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This paper analyzes two existing methods for securing Git repositories, Git-encrypt and Git-crypt, by comparing their performance relative to the default Git implementation. Securing a Git repository is necessary when the repository contains sensitive or restricted data. This allows the repository to be stored on any third-party cloud provider with assurance that even if the repository data is leaked,...
This paper discusses the problem of clustering data changing over time, a research domain that is attracting increasing attention due to the increased availability of streaming data in the Web 2.0 era. In the analysis conducted throughout the paper we make use of the kernel spectral clustering with memory (MKSC) algorithm, which is developed in a constrained optimization setting. Since the objective...
In this paper, we highlight a design of Gaussian kernels for online model selection by the multikernel adaptive filtering approach. In the typical multikernel adaptive filtering, the maximum value that each kernel function can take is one. This means that, if one employs multiple Gaussian kernels with multiple variances, the one with the largest variance would become dominant in the kernelized input...
In this paper, we consider the characteristics of the kernel adaptive filters for the mixture of linear and non-linear environments. We first consider employing a linear kernel as one of the kernels in multi-kernel adaptive filters. It is pointed out that the convergence characteristics of the filter corresponding to the linear kernel is affected by the selection of the other kernels. Then, we propose...
The expandable and dynamic web which is a huge repository for information is growing at lightning speed and hence it is hard to find the relevant information from the web. Efficient algorithms reduce the burden of search engines up to a great extent. Query classification is one such aspect and thus a valuable asset for a search engine. Everyday millions of web queries are posted on the web. The main...
A method is presented for authenticating people on the basis of lip movement. It uses the kernel mutual subspace (KMS) method using fusion of canonical angles by kernel Fisher discriminant analysis. Its authentication accuracy is better than that of previously proposed lip-movement authentication methods when the distribution of lip images has a nonlinear structure. The similarity of KMS is canonical...
In this article, a model migration strategy based on subspace separation is proposed for process monitoring by taking advantage of common information between an old process and a new process. Firstly, a global basis vector is extracted and deemed to enclose the cross-set similar correlations. Then two different subspaces are separated from each other in the new dataset. The kernel principal component...
In this work, we put forward a new adaptation criterion, namely the hybrid criterion (HC), which is a mixture of the traditional mean square error (MSE) and the maximum correntropy criterion (MCC). The HC criterion is developed from the viewpoint of the least trimmed squares (LTS) estimator, a high breakdown estimator that can avoid undue influence from outliers. In the LTS estimator, the data are...
As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. Similarity search, as a basic operator for many machine learning and data mining algorithms, has been extensively studied before, leading to several efficient solutions...
Lasso simultaneously conducts variable selection and supervised regression. In this paper, we extend Lasso to multiple output prediction, which belongs to the categories of structured learning. Though structured learning makes use of both input and output simultaneously, the joint feature mapping in current framework of structured learning is usually application-specific. As a result, ad hoc heuristics...
In this paper, we present a rich image representation which is robust to illumination, facial expression and scale variations. For this aim, firstly, we propose a novel dense local image representation method based on Walsh Hadamard Transform (WHT) called Local WHT (LWHT). LWHT is the application of WHT to each pixel of an image to decompose it into multiple components, called LWHT maps. Secondly,...
Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an efficient and scalable MIL algorithm named miFV. Our algorithm maps the original MIL bags into a new feature...
Anomaly detection starts from a model of normalbehavior and classifies departures from this model as anomalies. This paper introduces a statistical non-parametric approach for anomaly detection that is based on a multivariate extension of the Poisson point process model for univariateextremes. The method is demonstrated on both a synthetic and a real-world data set, the latter being an unbalanced...
In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is...
We propose an approach suitable to learn multiple time-varying models jointly and discuss an application in data-driven weather forecasting. The methodology relies on spectral regularization and encodes the typical multi-task learning assumption that models lie near a common low dimensional subspace. The arising optimization problem amounts to estimating a matrix from noisy linear measurements within...
Food-related photos have become increasingly very popular, due to social networks, food recommendation and dietary assessment systems. Reliable annotation is essential in those systems, but user-contributed tags are often non-informative and inconsistent, and unconstrained automatic food recognition still has relatively low accuracy. Most works focus on exploiting only the visual content while ignoring...
This paper employs sparse Bayesian approach to enable the Probabilistic Classification Vector Machine (PCVM) to select a relevant subset of features. Because of probabilistic outputs and the ability to automatically optimize the regularization items, the sparse Bayesian framework has shown great advantages in real-world applications. However, the Gaussian priors that introduce the same prior to different...
The diffusion least mean squares (LMS) [1] algorithm gives faster convergence than the original LMS in a distributed network. Also, it outperforms other distributed LMS algorithms like spatial LMS and incremental LMS [2]. However, both LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable [3]. A variant of LMS called kernel-LMS (KLMS) has been...
Malware writers use increasingly complex evasion mechanisms to ensure the concealment of malware against standard anti-malware suites. To identify malware through its behaviour, rather than its approach is an interesting venue of exploration. System call traces are highly indicative of a process behaviour. However, it is difficult to acquire system calls of all processes running on a physical machine...
Chord represents the back-bone of occidental music genre as it contains rich harmonic information which is useful for various music applications such as music genre classification or music retrieval. Hence, chord recognition or transcription is of importance for music representation. In this paper we focus on chord recognition and especially investigate different features representation used in such...
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