Aplicação de Técnicas de Inteligência Computacional para Detecção de Fraude em Comércio Eletrônico

O volume de transações eletrônicas cresceu significativamente nosúltimos anos, devido principalmente à popularização dos serviços de comer-cio eletrônico. Com essa popularização aumentou consideravelmente os casosde fraudes, resultando assim em um prejuízo de bilhões de dólares em todoo mundo. Assim, é necessário prover mecanismos capazes de auxiliar na de-tecção de fraude, foco principal deste projeto de pesquisa. Este trabalho deiniciação científica visa a modelagem e aplicação de técnicas de inteligênciacomputacional para detectar fraudes em transações eletrônicas, mais especifi-camente em operações envolvendo cartão de crédito, utilizando as técnicas deRegressão Logística e Redes Bayesianas. Para avaliar a eficácia das técnicas,definimos o conceito de Eficiência Econômica e o aplicamos a um cenário realdo serviço de pagamento eletrônico mais popular do Brasil. Nossos resulta-dos apresentaram bom desempenho para detecção de fraude, com um ganho de35,61% em relação ao cenário real da empresa

Uso de Regressão Logística para Modelar e Avaliar a Credibilidade em Aplicações Web

A popularização das aplicações Web tem feito surgir novos serviços acada dia, demandado mecanismos que assegurem a credibilidade desses servi-ços. Neste trabalho utilizamos regressão logística para modelar e avaliar a cre-dibilidade de um serviço da Web, considerando diferentes critérios associadosao serviço e seus fornecedores. A fim de validar nossa metodologia, executamosexperimentos usando uma base de dados real, a partir da qual avaliamos essesmodelos de credibilidade. Os resultados obtidos são muito bons, apresentandoganhos representativos, quando comparados à linha-de-base, mostrando assimque a metodologia proposta é promissora e pode ser usada para fortalecer aconfiança dos usuários nos serviços providos na Web

Correlations between climatic variations and the spread of the new coronavirus in Brazil

Assim que foi identificada, em pleno inverno boreal, surgiram hipóteses otimistas a respeito do avanço da doença na zona intertropical. Entretanto, meses depois, o Brasil possui mais de 3 milhões de casos confirmados e 100 mil mortes, ocupando a segunda posição mundial, a despeito de enorme subnotificação (11/08/2020). A rápida propagação do vírus em tão pouco tempo tem desafiado pesquisadores de diversas áreas do conhecimento. No âmbito da Climatologia, os primeiros trabalhos publicados no Brasil e no exterior apresentaram resultados ambíguos sobre a relação entre a disseminação da doença e variações na temperatura do ar

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

The Brazilian Developments on the Regional Atmospheric Modeling System (BRAMS 5.2): An Integrated Environmental Model Tuned for Tropical Areas

We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System where different previous versions for weather, chemistry and carbon cycle were unified in a single integrated software system. The new version also has a new set of state-of-the-art physical parameterizations and greater computational parallel and memory usage efficiency. Together with the description of the main features are examples of the quality of the transport scheme for scalars, radiative fluxes on surface and model simulation of rainfall systems over South America in different spatial resolutions using a scale-aware convective parameterization. Besides, the simulation of the diurnal cycle of the convection and carbon dioxide concentration over the Amazon Basin, as well as carbon dioxide fluxes from biogenic processes over a large portion of South America are shown. Atmospheric chemistry examples present model performance in simulating near-surface carbon monoxide and ozone in Amazon Basin and Rio de Janeiro megacity. For tracer transport and dispersion, it is demonstrated the model capabilities to simulate the volcanic ash 3-d redistribution associated with the eruption of a Chilean volcano. Then, the gain of computational efficiency is described with some details. BRAMS has been applied for research and operational forecasting mainly in South America. Model results from the operational weather forecast of BRAMS on 5 km grid spacing in the Center for Weather Forecasting and Climate Studies, INPE/Brazil, since 2013 are used to quantify the model skill of near surface variables and rainfall. The scores show the reliability of BRAMS for the tropical and subtropical areas of South America. Requirements for keeping this modeling system competitive regarding on its functionalities and skills are discussed. At last, we highlight the relevant contribution of this work on the building up of a South American community of model developers

Lying in Wait: The Resurgence of Dengue Virus After the Zika Epidemic in Brazil

After the Zika virus (ZIKV) epidemic in the Americas in 2016, both Zika and dengue incidence declined to record lows in many countries in 2017-2018, but in 2019 dengue resurged in Brazil, causing ~2.1 million cases. In this study we use epidemiological, climatological and genomic data to investigate dengue dynamics in recent years in Brazil. First, we estimate dengue virus force of infection (FOI) and model mosquito-borne transmission suitability since the early 2000s. Our estimates reveal that DENV transmission was low in 2017-2018, despite conditions being suitable for viral spread. Our study also shows a marked decline in dengue susceptibility between 2002 and 2019, which could explain the synchronous decline of dengue in the country, partially as a result of protective immunity from prior ZIKV and/or DENV infections. Furthermore, we performed phylogeographic analyses using 69 newly sequenced genomes of dengue virus serotype 1 and 2 from Brazil, and found that the outbreaks in 2018-2019 were caused by local DENV lineages that persisted for 5-10 years, circulating cryptically before and after the Zika epidemic. We hypothesize that DENV lineages may circulate at low transmission levels for many years, until local conditions are suitable for higher transmission, when they cause major outbreaks

The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated environmental model tuned for tropical areas

We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS), in which different previous versions for weather, chemistry, and carbon cycle were unified in a single integrated modeling system software. This new version also has a new set of state-of-the-art physical parameterizations and greater computational parallel and memory usage efficiency. The description of the main model features includes several examples illustrating the quality of the transport scheme for scalars, radiative fluxes on surface, and model simulation of rainfall systems over South America at different spatial resolutions using a scale aware convective parameterization. Additionally, the simulation of the diurnal cycle of the convection and carbon dioxide concentration over the Amazon Basin, as well as carbon dioxide fluxes from biogenic processes over a large portion of South America, are shown. Atmospheric chemistry examples show the model performance in simulating near-surface carbon monoxide and ozone in the Amazon Basin and the megacity of Rio de Janeiro. For tracer transport and dispersion, the model capabilities to simulate the volcanic ash 3-D redistribution associated with the eruption of a Chilean volcano are demonstrated. The gain of computational efficiency is described in some detail. BRAMS has been applied for research and operational forecasting mainly in South America. Model results from the operational weather forecast of BRAMS on 5 km grid spacing in the Center for Weather Forecasting and Climate Studies, INPE/Brazil, since 2013 are used to quantify the model skill of near-surface variables and rainfall. The scores show the reliability of BRAMS for the tropical and subtropical areas of South America. Requirements for keeping this modeling system competitive regarding both its functionalities and skills are discussed. Finally, we highlight the relevant contribution of this work to building a South American community of model developers.CNPqFAPESPEarth System Research Laboratory at the National Oceanic and Atmospheric Administration (ESRL/NOAA), Boulder, USAInst Nacl Pesquisas Espaciais, Ctr Previsao Tempo & Estudos Climat, Cachoeira Paulista, SP, BrazilDiv Ciência da Computação, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, BrazilUniv Estadual Paulista Unesp, Fac Ciencias, Bauru, SP, BrazilCtr Meteorol Bauru IPMet, Bauru, SP, BrazilUniv Fed Sao Paulo, Dept Ciencias Ambientais, Diadema, SP, BrazilUniv Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, Sao Paulo, SP, BrazilUniv Fed Campina Grande, Dept Ciencias Atmosfer, Campina Grande, PB, BrazilEmbrapa Informat Agr, Campinas, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos, SP, BrazilUniv Fed Rio Grande do Norte, Dept Ciencias Atmosfer & Climat, Programa Pos Grad Ciencias Climat, Natal, RN, BrazilInst Nacl Pesquisas Espaciais, Ctr Ciencias Sistema, Sao Jose Dos Campos, SP, BrazilUniv Fed Sao Joao Del Rei, Dept Geociencias, Sao Joao Del Rei, MG, BrazilInst Nacl Pesquisas Espaciais, Lab Associado Computacao & Matemat Aplica, Sao Jose Dos Campos, BrazilUniv Evora, Inst Ciencias Agr & Ambientais Mediterr, Evora, PortugalUniv Lusofona Humanidades & Tecnol, Ctr Interdisciplinar Desenvolvimento Ambient Gest, Lisbon, PortugalUniv Fed Pelotas, Fac Meteorol, Pelotas, RS, BrazilUnive Tecnol Fed Parana, Londrina, PR, BrazilNASA, Goddard Space Flight Ctr, Univ Space Res Assoc, Goddard Earth Sci Technol & Res Global Modeling &, Greenbelt, MD USAUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos, SP, BrazilCNPq: 306340/2011-9FAPESP: 2014/01563-1FAPESP: 2015/10206-0FAPESP: 2014/01564-8Web of Scienc

Association Between Preexisting Versus Newly Identified Atrial Fibrillation and Outcomes of Patients With Acute Pulmonary Embolism

Background Atrial fibrillation (AF) may exist before or occur early in the course of pulmonary embolism (PE). We determined the PE outcomes based on the presence and timing of AF. Methods and Results Using the data from a multicenter PE registry, we identified 3 groups: (1) those with preexisting AF, (2) patients with new AF within 2 days from acute PE (incident AF), and (3) patients without AF. We assessed the 90-day and 1-year risk of mortality and stroke in patients with AF, compared with those without AF (reference group). Among 16 497 patients with PE, 792 had preexisting AF. These patients had increased odds of 90-day all-cause (odds ratio [OR], 2.81; 95% CI, 2.33-3.38) and PE-related mortality (OR, 2.38; 95% CI, 1.37-4.14) and increased 1-year hazard for ischemic stroke (hazard ratio, 5.48; 95% CI, 3.10-9.69) compared with those without AF. After multivariable adjustment, preexisting AF was associated with significantly increased odds of all-cause mortality (OR, 1.91; 95% CI, 1.57-2.32) but not PE-related mortality (OR, 1.50; 95% CI, 0.85-2.66). Among 16 497 patients with PE, 445 developed new incident AF within 2 days of acute PE. Incident AF was associated with increased odds of 90-day all-cause (OR, 2.28; 95% CI, 1.75-2.97) and PE-related (OR, 3.64; 95% CI, 2.01-6.59) mortality but not stroke. Findings were similar in multivariable analyses. Conclusions In patients with acute symptomatic PE, both preexisting AF and incident AF predict adverse clinical outcomes. The type of adverse outcomes may differ depending on the timing of AF onset.info:eu-repo/semantics/publishedVersio