A dexterous feature selection artificial immune system algorithm for keystroke dynamics

The main problem in security protecting the computer or resources from intruders. The password and username are the most common means to provide security. But this method has many loop holes such as password sharing, shoulder surfing, brute-force attack, dictionary attack, guessing, and many more. Keystroke dynamics is one popular and inexpensive behavioral biometric technology, which identifies the authenticity of a user when the user is working via a keyboard. Keystroke features like dwell time and flight time of every user are evaluated in this paper by preprocessing techniques such as Hausdroff timing, mean, median, and standard deviation. The artificial immune system is used for feature selection, and comparison between preprocessing techniques is shown.     

具有间断扩散性质的线性约束全局优化随机算法

研究带线性约束的非凸全局优化问题,在有效集算法的基础上提出了一个具有间断扩散性质的随机微分方程算法,讨论了算法的理论性质和收敛性,证明了算法以概率收敛到问题的全局最优解,最后列出了数值实验效果.     

Analytic solution and estimation of parameters on a stochastic exponential model for a technological diffusion process

In this paper we examine the behaviour of a stochastic model that describes a technological diffusion process (continuously increasing process). Furthermore we obtain a solution for the proposed model through the estimation of the volatility using three different approximations. The adjustment of real data to the final stochastic model confirms its ability of describing and forecasting real cases.     

Modelling the CPI using a lognormal diffusion process and implications on forecasting inflation

The Maximum Likelihood estimator is used within a lognormaldiffusion process and closed form analytical solutions are obtained.The monthly CPI forecasts are estimated for the period between1970 and 2002. The quarterly estimates of inflation rates areobtained from monthly forecasts rather than from quarterly data.This has significantly improved the estimates of inflation rates.The model also produced a superior fit as compared to randomwalk and GARCH(p,q)-M models. The adopted approach is foundto be simple, economical and generally suitable for modellingstochastic processes that reflect aggregation over time stemmingfrom many factors, and in which the transition path betweenconsecutive states is relatively smooth.     

Modelling and forecasting vehicle stocks using the trends of stochastic Gompertz diffusion models: The case of Spain

In the present study, we treat the stochastic homogeneous Gompertz diffusion process (SHGDP) by the approach of the Kolmogorov equation. Firstly, using a transformation in diffusion processes, we show that the probability transition density function of this process has a lognormal time‐dependent distribution, from which the trend and conditional trend functions and the stationary distribution are obtained. Second, the maximum likelihood approach is adapted to the problem of parameters estimation in the drift and the diffusion coefficient using discrete sampling of the process, then the approximated asymptotic confidence intervals of the parameter are obtained. Later, we obtain the corresponding inference of the stochastic homogeneous lognormal diffusion process as limit from the inference of SHGDP when the deceleration factor tends to zero. A statistical methodology, based on the above results, is proposed for trend analysis. Such a methodology is applied to modelling and forecasting vehicle stocks. Finally, an application is given to illustrate the methodology presented using real data, concretely the total vehicle stocks in Spain. Copyright © 2008 John Wiley & Sons, Ltd.     

Nonlinear stochastic time-fractional slow and fast diffusion equations on Rd

This paper studies the nonlinear stochastic partial differential equation of fractional orders both in space and time variables: $\left({?}^{\beta }+\frac{\nu }{2}{(?\Delta )}^{\alpha ∕2}\right)u(t,x)=I_t^{\gamma }\left[\rho \left(u(t,x)\right)\stackrel{?}{W}(t,x)\right],t>0,x\in {\mathbb{R}}^d,$where $\stackrel{?}{W}$ is the space–time white noise, $\alpha \in (0,2]$, $\beta \in (0,2)$, $\gamma \ge 0$ and $\nu >0$. Fundamental solutions and their properties, in particular the nonnegativity, are derived. The existence and uniqueness of solution together with the moment bounds of the solution are obtained under Dalang’s condition: $d<2\alpha +\frac{\alpha }{\beta }min(2\gamma ?1,0)$. In some cases, the initial data can be measures. When $\beta \in (0,1]$, we prove the sample path regularity of the solution.     

Design of a fixed-interval smoother using covariance information based on the innovations approach in linear discrete-time stochastic systems

This paper describes a design for a recursive least-squares Wiener fixed-interval smoother using the covariance information in linear discrete-time stochastic systems. The estimators require information from the observation matrix, the system matrix for the state variable, related to the signal, the variance of the state variable, the cross-variance function of the state variable with the observed value and the variance of the white observation noise. It is assumed that the signal is observed with additive white noise.