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2,065 results on '"Ribeiro, Rita"'

1. Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey

Author

Jakubowski, Jakub, Wojak-Strzelecka, Natalia, Ribeiro, Rita P., Pashami, Sepideh, Bobek, Szymon, Gama, Joao, and Nalepa, Grzegorz J

Subjects
Computer Science - Artificial Intelligence
Abstract

Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research., Comment: Preprint submitted to Engineering Applications of Artificial Intelligence

Published
2024

2. Aequitas Flow: Streamlining Fair ML Experimentation

Author

Jesus, Sérgio, Saleiro, Pedro, Silva, Inês Oliveira e, Jorge, Beatriz M., Ribeiro, Rita P., Gama, João, Bizarro, Pedro, and Ghani, Rayid

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Aequitas Flow is an open-source framework for end-to-end Fair Machine Learning (ML) experimentation in Python. This package fills the existing integration gaps in other Fair ML packages of complete and accessible experimentation. It provides a pipeline for fairness-aware model training, hyperparameter optimization, and evaluation, enabling rapid and simple experiments and result analysis. Aimed at ML practitioners and researchers, the framework offers implementations of methods, datasets, metrics, and standard interfaces for these components to improve extensibility. By facilitating the development of fair ML practices, Aequitas Flow seeks to enhance the adoption of these concepts in AI technologies.

Published
2024

3. A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance

Author

Gama, João, Ribeiro, Rita P., Mastelini, Saulo, Davarid, Narjes, and Veloso, Bruno

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Predictive Maintenance applications are increasingly complex, with interactions between many components. Black box models are popular approaches based on deep learning techniques due to their predictive accuracy. This paper proposes a neural-symbolic architecture that uses an online rule-learning algorithm to explain when the black box model predicts failures. The proposed system solves two problems in parallel: anomaly detection and explanation of the anomaly. For the first problem, we use an unsupervised state of the art autoencoder. For the second problem, we train a rule learning system that learns a mapping from the input features to the autoencoder reconstruction error. Both systems run online and in parallel. The autoencoder signals an alarm for the examples with a reconstruction error that exceeds a threshold. The causes of the signal alarm are hard for humans to understand because they result from a non linear combination of sensor data. The rule that triggers that example describes the relationship between the input features and the autoencoder reconstruction error. The rule explains the failure signal by indicating which sensors contribute to the alarm and allowing the identification of the component involved in the failure. The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure. We evaluate the proposed system in a real-world case study of Metro do Porto and provide explanations that illustrate its benefits., Comment: 26 pages

Published
2024

4. Super-Resolution Analysis for Landfill Waste Classification

Author

Molina, Matias, Ribeiro, Rita P., Veloso, Bruno, and Gama, João

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning., Comment: This article has been accepted by the Symposium on Intelligent Data Analysis (IDA 2024)

Published
2024

5. Explainable Predictive Maintenance

Author

Pashami, Sepideh, Nowaczyk, Slawomir, Fan, Yuantao, Jakubowski, Jakub, Paiva, Nuno, Davari, Narjes, Bobek, Szymon, Jamshidi, Samaneh, Sarmadi, Hamid, Alabdallah, Abdallah, Ribeiro, Rita P., Veloso, Bruno, Sayed-Mouchaweh, Moamar, Rajaoarisoa, Lala, Nalepa, Grzegorz J., and Gama, João

Subjects
Computer Science - Artificial Intelligence, I.2.1
Abstract

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research., Comment: 51 pages, 9 figures

Published
2023

7. Community-Based Topic Modeling with Contextual Outlier Handling

Author

Andrade, Cesar, Ribeiro, Rita P., Gama, João, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Alonso-Betanzos, Amparo, editor, Guijarro-Berdiñas, Bertha, editor, Bolón-Canedo, Verónica, editor, Hernández-Pereira, Elena, editor, Fontenla-Romero, Oscar, editor, Camacho, David, editor, Rabuñal, Juan Ramón, editor, Ojeda-Aciego, Manuel, editor, Medina, Jesús, editor, Riquelme, José C., editor, and Troncoso, Alicia, editor

Published
2024
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12. Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

Author

Jesus, Sérgio, Pombal, José, Alves, Duarte, Cruz, André, Saleiro, Pedro, Ribeiro, Rita P., Gama, João, and Bizarro, Pedro

Subjects
Computer Science - Machine Learning
Abstract

Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks. However, tabular data -- which is prevalent in many high-stakes domains -- has been lagging behind. To bridge this gap, we present Bank Account Fraud (BAF), the first publicly available privacy-preserving, large-scale, realistic suite of tabular datasets. The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized,real-world bank account opening fraud detection dataset. This setting carries a set of challenges that are commonplace in real-world applications, including temporal dynamics and significant class imbalance. Additionally, to allow practitioners to stress test both performance and fairness of ML methods, each dataset variant of BAF contains specific types of data bias. With this resource, we aim to provide the research community with a more realistic, complete, and robust test bed to evaluate novel and existing methods., Comment: Accepted at NeurIPS 2022. https://openreview.net/forum?id=UrAYT2QwOX8

Published
2022

13. A Benchmark dataset for predictive maintenance

Author

Veloso, Bruno, Gama, João, Ribeiro, Rita P., and Pereira, Pedro M.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

Published
2022

14. Model Optimization in Imbalanced Regression

Author

Silva, Aníbal, Ribeiro, Rita P., and Moniz, Nuno

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare) values. Recently, an evaluation metric was introduced: Squared Error Relevance Area (SERA). This metric posits a bigger emphasis on the errors committed at extreme values while also accounting for the performance in the overall target variable domain, thus preventing severe bias. However, its effectiveness as an optimization metric is unknown. In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used SERA as an objective function are practically better than the models produced by their respective standard boosting algorithms at the prediction of extreme values. This confirms that SERA can be embedded as a loss function into optimization-based learning algorithms for imbalanced regression scenarios.

Published
2022

15. ACTIVITIES OF DAILY LIVING, DEPRESSION AND ANXIETY INCIDENCE IN THE ELDERLY ON HEMODIALYSIS: A CROSS-SECTIONAL STUDY/ATIVIDADES COTIDIANAS E INCIDENCIA DE ANSIEDADE E DEPRESSAO DOS IDOSOS EM HEMODIALISE: ESTUDO TRANSVERSAL/ACTIVIDADES COTIDIANAS, DEPRESION E INCIDENCIA DE ANSIEDAD EN ANCIANOS EN HEMODIALISIS: ESTUDIO TRANSVERSAL

Author

Rodrigues, Beatriz dos Santos, de Azevedo, Larissa Gomes, de Souza Menezes, Joao Daniel, da Silva, Matheus Querino, Ribeiro, Renato Mendonca, Parro, Maria Claudia, Sasaki, Natalia Sperli Geraldes Marin dos Santos, and de Cassia Helu Mendonca Ribeiro, Rita

Published
2024
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16. Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set

Author

Davari, Narjes, Veloso, Bruno, Ribeiro, Rita P., Gama, João, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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17. An Online Data-Driven Predictive Maintenance Approach for Railway Switches

Author

Sousa Tomé, Emanuel, Ribeiro, Rita P., Veloso, Bruno, Gama, João, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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18. Online Anomaly Explanation: A Case Study on Predictive Maintenance

Author

Ribeiro, Rita P., Mastelini, Saulo Martiello, Davari, Narjes, Aminian, Ehsan, Veloso, Bruno, Gama, João, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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19. Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data

Author

Salles, Rodrigo, Mendes, Jérôme, Ribeiro, Rita P., Gama, João, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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20. Study on Correlation Between Vehicle Emissions and Air Quality in Porto

Author

Shaji, Nirbhaya, Andrade, Thiago, Ribeiro, Rita P., Gama, João, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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26. Topic Model with Contextual Outlier Handling: a Study on Electronic Invoice Product Descriptions

Author

Andrade, Cesar, Ribeiro, Rita P., Gama, João, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moniz, Nuno, editor, Vale, Zita, editor, Cascalho, José, editor, Silva, Catarina, editor, and Sebastião, Raquel, editor

Published
2023
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27. Pollution Emission Patterns of Transportation in Porto, Portugal Through Network Analysis

Author

Andrade, Thiago, Shaji, Nirbhaya, Ribeiro, Rita P., Gama, João, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moniz, Nuno, editor, Vale, Zita, editor, Cascalho, José, editor, Silva, Catarina, editor, and Sebastião, Raquel, editor

Published
2023
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28. Predicting tick-borne disease risk : improving the distribution mapping of ticks and tick bite risk in Scotland

Author

Cardoso Ribeiro, Rita Cláudia, Macrae, Alastair, Gunn, George, Eze, Jude, Auty, Harriet, and Gilbert, Lucy

Subjects
tick distribution, tick bite risk mapping, human-tick encounters
Abstract

Maps of Ixodes ricinus distribution and maps depicting tick bite risk are essential for government and health organisations to target prevention and control strategies for tick-borne diseases. However, researchers often lack robust long-term and geographically extensive tick distribution data, and information about human exposure to ticks to measure risk. Citizen science projects, through the collective effort of many volunteers, have the potential to provide valuable data on tick bite risk and tick distribution, but are often based on opportunistic submission of reports. The overarching aim of this study was to assess methodologies to improve public health decision-making through distribution mapping of ticks and tick bite risk for Scotland. Research was undertaken to: compare the quality and robustness of predictive mapping with the three types of tick data most often used for predictive mapping; use statistical approaches to improve the quality of predictions of the distribution of I. ricinus in Scotland, including the predicted uncertainty; assess whether questing tick surveys reflect human-tick encounter risk; and finally, test the feasibility of a new citizen science approach to assess human risk of tick encounters. Analysis of the three existing datasets with I. ricinus distribution in Scotland showed that whereas data from questing tick surveys generate detailed predictive maps at local scale, at the country level, predictions were affected by poor data coverage. Additionally, dissimilarities in the predicted distribution pattern of I. ricinus between data from passive submission and from questing tick surveys were identified. This suggests the need for data from public submissions to gather information on absences and to account for volunteer effort. A predictive map of I. ricinus distribution in Scotland developed with a sophisticated Bayesian statistical technique (the stochastic partial differential equation (SPDE)) which accounted for several sources of variation was successful in improving the predictions in areas with poor data coverage, and the associated uncertainty. The relationship between questing tick surveys and human tick bite rate was then assessed. Questing tick surveys were carried out whilst collecting contemporaneous data on tick encounters from orienteers running the same areas in 11 events at world, national, regional and local orienteering events. This novel approach found that questing tick surveys are a good indicator of tick bite risk. Also, the number of people multiplied by the hours of exposure is the most meaningful denominator for human exposure to tick bites (correlation coefficient with questing tick abundance of 0.8, p=0.0052). From 340 reports from orienteers recorded across all events, a mean incidence of 409 tick bites per 1,000 person-hours exposure was recorded. Significant correlations were found between tick bite rate and temperature on the event day, the proportion of pastures around the track used by orienteers and the start time of the activity. A citizen science project was implemented in Scotland between May and November 2018 and again between March and November 2019. The project used a novel approach that included collection of denominator data (number of people exposed, and time spent) and additionally asked people to report both when they did, and importantly, when they did not encounter ticks. Tick bite and tick encounter rates calculated from participant reports were compared with predictions of questing tick abundance in two study areas, Lochaber and the Cairngorms using data collected from questing tick surveys. A total of 1,914 reports from 65 volunteers were received, with 231 and 118 reports received, respectively, from the Cairngorms and Lochaber areas. On average, the Cairngorms area registered 0.083 tick bites per person per hour of activity and 0.268 tick encounters per person per hour. Lochaber area registered 0.018 tick bites and 0.028 tick encounters per person per hour. Tick bite and tick encounter rates in the Cairngorms correlated better with predicted tick abundance in the area (correlation coefficient of 0.27 and 0.31, respectively) compared to Lochaber (correlation coefficient of 0.15 in both cases). Tick bite and tick encounter rates were found to depend both on questing tick abundance, and on factors related to human activity and behaviour. Type of human activity explained more variation in tick bite rate than questing tick abundance. Tick bite and tick encounter rates were quantified by activity type. These findings are valuable in identifying high risk activities and targeting public health messages. This study resulted in new methodologies to improve predictive mapping of ticks, and better understanding of tick bite risk and the factors that drive it, with the overall aim of improving control and prevention of tick-borne diseases.

Published
2021
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32. A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set

Author

Davari, Narjes, Pashami, Sepideh, Veloso, Bruno, Nowaczyk, Sławomir, Fan, Yuantao, Pereira, Pedro Mota, Ribeiro, Rita P., Gama, João, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bouadi, Tassadit, editor, Fromont, Elisa, editor, and Hüllermeier, Eyke, editor

Published
2022
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33. Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph

Author

Shaji, Nirbhaya, Gama, João, Ribeiro, Rita P., Gomes, Pedro, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bouadi, Tassadit, editor, Fromont, Elisa, editor, and Hüllermeier, Eyke, editor

Published
2022
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34. Combining Multiple Data Sources to Predict IUCN Conservation Status of Reptiles

Author

Soares, Nádia, Gonçalves, João F., Vasconcelos, Raquel, Ribeiro, Rita P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bouadi, Tassadit, editor, Fromont, Elisa, editor, and Hüllermeier, Eyke, editor

Published
2022
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38. Improving Smart Waste Collection Using AutoML

Author

Teixeira, Sónia, Londres, Guilherme, Veloso, Bruno, Ribeiro, Rita P., Gama, João, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kamp, Michael, editor, Koprinska, Irena, editor, Bibal, Adrien, editor, Bouadi, Tassadit, editor, Frénay, Benoît, editor, Galárraga, Luis, editor, Oramas, José, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Kang, Bo, editor, Largeron, Christine, editor, Lijffijt, Jefrey, editor, Viard, Tiphaine, editor, Welke, Pascal, editor, Ruocco, Massimiliano, editor, Aune, Erlend, editor, Gallicchio, Claudio, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Fröning, Holger, editor, Schindler, Günther, editor, Guidotti, Riccardo, editor, Monreale, Anna, editor, Rinzivillo, Salvatore, editor, Biecek, Przemyslaw, editor, Ntoutsi, Eirini, editor, Pechenizkiy, Mykola, editor, Rosenhahn, Bodo, editor, Buckley, Christopher, editor, Cialfi, Daniela, editor, Lanillos, Pablo, editor, Ramstead, Maxwell, editor, Verbelen, Tim, editor, Ferreira, Pedro M., editor, Andresini, Giuseppina, editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Fournier-Viger, Philippe, editor, Nawaz, M. Saqib, editor, Ventura, Sebastian, editor, Sun, Meng, editor, Zhou, Min, editor, Bitetta, Valerio, editor, Bordino, Ilaria, editor, Ferretti, Andrea, editor, Gullo, Francesco, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Ribeiro, Rita, editor, Gama, João, editor, Gavaldà, Ricard, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Roqueiro, Damian, editor, Saldana Miranda, Diego, editor, Sechidis, Konstantinos, editor, and Graça, Guilherme, editor

Published
2021
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40. ATIVIDADES COTIDIANAS E INCIDÊNCIA DE ANSIEDADE E DEPRESSÃO DOS IDOSOS EM HEMODIÁLISE: ESTUDO TRANSVERSAL

Author

Rodrigues, Beatriz dos Santos, primary, Azevedo, Larissa Gomes de, additional, Menezes, João Daniel de Souza, additional, Silva, Matheus Querino da, additional, Ribeiro, Renato Mendonça, additional, Parro, Maria Cláudia, additional, Sasaki, Natália Sperli Geraldes Marin dos Santos, additional, and Ribeiro, Rita de Cássia Helú Mendonça, additional

Published
2024
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44. Building Future Societies? A Brief Analysis of Braga’s School Bus Project

Author

Araújo, Emília, Silva, Márcia, Ribeiro, Rita, Corais, Filipa, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Pereira, Paulo, editor, Ribeiro, Rita, editor, Oliveira, Ivo, editor, and Novais, Paulo, editor

Published
2020
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47. Caring for the family with one of its members with dementia according to the MDAIF

Author

Quintão, Cláudia, Ribeiro, Rita, Fonseca, Sandra, Lebreiro da Silva, Marlene, Amaral, Odete, Quintão, Cláudia, Ribeiro, Rita, Fonseca, Sandra, Lebreiro da Silva, Marlene, and Amaral, Odete

Abstract

Introduction: The World Health Organization [WHO] (2012) identifies dementia as a growing public health problem, with the family being the first line of care for people with this pathology. The Dynamic Model of Family Assessment and Intervention (MDAIF) constitutes a theoretical and operational reference, guiding the nursing process, which intends to respond to the needs of nurses in caring for families, in the development of family-centered practices. (Figueiredo, 2009, 2012). Objective: Analyze the impact of the care provided by the specialist nurse in family health nursing, with the family as the unit of care. Methods: Case study, descriptive and exploratory, in the context of UCSP, with the MDAIF being the theoretical and operational reference. As data collection, in depth interviews were carried out. All ethical principles were taken into account. Results: The following nursing diagnoses in need of intervention were identified: residential building not safe, marital satisfaction not maintained, role of care provider not adequate, dysfunctional family process. Implementation of interventions arising from the expression of emotions, role saturation and coping using narrative therapy. Conclusion: This study made it possible to identify the needs of the family and the interventions that promote change. We verified the effectiveness of the implemented interventions. We recognize the MDAIF as an essential tool for Nurses in assessing and intervening in the family as a unit of care.

Published
2024

48. Artefatos industriais e representatividade negra: estudos de caso no Brasil

Author

Ramos de Freitas, Matheus Alberto, Aparecida da Conceição Ribeiro, Rita, Ramos de Freitas, Matheus Alberto, and Aparecida da Conceição Ribeiro, Rita

Abstract

This paper intended to reveal Afro-centered industrial artifacts for consumption in the Brazilian market between the years 2018 and 2020. Based on anti-racist theories such as racial plurality and multiculturalism, the work aimed the objects from a counter-hegemonic perspective. In addition, it adopted a qualitative approach with intentional sampling to describe and explain the portrayed artifacts based on the technical procedure multiple case study. The analysis revealed three categories of industrial products (dolls, pointe shoes and coloring tools) that have nude as a similarity, that is, the artifacts seek to represent different skin tones. The results showed the relevance that the media have in the exchange between companies and society in the context of strengthening black representation., O presente artigo pretendeu revelar artefatos industriais com representatividade negra para o consumo no mercado brasileiro entre os anos de 2018 e 2020. Possuindo como base as teorias antir racistas como a pluralidade racial e o multiculturalismo, o trabalho visou os objetos sob uma perspectiva contra-hegemônica. Além disso, adotou a abordagem qualitativa com amostragem intencional para descrever e explicar os artefatos retratados que possuíram como base o procedimento técnico estudo de caso múltiplo. A análise revelou três categorias de produtos industriais (bonecas e bonecos, sapatilhas de ponta e ferramentas para colorir) que possuem como semelhança o nude, ou seja, os artefatos buscam representar variados tons de pele. Os resultados apontaram a relevância que as mídias possuem no intercâmbio entre as empresas e a sociedade no contexto de fortalecimento da representatividade negra., Este artículo tiene como objetivo investigar artefactos industriales, con representatividad negra, para consumo del mercado brasileño entre los años 2018 y 2020. Basado en teorías antirracistas como la pluralidad racial y el multiculturalismo, el trabajo apuntó a los objetos desde una perspectiva contrahegemónica. Además, adoptó un enfoque cualitativo con muestreo intencional para describir y explicar los artefactos retratados basándose en el procedimiento técnico del estudio de casos múltiples. El análisis reveló tres categorías de productos industriales (muñecas y muñecos, zapatos de punta y utensilios para colorear) que son similares al nude, es decir, los artefactos buscan representar diferentes tonos de piel. Los resultados resaltaron la relevancia que tienen los medios de comunicación en el intercambio entre empresas y sociedad en el contexto del fortalecimiento de la representatividad negra.

Published
2024

49. Design e inclusão: o “design para o bem-estar” na transmissão de conhecimento para crianças TEA

Author

Cotrim, Michelle, Ribeiro, Rita, Cotrim, Michelle, and Ribeiro, Rita

Abstract

This article sought to contribute to the inclusion of children/adolescents diagnosed with Autism Spectrum Disorder (ASD), with regard to the school environment in order to collaborate in building their independence and autonomy. The article is based on research carried out with the help of Capes (Junior Post-doctoral scholarship) by the author together with educational actors, which sought to assist in the design of strategies that provide the transmission of didactic content for ASD children, supported by methodology “Design for well-being well” (D.BE), developed during the author’s doctoral thesis., O presente artigo buscou contribuir para a inclusão das crianças/adolescentes diagnosticados com Transtorno do Espectro Autista (TEA), no que diz respeito ao ambiente escolar a fim de colaborar para construção da independência e autonomia destes. O artigo tem como base a pesquisa realizada com auxílio da Capes (bolsa Pós-doutorado Júnior) pela autora junto aos atores da educação, a qual buscou auxiliar na concepção de estratégias que propiciem a transmissão de conteúdos didáticos para crianças TEA, tendo como suporte a metodologia “Design para o bem-estar bem” (D.BE), desenvolvida durante a tese de doutorado da autora., Este artículo buscó contribuir a la inclusión de niños/adolescentes diagnosticados con Trastorno del Espectro Autista (TEA), en el ámbito escolar, para colaborar en la construcción de su independencia y autonomía. El artículo se basa en una investigación realizada con el apoyo de la Capes (Beca Postdoctoral Junior) por el autor junto con actores educativos, que buscó coadyuvar en el diseño de estrategias que propicien la transmisión de contenidos didácticos para niños con TEA, apoyados por Metodología “Diseño para el bienestar” (D.BE), desarrollada durante la tesis doctoral del autor.

Published
2024

50. Current Trends in Learning from Data Streams

Author

Gama, João, Veloso, Bruno, Aminian, Ehsan, Ribeiro, Rita P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Srirama, Satish Narayana, editor, Lin, Jerry Chun-Wei, editor, Bhatnagar, Raj, editor, Agarwal, Sonali, editor, and Reddy, P. Krishna, editor

Published
2021
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