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  4. Marjory Da Costa Abreu

Dr. Marjory Da Costa Abreu PhD, MPhil, BSc, SFHEA

Senior Lecturer


Summary

Marjory is a permanent academic at Sheffield Hallam University. She has a degree in computer science and a PhD in electronic engineering. Her research focuses on artificial intelligence applied in several areas such as: information security applications, surveillance, predator identification and fake news on social networks.

She received the Senior Fellow of Higher Education Academy award Kingdom) and currently works as a Tutor for the Postgraduate Research in Computing and Informatics.

She actively acts on the SBC information security committee (Brazilian Computer Society) and is an associate editor of the IET Biometrics.

She has more than 60 published scientific papers and more than 20 completed research students.

FEMINIST and ambassador for women in science!

About

Marjory is passionate about computing and artificial intelligence.

Her main area of research is applied artificial intelligence, more specifically, data-mining and machine learning applied to user data analysis and biometrics (face analysis, emotion prediction, keystroke, mouse and touch dynamics, fingerprint, handwritten text, signature and voice/speech). Some of my ongoing projects are: medieval document analysis ; transparency in user behaviour in commercial game playing; signal processing (speech analysis) applied to medical diagnosis and neurological diseases therapy; forensics-based keystroke dynamics analysis applied to accountability concerns in social networks; mining judges’ sentences for analysing fairness; mining hard real-time network environments and investigating machine learning-based decision engines for network intrusion detection systems; and designing cheaper technological solutions for biometrics applications in different scenarios.

She has won the Newton Research Collaboration Programme Award and has strong collaborations with the University of Kent (UK) and the University of York (UK) and she has been working in several different projects.

She worked as a Reader in Artificial Intelligence at UFRN until 2019.

Biography

Teaching

Department of Computing

Business, Technology and Enterprise

I have been teaching in higher education subjects tha are related with programming, machine learning, research methods and artificial intelligence.

Subject Area

Games and Artificial Intelligence

Modules

Research Methods.
Artificial Intelligence.
Learning Systems.
Artificial Intelligence Seminar.

Research

Some of my ongoing projects are: medieval document analysis (https://www.facebook.com/watch/?v=1979841978711461); transparency in user behaviour in commercial game playing; signal processing (speech analysis) applied to medical diagnosis and neurological diseases therapy; forensics-based keystroke dynamics analysis applied to accountability concerns in social networks; mining judges’ sentences for analysing fairness; mining hard real-time network environments and investigating machine learning-based decision engines for network intrusion detection systems; and designing cheaper technological solutions for biometrics applications in different scenarios.

Relevant Projects

  • Exploring Applied Artificial Intelligence.
  • Visualisation and dynamic analysis of medieval writing processes in the context of neurological diseases and disorders.
  • DeepEyes: Visual Computing and Machine Intelligence Solutions for Computer Forensics and Electronic Surveillance.
  • Identity management using biometric data (UFRN / Propesq - PVB9148-2013).
  • Aging and biometric processing (UFRN / Propesq - PVB9756-2013).
  • Collection and analysis of a new multibiometric database based on behavioral metrics of the hand (CNPq Universal 14/2013 - 472717 / 2013-8).
  • Multi-source biometric processing.
  • Establishing the Parameters of Soft Biometrics Integration in an Agent-Based Identification Framework.
  • Investigating of Measures of Communication Effectiveness.
  • Perspectives on identity, identity protection and biometrics among young people.
  • SimOrg - Simulation of Organizations (PDPG-TI / CNPq 552431 / 2002-8) .
  • Brazilian Genome Project: RL group.

Publications

Journal articles

Da Costa Abreu, M., & Silva, B. (2020). A critical analysis of ’Law 4.0’: The use of Automation and Artificial Intelligence and their impact on the judicial landscape of Brazil. Revista de Direitos Fundamentais e Tributação, 1 (3), 1-16. http://doi.org/10.47319/rdft.v1i3.30

Goncalves de A. S. Marques, J.C., Lima Do Nascimento, T.M., Vasiljevic, B., Alves dos Santos Santana, L.E., & Da Costa Abreu, M. (2020). An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach. Journal of the Brazilian Computer Society, 26 (1), 8. http://doi.org/10.1186/s13173-020-00102-6

da Silva, R.S., Da Costa Abreu, M., & Smith, S. (2020). Investigating the use of an ensemble of evolutionary algorithms for letter identification in tremulous medieval handwriting. Evolutionary Intelligence. http://doi.org/10.1007/s12065-020-00427-3

Aguiar de Lima, T., & da Costa-Abreu, M. (2019). A survey on Automatic Speech Recognition systems for Portuguese language and its variations. Computer Speech & Language, 101055. http://doi.org/10.1016/j.csl.2019.101055

Silva, J., Kreutz, M., Pereira, M., & Da Costa Abreu, M. (2019). An investigation of latency prediction for NoC-based communication architectures using machine learning techniques. Journal of Supercomputing, 1-19. http://doi.org/10.1007/s11227-019-02971-x

Da Costa Abreu, M., & Bezerra, G.S. (2019). FAMOS: a framework for investigating the use of face features to identify spontaneous emotions. Pattern Analysis and Applications, 22 (2), 683-701. http://doi.org/10.1007/s10044-017-0675-y

Da Costa Abreu, M.C., & Fairhurst, M. (2011). Enhancing identity prediction using a novel approach to combining hard- and soft-biometric information. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 41 (5), 599-607. http://doi.org/10.1109/TSMCC.2010.2056920

Abreu, M.C.D.C., & Fairhurst, M. (2009). Analyzing the benefits of a novel multiagent approach in a multimodal biometrics identification task. IEEE Systems Journal, 3 (4), 410-417. http://doi.org/10.1109/JSYST.2009.2035978

Fairhurst, M.C., & Abreu, M.C.C. (2009). Balancing performance factors in multisource biometric processing platforms. IET Signal Processing, 3 (4), 342-351. http://doi.org/10.1049/iet-spr.2008.0140

Canuto, A.M.P., Campos, A.M.C., Bezerra, V.M.S., & Abreu, M.C.D.C. (2007). Investigating the use of a multi-agent system for knowledge discovery in databases. International Journal of Hybrid Intelligent Systems, 4 (1), 27-38. http://doi.org/10.3233/his-2007-4104

Canuto, A.M.P., Abreu, M.C.C., de Melo Oliveira, L., Xavier, J.C., & Santos, A.D.M. (2007). Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recognition Letters, 28 (4), 472-486. http://doi.org/10.1016/j.patrec.2006.09.001

Canuto, A.M.P., Fagundes, D., Abreu, M.C.C., & Junior, J.C.X. (2006). Using weighted dynamic classifier selection methods in ensembles with different levels of diversity. International Journal of Hybrid Intelligent Systems, 3 (3), 147-158. http://doi.org/10.3233/his-2006-3303

Canuto, A.M., Santos, A.M., Abreu, M.C., Bezerra, V.M., Souza, F.M., & Gomes, M.F. (2004). Investigating the use of an agent-based multi-classifier system for classification tasks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3316, 854-859. http://doi.org/10.1007/978-3-540-30499-9_131

Conference papers

Da Costa Abreu, M., & Aguiar, T. (2021). Investigating the use of multiple languages for crisp and fuzzy speaker identification. In 11th International Conference on Pattern Recognition Systems, Curico, Chile, 17 March 2021 - 19 March 2021. IET

Da Costa Abreu, M., & Rannow Budke, J. (2021). Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions. In 11th International Conference on Pattern Recognition Systems, Curico, Chile, 17 March 2021 - 19 March 2021. IET

Araujo De Souza, G., & Da Costa Abreu, M. (2020). Automatic offensive language detection from Twitter data using machine learning and feature selection of metadata. In IEEE World Congress on Computational Intelligence (IEEE WCCI), Glasgow, UK, 19 July 2020 - 24 July 2020. IEEE: http://doi.org/10.1109/IJCNN48605.2020.9207652

Cavalcante Bandeira, D.R., De Paula Canuto, A.M., Da Costa Abreu, M., Fairhurst, M., Li, C., & Nascimento, D.S.C. (2019). Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction. Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019, 126-131. http://doi.org/10.1109/BRACIS.2019.00031

Da Costa Abreu, M., & Bruno, S. (2019). An empirical analysis of Brazilian courts law documents using learning techniques. In XIX Brazilian Symposium on Information and Computational Systems Security, São Paulo, Brazil, 2 September 2019 - 5 September 2019 (pp. 1-7). Brazilian Computer Society (SBC): https://sbseg2019.ime.usp.br/anais/198728.pdf

Lima do Nascimento, T.M., Monteiro de Oliveira, A.V., Da Costa Abreu, M., & Oliveira, L. (2019). An investigation of genetic algorithm-based feature selection techniques applied to keystroke dynamics biometrics. In 19th Brazilian Symposium on Information and Computer System Security (SBSeg 2019), São Paulo, Brazil, 2 September 2019 - 5 September 2019 (pp. 1-4). Brazilian Computer Society (SBC): https://sbseg2019.ime.usp.br/anais/195899.pdf

Da Costa Abreu, M., & Goncalve, J.C. (2019). An evaluation of a three-modal hand-based database to forensic-based gender recognition. In 19th Brazilian Symposium on Information and Computer System Security (SBSeg 2019), São Paulo, Brazil, 2 September 2019 - 5 September 2019 (pp. 1-6). Brazilian Computer Society (SBC): https://sbseg2019.ime.usp.br/anais/195970.pdf

Da Costa Abreu, M., & Pereira, M. (2019). Women in Resistance: Reporting the impact of IEEEWiEUFRNexhibition at Science and Tech Week at UFRN. In 39th Brazilian Computer Society Congress (CSBC 2019), Belem, Brazil, 14 July 2019 - 18 July 2019 (pp. 99-103). Brazilian Computer Society (SBC): http://doi.org/10.5753/wit.2019.6717

Da Costa Abreu, M., & Silveira Silva, R. (2018). Smart Rescue Drones to Find Snowslide Victims. In 18th Brazilian Symposium on Information and Computational Systems Security (SBSeg 2018), Natal, Brazil, 22 October 2018 - 25 October 2018 (pp. 181-184). Brazilian Computer Society (SBC): https://sol.sbc.org.br/index.php/sbseg_estendido/article/view/4155/4084

Da Silva, V.R., & Da Costa Abreu, M. (2018). An empirical biometric-based study for user identification with different neural networks in the online game League of Legends. Proceedings of the International Joint Conference on Neural Networks, 2018-J (2018-J). http://doi.org/10.1109/IJCNN.2018.8489164

Da Silva, V.R., De AraújoSilva, J.C.G., & Da Costa Abreu, M. (2018). A new Brazilian hand-based behavioural biometrics database: Data collection and analysis. IET Seminar Digest, 2016 (2016). http://doi.org/10.1049/ic.2016.0085

Bittencourt, V.G., Da Costa Abreu, M., Souto, M.C.P.D., Costa, J.A.F., & Canuto, A.M.P. (2016). Aplicação de Multiclassificadores Heterogêneos no Reconhecimento de Classes Estruturais de Proteínas. Anais do 7. Congresso Brasileiro de Redes Neurais. http://doi.org/10.21528/cbrn2005-186

Da Silva Beserra, I., Camara, L., & Da Costa Abreu, M. (2016). Using keystroke and mouse dynamics for user identification in the online collaborative game League of Legends. IET Seminar Digest, 2016 (2016). http://doi.org/10.1049/ic.2016.0076

Erbilek, M., Fairhurst, M., & Da Costa Abreu, M. (2013). Age prediction from iris biometrics. 5th International Conference on Imaging for Crime Detection and Prevention, ICDP 2013. http://doi.org/10.1049/ic.2013.0258

Fairhurst, M., & Da Costa Abreu, M. (2012). Using keystroke dynamics for gender identification in social network environment. 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011). http://doi.org/10.1049/ic.2011.0124

Abreu, M., & Fairhurst, M. (2011). Combining multiagent negotiation and an interacting verification process to enhance biometric-based identification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6583 LNCS, 95-105. http://doi.org/10.1007/978-3-642-19530-3_9

Fairhurst, M., & Abreu, M. (2009). An investigation of predictive profiling from handwritten signature data. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1305-1309. http://doi.org/10.1109/ICDAR.2009.94

Abreu, M., & Fairhurst, M. (2009). Improving forgery detection in off-line forensic signature processing. IET Seminar Digest, 2009 (2). http://doi.org/10.1049/ic.2009.0234

Abreu, M., & Fairhurst, M. (2009). Improving identity prediction in signature-based unimodal systems using soft biometrics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5707 LNCS, 348-356. http://doi.org/10.1007/978-3-642-04391-8_45

Abreu, M., & Fairhurst, M. (2008). Analyzing the impact of non-biometric information on multiclassifier processing for signature recognition applications. BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems. http://doi.org/10.1109/BTAS.2008.4699355

Abreu, M., & Fairhurst, M. (2008). An empirical comparison of individual machine learning techniques in signature and fingerprint classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5372 LNCS, 130-139. http://doi.org/10.1007/978-3-540-89991-4_14

Canuto, A.M.P., Santana, L.E.A., Abreu, M.C.C., & Xavier, J.C. (2008). An analysis of data distribution in the ClassAge system: An agent-based system for classification tasks. Neurocomputing, 71 (16-18), 3319-3325. http://doi.org/10.1016/j.neucom.2008.01.032

Abreu, M.C.C., & Canuto, A.M.P. (2007). An experimental study on the importance of the choice of the ensemble members in fuzzy combination methods. Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007, 723-728. http://doi.org/10.1109/ISDA.2007.4389693

Abreu, M.C.C., & Canuto, A.M.P. (2007). Evaluating the influence of the choice of the ensemble members in some fuzzy combination methods. IEEE International Conference on Neural Networks - Conference Proceedings, 448-453. http://doi.org/10.1109/IJCNN.2007.4370998

Da Costa Abreu, M., & Canuto, A.M.P. (2007). An Experimental Study on the Importance of the Choice of the Ensemble Members in Fuzzy Combination Methods. Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). http://doi.org/10.1109/isda.2007.35

Canuto, A.M.P., & Abreu, M.C.C. (2007). Using fuzzy, neural and fuzzy-neural combination methods in ensembles with different levels of diversity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4668 LNCS (PART 1), 349-359. http://doi.org/10.1007/978-3-540-74690-4_36

Santana, L.E.O., Canuto, A.M.P., & Abreu, M.C.C. (2006). A neuro-fuzzy-based agent system with data distribution among the agents for classification tasks. Proceedings of the Ninth Brazilian Symposium on Neural Networks, SBRN'06. http://doi.org/10.1109/SBRN.2006.6

Santana, L.E.O., Canuto, A.M.P., & Abreu, M.C.C. (2006). Analyzing the performance of an agent-based neural system for classification tasks using data distribution among the agents. IEEE International Conference on Neural Networks - Conference Proceedings, 2951-2958. http://doi.org/10.1109/ijcnn.2006.247229

Abreu, M.C.D.C., & Canuto, A.M.P. (2006). Analyzing the benefits of using a fuzzy-neuro model in the accuracy of the NeurAge system: An agent-based system for classification tasks. IEEE International Conference on Neural Networks - Conference Proceedings, 2959-2966. http://doi.org/10.1109/ijcnn.2006.247251

Abreu, M.C.C., Canuto, A.M.P., & Santana, L.E.A.S. (2005). A comparative analysis of negotiation methods for a multi-neural agent system. Proceedings - HIS 2005: Fifth International Conference on Hybrid Intelligent Systems, 2005, 451-456. http://doi.org/10.1109/ICHIS.2005.3

Bittencourt, V.G., Abreu, M.C.C., De Souto, M.C.P., & Canuto, A.M.D.P. (2005). An empirical comparison of individual machine learning techniques and ensemble approaches in protein structural class prediction. Proceedings of the International Joint Conference on Neural Networks, 1, 527-531. http://doi.org/10.1109/IJCNN.2005.1555886

Canuto, A.M.P., De Melo Oliveira, L., Xavier, J.C., Santos, A.D.M., & Abreu, M.C.C. (2005). Performance and diversity evaluation in hybrid and non-hybrid structures of ensembles. Proceedings - HIS 2005: Fifth International Conference on Hybrid Intelligent Systems, 2005, 285-290. http://doi.org/10.1109/ICHIS.2005.87

Da Costa Abreu, M., Nascimento, T.M.L.D., Oliveira, A.V.M.D., & Santana, L.E.A.D.S. (2021). Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics. In 11th International Conference on Pattern Recognition Systems, Curico, Chile, 17 March 2021 - 19 March 2021 (pp. 1). IET

Da Costa Abreu, M., Rocha, L., & Silla Jr., C. (2021). A methodology for procedural piano music composition with mood templates using genetic algorithms. In 11th International Conference on Pattern Recognition Systems, Curico, Chile, 17 March 2021 - 19 March 2021. IET

Book chapters

(2017). Age predictive biometrics: predicting age from iris characteristics. In Iris and Periocular Biometric Recognition. (pp. 213-234). Institution of Engineering and Technology: http://doi.org/10.1049/pbse005e_ch10

Other activities

Postgraduate Research Tutor in Computing and Informatics.

Postgraduate supervision

I have had more than 30 research-based supervions (http://lattes.cnpq.br/2234040548103596)

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