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Detalhes

Detalhes

  • Nome

    Inês Dutra
  • Cargo

    Investigador Colaborador Externo
  • Desde

    01 janeiro 2009
003
Publicações

2023

Improving the Characterization and Comparison of Football Players with Spatial Flow Motifs

Autores
Barbosa, A; Ribeiro, P; Dutra, I;

Publicação
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2

Abstract
Association Football is probably the world's most popular sport. Being able to characterise and compare football players is therefore a very important and impactful task. In this work we introduce spatial flow motifs as an extension of previous work on this problem, by incorporating both temporal and spatial information into the network analysis of football data. Our approach considers passing sequences and the role of the player in those sequences, complemented with the physical position of the field where the passes occurred. We provide experimental results of our proposed methodology on real-life event data from the Italian League, showing we can more accurately identify players when compared to using purely topological data.

2023

An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems

Autores
Tome, ES; Ribeiro, RP; Dutra, I; Rodrigues, A;

Publicação
SENSORS

Abstract
The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at risk. In this sense, it is essential to guarantee smoke detectors' correct functioning. Traditionally, these systems have been subject to periodic maintenance plans, which do not consider the state of the fire alarm sensors and are, therefore, sometimes carried out not when necessary but according to a predefined conservative schedule. Intending to contribute to designing a predictive maintenance plan, we propose an online data-driven anomaly detection of smoke sensors that model the behaviour of these systems over time and detect abnormal patterns that can indicate a potential failure. Our approach was applied to data collected from independent fire alarm sensory systems installed with four customers, from which about three years of data are available. For one of the customers, the obtained results were promising, with a precision score of 1 with no false positives for 3 out of 4 possible faults. Analysis of the remaining customers' results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area.

2023

Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST)

Autores
Kirkpatrick, CR; Coakley, KL; Christopher, J; Dutra, I;

Publicação
Data Sci. J.

Abstract

2023

Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST)

Autores
Kirkpatrick, CR; Coakley, KL; Christopher, J; Dutra, I;

Publicação
Data Sci. J.

Abstract
Seven years after the seminal paper on FAIR was published, that introduced the concept of making research outputs Findable, Accessible, Interoperable, and Reusable, researchers still struggle to understand how to implement the principles. For many researchers, FAIR promises long-term benefits for near-term effort, requires skills not yet acquired, and is one more thing in a long list of unfunded mandates and onerous requirements for scientists. Even for those required to, or who are convinced that they must make time for FAIR research practices, their preference is for just-in-time advice properly sized to the scientific artifacts and process. Because of the generality of most FAIR implementation guidance, it is difficult for a researcher to adjust to the advice according to their situation. Technological advances, especially in the area of artificial intelligence (AI) and machine learning (ML), complicate FAIR adoption, as researchers and data stewards ponder how to make software, workflows, and models FAIR and reproducible. The FAIR+ Implementation Survey Tool (FAIRIST) mitigates the problem by integrating research requirements with research proposals in a systematic way. FAIRIST factors in new scholarly outputs, such as nanopublications and notebooks, and the various research artifacts related to AI research (data, models, workflows, and benchmarks). Researchers step through a self-serve survey process and receive a table ready for use in their data management plan (DMP) and/or work plan. while gaining awareness of the FAIR Principles and Open Science concepts. FAIRIST is a model that uses part of the proposal process as a way to do outreach, raise awareness of FAIR dimensions and considerations, while providing timely assistance for competitive proposals. © 2023, Ubiquity Press. All rights reserved.

2023

Data and Knowledge for Overtaking Scenarios in Autonomous Driving

Autores
Pinto, M; Dutra, I; Fonseca, J;

Publicação
CoRR

Abstract

Teses
supervisionadas

2019

Exascale computing with custom Linear Mixed Model kernels and GPU accelerators for Genomic Wide Association Studies and personalized medicine

Autor
Christopher David Harrison

Instituição
UP-FCUP

2019

Towards Improving the Search for Multi-Relational Concepts in ILP

Autor
Alberto José Rajão Barbosa

Instituição
UP-FCUP

2017

Execução e Gestão de Aplicações Conteinerizadas

Autor
Diogo Cristiano dos Santos Reis

Instituição
UP-FCUP

2017

Weighted Multiple Kernel Learning for Breast Cancer Diagnosis applied to Mammograms

Autor
Tiago André Guedes Santos

Instituição
UP-FCUP

2017

Improving the search for multi-relational concepts in ILP

Autor
Alberto José Rajão Barbosa

Instituição
UP-FCUP