Kernel rootkits are a special category of malware that are deployed directly in the kernel and hence have unmitigated reign over the functionalities of the kernel itself. We seek to detect such rootkits that are deployed in the real world by first observing how the majority of kernel rootkits operate. To this end, comparable to how rootkits function in the real world, we write our own kernel ro...

This paper points out that many search relevance models in information retrieval, such as the Vector Space Model, BM25 and Language Models for Information Retrieval, can be viewed as a similarity function between pairs of objects of different types, referred to as an S-function. An S-function is specifically defined as the dot product between the images of two objects in a Hilbert space mapped ...

Abstract The Laplace transform method is applied in this article to study the semi-Hyers-Ulam-Rassias stability of a Volterra integro-differential equation order n, with convolution-type kernel. This kind extends original Hyers-Ulam whose originated 1940. A general integral formulated first, and then some particular cases (polynomial function exponential function) for from kernel are considered.

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

We construct genRBF kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data into the function space and to define a kernel between two missing data points based...

In the study of smoothing spline estimators, some convolution-kernellike properties of the Green’s function for an appropriate boundary value problem, depending on the design density, are needed. For the uniform density, the Green’s function can be computed more or less explicitly. Then, integral equation methods are brought to bear to establish the kernel-like properties of said Green’s functi...

Microarray technique measures gene expression levels under various conditions, simultaneously. Microarray data are successfully analyzed by kernel methods for a variety of applications. A major drawback of microarray is technically error prone. To gain accurate analysis, we propose a method which produces a noise-tolerant kernel matrix. First of all, we devise a new distance function for microa...

Abstract This study employed soft computing techniques, namely, support vector machine (SVM) and Gaussian process regression (GPR) to predict the properties of a scour hole [depth (ds) length (Ls)] in diversion channel flow system. The considered different geometries channels (angles bed widths) hydraulic conditions. Four kernel function models for each technique (polynomial function, normalize...

In most kernel based machine learning algorithms and non-parametric statistics the key computational task is to compute a linear combination of local kernel functions centered on the training data, i.e., f(x) = ∑N i=1 qik(x, xi), which is the discrete Gauss transform for the Gaussian kernel. f is the regression/classification function in case of regularized least squares, Gaussian process regre...