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    25 October 2019, Volume 40 Issue 10
    Papers
    TSL:predicting popularity of Facebook content based on tie strength
    Xiaomeng WANG, Binxing FANG, Hongli ZHANG, Xing WANG
    2019, 40(10):  1-9.  doi:10.11959/j.issn.1000-436x.2019207
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    The rapid development of online social networks leads to an explosion of information,however,there are great differences in the popularity of different messages,and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore,with the help of the weak ties theory in sociology,the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models,an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model,and the performance is better than the existing similar methods.

    Denoising and reconstruction of evaporation duct based on quadratic optimal threshold approximation message passing method
    Guosheng RUI, Ge LIU, Wenbiao TIAN, Daoguang DONG, Yanan ZHANG
    2019, 40(10):  10-19.  doi:10.11959/j.issn.1000-436x.2019200
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    To solve the problem that the mixed noise of compressed sensing observation stage had adverse effects on the high-precision reconstruction in the time-space situation awareness process of evaporative duct,an approximate message passing method based on quadratic optimal threshold was proposed.A quadratic stabilization threshold function which integrated advantages of soft and hard threshold functions was designed,and the optimal threshold setting policy was added to the iterative process based on conjugate gradient method to speed up convergence.Theoretical analysis and experiments show that,compared with several mainstream algorithms,the proposed algorithm has the faster convergence rate and lower denoising reconstruction error.

    Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature
    Hongguang LI, Ying GUO, Ping SUI, Zisen QI
    2019, 40(10):  20-29.  doi:10.11959/j.issn.1000-436x.2019191
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    For frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-frequency diagram was removed by two-dimensional Wiener filtering to improve the resolution of the time-frequency diagram under low SNR conditions.Then,the connected-domain detection algorithm was used to extract the time-frequency energy spectrum of each hop signal and convert it into a time-frequency gray-scale image.The histogram statistical features and the gray-scale co-occurrence matrix feature were combined to form a 22-dimensional eigenvector.Finally,the feature set was trained,classified and identified by optimized support vector machine classifier.Simulation experiments show that the multi-dimensional feature vector extracted by the algorithm has strong representation ability and avoids the misjudgment caused by the similarity of single features.The average recognition accuracy of the six modulation methods of frequency hopping signals BPSK,QPSK,SDPSK,QASK,64QAM and GMSK is 91.4% under the condition of -4 dB SNR.

    Research on space focusing virtual coverage based on orthogonal frequency time reversal method
    Yifang NIE, Fangwei LI
    2019, 40(10):  30-41.  doi:10.11959/j.issn.1000-436x.2019211
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    To solve the problem of information leakage and coverage efficiency in ultra-dense wireless network,the orthogonal frequency-time reversal virtual coverage scheme was proposed.At first,different wireless reversal channels with lower correlation were distinguished by orthogonal carries.Next,after time reversal,the space focusing of each target signal was realized around the terminal.Then,theoretical expressions of the signal coverage and the signal-interference-to-noise ratio were derived and proved.Ultimately,the virtual coverage was shaped,meeting the requirements of users.The coverage of unrelated user not belonging to system was reduced.The results show that those unrelated users are difficult to receive the target signal because of virtual coverage.In addition,the system has higher capacity and security rate of reliable transmission without any more power or antennas.

    Task distribution algorithm based on community in mobile crowd sensing
    Hao LONG, Shukui ZHANG, Yang ZHANG, Li ZHANG
    2019, 40(10):  42-54.  doi:10.11959/j.issn.1000-436x.2019213
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    A community-based task distribution algorithm was proposed to solve the problem of the validity of mobile crowd sensing (MCS) task distribution.By calculating the minimum spanning tree (MST),the connection parameter (CP) and the community convergence degree (CI) between the mobile nodes,the behavior patterns of the users were abstracted and identified to rationally divide the nodes into different communities.Then,the eigenvalue matching degree of the community behavior patterns with the sensing task was calculated.According to the matching degree,the distribution of the corresponding tasks was completed by the central node of the community.The simulation results show that the proposed method can effectively improve the accuracy of the task distribution and the task completion rate,and save the time cost of the task completion.

    Hybrid anonymous channel for recipient untraceability via SDN-based node obfuscation scheme
    Hui ZHAO, Liangmin WANG
    2019, 40(10):  55-66.  doi:10.11959/j.issn.1000-436x.2019155
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    Leveraging the advantages of software defined networking (SDN),a new anonymous communication solution was de-signed for recipient untraceability.An obfuscation scheme in SDN domain was proposed to build a hybrid anonymous channel to solve the problem of large and highly variable delays and download time when using existing anonymous com-munication system such as Tor.The hybrid anonymous channel concatenated two sender anonymous channels in Tor and SDN to provide both sender and receiver anonymity.Adversaries can trace smaller portions of the path in hybrid channel.Experimental results show that the hybrid channel is as anonymous as two connected Tor circuits,with only a small larger latency (15%) compared with Tor.

    Measurement of video initial buffer size for mobile network
    Guang CHENG, Min FANG, Hua WU
    2019, 40(10):  67-78.  doi:10.11959/j.issn.1000-436x.2019179
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    To resolve the difficulty in accurately measure the length of video initial buffering queue,two video platforms,non-encrypted Youku and encrypted YouTube,were selected to research,and the video initial buffer queue length measurement method was proposed.By identifying and analyzing the characteristics of video traffic,correlating the traffic behavior with the playing state,constructing video fingerprint database,accurate measurement of queue length was realized.The experimental results show that the measurement results of the two types could be accurate to the frame,fully meeting the need to accurately evaluate the quality of the video experience.

    Finite automaton intrusion tolerance system model based on Markov
    Zhiyong LUO, Xu YANG, Guanglu SUN, Zhiqiang XIE, Jiahui LIU
    2019, 40(10):  79-89.  doi:10.11959/j.issn.1000-436x.2019196
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    To ensure that the system could still provide normal service for legitimate users when the LAN being invaded,a finite automaton intrusion tolerance model was designed.Based on Markov’s theory,the state transformation relationship of the service provided by the system by establishing the state transition probability matrix was described,quantified the transition state and found the key nodes in the system.The maintenance of key nodes could enhance the tolerance of the system and improve the reliability of the service.Experimental comparison shows that this model not only has strong intrusion tolerance,but also has obvious advantages in the integrity of the security system when it is invaded.

    Shamir-based virtual machine placement policy
    Junfeng TIAN, Zilong WANG, Xinfeng HE, Zhen LI
    2019, 40(10):  90-100.  doi:10.11959/j.issn.1000-436x.2019141
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    In order to alleviate the harm of co-location attack in cloud environment,a virtual machine placement strategy based on Shamir was proposed,and a virtual machine placement framework was designed.The security of key data was ensured by the blockchain in the proposed placement strategy.The virtual machine placement policy could effectively improve the security of the virtual machine and the load balancing capability of the cloud environment,and reduce resource waste.Finally,the effectiveness of the scheme is proved by simulation experiments.

    Feature selection algorithm based on XGBoost
    Zhanshan LI, Zhaogeng LIU
    2019, 40(10):  101-108.  doi:10.11959/j.issn.1000-436x.2019154
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    Feature selection in classification has always been an important but difficult problem.This kind of problem requires that feature selection algorithms can not only help classifiers to improve the classification accuracy,but also reduce the redundant features as much as possible.Therefore,in order to solve feature selection in the classification problems better,a new wrapped feature selection algorithm XGBSFS was proposed.The thought process of building trees in XGBoost was used for reference,and the importance of features from three importance metrics was measured to avoid the limitation of single importance metric.Then the improved sequential floating forward selection (ISFFS) was applied to search the feature subset so that it had high quality.Compared with the experimental results of eight datasets in UCI,the proposed algorithm has good performance.

    Identifying vital nodes algorithm in social networks fusing higher-order information
    Guanghui YAN, Meng ZHANG, Hao LUO, Shikui LI, Ting LIU
    2019, 40(10):  109-118.  doi:10.11959/j.issn.1000-436x.2019198
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    Identifying vital nodes is a basic problem in complex network research.The existing theoretical framework,mainly considered from the lower-order structure of node-based and edge-based relations often ignores important factors such as interactivity and transitivity between multiple nodes.To identify vital nodes more accurately,the motif,the high-er-order structure of the network,was studied as the basic unit.Firstly,a notion of higher-order degree of nodes in a com-plex network was proposed.Then,the higher-order structure and lower-order structure of nodes were fused into evidence theory.A semi-local identifying vital nodes algorithm fusing higher-order information of nodes was designed.The results of experiments on three real social networks show that the proposed algorithm can identify vital nodes more accurately in the network than the existing methods which only focus on the low-order structure.

    Enhancement of underwater acoustic signal based on denoising automatic-encoder
    Jingwei YIN, Wuxiong LUO, Li LI, Xiao HAN, Longxiang GUO, Jianfeng WANG
    2019, 40(10):  119-126.  doi:10.11959/j.issn.1000-436x.2019181
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    Aiming at the difficulty of feature extraction of echo signal in active sonar,a self-encoder algorithm based on the combination of denoising self-encoder and convolution denoising self-encoder was proposed.Firstly,the preprocessing of noisy signal was carried out by using the advantage of denoising self-encoder in signal as a whole,and then the local feature of signal was optimized by combining convolutional denoising self-encoder to denoise the signal locally,so as to enhance the signal.The time domain waveform of the received signal is used as the feature input by the algorithm,and retains the signal’s amplitude and phase characteristics.The experimental results show that the algorithm not only effectively reduces the noise component in the signal,but also achieves better recovery effect in both time and frequency domains.

    On the performance optimization for the cloud architecture with sleep-mode and registration service
    Shunfu JIN, Haixing WU, Tiantian HUO, Wenjuan ZHAO
    2019, 40(10):  127-136.  doi:10.11959/j.issn.1000-436x.2019202
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    In order to balance the energy conservation and the service quality for anonymous users,a novel sleep-mode based architecture with the free cloud service and the registration cloud service was proposed.Regarding the free cloud service as the first service,the registration cloud service as the second service and the sleep sate as the vacation,a partial asynchronous multiple vacation queueing model with a second optional service was built.Applying the method of a matrix-geometric solution,the steady-state distribution of the queueing model was derived,and then the energy saving rate of system as well as the average response time of anonymous users were estimated.By considering the benefits from accessing the cloud service and the time cost on waiting for the cloud service,benefit functions were constructed.Numerical results were carried out to reveal the relationship between Nash equilibrium arrival rate and social optimal arrival rate of anonymous users.The proposed cloud architecture provides a theoretical basis for social optimization.

    Energy-saving algorithm considering idle light-path prediction in SDM-EON
    Yu XIONG, Jinyou HE, Baohua WANG, Bin ZHOU
    2019, 40(10):  137-148.  doi:10.11959/j.issn.1000-436x.2019203
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    To effectively reduce the energy consumption,blocking rate and crosstalk between adjacent cores in a multi-core fiber for space division multiplexing elastic optical network (SDM-EON),an energy-saving algorithm considering idle light-path prediction was proposed.The extreme learning machine model was used to predict the traffic volume of each light-path in the network.Thus the idle light-path set and the maintenance time threshold of each idle light-path were obtained.Then,the actual maintenance time of the idle light-path was perceived by the prediction algorithm.Finally,in the light-paths where the actual maintenance time do not exceed the minimum maintenance time threshold and the inter-core crosstalk are lower than the crosstalk threshold,the idle light-path with the least loading energy consumption was selected to carry the new traffic.The simulation results show that compared with the traditional energy-saving algorithm,when the SDM-EON crosstalk limitation is satisfied,the proposed algorithm can lead better energy-saving while maintain the blocking rate at levels compatible.

    Energy efficiency analysis of heterogeneous cellular network based on Thomas cluster process
    Minglu JIN, Nan GUO
    2019, 40(10):  149-156.  doi:10.11959/j.issn.1000-436x.2019184
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    In the existing heterogeneous cellular networks (HCN),the rapid increasing of small cells caused a problem that the hotspots where users were clustered generated a huge amount of energy consumption.To improve the energy efficiency of the networks,the spatial model was researched.The users in hotspots in the HCN were modeled as the Thomas cluster process,combined with the max mean bias received signal power (max-BRP) strategy to achieve base station load balancing,and the user’s association probability,coverage probability and the network’s energy efficiency were derived using tool from stochastic geometry.The simulation shows that the derived formulas are accurate,and the network’s energy efficiency can be greatly improved by setting appropriate bias factors.

    Comprehensive Review
    Differential privacy protection technology and its application in big data environment
    Yu FU, Yihan YU, Xiaoping WU
    2019, 40(10):  157-168.  doi:10.11959/j.issn.1000-436x.2019209
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    The privacy protection in big data is a research hotspot in the field of cyberspace security.As a strict and provable definition of privacy protection,studying application status of differential privacy protection in big data environment can provide reference and guidance for its subsequent system applications.Based on the analysis of the related concepts and technical characteristics of differential privacy protection,the application of differential privacy protection technology was reviewed in data distribution and analysis,cloud computing and big data computing,location and trajectory services and social networks,which expounded the current representative research results and analyzed its existing problems.The research shows that the existing results have made effective innovation and exploration of differential privacy protection applications from the aspects of differential privacy protection mechanism,noise addition mechanism and location,and data processing methods,and the related results have been cross-applied in different scenarios.Finally,four major problems that need to be studied in the further systematic application of differential privacy protection in the big data environment are proposed.

    Correspondences
    Asymptotic RZF cooperative beamforming algorithm based on energy efficiency
    Yinghui ZHANG, Biao ZHANG, Xiaoting LU, Yang LIU
    2019, 40(10):  169-179.  doi:10.11959/j.issn.1000-436x.2019174
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    A low complexity asymptotic regularized zero forcing cooperative beamforming algorithm based on energy efficiency in heterogeneous massive MIMO system was proposed,aiming at the problem that the current multi-flow regularization zero forcing beamforming algorithm sets the power constraint of each antenna in the regularization term as a fixed value and ignores the influences of factors such as the number of antennas,the number of users and QoS.The algorithm selects the optimal antenna power constraint set through the optimization method,and the optimal beamforming was asymptotically ob-tained to balance the interference among users to achieve the optimal energy efficiency,considering the impact of the number of antennas and users with the constraints of the antenna power and QoS.In view of the importance of backhaul in massive MIMO system,a backhaul power consumption model and the impact of backhaul power consumption on system performance was analyzed.Analysis and simulation results show that the proposed algorithm has great improvement of the performance,especially when the number of antennas is large.The algorithm is close to optimal performance,especially suitable for massive MIMO system of next generation communication.

    Blind equalization algorithm based on complex support vector regression
    Ling YANG, Liang CHEN, Bin ZHAO, Guolong ZHANG, Yuan LI
    2019, 40(10):  180-188.  doi:10.11959/j.issn.1000-436x.2019199
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    A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method.

    Spatiotemporal squeeze-and-excitation residual multiplier network for video action recognition
    Huilan LUO, Kang TONG
    2019, 40(10):  189-198.  doi:10.11959/j.issn.1000-436x.2019194
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    Aiming at the shortcomings of shallow networks and general deep models in two-stream network structure,which could not effectively learn spatial and temporal information,a squeeze-and-excitation residual network was proposed for action recognition with a spatial stream and a temporal stream.Meanwhile,the long-term temporal dependence was captured by injecting the identity mapping kernel into the network as a temporal filter.Spatiotemporal feature multiplication fusion was used to further enhance the interaction between spatial information and temporal information of squeeze-and-excitation residual networks.Simultaneously,the influence of spatial-temporal stream multiplication fusion methods,times and locations on the performance of action recognition was studied.Given the limitations of performance achieved by a single model,three different strategies were proposed to generate multiple models,and the final recognition result was obtained by integrating these models through averaging and weighted averaging.The experimental results on the HMDB51 and UCF101 datasets show that the proposed spatiotemporal squeeze-and-excitation residual multiplier networks can effectively improve the performance of action recognition.

Copyright Information
Authorized by: China Association for Science and Technology
Sponsored by: China Institute of Communications
Editor-in-Chief: Zhang Ping
Associate Editor-in-Chief:
Zhang Yanchuan, Ma Jianfeng, Yang Zhen, Shen Lianfeng, Tao Xiaofeng, Liu Hualu
Editorial Director: Wu Nada, Zhao Li
Address: F2, Beiyang Chenguang Building, Shunbatiao No.1 Courtyard, Fengtai District, Beijing, China
Post: 100079
Tel: 010-53933889、53878169、
53859522、010-53878236
Email: xuebao@ptpress.com.cn
Email: txxb@bjxintong.com.cn
ISSN 1000-436X
CN 11-2102/TN
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