitness and Psychological motives to be physically active, although these are not related to their PA behaviour. Findings highlight the complex nature of PA and the need to further explore factors influencing PA and motivation in this population.
- Perfil de atividade física de pessoas com Doença Pulmonar Obstrutiva Crónica (DPOC) em PortugalPublication . Raposo, João; Pimenta, Sara; Alves-Guerreiro, José; Flora, Sofia; Caceiro, Rúben; Morais, Nuno; Oliveira, Ana; Silva, Cândida G.; Ribeiro, José; Silva, Fernando; Januário, Filipa; Carreira, Bruno P.; Rodrigues, Fátima; Marques, Alda; Cruz, JoanaIntrodução e objetivos: A participação em atividade física (AF) regular está associada a um menor risco de mortalidade e melhor qualidade de vida relacionada com a saúde. Apesar de se saber que as pessoas com Doença Pulmonar Obstrutiva Crónica (DPOC) apresentam níveis baixos de AF quando comparadas com indivíduos saudáveis da mesma idade e sexo, desconhece-se ainda a caracterização diária dos níveis de AF destas pessoas em Portugal. Este estudo teve como objetivos caracterizar o perfil de AF de pessoas com DPOC portuguesas e explorar a sua relação com características clínicas. Material e Métodos: Foi realizado um estudo observacional transversal em pessoas com DPOC clinicamente estáveis, nas regiões Centro e Lisboa e Vale do Tejo. Foram recolhidos dados sociodemográficos, antropométricos, função pulmonar [Volume Expiratório Forçado no 1º segundo (FEV1)], sintomas e exacerbações (GOLD ABCD), dispneia (modified Medical Research Council), tolerância ao exercício (teste de marcha dos 6-min) e estado de saúde (COPD Assessment Test). A AF foi avaliada através de acelerometria (ActiGraph GT3X+) durante 7 dias e consistiu em: tempo despendido em AF Moderada a Vigorosa (AFMV) e em AF Total (min/dia), e número de passos/dia. Realizou-se estatística descritiva e correlações de Spearman (ρ) entre as variáveis de AF e as medidas clínicas. Resultados: Os participantes (n=102, 82 do sexo masculino, FEV1=48±19%previsto) apresentaram uma mediana [Q1–Q3] de 20 [9–41] min/dia em AFMV, 144 [100–208] min em AF Total e realizaram 4438 [2821–6944] passos/dia. Apenas 24% dos participantes atingiram ≥7000 passos/dia e 41% os ≥30 min/dia de AFMV recomendados na literatura. O tempo despendido em AFMV e o n.º de passos/dia apresentaram correlações moderadas com a dispneia (ρ=-0.401 e ρ=0.537, respetivamente; p<0.001) e com a tolerância ao exercício (ρ=0.560 e ρ=0.525, respetivamente; p<0.001). O tempo em AFMV apresentou ainda correlação com os graus ABCD (ρ=-0.430, p<0.001). Conclusões: A maioria das pessoas com DPOC é fisicamente inativa. Os sintomas, exacerbações e tolerância ao esforço estão associados à AF nesta população e devem ser considerados em intervenções de promoção de AF.
- Phenotyping Adopters of Mobile Applications Among Patients With COPD: A Cross-Sectional StudyPublication . Flora, Sofia; Hipólito, Nádia; Brooks, Dina; Marques, Alda; Morais, Nuno; Silva, Cândida; Silva, Fernando; Ribeiro, José; Caceiro, Rúben; Carreira, Bruno; Burtin, Chris; Pimenta, Sara; Cruz, Joana; Oliveira, AnaEffectiveness of technology-based interventions to improve physical activity (PA) in people with COPD is controversial. Mixed results may be due to participants' characteristics influencing their use of and engagement with mobile health apps. This study compared demographic, clinical, physical and PA characteristics of patients with COPD using and not using mobile apps in daily life. Patients with COPD who used smartphones were asked about their sociodemographic and clinic characteristics, PA habits and use of mobile apps (general and PA-related). Participants performed a six-minute walk test (6MWT), gait speed test and wore an accelerometer for 7 days. Data were compared between participants using (App Users) and not using (Non-App Users) mobile apps. A sub-analysis was conducted comparing characteristics of PA–App Users and Non-Users. 59 participants were enrolled (73% Male; 66.3 ± 8.3 yrs; FEV1 48.7 ± 18.4% predicted): 59% were App Users and 25% were PA-App Users. Significant differences between App Users and Non-App Users were found for age (64.2 ± 8.9 vs. 69.2 ± 6.3yrs), 6MWT (462.9 ± 91.7 vs. 414.9 ± 82.3 m), Gait Speed (Median 1.5 [Q1–Q3: 1.4–1.8] vs. 2.0 [1.0–1.5]m/s), Time in Vigorous PA (0.6 [0.2–2.8] vs. 0.14 [0.1–0.7]min) and Self-Reported PA (4.0 [1.0–4.0] vs. 1.0 [0.0–4.0] Points). Differences between PA–App Users and Non-Users were found in time in sedentary behavior (764.1 [641.8–819.8] vs. 672.2 [581.2–749.4] min) and self-reported PA (4.0 [2.0–6.0] vs. 2.0 [0.0–4.0] points). People with COPD using mobile apps were younger and had higher physical capacity than their peers not using mobile apps. PA-App Users spent more time in sedentary behaviors than Non-Users although self-reporting more time in PA.
- Relationship between fatigue, physical activity and health-related factors in COPDPublication . Vieira, Ana; Dias, Diana; Miguel, Eunice; Matos, Telma; Flora, Sofia; Silva, Cândida G.; Morais, Nuno; Oliveira, Ana; Caceiro, Rúben; Silva, Fernando; Ribeiro, José; Silva, Sónia; Martins, Vitória; Valente, Carla; Burtin, Chris; Brooks, Dina; Marques, Alda; Cruz, JoanaFatigue is highly prevalent in COPD and may be associated with reduced physical activity (PA) and poor outcomes. This study explored the relationship between fatigue, objectively measured PA and health-related factors in people with COPD. Fatigue was assessed with the Checklist of Individual Strength (CIS20) and CIS20-Subjective Fatigue (CIS20-SF) and PA with Actigraph GT3X monitors (moderate-to-vigorous PA, MVPA; total PA; steps/day). Dyspnoea (modified Medical Research Council, mMRC), exercise tolerance (6-min walk distance, 6MWD), lung function (spirometry) and GOLD A-D were collected. Spearman (ρ) and Pearson (r) correlations and multiple regressions were performed. Variables entered the model if correlation≥0.2. 54 patients participated (68±7 years; 82% men) and 69% reported fatigue (CIS20-SF≥27). Fatigue was significantly correlated with MVPA, steps/day, mMRC, 6MWD, GOLD A-D and FEV1pp (Table 1). In regression models for CIS20 (p=.001; r2=.61) and CIS20-SF (p=.003; r2=.56), dyspnoea was the only significant variable. People with higher scores of fatigue present lower PA levels, although the relationship is weak. Dyspnoea appears to have the largest influence on fatigue.
- Synthetic image generation for effective deep learning model training for ceramic industry applicationsPublication . Gaspar, Fábio; Daniel Carreira; Rodrigues, Nuno; Miragaia, Rolando; Ribeiro, José; Costa, Paulo; Pereira, AntónioIn the rapidly evolving field of machine learning engineering, access to large, high-quality, and well-balanced labeled datasets is indispensable for accurate product classification. This necessity holds particular significance in sectors such as the ceramics industry, in which effective production line activities are paramount and deep learning classification mechanisms are particularly relevant for streamlining processes; but real-world image samples are scarce and difficult to obtain, hindering dataset building and consequently model training and deployment. This paper presents a novel approach for dataset building in the context of the ceramic industry, which involves employing synthetic images for building or complementing datasets for image classification problems. The proposed methodology was implemented in CeramicFlow, an innovative computer graphics rendering pipeline designed to create synthetic images by employing computer-aided design models of ceramic objects and incorporating domain randomization techniques. As a result, a fully synthetic image dataset named Synthetic CeramicNet was created and validated in real-world ceramic classification problems. The results demonstrate that synthetic images provide an adequate basis for datasets and can significantly reduce reliance on real-world data when developing deep learning approaches for image classification problems in the ceramic industry. Furthermore, the proposed approach can potentially be applied to other industrial fields.
- Systematic Review of Emotion Detection with Computer Vision and Deep LearningPublication . Pereira, Rafael; Mendes, Carla; Ribeiro, José; Ribeiro, Roberto; Miragaia, Rolando; Rodrigues, Nuno; Costa, Nuno; Pereira, AntónioEmotion 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 MetaAnalyses (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.
- Trends on empty exception handlers for Java open source librariesPublication . Nogueira, Ana Filipa; Ribeiro, José; Zenha-Rela, Mario A.Exception-handling structures provide a means to recover from unexpected or undesired flows that occur during software execution, allowing the developer to put the program in a valid state. Still, the application of proper exception-handling strategies is at the bottom of priorities for a great number of developers. Studies have already discussed this subject pinpointing that, frequently, the implementation of exception-handling mechanisms is enforced by compilers. As a consequence, several anti-patterns about Exception-handling are already identified in literature. In this study, we have picked several releases from different Java programs and we investigated one of the most well-known anti-patterns: the empty catch handlers. We have analysed how the empty handlers evolved through several releases of a software product. We have observed some common approaches in terms of empty catches’ evolution. For instance, often an empty catch is transformed into a empty catch with a comment. Moreover, for the majority of the programs, the percentage of empty handlers has decreased when comparing the first and last releases. Future work includes the automation of the analysis allowing the inclusion of data collected from other software artefacts: test suites and data from issue tracking systems.