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About

About

I am a lecturer in the Department of Computer Science, School of Sciences of University of Porto, Portugal. I obtained a B.Sc. degree in Computer Science from State University of Rio de Janeiro, Brazil, in 1985, and an M.Sc. degree in the Systems Engineering and Computer Science department of Federal University of Rio de Janeiro, Brazil, in 1988. My Ph.D. degree was obtained from Bristol University, UK, in 1995. In 1998, I started as a lecturer in the Department of Systems Engineering and Computer Science of COPPE, an institution for postgraduate studies in Engineering, at Federal University of Rio de Janeiro, where I taught courses on Operating Systems, Concurrent Programming and Topics on High Performance Computing, at M.Sc. and Ph.D. levels, and Artificial Intelligence and Logic Programming, at undergraduate level. In Februrary 2007 I moved to Portugal where I am now located. During the periods between October 2001 and December 2002, April 2004 and March 2005, Aug 2010 and Feb 2011, and Oct 2014 and Mar 2015, I worked as a visiting researcher at University of Wisconsin-Madison, USA, in the department of Biostatistics and Medical Informatics, and at the Radiology Department of the School of Sciences and Public Health. During these periods, I worked for machine learning projects funded by NSF, DARPA and American Air Force (projects COLLEAGUE, EELD and EAGLE), and NLM (Project ABLe) and started to work with applications that demanded a huge amount of resources. At this time, I had the opportunity to work with the Condor team, and to largely use the Condor resource manager to run experiments. My main research areas are Logic programming, Inductive Logic Programming, and Parallel Logic Programming systems. I served as Program Comittee member of several workshops and conferences in these areas. I supervised several M.Sc. and Ph.D. students in these areas. I have more than 80 publications in conferences and journals. I also participated or was the principal investigator of several projects funded by CNPq (Brazil), FCT (Portugal) and the EU. I am a member of the EELA (E-science grid facility for Europe and Latin America) initiative, whose main objective is to promote and maintain the infrastructure of hardware and software between Europe and Latin America. Currently, I have been working on machine learning techniques based on Inductive Logic programming, but still using parallelzation and grid environments to be able to perform machine learning experiments.

Interest
Topics
Details

Details

  • Name

    Inês Dutra
  • Role

    External Research Collaborator
  • Since

    01st January 2009
  • Nationality

    Brasil
  • Contacts

    +351220402963
    ines.dutra@inesctec.pt
003
Publications

2023

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

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

Publication
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

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

Publication
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)

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

Publication
Data Sci. J.

Abstract

2023

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

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

Publication
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

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

Publication
CoRR

Abstract

Supervised
thesis

2019

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

Author
Christopher David Harrison

Institution
UP-FCUP

2019

Towards Improving the Search for Multi-Relational Concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP

2017

Execução e Gestão de Aplicações Conteinerizadas

Author
Diogo Cristiano dos Santos Reis

Institution
UP-FCUP

2017

Weighted Multiple Kernel Learning for Breast Cancer Diagnosis applied to Mammograms

Author
Tiago André Guedes Santos

Institution
UP-FCUP

2017

Improving the search for multi-relational concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP