Instituto Superior de Engenharia
URI permanente desta comunidade:
Notícias
A definir... (EST)
http://est.ualg.pt/
Navegar
Percorrer Instituto Superior de Engenharia por Objetivos de Desenvolvimento Sustentável (ODS) "04:Educação de Qualidade"
A mostrar 1 - 10 de 12
Resultados por página
Opções de ordenação
- Continual learning for object classification: integrating AutoML for binary classification tasks within a modular dynamic architecturePublication . Turner, Daniel; Cardoso, Pedro; Rodrigues, JoaoFor humans it is quite easy to identify a new object after learning to identify existing ones, but not for a machine. Deep neural networks (DNN) are the foundation of the current state-of-the-art methods for training machines to recognize sets of objects. The issue is that any modification to the DNN weights that were trained to classify an initial set of objects has the potential to seriously impair the network’s ability to make those initial classifications; this behaviour is referred to as catastrophic forgetting (CF). This paper presents a continual learning (CL) architecture that can deal with CF. The architecture is composed of two primary parts: (i) The feature extraction component, which is based on the ResNet50 backbone and (ii) the modular dynamic classification (MDC) component. The latter is made up of multiple sub-networks that gradually assemble themselves into a tree-like structure that reorganizes itself as it learns over time, so that each sub-network can operate independently. The MDC relies heavily on binary classification, and here the application of automated machine learning (AutoML) was introduced, where each binary classifier is tailored on-the-fly, and is/can be different from object to object. The strategy involves a calculated selection from a predefined list of model types and parameters, optimizing them for their respective tasks. Results demonstrate that we advanced the adaptability and performance of the network, emphasizing the transformative potential of AutoML in modular CL approaches. Tests on the CORe50 dataset showed accuracy results of 81.1%, which are above the state of the art for CL architectures.
- CTCovid19: automatic Covid-19 model for computed tomography scans using deep learningPublication . Antunes, Carlos; Rodrigues, Joao; Cunha, AntónioCOVID-19 is an extremely contagious respiratory sickness instigated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Common symptoms encompass fever, cough, fatigue, and breathing difficulties, often leading to hospitalization and fatalities in severe cases. CTCovid19 is a novel model tailored for COVID-19 detection, specifically honing in on a distinct deep learning structure, ResNet-50 trained with ImageNet serves as the foundational framework for our model. To enhance its capability to capture pertinent features related to COVID-19 patterns in Computed Tomography scans, the network underwent fine-tuning through layer adjustments and the addition of new ones. The model achieved accuracy rates that went from 97.0 % to 99.8 % across three widely recognized and documented datasets dedicated to COVID-19 detection.
- Digital cultural heritagePublication . Portalés, Cristina; Rodrigues, Joao; Rodrigues Gonçalves, Alexandra; Alba, Ester; Sebastián, JorgeMost contemporary thinkers agree that we are going through a time of historical change, building a different concept and model of social interrelation. Our ways of life and work have changed, as have the ways in which we communicate and relate to each other. Likewise, an increasing consensus indicates the need to reconfigure traditional social and cultural structures. The Internet, the virtual social networks, and the Information and Communication Technologies (ICTs) have coalesced into a new collective consciousness—a world intercommunicated from the local to the global [1]. The fusion of tradition, culture, history, and legacy with technology, innovation, and interaction provides an attractive system that serves both as an artistic expression and as a fundamental tool for diffusion in cultural institutions [2]. For instance, the usage of interactive technologies such as virtual reality (VR) or augmented reality (AR), combined with multidimensional or multimodal representations [3], provides a significant novelty. User interaction offers a broader perspective, making people more aware of their actions, helping them become the true center of the application. It also enables interactive artistic expression through alternative realities, as well as narration supported by the use of virtual avatars.
- Enhancing MILAGE LEARN+ with Machine Learning to improve students’ performancePublication . Figueiredo, Mauro; rodrigues, jose; Martins, Paula Ventura; Zacarias, Marielba; Milharó, DanielaStudents currently attending school were born after the year 2000 and have grown up surrounded by technology, including smartphones, tablets, the Internet, video games, and social media. Typically, conventional educational activities in schools fail to engage these students, leading many of them to struggle academically. This paper presents the strategy adopted by the free MILAGE LEARN+ platform to address these challenges. Artificial Intelligence supports learning personalization by recommending suitable activities tailored to each student’s individual needs, enabling both lower-performing and higherperforming learners to enhance their academic progress. This paper investigates how Artificial Intelligence, specifically through various machine learning methods, can enrich the learning experience offered by the MILAGE LEARN+ platform. Several machine learning approaches are evaluated and analysed based on data from the platform and student outcomes collected during a Mathematics course.
- From cues to engagement: a comprehensive survey and holistic architecture for computer vision-based audience analysis in live eventsPublication . Lemos, Marco; Cardoso, Pedro; Rodrigues, JoaoThe accurate measurement of audience engagement in real-world live events remains a significant challenge, with the majority of existing research confined to controlled environments like classrooms. This paper presents a comprehensive survey of Computer Vision AI-driven methods for real-time audience engagement monitoring and proposes a novel, holistic architecture to address this gap, with this architecture being the main contribution of the paper. The paper identifies and defines five core constructs essential for a robust analysis: Attention, Emotion and Sentiment, Body Language, Scene Dynamics, and Behaviours. Through a selective review of state-of-the-art techniques for each construct, the necessity of a multimodal approach that surpasses the limitations of isolated indicators is highlighted. The work synthesises a fragmented field into a unified taxonomy and introduces a modular architecture that integrates these constructs with practical, businessoriented metrics such as Commitment, Conversion, and Retention. Finally, by integrating cognitive, affective, and behavioural signals, this work provides a roadmap for developing operational systems that can transform live event experience and management through data-driven, real-time analytics.
- From cues to engagement: a comprehensive survey and holistic architecture for computer vision-based audience analysis in live eventsPublication . Lemos, Marco; Cardoso, Pedro; Rodrigues, JoaoThe accurate measurement of audience engagement in real-world live events remains a significant challenge, with the majority of existing research confined to controlled environments like classrooms. This paper presents a comprehensive survey of Computer Vision AI-driven methods for real-time audience engagement monitoring and proposes a novel, holistic architecture to address this gap, with this architecture being the main contribution of the paper. The paper identifies and defines five core constructs essential for a robust analysis: Attention, Emotion and Sentiment, Body Language, Scene Dynamics, and Behaviours. Through a selective review of state-of-the-art techniques for each construct, the necessity of a multimodal approach that surpasses the limitations of isolated indicators is highlighted. The work synthesises a fragmented field into a unified taxonomy and introduces a modular architecture that integrates these constructs with practical, businessoriented metrics such as Commitment, Conversion, and Retention. Finally, by integrating cognitive, affective, and behavioural signals, this work provides a roadmap for developing operational systems that can transform live event experience and management through data-driven, real-time analytics.
- Guest intelligence applications acceptance model: an approach with the UTAUT Model and PLS-SEMPublication . Ramos, Celia; Ashqar, RashedIn a world where innovations are made daily, how people interact with and use technology is becoming increasingly important. It is relevant to investigate how new technologies are used and accepted in this environment. Acceptance assessment has been done in research on creating information systems, considering the UTAUT model and the data analysis technique PLS-SEM. Although this digital environment is pertinent to society in general, it is even more appropriate when interacting with tourists because it may give personalised goods and services. In order to assess acceptance in terms of guest insights proportioned by technologies to recommend personalised services and products by evaluating an application that recommends products and services personalised, considering guests’ intelligence, this article analyses the use and acceptance of a technological application whose characteristics meet the aforementioned.
- Identificação da ocupação e uso do solo com base em imagens provenientes de deteção remota e em algoritmos de machine learning: a Reserva da Faia Brava como caso de estudoPublication . Pacheco, Paula Maria de Fraga Borges; Luís, Joaquim; Loureiro, Nuno de SantosEsta dissertação procura identificar a ocupação e uso do solo com base em imagens de deteção remota e algoritmos de Machine Learning (ML), utilizando como estudo de caso a Reserva da Faia Brava, situada no vale do Côa, distrito da Guarda, Portugal. O estudo avalia a exequibilidade de ferramentas de código aberto, como o QGIS e o plugin Orfeo Toolbox, para implementar fluxos de trabalho de classificação supervisionada. Foram processadas imagens de alta resolução obtidas por UAV (2,7 cm/pixel) e pelo satélite Pléiades-Neo (30 cm/pixel), integrando índices de vegetação (GLI, NDVI, SAVI e MSAVI) e métricas texturais de Haralick. O treino dos modelos foi realizado numa quadrícula com 500 metros de lado, selecionada pela sua diversidade ecológica, e posteriormente testada em outras áreas da reserva para avaliar a capacidade de generalização. Dois algoritmos de ML, Random Forest (RF) e Support Vector Machine (SVM), foram testados, com desempenhos avaliados através de matrizes de confusão, F1-scores e coeficientes Kappa. Os resultados evidenciaram a superioridade dos ortofotomosaicos UAV face às imagens de satélite, especialmente quando combinados com análise textural, embora tenham sido identificadas limitações relacionadas com variações sazonais da vegetação e a interoperabilidade entre sensores. O algoritmo RF mostrou maior consistência enquanto o SVM revelou sensibilidade à complexidade espectral. O estudo destaca a aplicabilidade prática destes métodos para a monitorização ambiental, sublinhando a importância das soluções open source para a democratização das tecnologias de deteção remota. Como produto final foi produzido, com base nos modelos de ML, uma carta temática para a área total da Reserva da Faia Brava, abrangendo quatro classes: árvores e arbustos, vegetação herbácea, afloramentos rochosos e outras ocupações e usos do solo.
- Intervenções de segurança na prevenção de quedas na construção civil - uma revisão sistemáticaPublication . Silva, Ingrid Polyana Gomes da; Costa, Rui Carlos Gonçalves Graça eA construção civil é um dos setores com maior incidência de acidentes de trabalho, sendo as quedas uma das principais causas de lesões graves e fatalidades. Esta revisão sistemática teve como objetivo identificar os fatores de risco mais comuns associados aos acidentes por quedas no setor da construção civil, e avaliar as estratégias mais eficazes para sua prevenção. A pesquisa seguiu as diretrizes do PRISMA 2020 e utilizou o modelo PICO para a formulação das perguntas de pesquisa. Foram incluídos seis estudos primários (quantitativos e mistos), publicados entre 2020 e 2024, em idioma inglês ou português, extraídos das bases de dados: B-On, Web of Science e PubMed. A análise qualitativa revelou que os principais fatores associados às quedas são: comportamentos inseguros dos trabalhadores, condições inseguras no local de trabalho, falhas na gestão e supervisão, além de barreiras de comunicação. As intervenções mais eficazes incluem treino contínuo, uso e fiscalização de EPIs (Equipamentos de Proteção Individual) e EPCs (Equipamentos de Proteção Coletiva), planeamento seguro das atividades e promoção de uma cultura organizacional de segurança. Conclui-se que a prevenção de quedas exige uma abordagem integrada que vá além das normas técnicas, incorporando gestão eficiente, formação constante e compromisso coletivo com a segurança.
- MILAGE LEARN+: motivation and grade benefits in computer science university studentsPublication . Dorin, Audrey; Moraes, Marcia C.; Figueiredo, MauroThis innovative practice full paper describes an experience of using MILAGE LEARN+ with Computer Science university students. Students motivation when in an academic setting is a very important aspect of how well they will learn the material provided. Many computer science students own smart phones, tablets, and computers in order to complete their work and study. Here we introduce MILAGE LEARN+ to fourth year computer science university students. MILAGE LEARN+ is a mobile educational application where students take quizzes, watch videos, and do assignments in worksheet format. This application integrates the gamification pedagogy with the usage of difficulty levels, a leader board, as well as self and peer-review in order to benefit students’ motivation, autonomy, and their grades. While MILAGE LEARN+ has been used in many European countries with a variety of age groups and fields of study there has been no research done on how American students connect with the application. In this study we are examining how university students in computer science react to the usage of this application.