Coleção de Artigos Acadêmicos
URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227
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18 resultados
Resultados da Pesquisa
Artigo Científico SNA-Based Reasoning for Multi-Agent Team Composition(2015) ANDRE FILIPE DE MORAES BATISTA; Marietto, Maria das Graças BrunoThe social network analysis (SNA), branch of complex systems can be used in the construction of multi agent systems. This paper proposes a study of how social network analysis can assist in modeling multi agent systems, while addressing similarities and differences between the two theories. We built a prototype of multi-agent systems for resolution of tasks through the formation of teams of agents that are formed on the basis of the social network established between agents. Agents make use of performance indicators to assess when should change their social network to maximize the participation in teams.Artigo Científico An investigation of the distribution of gaze estimation errors in head mounted gaze trackers using polynomial functions(2018) Mardanbegi, Diako; ANDREW TOSHIAKI NAKAYAMA KURAUCHI; Morimoto, Carlos H.Second order polynomials are commonly used for estimating the point-of-gaze in head-mounted eye trackers. Studies in remote (desktop) eye trackers show that although some non-standard 3rd order polynomial models could provide better accuracy, high-order polynomials do not necessarily provide better results. Different than remote setups though, where gaze is estimated over a relatively narrow field-of-view surface (e.g. less than 30x20 degrees on typical computer displays), head-mounted gaze trackers (HMGT) are often desired to cover a relatively wider field-of-view to make sure that the gaze is detected in the scene image even for extreme eye angles. In this paper we investigate the behavior of the gaze estimation error distribution throughout the image of the scene camera when using polynomial functions. Using simulated scenarios, we describe effects of four different sources of error: interpolation, extrapolation, parallax, and radial distortion. We show that the use of third order polynomials result in more accurate gaze estimates in HMGT, and that the use of wide angle lenses might be beneficial in terms of error reduction.Artigo Científico Identification of alterations associated with age in the clustering structure of functional brain networks(2018) Guzman, Grover E. C.; Sato, Joao R.; MACIEL CALEBE VIDAL; Fujita, AndreInitial studies using resting-state functional magnetic resonance imaging on the trajectories of the brain network from childhood to adulthood found evidence of functional integration and segregation over time. The comprehension of how healthy individuals’ functional integration and segregation occur is crucial to enhance our understanding of possible deviations that may lead to brain disorders. Recent approaches have focused on the framework wherein the functional brain network is organized into spatially distributed modules that have been associated with specific cognitive functions. Here, we tested the hypothesis that the clustering structure of brain networks evolves during development. To address this hypothesis, we defined a measure of how well a brain region is clustered (network fitness index), and developed a method to evaluate its association with age. Then, we applied this method to a functional magnetic resonance imaging data set composed of 397 males under 31 years of age collected as part of the Autism Brain Imaging Data Exchange Consortium. As results, we identified two brain regions for which the clustering change over time, namely, the left middle temporal gyrus and the left putamen. Since the network fitness index is associated with both integration and segregation, our finding suggests that the identified brain region plays a role in the development of brain systems.Artigo Científico Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder(2021) Ribeiro, Adèle Helena; MACIEL CALEBE VIDAL; Sato, João Ricardo; Fujita, AndréGraphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks’ time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models’ parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal’s application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain’s right to the left hemisphere is different between ASD and controls.Artigo Científico Gabor filters and phase portraits for the detection of architectural distortion in mammograms(2006) Rangayyan, Rangaraj M.; FABIO JOSE AYRESSegmentation 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.Artigo Científico Three-Dimensional Segmentation of the Tumor in Computed Tomographic Images of Neuroblastoma(2007) Deglint, Hanford J.; Rangayyan, Rangaraj M.; FABIO JOSE AYRES; Boag, Graham S.; Zuffo, Marcelo K.Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost Always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to therapy and in the planning of delayed surgery for resection of the tumor. We propose methods to achieve 3-dimensional segmentation of the neuroblastic tumor. In our scheme, some of the normal structures expected in abdominal CT images are delineated and removed from further consideration; the remaining parts of the image volume are then examined for the tumor mass. Mathematical morphology, fuzzy connectivity, and other image processing tools are deployed for this purpose. Expert knowledge provided by a radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are incorporated into the segmentation algorithm. In this preliminary study, the methods were tested with 10 CT exams of four cases from the Alberta Children’s Hospital. False-negative error rates of less than 12% were obtained in eight of the 10 exams; however, seven of the exams had false-positive error rates of more than 20% with respect to manual segmentation of the tumor by a radiologist.Artigo Científico A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs(2007) Rangayyan, Rangaraj M.; FABIO JOSE AYRES; Desautels, J.E. LeoMammography is the best available tool for screening for the early detection of breast cancer. Mammographic screening has been shown to be effective in reducing breast cancer mortality rates: screening programs have reduced mortality rates by 30–70%. Mammograms are difficult to interpret, especially in the screening context. The sensitivity of screening mammography is affected by image quality and the radiologist's level of expertise. Computer-aided diagnosis (CAD) technology can improve the performance of radiologists, by increasing sensitivity to rates comparable to those obtained by double reading, in a cost-effective manner. Current research is directed toward the development of digital imaging and image analysis systems that can detect mammographic features, classify them, and provide visual prompts to the radiologist. Radiologists would like the ability to change the contrast of a mammogram, either manually or with pre-selected settings. Computer techniques for detecting, classifying, and annotating diagnostic features on the images would be desirable. This paper presents an overview of digital image processing and pattern analysis techniques to address several areas in CAD of breast cancer, including: contrast enhancement, detection and analysis of calcifications, detection and analysis of masses and tumors, analysis of bilateral asymmetry, and detection of architectural distortion. Although a few commercial CAD systems have been released, the detection of subtle signs of breast cancer such as global bilateral asymmetry and focal architectural distortion remains a difficult problem. We present some of our recent works on the development of image processing and pattern analysis techniques for these applications.Artigo Científico Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis(2008) Rangayyan, Rangaraj M.; Prajna, Shormistha; FABIO JOSE AYRES; Desautels, J. E. LeoObjective Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms taken prior to the detection of breast cancer. Materials and methods A method based on Gabor filters and phase portrait analysis was used to detect initial candidates for sites of architectural distortion. A total of 386 regions of interest (ROIs) were automatically obtained from 14 “prior mammograms”, including 21 ROIs related to architectural distortion. From the corresponding set of 14 “detection mammograms”, 398 ROIs were obtained, including 18 related to breast cancer. For each ROI, the fractal dimension and Haralick’s texture features were computed. The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. Results The average fractal dimension of the normal (false-positive) ROIs was significantly higher than that of the ROIs with architectural distortion (p = 0.006). For the “prior mammograms”, the best receiver operating characteristics (ROC) performance achieved, in terms of the area under the ROC curve, was 0.80 with a Bayesian classifier using four features including fractal dimension, entropy, sum entropy, and inverse difference moment. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.79 at 8.4 false positives per image in the detection of sites of architectural distortion in the “prior mammograms”. Conclusion Fractal dimension offers a promising way to detect the presence of architectural distortion in prior mammograms.Artigo Científico Particle learning for Bayesian semi-parametric stochasticvolatility model(2019) Virbickaitė, Audronė; HEDIBERT FREITAS LOPES; Ausín, M. Concepción; Galeano, PedroThis article designs a Sequential Monte Carlo (SMC) algorithm for estimation of Bayesian semi-parametric Stochastic Volatility model for financial data. In particular, it makes use of one of the most recent particle filters called Particle Learning (PL). SMC methods are especially well suited for state-space models and can be seen as a cost-efficient alternative to Markov Chain Monte Carlo (MCMC), since they allow for online type inference. The posterior distributions are updated as new data is observed, which is exceedingly costly using MCMC. Also, PL allows for consistent online model comparison using sequential predictive log Bayes factors. A simulated data is used in order to compare the posterior outputs for the PL and MCMC schemes, which are shown to be almost identical. Finally, a short real data application is included.Artigo Científico Dynamically consistent objective and subjective rationality(2022) Bastianello, Lorenzo; JOSÉ HELENO FARO; Santos, AnaA group of experts, for instance climate scientists, is to advise a decision maker about the choice between two policies f and g. Consider the following decision rule. If all experts agree that the expected utility of f is higher than the expected utility of g, the unanimity rule applies, and f is chosen. Otherwise, the precautionary principle is implemented and the policy yielding the highest minimal expected utility is chosen. This decision rule may lead to time inconsistencies when adding an intermediate period of partial resolution of uncertainty. We show how to coherently reassess the initial set of experts’ beliefs so that precautionary choices become dynamically consistent: new beliefs should be added until one obtains the smallest “rectangular set” that contains the original one. Our analysis offers a novel behavioral characterization of rectangularity and a prescriptive way to aggregate opinions in order to avoid sure regret.