Source: https://recodbr.wordpress.com/
Timestamp: 2019-04-18 23:30:53+00:00

Document:
The list below highlights some recent publication of Prof. Ricardo Torres and his research team from the last two years. Enjoy!
Werneck, R. O. ; Almeida, W. ; Stein, B. ; Pazinato, D. ; Mendes, P. R. ; Penatti, O. A. B. ; Rocha, A. ; Torres, R. da S. . Kuaa: A unified framework for design, deployment, execution, and recommendation of machine learning experiments. Future Generation Computer Systems-The International Journal of eScience, v. 78, p. 59-76, 2018.
Silva, F. B. ; Werneck, R. O. ; Goldenstein, S. K. ; Tabbone, S. ; Torres, R. da S. . Graph-based Bag-of-Words for Classification. PATTERN RECOGNITION, v. 74, p. 266-285, 2018.
Pereira, L. A.M. ; Torres, R. da S. . Semi-Supervised Transfer Subspace for Domain Adaptation. PATTERN RECOGNITION, v. 75, p. 235-249, 2018.
Neira, M. A. C. ; Mendes Junior, P. R. ; Rocha, A. ; Torres, R. da S. . Data-Fusion Techniques for Open-set Recognition Problems. IEEE Access, v. 6, p. 21242-21265, 2018.
Esmael, A. ; Santos, J. A. ; Torres, R. da S. . On the ensemble of multiscale object-based classifiers for aerial images: a comparative study. MULTIMEDIA TOOLS AND APPLICATIONS, v. 77, p. 24565-24592, 2018.
Mariano, G. C. ; Staggemeier, V. G. ; Morellato, L. P. C. ; Torres, R. da S. . Multivariate cyclical data visualization using radial visual rhythms: A case study in phenology analysis. Ecological Informatics, v. 46, p. 19-35, 2018.
Nogueira, K. ; Fadel, S. G. ; Dourado, I. C. ; Werneck, R. O. ; Muñoz, J. M. ; Penatti, O. A. B. ; Calumby, R. T. ; Li, L. T. ; Santos, J. A. ; TORRES, R. da Silva . Exploiting ConvNet Diversity for Flooding Identification. IEEE Geoscience and Remote Sensing Letters, v. 15, p. 1446-1450, 2018.
Calumby, R. T. ; Gonçalves, M. A. ; Torres, R. da S. . Diversity-based Interactive Learning meets Multimodality. NEUROCOMPUTING, v. 259, p. 159-175, 2017.
Pisani, F. ; Pedronette, D. C. G. ; Torres, R. da S. ; Borin, E. . Contextual Spaces Re-Ranking: accelerating the Re-sort Ranked Lists step on heterogeneous systems. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, v. 29, p. e3962, 2017.
Santos, J. M. ; Moura, E. S. ; Silva, A. S. ; Torres, R. da S. . Color and texture applied to a signature-based bag of visual words method for image retrieval. MULTIMEDIA TOOLS AND APPLICATIONS, v. 76, p. 16855-16872, 2017.
Mendes Junior, P. R. ; de Souza, R. M. ; Werneck, R. de O. ; Stein, B. V. ; Pazinato, D. V. ; de Almeida, W. R. ; Penatti, O. A. B. ; Torres, R. da S. ; Rocha, A. . Nearest neighbors distance ratio open-set classifier. MACHINE LEARNING, v. 106, p. 359-386, 2017.
Pedronette, D. C. G. ; Torres, R. da S. . Unsupervised Rank Diffusion for Content-Based Image Retrieval. NEUROCOMPUTING, v. 260, p. 478-489, 2017.
Almeida, A. E. ; Torres, R. da S. . Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions. IEEE Geoscience and Remote Sensing Letters, v. 14, p. 1499-1503, 2017.
Alberton, B. ; Torres, R. da Silva ; Cancian, L. ; Borges, B. D. ; Almeida, J. G. A. ; Mariano, G. ; Santos, J. A. ; Morellato, L. P. C. . Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation. Perspectives in Ecology and Conservation, v. 15, p. 82-90, 2017.
Moura, F. A. ; Marche, A. L. ; Caetano, F. G. ; Torres, R. da S. ; Martins, L. E. B. ; Cunha, S. . Analysis of high-intensity efforts of Brazilian professional soccer players. Human Movement, v. 0, p. 3-10, 2017.
A team of RECOD researchers participated for the second time on the melanoma classification task at the melanoma diagnostic challenge promoted by the International Skin Imaging Collaboration. This year the number of participants grew 6 times regarding last edition, reaching 77 participants — our team got 6th place. Competitors all around the world submitted their approaches, including private companies focused on smart skin analytics.
RECOD also participated on the tasks of lesion segmentation and lesion attribute detection. RECOD’s participation in those tasks is detailed in a technical report (submitted before the official ranking was announced).
The global results will be discussed at the upcoming Medical Image Computing & Computer Assisted Intervention (MICCAI 2018), where RECOD team will also present two papers (out of ten selected) at the ISIC Skin Image Analysis Workshop, regarding new approaches for dermatologic dataset extension: techniques for data augmentation and synthetic image generation with GANs.
We gave a preview of our synthetic image generation with GANs at the 2nd International Educational Symposium of the Melanoma World on Rio de Janeiro (August, 2018), were it got the 2nd place at the best poster awards.
The melanoma team is composed by professors Eduardo Valle and Sandra Avila, Ph.D. student Michel Fornaciali, and M.Sc. students Alceu Bissoto, Fábio Perez and Vinícius Ribeiro, all RECOD members.
A recent research article in partnership of Recodists Prof. Anderson Rocha, Prof. Sandra Avila and Luiz Navarro and researchers at the Faculty of Pharmacy led by Prof. Rodrigo Catharino published at Frontiers in Bioengineering and Biotechnology journal is getting the media attention. The article, entitled “A machine learning application based in random forest for integrating mass spectrometry-based metabolomic data: a simple screening method for patients with zika virus”, presents a powerful solution against the analysis of the Zika virus presence based on high-resolution mass spectrometry data and machine-learning prediction model.
Since both mass spectrometry and machine learning approaches are well-established and largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening — faster and more accurate — with improved cost-effectiveness when compared to existing technologies.

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