FABIO JOSE AYRES

FABIO JOSE AYRES

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Exploração de dados textuais abertos usando IBM Watson
(2021) Moço, Emanuelle Silva; Klabin, Gabriel Magalhães Duarte; Bicalho, Maria Eduarda Peppes; Pina, Roger Ribeiro Fava
Este projeto teve como objetivo criar um showcase de aplicação do Watson, ferramenta de inteligência artificial da IBM, para possíveis clientes da empresa. Para isso, quatro diferentes soluções da suíte IBM Watson foram utilizadas para criar um chatbot que ajuda um usuário a explorar notícias e também descobrir tendências de assuntos na mídia brasileira. O Watson Assistant foi utilizado na estruturação do chatbot; o Watson Natural Language Understanding para enriquecer os dados, fazer reconhecimento de temas e criação de um modelo por aprendizado de máquina que reconhece a polaridade política de um texto; o Cloudant para armazenamento dos dados. Uma interface web foi criada usando o framework ReactJS, para a interação com o usuário, e um servidor em Node.js, cujo deploy foi realizado com a ferramenta Cloud Foundry, para demonstrar o produto. Por fim, um tutorial de construção deste showcase também foi produzido para atrair clientes que busquem desenvolver suas próprias aplicações Watson in-house.
Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms
(2010) Rangayyan, Rangaraj M.; Nguyen, Thanh M.; FABIO JOSE AYRES; Nandi, Asoke K.
The effect of pixel resolution on texture features computed using the gray-level co-occurrence matrix (GLCM) was analyzed in the task of discriminating mammographic breast lesions as benign masses or malignant tumors. Regions in mammograms related to 111 breast masses, including 65 benign masses and 46 malignant tumors, were analyzed at pixel sizes of 50, 100, 200, 400, 600, 800, and 1,000 μm. Classification experiments using each texture feature individually provided accuracy, in terms of the area under the receiver operating characteristics curve (AUC), of up to 0.72. Using the Bayesian classifier and the leave-one-out method, the AUC obtained was in the range 0.73 to 0.75 for the pixel resolutions of 200 to 800 μm, with 14 GLCM-based texture features using adaptive ribbons of pixels around the boundaries of the masses. Texture features computed using the ribbons resulted in higher classification accuracy than the same features computed using the corresponding regions within the mass boundaries. The t test was applied to AUC values obtained using 100 repetitions of random splitting of the texture features from the ribbons of masses into the training and testing sets. The texture features computed with the pixel size of 200 μm provided the highest average AUC with statistically highly significant differences as compared to all of the other pixel sizes tested, except 100 μm.
Well-Connected Communities in Real-World and Synthetic Networks
(2023) Park, Minhyuk; Tabatabaee, Yasamin; Ramavarapu, Vikram; Liu, Baqiao; Pailodi, Vidya Kamath; Ramachandran, Rajiv; Korobskiy, Dmitriy; FABIO JOSE AYRES; Chacko, George; Warnow, Tandy
Integral to the problem of detecting communities through graph clustering is the expectation that they are "well connected". In this respect, we examine five different community detection approaches optimizing different criteria: the Leiden algorithm optimizing the Constant Potts Model, the Leiden algorithm optimizing modularity, Iterative K-Core Clustering (IKC), Infomap, and Markov Clustering (MCL). Surprisingly, all these methods produce, to varying extents, communities that fail even a mild requirement for well connectedness. To remediate clusters that are not well connected, we have developed the "Connectivity Modifier" (CM), which, at the cost of coverage, iteratively removes small edge cuts and re-clusters until all communities produced are well connected. Results from real-world and synthetic networks illustrate a tradeoff users make between well connected clusters and coverage, and raise questions about the "clusterability" of networks and models of community structure.
Extração e classificação de licitações do Diário Oficial do Estado de SP
(2021) Satyro, Vitor; Liu, Vitor; Delchiaro, Lucca; Schoueri, Guilherme
Este projeto tem como objetivo o desenvolvimento de uma ferramenta modularizada de extração dos documentos do Diário Oficial do estado de São Paulo e identificação de licitações. Essa ferramenta é a base para um desenvolvimento de um produto capaz de categorizar e resumir informações de grande quantidade de documentos, sendo também um produto customizável às necessidades específicas da DELL. De acordo com o cliente, a aplicação precisa de módulos de coleta de dados (scraper), identificação de textos dos documentos, separação de seções de documentos, identificador de licitações e mecanismo de busca entre licitações, sendo todos esses módulos adaptáveis à demanda. Para isso, foi necessário estudar tecnologias como: scrapper, tratamento de imagens, reconhecimento óptico de caracteres, modelos de classificação, mecanismos de buscas e utilização de um banco de dados não relacional. Dado que a principal demanda do cliente é o tratamento dos textos do diário oficial e identificação de licitações, todos os módulos da ferramenta apresentaram-se eficazes no que diz respeito ao tempo esperado e da saída obtida, incluindo a extração de textos de arquivos pdf e classificação com random forest.
Search of shipwrecked people using drone swarms (part 2)
(2024) Oliveira, Jorás Custódio Campos de; Andrade, Pedro Henrique Britto Aragão; Falcão, Renato Laffranchi; Rodrigues, Ricardo Ribeiro
The project's purpose is to iterate on the given multi-agent Drone Swarm Search Environment (DSSE) and research into Reinforcement Learning methods. The DSSE was created with the direct purpose of using reinforcement learning algorithms to train swarms of drones to execute autonomous maritime search and rescue missions of shipwrecked people in the ocean. The environment simulates the movement of persons-in-water (PIW) considering the ocean's dynamic circumstances and calculates a dynamic map of probabilities to be given to the agents, with two distinct environments, one for rescue scenarios with simulated PIW and a second expanding on state-of-the-art research for maritime coverage search path planning. The DSSE facilitates the training and visualization of drone behavior, the project emphasizes continuous improvement and open accessibility, with the release of the DSSE as an open-source Python package and documentation. The focus is on the continuous improvement of simulation quality and applicability of the environments for research purposes, with development, training and evaluation of Reinforcement learning algorithms to improve the path planning of autonomous agents, for search and rescue maritime scenarios.
The Lysyl Oxidase Inhibitor, b-Aminopropionitrile, Diminishes the Metastatic Colonization Potential of Circulating Breast Cancer Cells
(2009) Bondareva, Alla; Downey, Charlene M.; FABIO JOSE AYRES; Liu, Wei; Boyd, Steven K.; Hallgrimsson, Benedikt; Jirik, Frank R.
Lysyl oxidase (LOX), an extracellular matrix remodeling enzyme, appears to have a role in promoting breast cancer cell motility and invasiveness. In addition, increased LOX expression has been correlated with decreases in both metastases-free, and overall survival in breast cancer patients. With this background, we studied the ability of b-aminopropionitrile (BAPN), an irreversible inhibitor of LOX, to regulate the metastatic colonization potential of the human breast cancer cell line, MDA MB-231. BAPN was administered daily to mice starting either 1 day prior, on the same day as, or 7 days after intracardiac injection of luciferase expressing MDA-MB-231-Luc2 cells. Development of metastases was monitored by in vivo bioluminescence imaging, and tumor-induced osteolysis was assessed by micro-computed tomography (mCT). We found that BAPN administration was able to reduce the frequency of metastases. Thus, when BAPN treatment was initiated the day before, or on the same day as the intra-cardiac injection of tumor cells, the number of metastases was decreased by 44%, and 27%, and whole-body photon emission rates (reflective of total tumor burden) were diminished by 78%, and 45%, respectively. In contrast, BAPN had no effect on the growth of established metastases. Our findings suggest that LOX activity is required during extravasation and/or initial tissue colonization by circulating MDA-MB-231 cells, lending support to the idea that LOX inhibition might be useful in metastasis prevention.
Criação automática de legendas para fotos de imóveis
(2021) Teracini, Felippe Nalin; Azambuja, Pedro Oliveira de; Moraes, Rachel Pereira Bottino de; Rosenzvaig, Rafael Vieira
Este projeto teve como objetivo desenvolver um método de criação de legendas automáticas para fotos de cômodos de imóveis, a fim de facilitar o trabalho de fotógrafos que gastam tempo classificando manualmente um grande volume de fotos diariamente. O método desenvolvido consiste na criação e treinamento de modelos de redes neurais utilizando a biblioteca Keras. Além da criação de modelos de aprendizado de máquina, também foi desenvolvida uma pipeline que auxilia na análise da performance do treinamento dos modelos
Identificação de culturas de plantio por imagens de satélite
(2023) Carmo, Amanda Rosa do; Hirschheimer, Carolina; Mitu, Gabriela Yukari; Queiroga, Nicolas Maciel
Este projeto tem por objetivo desenvolver uma solução de análise e predição em imagens de satélites de culturas de plantio, que seja capaz de segmentar as imagens e identificar as diferentes plantações agrícolas nela presentes. Para tanto, será investigado o uso de um algoritmo de deep learning com arquitetura baseada em redes neurais convolucionais para criar um modelo de segmentação de imagens provenientes da missão de satélites Sentinel-2 e predição dos padrões de cultivo. O algoritmo funcional será então conectado a uma interface web, que permitirá aos potenciais usuários buscarem por características de plantio de determinada região do Brasil.
Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis
(2010) Rangayyan, Rangaraj M.; Zhu, Xiaolu; FABIO JOSE AYRES; Ells, Anna L.
We propose a method using Gabor filters and phase portraits to automatically locate the optic nerve head (ONH) in fundus images of the retina. Because the center of the ONH is at or near the focal point of convergence of the retinal vessels, the method includes detection of the vessels using Gabor filters, detection of peaks in the node map obtained via phase portrait analysis, and an intensity-based condition. The method was tested on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database and 81 images from the Structured Analysis of the Retina (STARE) database. An ophthalmologist independently marked the center of the ONH for evaluation of the results. The evaluation of the results includes free-response receiver operating characteristics (FROC) and a measure of distance between the manually marked and detected centers. With the DRIVE database, the centers of the ONH were detected with an average distance of 0.36 mm (18 pixels) to the corresponding centers marked by the ophthalmologist. FROC analysis indicated a sensitivity of 100% at 2.7 false positives per image. With the STARE database, FROC analysis indicated a sensitivity of 88.9% at 4.6 false positives per image.
Gabor filters and phase portraits for the detection of architectural distortion in mammograms
(2006) Rangayyan, Rangaraj M.; FABIO JOSE AYRES
Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost Always heterogeneous in nature; furthermore, viable Architectural distortion is a subtle abnormality in mammograms, and a source of overlooking errors by radiologists. Computer-aided diagnosis (CAD) techniques can improve the performance of radiologists in detecting masses and calcifications; however, most CAD systems have not been designed to detect architectural distortion. We present a new method to detect and localise architectural distortion by analysing the oriented texture in mammograms. A bank of Gabor filters is used to obtain the orientation field of the given mammogram. The curvilinear structures (CLS) of interest (spicules and fibrous tissue) are separated from confounding structures (pectoral muscle edge, parenchymal tissue edges, breast boundary, and noise). The selected core CLS pixels and the orientation field are filtered and downsampled, to reduce noise and also to reduce the computational effort required by the subsequent methods. The downsampled orientation field is analysed to produce three phase portrait maps: node, saddle, and spiral. The node map is further analysed in order to detect the sites of architectural distortion. The method was tested with 19 mammograms containing architectural distortion. In a preliminary experiment, a sensitivity of 84% was obtained at 7.8 false positives per image.

FABIO JOSE AYRES

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