Happy Holidays!

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MSc. Guilherme Fôlego and Ph.D. José Ramon Pires defenses

The RECODists Guilherme Adriano Fôlego and José Ramon Trindade Pires have earned their MSc. and Ph.D. degrees, respectively, recently at IC/Unicamp. The details are shown below:


Guilherme Adriano Fôlego 

Date/time: 18/12/2018 at 2pm

Advisor: Anderson Rocha

Co-advisor:  Marina Weiler

Title: ADNet: Diagnóstico Assistido por Computador para Doença de Alzheimer Usando Rede Neural Convolucional 3D com Cérebro Inteiro

Examination board:

  1. Leticia Rittner (FEEC/Unicamp)
  2. Sandra Eliza Fontes de Avila (IC/Unicamp)

José Ramon Trindade Pires

Date/time: 20/12/2018 at 2pm

Advisor: Anderson Rocha

Co-advisor:  Jacques Wainer

Title: Image Analytics Techniques for Diabetic Retinopathy Detection

Examination board:

  1. Agma Juci Machado Traina (ICMC/USP)
  2. Roberto Marcondes César Junior (IME/USP)
  3. Ricardo da Silva Torres ( IC/Unicamp)
  4. Levy Boccato (FEEC/Unicamp)
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Prof. Ricardo Torres recent published papers

The list below highlights some recent publication of Prof. Ricardo Torres and his research team from the last two years. Enjoy!

  1. 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.
  2. 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.
  3. Pereira, L. A.M. ; Torres, R. da S. . Semi-Supervised Transfer Subspace for Domain Adaptation. PATTERN RECOGNITION, v. 75, p. 235-249, 2018.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Calumby, R. T. ; Gonçalves, M. A. ; Torres, R. da S. . Diversity-based Interactive Learning meets Multimodality. NEUROCOMPUTING, v. 259, p. 159-175, 2017.
  9. 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.
  10. 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.
  11. 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.
  12. Pedronette, D. C. G. ; Torres, R. da S. . Unsupervised Rank Diffusion for Content-Based Image Retrieval. NEUROCOMPUTING, v. 260, p. 478-489, 2017.
  13. 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.
  14. 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.
  15. 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.
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DéjàVu on the news against child pornography and fake news

FAPESP DéjàVu project keeps attracting the worldwide attention uppon our solutions applied against child pornography and fake news, in particular during the 2018 presidential elections. They are (in Portuguese):

Saiba como inteligência artificial é usada para criar fake news (Câmera Record – 15/10/2018)

 

Brasileiros usam tecnologia para ‘salvar o mundo’ e ganham grana do Google (UOL Tecnologia – 23/10/2018)

Agora vai? Cientistas entram na caçada às notícias falsas e seus autores (UOL Tecnologia – 25/10/2018)

Invited by INTERPOL to present our research work “Leveraging deep neural networks to fight child pornography in the age of social media” in the 36th Interpol’s Expert Meeting in Singapore

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Three Ph.D. thesis defense: Allan, Luis e Pedro

The RECODists Allan da Silva Pinto, Luís Augusto Martins Pereira and Pedro Ribeiro Mendes Júnior have earned their Ph.D. degrees recently at IC/Unicamp. The details are shown below:


Allan da Silva Pinto

Date/time: 06/09/2018 at 2pm

Advisor: Anderson Rocha

Co-advisor: Hélio Pedrini

Title: Analysis of Intrinsic and Extrinsic Properties of Biometric Samples for Presentation Attack Detection

Examination board:

  1. Aparecido Nilceu Marana (DC/UNESP)
  2. Siovani Cintra Felipussi (CCGT/UFSCar)
  3. Sandra Eliza Fontes de Avila (IC/Unicamp)
  4. José Mário De Martino (FEEC/Unicamp)

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Luís Augusto Martins Pereira

Date/time: 27/07/2018 at 2pm

Advisor: Ricardo Torres

Title: Domain Adaptation via Subspace Learning and Kernel Methods

Examination board:

  1. Aparecido Nilceu Marana (DC/UNESP)
  2. Alexandre Luís Magalhães Levada (DC/UFSCar)
  3. Marco Antonio Garcia de Carvalho (FT/Unicamp)
  4. Roberto Alencar Lotufo (FEEC/Unicamp)

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Pedro Ribeiro Mendes Júnior

Date/time: 14/09/2018 at 2pm

Advisor: Anderson Rocha

Title: Open-set recognition for different classifiers

Examination board:

  1. Roberto Hirata Junior (IME/USP)
  2. André Carlos Ponce de Leon Ferreira de Carvalho (ICMC/USP)
  3. Hélio Pedrini (IC/Unicamp)
  4. Fernanda Alcântara Andaló (IC/Unicamp)

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Granted Patent: Open set classifier

Samsung and RECOD have the honor to inform a GRANTED PATENT at the USPTO (USA).

The document is: “METHOD FOR MULTICLASS CLASSIFICATION IN OPEN-SET SCENARIOS AND USES THEREOF”, publication number US 14/532,580 or as it was known by the nickname of “OPEN SET CLASSIFIER”.

This patent was filed in November 04th, 2014 and allowanced at November 01st , 2018.

This application claims the priority benefit of Brazilian Patent Application No. BR 10 2014 023780-1, filed in September 25th, 2014, at INPI (BR).

“The proposed method is used for classification in open-set scenarios, wherein often it is not possible to first obtain the training data for all possible classes that may arise during the testing stage. During the test phase, test samples belonging to one of the classes used in the training phase are classified based on a ratio between similarity scores, as known correct class and test samples belonging to any other class are to be rejected and classified as unknown.”

Congratulations to all inventors from Unicamp and Samsung who worked jointly in this project:

Anderson Rocha (Coordinator)
Pedro Ribeiro Mendes Júnior
Roberto Medeiros de Souza
Ricardo da Silva Torres
Bernardo Vecchia Stein
Daniel Vatanabe Pazinato
Waldir Rodrigues de Almeida
Rafael de Oliveira Werneck
Otavio Augusto Bizetto Penatti – Samsung

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RECOD is Top 10 on international competition for melanoma classification

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.

Ph.D. student Michel Fornaciali at the 2nd International Educational Symposium of the Melanoma World on Rio de Janeiro (August, 2018)

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DéjàVu Talks on August 2018

Graph models for machine learning: K-associated graphs and Attribute-based Decision Graphs
Prof. João Bertini (FT/Limeira) [slides] [video]

Abstract: Graph-based methods consist of a powerful form of data representation and abstraction. Among other features, they allow uncovering topological relationships, visualizing structures, representing groups of data with different formats, and providing alternative measures to characterize the data. However, the majority of traditional graph-based methods, in general, presents a high computational complexity order, which limits the scope of application of these methods. In this context, alternative approaches based on graphs, such as K-associated graphs and Attribute-based Decision Graphs, present low computational complexity and, at the same time, have the advantages of graph-based learning. This talk will address general aspects of both models, as well as some of their applications to machine learning tasks, such as supervised and semi-supervised classification, learning from data streams, imputation of missing attribute values and enhancement of the data quality for classification tasks.

When: Thursday August 16th @11am.
Where: IC Amphitheater.

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Reverse engineering of video content for forensic analysis: model-based and data-driven approaches.
Prof. Paolo Bestagini (Politecnico di Milano, Italy) [slides] [video]

Abstract: The recent development of multimedia devices and editing tools, together with the proliferation of video sharing websites, has made the acquisition, alteration, and diffusion of video content relatively easy tasks. As a consequence, it is possible to find more and more video sequences available on the Internet, but each of them has been potentially tampered with by anyone. It is then clear that the development of tools that enable the recovery of past history of video sequences in order to prove their origin and authenticity is more than an urgent necessity. In this talk, we discuss some of the video forensic techniques that have proposed in the last few years. Specifically, we will focus on blind forensic methods exploiting either model-based or data-driven solutions.

Mini vita: Paolo Bestagini received the B.Sc. and M.Sc. in Telecommunications Engineering at the Politecnico di Milano in 2008 and 2010, respectively. Since 2011, he joined the Image and Sound Processing Group (ISPG) at the Politecnico di Milano, and he received his Ph.D. in Information Technology in 2014. Since 2016 he is Assistant Professor at the Image and Sound Processing Group, Politecnico di Milano. His research interests focus on multimedia forensics and acoustic signal processing for microphone arrays. He is an elected member of the IEEE Information Forensics and Security Technical Committee, and a co-organizer of the IEEE Signal Processing Cup 2018.

When: Friday August 17th @2pm.
Where: IC Amphitheater

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A simple screening method for patients with zika virus

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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Image Provenance Analysis at Scale

In this article, the authors present a fully automated large-scale end-to-end pipeline that starts with the step of provenance image filtering (over millions of images) and ends up with the provenance graphs. The most immediate application of provenance image filtering is forensics, where the detection of manipulated images spans traditional policing to analysis for strategic intelligence. The question of the origins of suspect images has taken a prominent role recently, with the rise of so-called “fake news” on the Internet.

A comprehensive set of experiments for each stage of the pipeline is provided, comparing the proposed solution with state-of-the-art results, employing previously published datasets. In addition, this work introduces a new dataset of real-world provenance cases from the social media site Reddit, along with baseline results.

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