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This paper proposes a new image watermarking scheme based on Generalized Singular Value Decomposition (GSVD). This image watermarking method embeds the principal components of watermark image into the host image using a specific scaling factor to control the imperceptibility and robustness aspects. Using this strategy, the GSVD-based image watermarking has ability to avoid the false positive problem...
Emerging Smart Grids have given rise to deployment of advanced metering infrastructure (AMI) which produces voluminous load data with respect to time. The information extracted from this data can potentially be used to design Demand Side Management (DSM) policies. The major impediment in the deployment of DSM is the lack of knowledge of individual consumers' load demand to determine target consumers...
We are concerned with the issue of discovering behavioral patterns on the web. When a large amount of web access logs are given, we are interested in how they are categorized and how they are related to activities in real life. In order to conduct that analysis, we develop a novel algorithm for sparse non-negative matrix factorization (SNMF), which can discover patterns of web behaviors. Although...
In this paper, we aim to address the issue that semi-supervised learning is prone to be influenced by the quality and quantity of initial seeds. In order to expand the initial labeled data, we select credible samples from unlabeled data by a proposed bilateral latent information miner. The miner can extract information from unlabeled data for both positive and negative class respectively. Then we...
A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify...
The number of devices capable of measurement Power Quality (PQ) parameters is increasing continuously in all voltage levels. Consequently, the amount of available PQ data is also growing very fast. These data contain a lot of valuable information about the behavior of PQ, but up to now it is in the most cases used only to assess compliance with limits (e.g. EN 50160 in Europe). Beside long-term characteristics...
The number of new malwares is increasing everyday. Thus malware detection is nowadays a big challenge. The existing techniques for malware detection require a huge effort of engineering to manually extract the malicious behaviors. To avoid this tedious task, we propose in this paper an approach to automatically extract the malicious behaviors. We model a program using an API call graph, and we represent...
Feature learning from unlabeled times series data is an important component in data analysis. Shaplets are discriminative sub-sequence of time series that can best predict target variable. Therefore, shaplets discovery is very important for analysis of time-series. Recently, based on optimization model, a novel approach has been proposed to learning shaplets. To make shaplets learning model more robust,...
Identifying and detecting the unknown abnormal sparse signal has become an important issue for distributed networks. In this paper, we proposed a new detection scheme based on convex optimization for wireless sensor networks. Under the Neyman-Pearson testing framework, the detection scheme first estimates the unknown signal by employing the convex optimization at the fusion center. Then the sensor...
Communication security has taken vital role with the advancement in digital communication. The universal use of internet for communication has increased the attacks to users. The security of information is the present issue related to privacy and safety during storage and communication. Cryptography and Steganography are two popular ways of sending essential information in a confidential way. Cryptography...
In the past decade, the science of information hiding has gained tremendous significance due to advances in information and communication technology. The performance of any steganographic algorithm relies on the embedding efficiency, embedding payload, and robustness against attackers. Low hidden ratio, less security, and low quality of stego videos are the major issues of many existing steganographic...
People count is an important indicator in video surveillance. Due to the overlapping objects and cluttered background, counting people accurately in actual crowded scene remains a non-trivial problem. Existing regression-based methods either learn a single model mapping the global feature to people count, or estimate localized count by training a large number of regressors. In this paper, we present...
In the paper the adaptive robust models for adaptive identification of nonstationary systems are proposed. These proposed models can be used for solving Dynamical Data and Data Stream Mining tasks. These adaptive robust models are characterized by computational simplicity and high speed operation that allow the signal processing in on-line mode.
During the past years, malicious PDF files have become a serious threat for the security of modern computer systems. They are characterized by a complex structure and their variety is considerably high. Several solutions have been academically developed to mitigate such attacks. However, they leveraged on information that were extracted from either only the structure or the content of the PDF file...
This paper focuses on the problem of built-up areas detection in single high-resolution SAR images. In consideration of the rich structure information of built-up areas in high-resolution SAR images, we put forward a multiscale CNN model to extract multiscale trained features directly from image patches to detect built-up areas. By processing features extraction and classification as a whole, we overcome...
For the character of traditional clustering algorithms can only deal with low-dimensional linear data, not sensitive to high-dimensional nonlinear data sets, this paper proposes to use normalized cut (N-cut) spectral clustering algorithm to process high-dimensional data, and combines with sparse subspace clustering algorithm to generate similarity matrix, the method overcomes the insensitivity of...
Improving the quality of data is a crucial task for business, health, and scientific data. Several data cleaning algorithms have been translated into tools to identify and repair data errors such as outlying values, duplicate records, typos, missing values, and violations of rules in general [1], [2], [3], [4].
Crowdclustering clusters data items in a crowdsourcing manner, which makes discovered item categories more consistent with human perception. However, due to diversity of crowdsourcing workers and fluctuation of the number of tasks assigned to each worker, inferring stable and reliable clusters is challenging. Moreover, an item may be associated with multiple attributes, and such items should be put...
The issue of protecting scientific and intellectual products and publications ownership constitutes an important deal within the domain of securing and protecting information. Digital watermarking is used to identify the ownership of the copyright and in authentication process. In this paper, an invisible blind watermarking method utilizing the word space in Arabic text is proposed. Since the pseudo-space...
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