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This paper examines the existence of efficiently implementable approximations of a general real linear dimensionality reduction (LDR) operator. The specific focus is on approximating a given LDR operator with a partial circulant structured matrix (a matrix whose rows are related by circular shifts) as these constructions allow for low-memory footprint and computationally efficient implementations...
We present an algorithm that computes exactly (optimally) the S-sparse (1≤S<D) maximum-L1-norm-projection principal component of a real-valued data matrix X ∈ ℝD×N that contains N samples of dimension D. For fixed sample support N, the optimal L1-sparse algorithm has linear complexity in data dimension, O(D). For fixed dimension D (thus, fixed sparsity S), the optimal L1-sparse algorithm has polynomial...
Keyframe extraction is one of the basic procedures relating to video retrieval and summary. It consists on presenting an abstract of the video with the most representative frames. This paper presents an efficient keyframe extraction approach based on local description and graph modularity clustering. The first step is to generate a set of candidate keyframes using a windowing rule in order to reduce...
Static program analysis is a technique to analyse code without executing it, and can be used to find bugs in source code. Many open source and commercial tools have been developed in this space over the past 20 years. Scalability and precision are of importance for the deployment of static code analysis tools - numerous false positives and slow runtime both make the tool hard to be used by development,...
State-of-the-art methods for traffic signs detection based feature extraction have got a high recall rate, but the detection rates are not ideal for some mistakenly detected. In this work, this paper presents a method of traffic signs detection based on HOG and Boolean Convolutional Neural Networks (HOG-BCNN). A cascade classifier is trained based on HOG to detect the candidate regions of traffic...
Sparse code multiple access (SCMA), as a promising non-orthogonal multiple access scheme for the 5G system, aims to achieve massive connections and grant-free transmission in the radio access scenario. In this paper, we propose a blind detection scheme for the uplink grant-free SCMA transmission based on a novel sparsity-inspired sphere decoding (SI-SD) algorithm. By introducing one additional all-zero...
Falls are a critical public health issue among elderly people that requires continuous monitoring. This paper presents a patient-specific single sensor fall detection system that utilizes a tri-axial accelerometer data measured from the patient's trouser pocket to distinguish between activities of daily living (ADL) and falls. The proposed system which is implemented on FPGA provides the following...
Spam over Internet telephony (SPIT) is recognized as a new threat for voice communication services such as voice over Internet protocol (VoIP). Due to the privacy reason, it is desired to detect SPITters (SPIT callers) in a VoIP service without training data. Although a clustering-based unsupervised SPITters detection scheme has been proposed, it does not work well when the SPITters account for a...
Epileptic seizures are recurring brief episodes of abnormal excessive or synchronous neuronal activity in the brain, and are often accompanied by changes in various autonomic functions like heart rate (HR). A better approach for detecting epileptic seizures is by using electrocardiogram (ECG) signals because ECG acquisition is relatively easier as compared to EEG. In this paper a new technique is...
A drawback of the Search Engine (SE) based anti-phishing technique is that authentic websites that are newly launched over the web are classified as phishing websites due to their low ranking. This causes a noticeable reduction in the True Negative Rate (TNR) of such schemes. Some SE based techniques use other complex anti-phishing techniques or features in combination to increase the TNR which results...
Image quality assessment gains a greater interest due to development of digital imaging and storage. In that field, structural similarity (SSIM) index has been shown to favorably agree with human perceptual assessment, significantly outperforming the method of mean squared error, i.e., L2 distance. The similarity measure function in SSIM which compares a target (distorted) image with its reference...
Spatial image classification meant to the mechanism of extracting meaningful knowledge information classes from spatial images dataset. Many traditional pixel based image classification techniques such as Support Vector Machines (SVM), ANN, Fuzzy methods, Decision Trees (DT) etc. exist. The performance and accuracy of these image classification methods depends upon the network structure and number...
Cocaine dependence devastates millions of human lives. Despite of a variety of treatments, there is a very high rate of individual relapse to drug use. In the last decade, functional magnetic resonance imaging (fMRI) proved to be a powerful tool to diagnose and understand different pathologies. This work provides advances in the identification of cocaine dependence and in the relapse prediction based...
Multi-label classification has attracted many attentions in various fields, such as text categorization and semantic image annotation. Aiming to classify an instance into multiple labels, various multi-label classification methods have been proposed. However, the existing methods typically build models in the identical feature (sub)space for all labels, possibly inconsistent with real-world problems...
our task of video copy detection system aims to locate vicdeo segments that are partially copied or near-duplicated versions from an archive of reference videos. In 2010, video copy detection problem was sometimes considered as a solved problem, since previous research within this area used either small-scale or large-scale datasets (e.g. TRECVID 2009, Muscle-VCD) with pre-defined simulated videos...
The Complexity-Entropy Causality Plane (CECP) is a representation space with two dimensions: normalized permutation entropy (Hs) and Jensen-Shannon complexity (Cjs). CECP has wide found applications in non-linear dynamic analysis to classify a given signal according to its randomness and complexity which is a motivation to investigate its application for machine fault diagnostics. In this work we...
The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the...
We investigate where and how key dependency structure between measures of network activity change throughout the course of daily activity. Our approach to data-mining is probabilistic in nature, we formulate the identification of dependency patterns as a regularised statistical estimation problem. The resulting model can be interpreted as a set of time-varying graphs and provides a useful visual interpretation...
Ensemble methods for classification have been effectively used for decades, while for outlier detection it has only been studied recently. In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing error through both bias and variance by considering outlier detection as a binary classification task with unobserved...
Generalized canonical correlation analysis (GCCA) aims at extracting common structure from multiple 'views', i.e., high-dimensional matrices representing the same objects in different feature domains – an extension of classical two-view CCA. Existing (G)CCA algorithms have serious scalability issues, since they involve square root factorization of the correlation matrices of the views. The memory...
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