Leal, Fátima
A carregar...
Endereço de Email
Data de nascimento
Cargo
Último Nome
Leal
Primeiro Nome
Fátima
Nome
Fátima Leal
Biografia
Fátima Leal holds a Ph.D. degree in Information and Communication Technologies from University of Vigo, Spain. She is an Auxiliar Professor at Universidade Portucalense in Porto, Portugal and a researcher in REMIT (Research on Economics, Management and Information Technologies). Following a full-time postdoctoral fellowship funded by the European Commission, she continues collaborating with The Cloud Competency Centre at the National College of Ireland in Dublin. Her research is based on crowdsourced information, including, Trust and Reputation, Big Data, Data Streams, and Recommendation Systems. Recently, she has been exploring blockchain technologies for a responsible data processing.
Afiliação:
REMIT – Research on Economics, Management and Information Technologies.
DCT - Departamento de Ciência e Tecnologia.
Identificadores
2211-3EC7-B4B6
57190765181
Y-3460-2019Projetos de investigação
Unidades organizacionais
REMIT – Research on Economics, Management and Information Technologies
Centro de investigação que que tem como objetivo principal produzir e disseminar conhecimento teórico e aplicado que possibilite uma maior compreensão das dinâmicas e tendências económicas, empresariais, territoriais e tecnológicas do mundo contemporâneo e dos seus efeitos socioeconómicos. O REMIT adota uma perspetiva multidisciplinar que integra vários domínios científicos: Economia e Gestão; Ciências e Tecnologia; Turismo, Património e Cultura.
Founded in 2017, REMIT – Research on Economics, Management and Information Technologies is a research unit of Portucalense University. Based on a multidisciplinary and interdisciplinary perspective it aims at responding to social challenges through a holistic approach involving a wide range of scientific fields such as Economics, Management, Science, Technology, Tourism, Heritage and Culture.
Grounded on the production of advanced scientific knowledge, REMIT has a special focus on its application to the resolution of real issues and challenges, having as strategic orientations:
- the understanding of local, national and international environment;
- the development of activities oriented to professional practice, namely in the business world.
35 resultados
Resultados da pesquisa
A mostrar 1 - 10 de 35
Publicação Acesso Restrito Explanation plug-In for stream-based collaborative filtering2022-05-11 - García-Méndez, Silvia; Malheiro, Benedita; Burguillo, Juan C.; Leal, FátimaCollaborative filtering is a widely used recommendation technique, which often relies on rating information shared by users, i.e., crowdsourced data. These filters rely on predictive algorithms, such as, memory or model based predictors, to build direct or latent user and item profiles from crowdsourced data. To predict unknown ratings, memory-based approaches rely on the similarity between users or items, whereas model-based mechanisms explore user and item latent profiles. However, many of these filters are opaque by design, leaving users with unexplained recommendations. To overcome this drawback, this paper introduces Explug, a local model-agnostic plug-in that works alongside stream-based collaborative filters to reorder and explain recommendations. The explanations are based on incremental user Trust & Reputation profiling and co-rater relationships. Experiments performed with crowdsourced data from TripAdvisor show that Explug explains and improves the quality of stream-based collaborative filter recommendations.Publicação Acesso Restrito Trust and reputation smart contracts for explainable recommendations2020-05-18 - Veloso, Bruno; Malheiro, Benedita; González-Vélez, Horacio; Leal, FátimaRecommendation systems are usually evaluated through accuracy and classification metrics. However, when these systems are supported by crowdsourced data, such metrics are unable to estimate data authenticity, leading to potential unreliability. Consequently, it is essential to ensure data authenticity and processing transparency in large crowdsourced recommendation systems. In this work, processing transparency is achieved by explaining recommendations and data authenticity is ensured via blockchain smart contracts. The proposed method models the pairwise trust and system-wide reputation of crowd contributors; stores the contributor models as smart contracts in a private Ethereum network; and implements a recommendation and explanation engine based on the stored contributor trust and reputation smart contracts. In terms of contributions, this paper explores trust and reputation smart contracts for explainable recommendations. The experiments, which were performed with a crowdsourced data set from Expedia, showed that the proposed method provides cost-free processing transparency and data authenticity at the cost of latency.Publicação Acesso Restrito The European Union’s Potential in Mitigating Climate Change Caused by Tourism: Classification of the 27 EU Member States Tourists’ Sustainable Behaviours2025-10-01 - Pinho, Micaela; Leal, FátimaClimate change is now a global phenomenon with severe social and economic implications, including for tourism. Tourism is currently one of the most dynamic economic sectors in the world and one of the main ones responsible for greenhouse gas emissions. At the same time as it contributes to global warming, the tourism sector is also one of the primary victims of climate change. Strengthening climate change mitigation measures is paramount for the sectors’ resilience. These changes require a collective and concerted effort by all stakeholders, including tourists, to transition to a tourism sector that minimises its environmental footprint. The question arises whether tourists are willing to change their habits towards an environmentally sustainable tourism. This study proposes a system for classifying citizens from the 27 European Union (EU) member states as future sustainable tourists. The data used were retrieved from the European survey—Flash Eurobarometer 499, involving 25711 European citizens. The proposed method relies on a decision tree-based algorithm—Random Forest, to achieve intelligent classifications based on Machine Learning. The implementation of this classification system was preceded by the exploration of respondents’ willingness to change, in the future, their travel and tourist habits towards environmental sustainability by gender, age, and region of residence. The main findings suggest a notable trend in EU countries towards greater tourism sustainability among women belonging to Generation Y (aged between 30 and 44) and from the Eastern European region. With this information, the implemented algorithm can classify the environmental sustainability of EU citizens as future tourists with an accuracy of 63%. This study enriches the theoretical understanding of the intention of tourists’ pro-environmental behaviour. It highlights the need to adopt specific measures to increase awareness of human action in tourism for male citizens, for older generations, and for south, west, and north European regions.Publicação Acesso Aberto An intelligent community-based system for healthcare prioritisation2025-09-30 - Pinho, Micaela; Leal, FátimaHealthcare rationing is unavoidable in systems constrained by limited resources. While decisions about who should be treated are ethically complex, they must reflect not only efficiency concerns but also socially accepted values. This study aims to develop a multi-criteria decision-support system - Vital Priority System, that prioritise patients using a Random Forest algorithm trained on multiple rationing criteria endorsed by Portuguese civil society. Based on a Portuguese online survey data, the model incorporates nine dimensions: clinical need, life expectancy gain, quality of life improvement, age, waiting time, parental status, lifestyle responsibility, and social role. Our results show that clinical need, expected treatment effectiveness, waiting time and age were the most influential, followed by parental status. Lifestyle and social role factors were least weighted. The proposed system enables the classification of patients as ‘priority’ or ‘non-priority’, providing healthcare professionals with a transparent, consistent, and ethically grounded tool to support decision-making. This study advances the literature by operationalising, for the first time in the Portuguese context, public preferences in a replicable AI-based framework for fairer patient prioritisation.Publicação Acesso Aberto Multi-service model for blockchain networks2021-01-20 - Chis, Adriana E.; González–Vélez, Horacio; Leal, FátimaMulti-service networks aim to efficiently supply distinct goods within the same infrastructure by relying on a (typically centralised) authority to manage and coordinate their differential delivery at specific prices. In turn, final customers constantly seek to lower costs whilst maximising quality and reliability. This paper proposes a decentralised business model for multiservice networks using Ethereum blockchain features – gas, transactions, and smart contracts – to execute multiple services at different prices. By employing the Ethereum cryptocurrency token, Ether, to quantify the quality of service and reliability of distinct private Ethereum networks, our model concurrently processes streams of services at different gas prices while differentially delivering reliability and service quality. This multi-service business model has been extensively tested on five concurrent Ethereum networks with various combinations of gas prices, miners, and regular nodes using a Proof of Authority consensus algorithm and throughput as the evaluation metric. It has exhibited linear scalability, providing increased throughput in high-quality Ethereum networks, i.e., composed of more validator nodes. The results also indicate that different mining prices do not impact the network performance, but networks with more miners had limited scalability and an increased level of trustworthiness and reliability.Publicação Acesso Restrito Emotional evaluation of open-ended responses with Transformer Models2024-05-11 - Pajón-Sanmartín, Alejandro; Arriba-Pérez, Francisco de; García-Méndez, Silvia; Burguillo, Juan C.; Leal, Fátima; Malheiro, BeneditaThis work applies Natural Language Processing (NLP) techniques, specifically transformer models, for the emotional evaluation of open-ended responses. Today’s powerful advances in transformer architecture, such as ChatGPT, make it possible to capture complex emotional patterns in language. The proposed transformer-based system identifies the emotional features of various texts. The research employs an innovative approach, using prompt engineering and existing context, to enhance the emotional expressiveness of the model. It also investigates spaCy’s capabilities for linguistic analysis and the synergy between transformer models and this technology. The results show a significant improvement in emotional detection compared to traditional methods and tools, highlighting the potential of transformer models in this domain. The method can be implemented in various areas, such as emotional research or mental health monitoring, creating a much richer and complete user profile.Publicação Acesso Restrito Predictive Maintenance Using Autoencoders and Messaging Systems2025-11-18 - Carvalho, Rui; Sousa, Diogo; Leal, FátimaThis article presents an anomaly detection system for bearing data, leveraging Apache Kafka for efficient data streaming and an LSTM autoencoder. The system relies on a data producer that reads bearing vibration data, computes the Root Mean Square values, and streams both raw and processed data to a Kafka topic. The consumer processes this data, storing the raw values in a PostgreSQL database, and performs anomaly detection. By utilising pre-fitted scalers and defined statistical thresholds, the system effectively identifies deviations from normal operating conditions. The architecture ensures seamless data ingestion, storage, and processing, enabling timely interventions to prevent equipment failures. Experimental results demonstrate the system’s efficacy in detecting anomalies, highlighting its scalability, adaptability, and practical applicability in industrial predictive maintenance.Publicação Acesso Aberto Profiling Decision-Making Styles Under Healthcare Resource Scarcity: An Interdisciplinary Clustering Approach2026-03-13 - Pinho, Micaela; Leal, Fátima; Miguel, IsabelScarcity of healthcare resources requires prioritisation decisions that raise complex ethical, economic, and social challenges. While normative frameworks provide guidance on how such decisions ought to be made, growing evidence suggests that individuals differ substantially in how they approach morally charged allocation choices. This study investigates heterogeneity in decision-making styles and support for healthcare prioritisation criteria using an interdisciplinary approach that integrates health economics, social psychology, and computational methods to identify latent decision-making profiles among a sample of adults residing in Portugal. Data were collected from adults residing in Portugal using a structured online questionnaire comprising socio-demographic characteristics, decision-making styles, and preferences elicited through twenty hypothetical healthcare rationing scenarios. The results reveal three meaningful decision-making profiles characterised by different combinations of cognitive styles and ethical prioritisation patterns: analytically oriented decision-makers prioritising health gains; intuitive, context-sensitive decision-makers balancing clinical and social criteria; heuristic-driven decision-makers relying on simpler or less differentiated heuristics. These findings demonstrate that, within this sample, healthcare prioritisation preferences are shaped by systematic variations in decision style rather than a single moral or rational framework. By linking behavioural heterogeneity with ethical decision-making, this study contributes to theoretical debates on healthcare rationing and demonstrates the value of clustering techniques for uncovering latent structures in complex decision data. The results provide insights relevant for the design of decision-support systems and rationing policies, which may be adapted to accommodate heterogeneous decision styles in comparable settings.Publicação Acesso Aberto Interpretable success prediction in higher education institutions using pedagogical surveys2022-10-18 - Veloso, Bruno; Leal, Fátima; Moreira, Fernando; Santos-Pereira, Carla; Jesus-Silva, Natacha; Durão, NatérciaThe indicators of student success at higher education institutions are continuously analysed to increase the students’ enrolment in multiple scientific areas. Every semester, the students respond to a pedagogical survey that aims to collect the student opinion of curricular units in terms of content and teaching methodologies. Using this information, we intend to anticipate the success in higher- level courses and prevent dropouts. Specifically, this paper contributes with an interpretable student classification method. The proposed solution relies on (i) a pedagogical survey to collect student’s opinions; (ii) a statistical data analysis to validate the reliability of the survey; and (iii) machine learning algorithms to classify the success of a student. In addition, the proposed method includes an explainable mechanism to interpret the classifications and their main factors. This transparent pipeline was designed to have implications in both digital and sustainable education, impacting the three pillars of sustainability, i.e.,economic, social, and environmental, where transparency is a cornerstone. The work was assessed with a dataset from a Portuguese higher-level institution, contemplating multiple courses from different departments. The most promising results were achieved with Random Forest presenting 98% in accuracy and F-measure.Publicação Acesso Restrito Stream-based explainable recommendations via blockchain profiling2022 - Veloso, Bruno; Malheiro, Benedita; Burguillo, Juan C.; Chis, Adriana E.; González-Vélez, Horacio; Leal, FátimaExplainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.