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    National Open Distributed and Parallel Computing Conference 2020 (DPCS 2020)

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    Clustered wireless federated learning algorithm in high-speed internet of vehicles scenes
    WANG Jiarui, TAN Guoping, ZHOU Siyuan
    Journal of Computer Applications    2021, 41 (6): 1546-1550.   DOI: 10.11772/j.issn.1001-9081.2020121912
    Abstract425)      PDF (912KB)(618)       Save
    Existing wireless federated learning frameworks lack the effective support for the actual distributed high-speed Internet of Vehicles (IoV) scenes. Aiming at the distributed learning problem in such scenes, a distributed training algorithm based on the random network topology model named Clustered-Wireless Federated Learning Algorithm (C-WFLA) was proposed. In this algorithm, firstly, a network model was designed on the basis of the distribution situation of vehicles in the highway scene. Secondly, the path fading, Rayleigh fading and other factors during the uplink data transmission of the users were considered. Finally, a wireless federated learning method based on clustered training was designed. The proposed algorithm was used to train and test the handwriting recognition model. The simulation results show that under the situations of good channel state and little user transmit power limit, the loss functions of traditional wireless federated learning algorithm and C-WFLA can converge to similar values under the same training condition, but C-WFLA converges faster; under the situations of poor channel state and much user transmit power limit, C-WFLA can reduce the convergence value of loss function by 10% to 50% compared with the traditional centralized algorithm. It can be seen that C-WFLA is more helpful for model training in high-speed IoV scenes.
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    Deployment method of dockers in cluster for dynamic workload
    YIN Fei, LONG Lingli, KONG Zheng, SHAO Han, LI Xin, QIAN Zhuzhong
    Journal of Computer Applications    2021, 41 (6): 1581-1588.   DOI: 10.11772/j.issn.1001-9081.2020121913
    Abstract297)      PDF (981KB)(355)       Save
    Aiming at the problem of frequent migration of containers triggered by dynamic changes of cluster workload, a container deployment method based on resource reservation was proposed. Firstly, a dynamic change description mechanism of single-container resource demand based on Markov chain model was designed to describe the resource demand situation of single container. Secondly, the dynamic change of multi-container resource was analyzed based on the single-container Markov chain model to describe the container resource demand state. Thirdly, a container deployment and resource reservation algorithm for dynamic workload was proposed based on the multi-container Markov chain. Finally, the performance of the proposed algorithm was optimized based on the analysis of container resource demand characteristics. The simulation experimental environment was constructed based on the domestic software and hardware environment, and the simulation results show that in terms of resource conflict rate, the performance of the proposed method has the performance close to the optimal peak allocation strategy named Resource with Peak (RP), but its number of required hosts and container dynamic migration number are significantly less; in terms of resource utilization rate, the proposed method has the number of hosts used slightly more than the optimal valley allocation strategy named Resource with Valley (RV), but has less dynamic migration number and lower resource conflict rate; compared with the peak and valley allocation strategy named Resource with Valley and Peak (RVP), the proposed method has better comprehensive performance.
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    Intelligent recommendation method for lock mechanism in concurrent program
    ZHANG Yang, DONG Shicheng
    Journal of Computer Applications    2021, 41 (6): 1597-1603.   DOI: 10.11772/j.issn.1001-9081.2020121929
    Abstract282)      PDF (1311KB)(301)       Save
    The choices of Java locks are faced by the developers during parallel programming. To solve the problem of how to choose the appropriate lock mechanism to improve the program performance, a recommendation method named LockRec for developers of concurrent program to choose lock mechanism was proposed. Firstly, the program static analysis technology was used to analyze the use of lock mechanism in concurrent programs and determine the program feature attributes that affect the program performance. Then, the improved random forest algorithm was used to build a recommendation model of lock mechanism, so as to help the developers to choose the lock among synchronization lock, re-entrant lock, read-write lock, and stamped lock. Four existing machine learning datasets were selected to experiment with LockRec. The average accuracy of the proposed LockRec is 95.1%. In addition, the real-world concurrent programs were used to analyze the recommendation results of LockRec. The experimental results show that LockRec can effectively improve the execution efficiency of concurrent programs.
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    Traffic mode recognition algorithm based on residual temporal attention neural network
    LIU Shize, ZHU Yida, CHEN Runze, LUO Haiyong, ZHAO Fang, SUN Yi, WANG Baohui
    Journal of Computer Applications    2021, 41 (6): 1557-1565.   DOI: 10.11772/j.issn.1001-9081.2020121953
    Abstract309)      PDF (1075KB)(606)       Save
    Traffic mode recognition is an important branch of user behavior recognition, the purpose of which is to identify the user's current traffic mode. Aiming at the demand of the modern intelligent urban transportation system to accurately perceive the user's traffic mode in the mobile device environment, a traffic mode recognition algorithm based on the residual temporal attention neural network was proposed. Firstly, the local features in the sensor time sequence were extracted through the residual network with strong local feature extraction ability. Then, the channel-based attention mechanism was used to recalibrate the different sensor features, and the attention recalibration was performed by focusing on the data heterogeneity of different sensors. Finally, the Temporal Convolutional Network (TCN) with a wider receptive field was used to extract the global features in the sensor time sequence. The data-rich High Technology Computer (HTC) traffic mode recognition dataset was used to evaluate the existing traffic mode recognition algorithms and the residual temporal attention model. Experimental results show that the proposed residual temporal attention model has the accuracy as high as 96.07% with friendly computational overhead for mobile devices, and has the precision and recall for any single class reached or exceeded 90%, which verify the accuracy and robustness of the proposed model. The proposed model can be applied to intelligent transportation, smart city and other domains as a kind of traffic mode detection for supporting mobile intelligent terminal operation.
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    Traceable and revocable ciphertext-policy attribute-based encryption scheme based on cloud-fog computing
    CHEN Jiahao, YIN Xinchun
    Journal of Computer Applications    2021, 41 (6): 1611-1620.   DOI: 10.11772/j.issn.1001-9081.2020121955
    Abstract349)      PDF (1134KB)(361)       Save
    Focusing on the large decryption overhead of the resource limited edge devices and the lack of effective user tracking and revocation in attribute-based encryption, a traceable and revocable Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme supporting cloud-fog computing was proposed. Firstly, through the introduction of fog nodes, the ciphertext storage and outsourcing decryption were able to be carried out on fog nodes near the users, which not only effectively protected users' private data, but also reduced users' computing overhead. Then, in response to the behaviors such as user permission changes, users intentionally or unintentionally leaking their own keys in the attribute-based encryption system, user tracking and revocation functions were added. Finally, after the identity of malicious user with the above behaviors was tracked through the algorithm, the user would be added to the revocation list, so that user's access right was cancelled. The performance analysis shows that the decryption overhead at the user end is reduced to one multiplication and one exponential operation, which can save large bandwidth and decryption time for users; at the same time, the proposed scheme supports the tracking and revocation of malicious users. Therefore, the proposed scheme is suitable for data sharing of devices with limited computing resources in cloud-fog environment.
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    Lightweight anonymous mutual authentication protocol based on random operators for radio frequency identification system
    WU Kaifan, YIN Xinchun
    Journal of Computer Applications    2021, 41 (6): 1621-1630.   DOI: 10.11772/j.issn.1001-9081.2020121947
    Abstract255)      PDF (1437KB)(193)       Save
    The Radio Frequency Identification (RFID) system is vulnerable to malicious attacks in the wireless channel and the privacy of the tag owners is often violated. In order to solve the problems, a lightweight RFID authentication protocol supporting anonymity was proposed. Firstly, the random number generator was used to generate the unpredictable sequence for specifying the lightweight operators participating in the protocol. Then, the seed was specified to achieve the key negotiation between the reader and the tag. Finally, the mutual authentication and information updating were achieved. The comparison results with some representative lightweight schemes show that the proposed scheme saves the the tag storage overhead by up to 42% compared with the similar lightweight protocols, and the has the communication overhead also maintained at the low level of similar schemes at the same time, and is able to support the multiple security requirements. The proposed scheme is suitable for low-cost RFID systems.
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    Online short video content distribution strategy based on federated learning
    DONG Wentao, LI Zhuo, CHEN Xin
    Journal of Computer Applications    2021, 41 (6): 1551-1556.   DOI: 10.11772/j.issn.1001-9081.2020121936
    Abstract353)      PDF (958KB)(521)       Save
    To improve the accuracy of short video content distribution, the interest tendencies and the personalized demands for short video content of social groups that the users belong to were analyzed, and in the short video application scenarios based on the active recommendation approaches, a short video content distribution strategy was designed with the goal of maximizing the profit of video content providers. Firstly, based on the federated learning, the interest prediction model was trained by using the local album data of the user group, and the user group interest vector prediction algorithm was proposed and the interest vector representation of the user group was obtained. Secondly, using the interest vector as the input, the corresponding short video content distribution strategy was designed in real time based on the Combinatorial Upper Confidence Bound (CUCB) algorithm, so that the long-term profit obtained by the video content providers was maximized. The average profit obtained by the proposed strategy is relatively stable and significantly better than that obtained by the short video distribution strategy only based on CUCB; in terms of total profit of video providers, compared with the Upper Confidence Bound (UCB) strategy and random strategy, the proposed strategy increases by 12% and 30% respectively. Experimental results show that the proposed short video content distribution strategy can effectively improve the accuracy of short video distribution, so as to further increase the profit obtained by video content providers.
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    Deep neural network compression algorithm based on combined dynamic pruning
    ZHANG Mingming, LU Qingning, LI Wenzhong, SONG Hu
    Journal of Computer Applications    2021, 41 (6): 1589-1596.   DOI: 10.11772/j.issn.1001-9081.2020121914
    Abstract365)      PDF (1131KB)(330)       Save
    As a branch of model compression, network pruning algorithm reduces the computational cost by removing unimportant parameters in the deep neural network. However, permanent pruning will cause irreversible loss of the model capacity. Focusing on this issue, a combined dynamic pruning algorithm was proposed to comprehensively analyze the characteristics of the convolution kernel and the input image. Part of the convolution kernels were zeroized and allowed to be updated during the training process until the network converged, thereafter the zeroized kernels would be permanently removed. At the same time, the input images were sampled to extract their features, then a channel importance prediction network was used to analyze these features to determine the channels able to be skipped during the convolution operation. Experimental results based on M-CifarNet and VGG16 show that the combined dynamic pruning can respectively provide 2.11 and 1.99 floating-point operation compression ratios, with less than 0.8 percentage points and 1.2 percentage points accuracy loss respectively compared to the benchmark model (M-CifarNet、VGG16). Compared with the existing network pruning algorithms, the combined dynamic pruning algorithm effectively reduces the Floating-Point Operations Per second (FLOPs) and the parameter scale of the model, and achieves the higher accuracy under the same compression ratio.
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    Relay selection strategy for cache-aided full-duplex simultaneous wireless information and power transfer system
    SHI Anni, LI Taoshen, WANG Zhe, HE Lu
    Journal of Computer Applications    2021, 41 (6): 1539-1545.   DOI: 10.11772/j.issn.1001-9081.2020121930
    Abstract333)      PDF (1136KB)(466)       Save
    In order to improve the performance of the Simultaneous Wireless Information and Power Transfer (SWIPT) system, a new cache-aided full-duplex relay collaborative system model was constructed, and the free Energy Access Points (EAPs) were considered as the extra energy supplement of relay nodes in the system. For the system throughput optimization problem, a new SWIPT relay selection strategy based on power allocation cooperation was proposed. Firstly, a problem model on the basis of the constraints such as communication service quality and source node transmit power was established. Secondly, the original nonlinear mixed integer programming problem was transformed into a pair of coupling optimization problems through mathematical transformation. Finally, the Karush-Kuhn-Tucker (KKT) condition was used to solve the internal optimization problem with the help of Lagrange function, so that the closed-form solution of the power allocation factor and the relay transmit power was obtained, and the external optimization problem was solved based on this result, so as to select the best relay for the cooperative communication. The simulation experimental results show that, the free EAPs and the configuration of cache for the relay are feasible and effective, and the proposed system is significantly better than the traditional relay cooperative communication systems in terms of throughput gain.
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    Transportation mode recognition algorithm based on multi-scale feature extraction
    LIU Shize, QIN Yanjun, WANG Chenxing, GAO Cunyuan, LUO Haiyong, ZHAO Fang, WANG Baohui
    Journal of Computer Applications    2021, 41 (6): 1573-1580.   DOI: 10.11772/j.issn.1001-9081.2020121915
    Abstract377)      PDF (1478KB)(536)       Save
    Aiming at the problems of high power consumption and complex scene for scene perception in universal transportation modes, a new transportation mode detection algorithm combining Residual Network (ResNet) and dilated convolution was proposed. Firstly, the 1D sensor data was converted into the 2D spectral image by using Fast Fourier Transform (FFT). Then, the Principal Component Analysis (PCA) algorithm was used to realize the downsampling of the spectral image. Finally, the ResNet was used to mine the local features of transportation modes, and the global features of transportation modes were mined with dilated convolution, so as to detect eight transportation modes. Experimental evaluation results show that, compared with 8 algorithms including decision tree, random forest and AlexNet, the transportation mode recognition algorithm combining ResNet and dilated convolution has the highest accuracy in eight traffic patterns including static, walking and running, and the proposed algorithm has good identification accuracy and robustness.
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    Traffic flow prediction algorithm based on deep residual long short-term memory network
    LIU Shize, QIN Yanjun, WANG Chenxing, SU Lin, KE Qixue, LUO Haiyong, SUN Yi, WANG Baohui
    Journal of Computer Applications    2021, 41 (6): 1566-1572.   DOI: 10.11772/j.issn.1001-9081.2020121928
    Abstract469)      PDF (1116KB)(529)       Save
    In the multi-step traffic flow prediction task, the spatial-temporal feature extraction effect is not good and the prediction accuracy of future traffic flow is low. In order to solve these problems, a fusion model combining Long-Short Term Memory (LSTM) network, convolutional residual network and attention mechanism was proposed. Firstly, an encoder-decoder-based architecture was used to mine the temporal domain features of different scales by adding LSTM network into the encoder-decoder. Secondly, a convolutional residual network based on the Squeeze-and-Excitation (SE) block of attention mechanism was constructed and embedded into the LSTM network structure to mine the spatial domain features of traffic flow data. Finally, the implicit state information obtained from the encoder was input into the decoder to realize the prediction of high-precision multi-step traffic flow. The real traffic data was used for the experimental testing and analysis. The results show that, compared with the original graph convolution-based model, the proposed model achieves the decrease of 1.622 and 0.08 on the Root Mean Square Error (RMSE) for Beijing and New York traffic flow public datasets, respectively. The proposed model can predict the traffic flow efficiently and accurately.
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    Reverse hybrid access control scheme based on object attribute matching in cloud computing environment
    GE Lina, HU Yugu, ZHANG Guifen, CHEN Yuanyuan
    Journal of Computer Applications    2021, 41 (6): 1604-1610.   DOI: 10.11772/j.issn.1001-9081.2020121954
    Abstract269)      PDF (1071KB)(289)       Save
    Cloud computing improves the efficiency of the use, analysis and management of big data, but also brings the worry of data security and private information disclosure of cloud service to the data contributors. To solve this problem, combined with the role-based access control, attribute-based access control methods and using the architecture of next generation access control, a reverse hybrid access control method based on object attribute matching in cloud computing environment was proposed. Firstly, the access right level of the shared file was set by the data contributor, and the minimum weight of the access object was reversely specified. Then, the weight of each attribute was directly calculated by using the variation coefficient weighting method, and the process of policy rule matching in the attribute centered role-based access control was cancelled. Finally, the right value of the data contributor setting to the data file was used as the threshold for the data visitor to be allowed to access, which not only realized the data access control, but also ensured the protection of private data. Experimental results show that, with the increase of the number of visits, the judgment standards of the proposed method for malicious behaviors and insufficient right behaviors tend to be stable, the detection ability of the method becomes stronger and stronger, and the success rate of the method tends to a relatively stable level. Compared with the traditional access control methods, the proposed method can achieve higher decision-making efficiency in the environment of large number of user visits, which verifies the effectiveness and feasibility of the proposed method.
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2024 Vol.44 No.6

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Honorary Editor-in-Chief: ZHANG Jingzhong
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