To keep mobile devices secure, experts recommend turning on auto-updates for applications, but recent research has suggested that users often avoid auto-updating because updates can lead to undesirable consequences such as user interface changes or compatibility issues. Understanding whether there are commonalities amongst users who avoid auto-updates can help us create better mobile applicatio...

We introduce a novel training principle for generative probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework generalizes Denoising Auto-Encoders (DAE) and is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. The transition distribution is a conditiona...

In the research described by this paper, we implemented and evaluated a linear self-organized feedforward neural network for image compression. Based on the Generalized Hebbian Learning Algorithm (GHA), the neural network extracts the principle components from the auto-correlation matrix of the input images. To do so, an image is first divided into mutually exclusive square blocks of size m x m...

Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurat...