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A Distributed Dynamic Adaptive and Fast Balancing SDN Controller Management

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IOP Conference Series: Earth and Environmental Science 2019年第1期234卷

作者： Yang Liu Qian He Xiongying Li Shuiming Zhou Guangxi Transportation Research and Consulting Co. Ltd Nanning 530007 China Guangxi Key Laboratory of Cryptograph and Information Security Guangxi University Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin 541004 China

Tradition Software Defined network (SDN) suffers from single point failure and uneven load distribution problems. Multiple SDN controllers work together to construct a distributed dynamic adaptive cluster with dynamic load balancing for large scale SDN switch stream requesting. Introducing such features as CPU, memory and network load, a Dynamic Adaptive and Fast algorithm (DAF) can select source, target and destination SDN controllers to balance load dynamically. The results of experiments show that DAF works well and improve the cloud service system throughput.

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Dynamic network embedding via incremental skip-gram with negative sampling

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Yan, Hao Gong, Qiran Wang, Senzhang Liu, Lin Wang, Lihong Ren, Xiang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Department of Computer Science University of Southern California Los Angeles United States

network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O( 1/n2 ), and the second order moment of the objective difference can be stabilized in order of O(1). Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up

关键词： Classification (of information)

SPESC: A Specification Language for Smart Contracts

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SPESC: A Specification Language for Smart Contracts

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Xiao He Bohan Qin Yan Zhu Xing Chen Yi Liu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China National Computer Network Emergency Response Technical Team Coordination Center of China

ISBN: (纸本)9781538626672

The smart contract is an interdisciplinary concept that concerns business, finance, contract law and information technology. Designing and developing a smart contract may require the close cooperation of many experts coming from different fields. How to support such collaborative development is a challenging problem in blockchain-oriented software engineering. This paper proposes SPESC, a specification language for smart contracts, which can define the specification of a smart contract for the purpose of collaborative design. SPESC can specify a smart contract in a similar form to real-world contracts using a natural-language-like grammar, in which the obligations and rights of parties and the transaction rules of cryptocurrencies are clearly defined. The preliminary study results demonstrated that SPESC can be easily learned and understood by both IT and non-IT users and thus has greater potential to facilitate collaborative smart contract development.

关键词： Contracts Cryptography Contract law Syntactics Collaboration Software

Fingertip Tracking Based on Edge Feature and Pixel Ratio in Gesture

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Fingertip Tracking Based on Edge Feature and Pixel Ratio in ...

2018 International Conference on Computer,Communication and network technology(CCNT 2018)

作者： Bo-tao ZENG Zhi-quan FENG Tao XU Xiao-hui YANG Chang-sheng AI Wei XIE School of information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Mechanical Engineering University of Jinan School of Information and Electrial Engineering Harbin Institute of Technology at Weihai

Fingertip recognition and tracking is a key problem in gesture recognition. The current fingertip locating and tracking method is complicated, or needs to be labelled artificially. In this paper, a particle filtering method based on edge feature and pixel ratio is proposed to track the target finger in a complex background. We set the region of each particle as a fixed value, and calculate the edge orientation histograms and the proportion of the body pixels of each particle area. The similarity degree of edge features is measured by the Bhattacharyya distance, and a new similarity measure is defined to measure the similarity degree of pixel ratio. These two feature similarities will be linearly combined to track the target model. And then we calculate the farthest point from the center of the contour in the predicted model and update the target model again. The results show that the method can track the target fingertip accurately and effectively in real time, under the condition of interference.

关键词： Edge feature Particle filtering Kinect Fingertip tracking Pixel ratio

A Method of Gesture Recognition Using CNN-SVM Model with Error Correction Strategy

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A Method of Gesture Recognition Using CNN-SVM Model with Err...

2018 International Conference on Computer,Communication and network technology(CCNT 2018)

作者： Jian LI Zhi-quan FENG We XIE Chang-sheng AI School of information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information and Electrial Engineering Harbin Institute of Technology at Weihai School of Mechanical Engineering University of Jinan

The gesture recognition methods based on artificial feature extraction are time-consuming and low recognition rate. The generalization ability of hand gesture recognition using convolution neural network is not strong. Therefore, this paper combines the advantages of CNN and SVM to propose a hybrid model to automatically extract the features and improve the generalization ability, in addition, we use an error correction strategy to reduce the error recognition rate of confusing gestures. First, the segmentation preprocessing of gesture data collected by Kinect. Then, the hybrid model automatically extracts features from the data and generates the predictions. Finally, using the error correction strategy to adjust the prediction result. We get a recognition rate of 95.81% without error correction strategy on our database, the average recognition rate of 97.32% with error correction strategy.

关键词： Gesture recognition Convolution neural network Support vector machine Probability estimation Error correction strategy

A Gesture-based Augmented Reality System at Mobile Terminal

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A Gesture-based Augmented Reality System at Mobile Terminal

2018 International Conference on Computer,Communication and network technology(CCNT 2018)

作者： Meng-ting XIAO Zhi-quan FENG Tao XU Xiao-hui YANG Chang-sheng AI Wei XIE School of information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Mechanical Engineering University of Jinan School of Information and Electrial Engineering Harbin Institute of Technology at Weihai

The combination of Augmented Reality(AR) technology and gesture recognition has become increasingly important in the educational system. In the smart classroom application scenario, the virtual three-dimensional complex model is combined and interacted with real hands based on augmented reality technology. The main contribution of this paper is to determine the coordinates of the five-finger tip of the hand, and calculate the initial length from the center of the 3 D model. When the distance between five fingertips and three-dimension mode is shorter than the initial length during the process of fingers grasp, the three-dimension mode will get smaller. Light interaction is achieved by establishing the relationship of the two spatial models with coordinates. AR is achieved under Vuforia, and the three-dimensional model gradually becomes smaller with the five fingers grasping. The proposed method is used in an Intelligent Teaching System and run at the mobile terminal. The experimental results show that the proposed method works well in ITS.

关键词： Augmented Reality Gesture recognition Vuforia Light interaction

Polarization attack on continuous-variable quantum key distribution

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arXiv 2018年

作者： Zhao, Yijia Zhang, Yichen Huang, Yundi Xu, Bingjie Yu, Song Guo, Hong State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China State Key Laboratory of Advanced Optical Communication System and Network School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China

The shot-noise unit (SNU) is a crucial factor for the practical security of a continuous-variable quantum key distribution system. In the most widely used experimental scheme, the SNU should be calibrated first and acts as a constant during the key distribution. However, the SNU of a practical system is dependent on the various parameters of the local oscillator, which can be controlled by the eavesdropper in the open channel. In this paper, we report a quantum hacking method to control the practical SNU by using the limited compensation rate of the polarization compensation. Since the compensation is only based on of the polarization measurement results of part of local oscillator pulses, the polarization of other unmeasured pulses may not be compensated correctly, which can be utilized by the eavesdropper to control the practical SNU. The simulation and experiment results indicate that the practical SNU can be controlled by the eavesdropper. Thus, the eavesdropper can use the fact that the practical SNU is no longer equals to the calibrated one to control the excess noise and final key rate. Copyright © 2018, The Authors. All rights reserved.

关键词： Shot noise

Anatomy of an online misinformation network

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arXiv 2018年

作者： Shao, Chengcheng Hui, Pik-Mai Wang, Lei Jiang, Xinwen Flammini, Alessandro Menczer, Filippo Ciampaglia, Giovanni Luca College of Computer National University of Defense Technology China School of Informatics Computing and Engineering Indiana University Bloomington United States MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan University China Indiana University Network Science Institute Bloomington United States

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network. Copyright © 2018, The Authors. All rights reserved.

关键词： Social networking (online)

WISERNet: Wider Separate-then-reunion network for Steganalysis of Color Images

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography