Biblio

Style transfer is a research hotspot in computer vision. Up to now, it is still a challenge although many researches have been conducted on it for high quality style transfer. In this work, we propose an algorithm named ASTCNN which is a real-time Arbitrary Style Transfer Convolution Neural Network. The ASTCNN consists of two independent encoders and a decoder. The encoders respectively extract style and content features from style and content and the decoder generates the style transferred image images. Experimental results show that ASTCNN achieves higher quality output image than the state-of-the-art style transfer algorithms and the floating point computation of ASTCNN is 23.3% less than theirs.

On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.

The IP address attribution information includes the geographical information, the network routing information, the agency information, Internet Content Provider (ICP) information, etc. Nowadays, the attribution information is important to the network traffic engineering, which needs to be obtained in real time in network traffic analysis system. The existing proposed methods for IP address attribution information lookup cannot be employed in actual systems efficiently due to their low scalability or bad performance. They cannot address the backbone network's requirements for real-time IP address attribution information lookup, and most lookup methods do not support custom IP address attribution lookup. In response to these challenges, we propose a novel high-speed approach for IP address attribution information lookup. We first devise a data structure of IP address attribution information search tree (PATIP-TREE) to store custom IP address attribution information. Based on the PATIP-TREE, an effective algorithm for IP information lookup is proposed, which can support custom IP addresses attribution information lookup in real time. The experimental results show that our method outperforms the existing methods in terms of higher efficiency. Our approach also provides high scalability, which is suitable for many kinds network address such as IPv4 address, IPv6 address, named data networking address, etc.

The network intrusion detection problem domain is described with mathematical knowledge in this paper, and a novel IDS detection model based on immune mechanism is designed. We study the key modules of IDS system, detector tolerance module and the algorithms of IDS detection intensively. Then, the continuous bit matching algorithm for computing affinity is improved by further analysis. At the same time, we adopt controllable variation and random variation, as well as dynamic demotion to improve the dynamic clonal selection algorithm. Finally the experimental simulations verify that the novel artificial immune algorithm has better detection rate and lower noise factor.

Game theory can provide a useful tool to study the security problem in mobile ad hoc networks (MANETs). Most of existing works on applying game theories to security only consider two players in the security game model: an attacker and a defender. While this assumption may be valid for a network with centralized administration, it is not realistic in MANETs, where centralized administration is not available. In this paper, using recent advances in mean field game theory, we propose a novel game theoretic approach with multiple players for security in MANETs. The mean field game theory provides a powerful mathematical tool for problems with a large number of players. The proposed scheme can enable an individual node in MANETs to make strategic security defence decisions without centralized administration. In addition, since security defence mechanisms consume precious system resources (e.g., energy), the proposed scheme considers not only the security requirement of MANETs but also the system resources. Moreover, each node in the proposed scheme only needs to know its own state information and the aggregate effect of the other nodes in the MANET. Therefore, the proposed scheme is a fully distributed scheme. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.