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Data clustering is one of the popular tasks recently used in the educational data mining arena for grouping similar students by several aspects such as study performance, behavior, skill, etc. Many well-known clustering algorithms such as k-means, expectation-maximization, spectral clustering, etc. were employed in the related works for educational data clustering. None of them has taken into consideration...
In this paper, we present a multiple human tracking approach that takes the single frame human detection results as input, and associates them hierarchically to form trajectories while improving the original detection results by making use of reliable temporal information. It works by first forming tracklets, from which reliable temporal information can be extracted, and then refining the detection...
Outlier detection is now widely used in various fields. It attracts more and more interests in research. The density based outlier detection methods and the distance based outlier detection methods are the most frequently used outlier detection methods. In big data, the size and dimensions of data is very large. Those features make the conventional methods not suitable for big data. According to the...
Recently, the feature point matching based water-marking techniques have been paying attention for resisting the geometric attacks. We present a performance analysis of robust watermarking using linear and nonlinear feature. In particular, we consider the geometric attacks and the signal processing attacks for image watermarking. In order to analyze the efficiency of linear and nonlinear feature,...
The most effective and natural means for human beings is Facial expression that have the dexterity to communicate emotion and regulate inter-personal behaviour. We proposed a novel facial-expression analysis system design that focused on automatically recognize facial expressions and reducing the doubt and confusion between facial-expression classes. The information used in facial expression concentrates...
In this paper, a robust blind extracting audio watermarking scheme based on Improved Spread Spectrum (ISS) and Quadrature Phase Shift Keying (QPSK) is proposed. The frequency of direct spread spectrum sequence signal is reduced to limit its bandwidth, and then modulated to a higher frequency band with QPSK so as to lower the influence of watermarking to listener, meanwhile improve signal-noise ratio...
LBP (Local Binary Pattern) is a commonly used operator to extract LBPH (LBP histogram) of an image for local texture description. For gender classification, we proposed an innovative method by extracting multi-scale LBPH in DoG (Difference of Gaussian) space in this paper. Given a facial image, we firstly preprocess it meticulously to avert the local variations of images which probably be caused by...
Data clustering is one of the popular tasks recently used in the educational data mining arena for grouping similar students by several aspects such as study performance, behavior, skill, etc. Many well-known clustering algorithms such as k-means, expectation-maximization, spectral clustering, etc. were employed in the related works. None of them has taken into consideration the incompleteness of...
Accompany with the growth of the speed in network, people's living standard has greatly improved, but the threaten to its information's security has also appeared, as a important branch of information security, steganography is a useful method to protect the secret information safety. In view of the high complexity of current video steganographic algorithms, and combining with the trailing coefficient...
In this paper, a digital watermarking scheme has been implemented using Weighted Extreme Learning Machine (WELM) on images and results are compared with other existing methods. The neighbourhood relationship among the pixel in image can be used as an reference positions, WELM is used as a regressor. Digital watermarking problem can be treated as regression problem can be trained at the embedding procedure...
With the rapid progress in data mining and outlier detection, outlier detection methods have been widely used in various domains. The density based LOF method is the commonly used outlier detection method. In big data, the size and dimensions of data is very large, and the data is sparse. Those features make the LOF not suitable for big data. According to the features of big data, we propose a novel...
This paper addresses the problem of automated vehicle tracking and recognition from aerial image sequences. Motivated by its successes in the existing literature we focus on the use of linear appearance subspaces to describe multi-view object appearance and highlight the challenges involved in their application as a part of a practical system. A working solution which includes steps for data extraction...
Robustness and invisibility are all important points in video watermarking technology. In this paper, we propose the new scheme to increase the robustness and retain the invisibility as good. Through the experiment, we get the linear curve relationship between QP values and watermark strength values, and using that relationship, we get the better detection rate increased by 7%∼14%. In spite of increasing...
We propose a new technique of Distortion Compensation (DC) for the two dimensional Quincunx Lattice Quantization that is used in watermarking. The choice of a new direction of quantization is explained by the form of Voronoi cell of the lattice elements. Parameter a controls DC and can be adjusted to maximize the mutual information between embedded and detected message in a noisy channel. For a special...
How would considering the aesthetics of text layout, particularly line spacing with the Golden Ratio (GR) parameter, influence the ease of reading and retention? To answer this question we introduced a novel method by employing a wearable eye tracker instead of a table-mounted eye tracker. Because of their lower price, portability, and growing market, as well as convenience to setup, such head-mounted...
This paper describes the secondary research on feature extraction and selection for decoding the brain electroencephalograph (EEG) signals in designing a prosthetic arm, a Brain Computer Interface (BCI) system. It considers EEG pattern recognition using Principal Component Analysis (PCA) for Feature Extraction. The data used for this research is the EEG signal that is recorded during the imagination...
This paper describes the secondary research on feature extraction and selection for decoding the brain electroencephalograph (EEG) signals in designing a prosthetic arm, a Brain Computer Interface (BCI) system. It considers EEG pattern recognition using Principal Component Analysis (PCA) for Feature Extraction. The data used for this research is the EEG signal that is recorded during the imagination...
Unsupervised classification allows us to divide the dataset into several groups without knowing how the records should relate to each other. It is one of an interesting data mining topics that can be applied in many fields. A new method for solving this optimization problem is utilized. The method is based on the so-called Mesh Adaptive Direct Search method. This method does not explicitly use derivatives,...
Designing practical methods that target achieving the ideal data hiding capacity is one of the main foci of data hiding field. High dimensional quantization enables to narrow the gap between ideal and actual capacities. However, computational complexity becomes a prohibitive factor. Lattice based quantization appears to be a good candidate for practical solutions. In this paper, it is shown that TCQ-IS,...
Cloud platforms involve multiple independently developed components, often executing on diverse hardware configurations and across multiple data centers. This complexity makes tracking various key performance indicators (KPIs) and manual diagnosing of anomalies in system behavior both difficult and expensive. In this paper, we describe PerfAugur, an automated system for mining service logs to identify...
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