Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

Semisupervised Multiview Distance Metric Learning for Cartoon Synthesis

Abstract: In image processing, cartoon character classification, retrieval, and synthesis are critical, so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters. In this paper, we introduce three visual features, color histogram, shape context, and skeleton, to characterize the color, shape, and action, respectively, of a cartoon character. These three features are complementary to each other, and each feature set is regarded as a single view. However, it is improper to concatenate these three features into a long vector, because they have different physical properties, and simply concatenating them into a high-dimensional feature vector will suffer from the so-called curse of dimensionality. Hence, we propose a semisupervised multiview distance metric learning (SSM-DML). SSM-DML learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously, under the umbrella of graph-based semisupervised learning. SSM-DML discovers complementary characteristics of different feature sets through an alternating optimization-based iterative algorithm. Therefore, SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement. On the basis of SSM-DML, we develop a novel system that composes the modules of multiview cartoon character classification, multiview graph-based cartoon synthesis, and multiview retrieval-based cartoon synthesis. Experimental evaluations based on the three modules suggest the effectiveness of SSM-DML in cartoon applications.

PHY-Layer Spoofing Detection with Reinforcement Learning in Wireless Networks

Abstract: In this paper, we investigate the PHY-layer authentication that exploits radio channel information (such as received signal strength indicators) to detect spoofing attacks in wireless networks. The interactions between a legitimate receiver and spoofers are formulated as a zero-sum authentication game. The receiver chooses the test threshold in the hypothesis test to maximize its utility based on the Bayesian risk in the spoofing detection, whereas the spoofers determine their attack frequencies to minimize the utility of the receiver. The Nash equilibrium of the static authentication game is derived, and its uniqueness is discussed. We also investigate a repeated PHY-layer authentication game for a dynamic radio environment. As it is challenging for the radio nodes to obtain the exact channel parameters in advance, we propose spoofing detection schemes based on Q-learning and Dyna-Q, which achieve the optimal test threshold in the spoofing detection via reinforcement learning. We implement the PHY-layer spoofing detectors over universal software radio peripherals and evaluate their performance via experiments in indoor environments. Both simulation and experimental results have validated the efficiency of the proposed strategies.