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Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. In this paper, we propose a unified Convolutional Neural Network (CNN) model for this task. In order to reliably detect modern steganographic algorithms, we design the proposed model from two aspects. For the first, different from existing CNN based steganalytic algorithms that use a predefined highpass...
This paper presents an enhanced efficiency 3-D convolution operator based on optimal field programmable gate array (FPGA) accelerator platform. The proposed system takes advantages of the intermediate data delay lines, implemented in an FPGA, to avoid loading repetition of the input feature maps. This 3-D convolution accelerator performs 268.07 giga operations per second at 100-MHz operation frequency,...
The details of oriented visual stimuli are better resolved when they are horizontal or vertical rather than oblique. This "oblique effect" has been researched and confirmed in numerous research studies, including behavioral studies and neurophysiological and neuroimaging findings. Although the "oblique effect" has influence in many fields, little research integrated it into computational...
Artificial olfaction data are usually represented by a sensor array embedded in an electronic nose system (E-Nose), such that each observation can be expressed as a feature vector for pattern recognition. The concerns of this paper are threefold: 1) each feature can be represented by multiple different modalities; 2) manual labeling of sensory data in real application is difficult and hardly impossible,...
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...
Total projection to latent structures (T-PLS) has been used for quality-related process monitoring. Compared to PLS, the T-PLS is more effectively in detecting the quality-related abnormal situations for linear and static processes. To describe the nonlinear and dynamic process characteristics, a new monitoring approach, dynamic total kernel projection to latent structures (DT-KPLS), is proposed in...
Due to process disturbances and some uncertainties, the process operating performance will deviate from the optimal operating point along with time, so it is very important to develop strategies for online operating performance assessment on optimality. However, a little work has been published in this research area to our knowledge. In this study, a new online operating optimality assessment method...
Satellite information is an important source of the decision-level intelligence in battlefield. Research of the multi-source information decision-level fusion provides a key technical approach for distilling comprehensive satellite information and acquiring decision-level intelligences. A SVMs-DS model adopting statistics theory and uncertainty reasoning method in the article, ensures precision and...
As email playing an increasingly important role in the online world, analyzing and mining on email communication network draw more and more attention, in which community detection is one of the most key technologies and applications. Based on the intuition that the edge topic is more focused than the node topic in a short period, this paper proposes an edge-content based email network community detection...
The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of the Fisher vector is that it is high-dimensional...
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel machines are difficult to scale to large training sets, it has been proposed to perform an explicit mapping of the data and to learn directly linear classifiers in the new space. In this paper, we consider the problem of...
Emails play an important role in our daily life. It has been recognized that clustering emails into meaningful groups can greatly save cognitive load to process emails. Mailbox user becomes more and more concerned about how to organize and manage the emails as well as how to mine the meaningful data conveniently and effectively. This paper proposes a novel personal topics detection approach using...
We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. The main contribution of this paper is to model each vector set with a GMM adapted from a common ldquouniversalrdquo...
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