KBOC: Keystroke Biometrics OnGoing Competition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents the first Keystroke Biometrics Ongoing evaluation platform and a Competition (KBOC) organized to promote reproducible research and establish a baseline in person authentication using keystroke biometrics. The ongoing evaluation tool has been developed using the BEAT platform and includes keystroke sequences (fixedtext) from 300 users acquired in 4 different sessions. In addition, the results of a parallel offline competition based on the same data and evaluation protocol are presented. The results reported have achieved EERs as low as 5.32%, which represent a challenging baseline for keystroke recognition technologies to be evaluated on the new publicly available KBOC benchmarkA.M. and M. G.-B. are supported by a JdC contract (JCI-2012- 12357) and a FPU Fellowship from Spanish MINECO and MCD, respectively. J.M. and J.C. are supported by CAPES and CNPq (grant 304853/2015-1). This work was partially funded by the projects: CogniMetrics (TEC2015-70627-R) from MINECO FEDER and BEAT (FP7-SEC-284989) from E

Modelo probabilístico para análises de ruído do tráfego urbano usando sinais sonoros reais

Vehicular traffic is pointed out as a major source of urban noise pollution today. In this paper, we evaluated the precision of a new probabilistic model for urban traffic noise analyses. The proposed model adopts real sound signals and the Monte Carlo method in simulations. Probability distributions of traffic variables were obtained in-situ on two urban roads. The acoustic signals and corresponding energies of single pass-by of vehicles were obtained using sound signal recordings on test tracks under free-field condition. The model simulates vehicular traffic noise on urban roads in free or in traffic light controlled flow and considers the influence of bus stops. The proposed model calculates different acoustic descriptors, such as Statistical sound levels (LA10 and LA90), Equivalent continuous sound level (LAeq), Traffic noise index (TNI) and Noise pollution level (LNP). Furthermore, it allows the listening of simulated noise. The experimental results indicate that the proposed model is reliable and accurate for vehicular traffic noise prediction.O tráfego veicular é apontado como uma importante fonte de poluição sonora urbana nos dias de hoje. Neste artigo, foi avaliada a precisão de um novo modelo probabilístico para análises de ruído do tráfego urbano. O modelo proposto adota sinais sonoros reais e o método Monte Carlo nas simulações. As distribuições de probabilidade das variáveis de tráfego foram obtidas in-situ em duas ruas urbanas. Os sinais acústicos e as energias correspondentes das passagens individuais de veículos foram obtidos usando gravações de sinais sonoros em pistas de teste sob condições de campo livre. O modelo simula o ruído do tráfego veicular em ruas urbanas com fluxo livre ou fluxo controlado por semáforos, e considera a influência das paradas de ônibus. O modelo proposto calcula diferentes descritores acústicos, tais como, Níveis sonoros estatísticos (LA10 e LA90), Nível sonoro contínuo equivalente (LAeq), Índice de ruído de tráfego (TNI) e Nível de poluição sonora (LNP). Além disso, o modelo permite a escuta de ruídos simulados. Os resultados experimentais indicam que o modelo proposto é confiável e preciso para a previsão de ruído do tráfego veicular

Modelo probabilístico para análises de ruído do tráfego urbano usando sinais sonoros reais

Vehicular traffic is pointed out as a major source of urban noise pollution today. In this paper, we evaluated the precision of a new probabilistic model for urban traffic noise analyses. The proposed model adopts real sound signals and the Monte Carlo method in simulations. Probability distributions of traffic variables were obtained in-situ on two urban roads. The acoustic signals and corresponding energies of single pass-by of vehicles were obtained using sound signal recordings on test tracks under free-field condition. The model simulates vehicular traffic noise on urban roads in free or in traffic light controlled flow and considers the influence of bus stops. The proposed model calculates different acoustic descriptors, such as Statistical sound levels (LA10 and LA90), Equivalent continuous sound level (LAeq), Traffic noise index (TNI) and Noise pollution level (LNP). Furthermore, it allows the listening of simulated noise. The experimental results indicate that the proposed model is reliable and accurate for vehicular traffic noise prediction.O tráfego veicular é apontado como uma importante fonte de poluição sonora urbana nos dias de hoje. Neste artigo, foi avaliada a precisão de um novo modelo probabilístico para análises de ruído do tráfego urbano. O modelo proposto adota sinais sonoros reais e o método Monte Carlo nas simulações. As distribuições de probabilidade das variáveis de tráfego foram obtidas in-situ em duas ruas urbanas. Os sinais acústicos e as energias correspondentes das passagens individuais de veículos foram obtidos usando gravações de sinais sonoros em pistas de teste sob condições de campo livre. O modelo simula o ruído do tráfego veicular em ruas urbanas com fluxo livre ou fluxo controlado por semáforos, e considera a influência das paradas de ônibus. O modelo proposto calcula diferentes descritores acústicos, tais como, Níveis sonoros estatísticos (LA10 e LA90), Nível sonoro contínuo equivalente (LAeq), Índice de ruído de tráfego (TNI) e Nível de poluição sonora (LNP). Além disso, o modelo permite a escuta de ruídos simulados. Os resultados experimentais indicam que o modelo proposto é confiável e preciso para a previsão de ruído do tráfego veicular

Avaliação da acustica de recintos pelo metodo dos elementos finitos

Orientador: Jose Geraldo ChiquitoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia EletricaResumo: O comportamento das ondas acústicas é calculado aproximadamente para recintos com teto e piso paralelos, pelo método dos elementos finitos. Um conjunto de programas é concebido e implementado para encontrar as matrizes de elementos finitos e, por meio dessas, calcular as freqüências de ressonância dos modos e respectivas constantes de atenuação. Os resultados são analisados considerando a qualidade acústica dos recintosAbstract: The two-dimensional acoustic waves behavior is calculated in rooms with, parallel ceiling and floor using, the finite element approximation. Programs are developed in order to find the finite element matrices and to calculate the resonance frequencies of the modes and decay rates from the matrices. The results are analyzed having in mind the acoustic quality of the roomsMestradoMestre em Engenharia Elétric

Comparing GMM and parzen in automatic signature recognition : a step backward or forward ?

International audienceThe use of Gaussian Mixture Models (GMM), adapted through the Expectation Minimization algorithm, is quite widespread in automatic verification (Biometric) tasks. Its choice is, at a first glance, founded on the good qualities of GMM models when aimed at approximating Probability Density Functions (PDF) of random variables. But biometric models for verification are frequently adapted from small sample sets of biometric signals, since in real applications subjects are not willing to accept long enrollment sessions. This well known constraint raises a problem of balance between model complexity and sample size. From this perspective, we show, through simple online signature verification experiments, that constrained GMM with fewer degrees of freedom, compared to GMM with full covariance matrices, provide better performances. Moreover, pushing this argument even further, we also show that a Parzen model (seen here as a over-constrained GMM) can be even better than usual GMM, in terms of Equal Error Ratio (EER)O uso de Gaussian Mixture Models (GMM), adaptados atraves do algoritmo iterativo Expectation Minimization , e comum em tarefas de verificacao automatica de individuos (Biometria). Sua escolha, a primeira vista, e bem fundamentada nas boas caracteristicas do GMM como ferramenta para modelar Funcoes Densidade de Probabilidade de variaveis aleatorias. Mas os modelos em biometria, nao raramente, sao adaptados/aprendidos a partir de pequenos conjuntos de amostras, pois em aplicacoes reais, os individuos podem nao aceitar longas sessoes de cadastros. Esta restricao, bem conhecida em biometria, pode gerar problemas de balaco entre complexidade de um modelo de probabilidade e a quantidade de dados disponiveis para o seu aprendizado. A partir desta perspectiva, atraves de experimentos simples de verificacao pela assinatura online, este trabalho aponta evidencias de que modelos com menos garus de liberdade que os GMM com matrizes de covariancia cheias, incluindo GMM regularizados, provem melhores resultados. Mais ainda, tambem e mostrado que um simples modelo Parzen (visto aqui como um GMM sobre-regularizado) pode ser melhor que os GMM usuais, em termos de Equal Error Ratio (EER)

Sound event detection in remote health care - Small learning datasets and over constrained Gaussian Mixture Models

International audienceThe use of Gaussian Mixture Models (GMM), adapted through the Expectation Minimization(EM)algorithm, is not rare in Audio Analysis for Surveillance Applications and Environmental sound recognition. Their use, at a first glance, is founded on the good qualities of GMM models when aimed at approximating Probability Density Functions(PDF) of random variables. But in some cases, where models are to be adapted from small sample sets of specific and locally recorded signals, instead of large but generic databases, a problem of balance between model complexity and sample size may play an important role. From this perspective, we show, through simple sound classification experiments, that constrained GMM, with fewer degrees of freedom, as compared to GMM with full covariance matrices, provide better classification performances. Moreover, pushing this argument even further, we also show that a Parzen model (seen here as an over-constrained GMM) can do even better than usual GMM, in terms of classification error rati

A Michigan-like immune-inspired framework for performing independent component analysis over Galois fields of prime order

sem informaçãosem informaçãoIn this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial optimization problem associated with a minimal entropy configuration adopting a Michigan-like population structure. The simulation results reveal that the strategy is capable of reaching a performance similar to that of standard methods for lower-dimensional instances with the advantage of also handling scenarios with an elevated number of sources. (C) 2013 Elsevier B.V. All rights reserved.In this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial opt96B153163FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTIFICO E TECNOLOGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTIFICO E TECNOLOGICOsem informaçãosem informaçãosem informaçãosem informaçã