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    Survey of communication overhead of federated learning
    Xinyuan QIU, Zecong YE, Xiaolong CUI, Zhiqiang GAO
    Journal of Computer Applications    2022, 42 (2): 333-342.   DOI: 10.11772/j.issn.1001-9081.2021020232
    Abstract842)   HTML157)    PDF (1356KB)(1566)       Save

    To solve the irreconcilable contradiction between data sharing demands and requirements of privacy protection, federated learning was proposed. As a distributed machine learning, federated learning has a large number of model parameters needed to be exchanged between the participants and the central server, resulting in higher communication overhead. At the same time, federated learning is increasingly deployed on mobile devices with limited communication bandwidth and limited power, and the limited network bandwidth and the sharply raising client amount will make the communication bottleneck worse. For the communication bottleneck problem of federated learning, the basic workflow of federated learning was analyzed at first, and then from the perspective of methodology, three mainstream types of methods based on frequency reduction of model updating, model compression and client selection respectively as well as special methods such as model partition were introduced, and a deep comparative analysis of specific optimization schemes was carried out. Finally, the development trends of federated learning communication overhead technology research were summarized and prospected.

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    Federated learning survey:concept, technology, application and challenge
    Journal of Computer Applications    DOI: 10.11772/j.issn.1001-9081.2021101821
    Accepted: 23 December 2021

    Survey of anonymity and tracking technology in Monero
    Dingkang LIN, Jiaqi YAN, Nandeng BA, Zhenhao FU, Haochen JIANG
    Journal of Computer Applications    2022, 42 (1): 148-156.   DOI: 10.11772/j.issn.1001-9081.2021020296
    Abstract569)   HTML36)    PDF (723KB)(355)       Save

    Virtual digital currency provides a breeding ground for terrorist financing, money laundering, drug trafficking and other criminal activities. As a representative emerging digital currency, Monero has a universally acknowledged high anonymity. Aiming at the problem of using Monroe anonymity to commit crimes, Monero anonymity technology and tracking technology were explored as well as the research progresses were reviewed in recent years, so as to provide technical supports for effectively tackling the crimes based on blockchain technology. In specific, the evolution of Monero anonymity technology was summarized, and the tracking strategies of Monero anonymity technology in academic circles were sorted out. Firstly, in the anonymity technologies, ring signature, guaranteed unlinkability (one-off public key), guaranteed untraceability, and the important version upgrading for improving anonymity were introduced. Then, in tracking technologies, the attacks such as zero mixin attack, output merging attack, guess-newest attack, closed set attack, transaction flooding attack, tracing attacks from remote nodes and Monero ring attack were introduced. Finally, based on the analysis of anonymity technologies and tracking strategies, four conclusions were obtained: the development of anonymity technology and the development of tracking technology of Monero promote each other; the application of Ring Confidential Transactions (RingCT) is a two-edged sword, which makes the passive attack methods based on currency value ineffective, and also makes the active attack methods easier to succeed; output merging attack and zero mixin attack complement each other; Monero’s system security chain still needs to be sorted out.

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    Text multi-label classification method incorporating BERT and label semantic attention
    Xueqiang LYU, Chen PENG, Le ZHANG, Zhi’an DONG, Xindong YOU
    Journal of Computer Applications    2022, 42 (1): 57-63.   DOI: 10.11772/j.issn.1001-9081.2021020366
    Abstract503)   HTML35)    PDF (577KB)(579)       Save

    Multi-Label Text Classification (MLTC) is one of the important subtasks in the field of Natural Language Processing (NLP). In order to solve the problem of complex correlation between multiple labels, an MLTC method TLA-BERT was proposed by incorporating Bidirectional Encoder Representations from Transformers (BERT) and label semantic attention. Firstly, the contextual vector representation of the input text was learned by fine-tuning the self-coding pre-training model. Secondly, the labels were encoded individually by using Long Short-Term Memory (LSTM) neural network. Finally, the contribution of text to each label was explicitly highlighted with the use of an attention mechanism in order to predict the multi-label sequences. Experimental results show that compared with Sequence Generation Model (SGM) algorithm, the proposed method improves the F value by 2.8 percentage points and 1.5 percentage points on the Arxiv Academic Paper Dataset (AAPD) and Reuters Corpus Volume I (RCV1)-v2 public dataset respectively.

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    Unsupervised attributed graph embedding model based on node similarity
    Yang LI, Anbiao WU, Ye YUAN, Linlin ZHAO, Guoren WANG
    Journal of Computer Applications    2022, 42 (1): 1-8.   DOI: 10.11772/j.issn.1001-9081.2021071221
    Abstract436)   HTML109)    PDF (864KB)(430)       Save

    Attributed graph embedding aims to represent the nodes in an attributed graph into low-dimensional vectors while preserving the topology information and attribute information of the nodes. There are lots of works related to attributed graph embedding. However, most of algorithms proposed in them are supervised or semi-supervised. In practical applications, the number of nodes that need to be labeled is large, which makes these algorithms difficult and consume huge manpower and material resources. Above problems were reanalyzed from an unsupervised perspective, and an unsupervised attributed graph embedding algorithm was proposed. Firstly, the topology information and attribute information of the nodes were calculated respectively by using the existing non-attributed graph embedding algorithm and attributes of the attributed graph. Then, the embedding vector of the nodes was obtained by using Graph Convolutional Network (GCN), and the difference between the embedding vector and the topology information and the difference between the embedding vector and attribute information were minimized. Finally, similar embeddings was obtained by the paired nodes with similar topological information and attribute information. Compared with Graph Auto-Encoder (GAE) method, the proposed method has the node classification accuracy improved by 1.2 percentage points and 2.4 percentage points on Cora and Citeseer datasets respectively. Experimental results show that the proposed method can effectively improve the quality of the generated embedding.

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    Research advances in disentangled representation learning
    Keyang CHENG, Chunyun MENG, Wenshan WANG, Wenxi SHI, Yongzhao ZHAN
    Journal of Computer Applications    2021, 41 (12): 3409-3418.   DOI: 10.11772/j.issn.1001-9081.2021060895
    Abstract434)   HTML48)    PDF (877KB)(247)       Save

    The purpose of disentangled representation learning is to model the key factors that affect the form of data, so that the change of a key factor only causes the change of data on a certain feature, while the other features are not affected. It is conducive to face the challenge of machine learning in model interpretability, object generation and operation, zero-shot learning and other issues. Therefore, disentangled representation learning always be a research hotspot in the field of machine learning. Starting from the history and motives of disentangled representation learning, the research status and applications of disentangled representation learning were summarized, the invariance, reusability and other characteristics of disentangled representation learning were analyzed, and the research on the factors of variation via generative entangling, the research on the factors of variation with manifold interaction, and the research on the factors of variation using adversarial training were introduced, as well as the latest research trends such as a Variational Auto-Encoder (VAE) named β-VAE were introduced. At the same time, the typical applications of disentangled representation learning were shown, and the future research directions were prospected.

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    Research progress on binary code similarity search
    Bing XIA, Jianmin PANG, Xin ZHOU, Zheng SHAN
    Journal of Computer Applications    2022, 42 (4): 985-998.   DOI: 10.11772/j.issn.1001-9081.2021071267
    Abstract430)   HTML98)    PDF (841KB)(412)       Save

    With the rapid development of Internet of Things (IoT) and industrial Internet, the research of cyberspace security has been paid more and more attention by industry and academia. Because the source code cannot be obtained, binary code similarity search has become a key core technology for vulnerability mining and malware code analysis. Firstly, the basic concepts of binary code similarity search and the framework of binary code similarity search system were introduced. Secondly, the development status of binary code technology about syntax similarity search, semantic similarity search and pragmatic similarity search were discussed. Then, the existing solutions were summarized and compared from the perspectives of binary hash, instruction sequence, graph structure, basic block semantics, feature learning, debugging information recovery and advanced semantic recognition of functions. Finally, the future development direction and prospect of binary code similarity search were looked forward to.

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    Improved high-dimensional many-objective evolutionary algorithm based on decomposition
    Gangzhu QIAO, Rui WANG, Chaoli SUN
    Journal of Computer Applications    2021, 41 (11): 3097-3103.   DOI: 10.11772/j.issn.1001-9081.2020121895
    Abstract408)   HTML91)    PDF (525KB)(377)       Save

    In the reference vector based high-dimensional many-objective evolutionary algorithms, the random selection of parent individuals will slow down the speed of convergence, and the lack of individuals assigned to some reference vectors will weaken the diversity of population. In order to solve these problems, an Improved high-dimensional Many-Objective Evolutionary Algorithm based on Decomposition (IMaOEA/D) was proposed. Firstly, when a reference vector was assigned at least two individuals in the framework of decomposition strategy, the parent individuals were selected for reproduction of offspring according to the distance from the individual assigned to the reference vector to the ideal point, so as to increase the search speed. Then, for the reference vector that was not assigned at least two individuals, the point with the smallest distance from the ideal point along the reference vector was selected from all the individuals, so that at least two individuals and the reference vector were associated. Meanwhile, by guaranteeing one individual was related to each reference vector after environmental selection, the diversity of population was ensured. The proposed method was tested and compared with other four high-dimensional many-objective optimization algorithms based on decomposition on the MaF test problem sets with 10 and 15 objectives. Experimental results show that, the proposed algorithm has good optimization ability for high-dimensional many-objective optimization problems: the optimization results of the proposed algorithm on 14 test problems of the 30 test problems are better than those of the other four comparison algorithms. Especially, the proposed algorithm has certain advantage on the degradation problem optimization.

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    Safety helmet wearing detection algorithm based on improved YOLOv5
    Jin ZHANG, Peiqi QU, Cheng SUN, Meng LUO
    Journal of Computer Applications    2022, 42 (4): 1292-1300.   DOI: 10.11772/j.issn.1001-9081.2021071246
    Abstract392)   HTML18)    PDF (7633KB)(245)       Save

    Aiming at the problems of strong interference and low detection precision of the existing safety helmet wearing detection, an algorithm of safety helmet detection based on improved YOLOv5 (You Only Look Once version 5) model was proposed. Firstly, for the problem of different sizes of safety helmets, the K-Means++ algorithm was used to redesign the size of the anchor box and match it to the corresponding feature layer. Secondly, the multi-spectral channel attention module was embedded in the feature extraction network to ensure that the network was able to learn the weight of each channel autonomously and enhance the information dissemination between the features, thereby strengthening the network ability to distinguish foreground and background. Finally, images of different sizes were input randomly during the training iteration process to enhance the generalization ability of the algorithm. Experimental results show as follows: on the self-built safety helmet wearing detection dataset, the proposed algorithm has the mean Average Precision (mAP) reached 96.0%, the the Average Precision (AP) of workers wearing safety helmet reached 96.7%, and AP of workers without safety helmet reached 95.2%. Compared with the YOLOv5 algorithm, the proposed algorithm has the mAP of helmet safety-wearing detection increased by 3.4 percentage points, and it meets the accuracy requirement of helmet safety-wearing detection in construction scenarios.

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    Fall behavior detection algorithm for the elderly based on AlphaPose optimization model
    Jingqi MA, Huan LEI, Minyi CHEN
    Journal of Computer Applications    2022, 42 (1): 294-301.   DOI: 10.11772/j.issn.1001-9081.2021020331
    Abstract386)   HTML17)    PDF (7482KB)(452)       Save

    In order to detect the elderly fall high-risk behaviors quickly and accurately on the low-power and low-cost hardware platform, an abnormal behavior detection algorithm based on AlphaPose optimization model was proposed. Firstly, the pedestrian target detection model and pose estimation model were optimized to accelerate the human target detection and pose joint point reasoning. Then, the image coordinate data of human pose joint points were computed rapidly through the optimized AlphaPose model. Finally, the relationship between the head joint point linear velocity and the crotch joint linear velocity at the moment the human body falls was calculated, as well as the change of the angle between the midperpendicular of the torso and X-axis of the image, were calculated to determine the occurrence of the fall. The proposed algorithm was deployed to the Jetson Nano embedded development board, and compared with several main fall detection algorithms based on human pose at present: YOLO (You Only Look Once)v3+Pose, YOLOv4+Pose, YOLOv5+Pose, trt_pose and NanoDet+Pose. Experimental results show that on the used embedded platform when the image resolution is 320×240, the proposed algorithm has the detection frame rate of 8.83 frame/s and the accuracy of 0.913, which are both better than those of the algorithms compared above. The proposed algorithm has relatively high real-time performance and accuracy, and can timely detect the occurrence of the elderly fall behaviors.

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    Stock market volatility prediction method based on graph neural network with multi-attention mechanism
    Xiaohan LI, Jun WANG, Huading JIA, Liu XIAO
    Journal of Computer Applications    2022, 42 (7): 2265-2273.   DOI: 10.11772/j.issn.1001-9081.2021081487
    Abstract378)   HTML7)    PDF (2246KB)(134)       Save

    Stock market is an essential element of financial market, therefore, the study on volatility of stock market plays a significant role in taking effective control of financial market risks and improving returns on investment. For this reason, it has attracted widespread attention from both academic circle and related industries. However, there are multiple influencing factors for stock market. Facing the multi-source and heterogeneous information in stock market, it is challenging to find how to mine and fuse multi-source and heterogeneous data of stock market efficiently. To fully explain the influence of different information and information interaction on the price changes in stock market, a graph neural network based on multi-attention mechanism was proposed to predict price fluctuation in stock market. First of all, the relationship dimension was introduced to construct heterogeneous subgraphs for the transaction data and news text of stock market, and multi-attention mechanism was adopted for fusion of the graph data. Then, the graph neural network Gated Recurrent Unit (GRU) was applied to perform graph classification. On this basis, prediction was made for the volatility of three important indexes: Shanghai Composite Index, Shanghai and Shenzhen 300 Index, Shenzhen Component Index. Experimental results show that from the perspective of heterogeneous information characteristics, compared with the transaction data of stock market, the news information of stock market has the lagged influence on stock volatility; from the perspective of heterogeneous information fusion, compared with algorithms such as Support Vector Machine (SVM), Random Forest (RF) and Multiple Kernel k-Means (MKKM) clustering, the proposed method has the prediction accuracy improved by 17.88 percentage points, 30.00 percentage points and 38.00 percentage points respectively; at the same time, the quantitative investment simulation was performed according to the model trading strategy.

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    Cross-chain mechanism based on Spark blockchain
    Jiagui XIE, Zhiping LI, Jian JIN
    Journal of Computer Applications    2022, 42 (2): 519-527.   DOI: 10.11772/j.issn.1001-9081.2021020353
    Abstract377)   HTML37)    PDF (888KB)(396)       Save

    Considering different blockchains being isolated and the data interaction and sharing difficulties in the current rapid development process of blockchain technology, a cross-chain mechanism based on Spark blockchain was proposed. Firstly, common cross-chain technologies and current mainstream cross-chain projects were analyzed, the implementation principles of different technologies and projects were studied, and their differences, advantages and disadvantages were summarized. Then, using the blockchain architecture maned main-sub blockchain mode, the key core components such as smart contract component, transaction verification component, transaction timeout component were designed, and the four stages of cross-chain process were elaborated in detail, including transaction initiation, transaction routing, transaction verification and transaction confirmation. Finally, the feasible experiments were designed for performance test and security test, and the security was analyzed. Experimental results show that Spark blockchain has significant advantages compared to other blockchains in terms of transaction delay, throughput and spike testing. Besides, when the proportion of malicious nodes is low, the success rate of cross-chain transactions is 100%, and different sub chains can conduct cross-chain transactions safely and stably. This mechanism solves the problem of data interaction and sharing between blockchains, and provides technical reference for the design of Spark blockchain application scenarios in the next step.

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    Review of applications of natural language processing in text sentiment analysis
    Yingjie WANG, Jiuqi ZHU, Zumin WANG, Fengbo BAI, Jian GONG
    Journal of Computer Applications    2022, 42 (4): 1011-1020.   DOI: 10.11772/j.issn.1001-9081.2021071262
    Abstract344)   HTML56)    PDF (783KB)(240)       Save

    Text sentiment analysis has gradually become an important part of Natural Language Processing(NLP) in the fields of systematic recommendation and acquisition of user sentiment information, as well as public opinion reference for the government and enterprises. The methods in the field of sentiment analysis were compared and summarized by literature research. Firstly, literature investigation was carried out on the methods of sentiment analysis from the dimensions of time and method. Then, the main methods and application scenarios of sentiment analysis were summarized and compared. Finally, the advantages and disadvantages of each method were analyzed. According to the analysis results, in the face of different task scenarios, there are mainly three sentiment analysis methods: sentiment analysis based on emotion dictionary, sentiment analysis based on machine learning and sentiment analysis based on deep learning. The method based on multi-strategy mixture has become the trend of improvement. Literature investigation shows that there is still room for improvement in the techniques and methods of text sentiment analysis, and it has a large market and development prospects in e-commerce, psychotherapy and public opinion monitoring.

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    Time series classification by LSTM based on multi-scale convolution and attention mechanism
    Yinglü XUAN, Yuan WAN, Jiahui CHEN
    Journal of Computer Applications    2022, 42 (8): 2343-2352.   DOI: 10.11772/j.issn.1001-9081.2021061062
    Abstract343)   HTML33)    PDF (711KB)(231)       Save

    The multi-scale features of time series contain abundant category information which has different importance for classification. However, the existing univariate time series classification models conventionally extract series features by convolutions with a fixed kernel size, resulting in being unable to acquire and focus on important multi-scale features effectively. In order to solve the above problem, a Multi-scale Convolution and Attention mechanism (MCA) based Long Short-Term Memory (LSTM) model (MCA-LSTM) was proposed, which was capable of concentrating and fusing important multi-scale features to achieve more accurate classification effect. In this structure, by using LSTM, the transmission of series information was controlled through memory cells and gate mechanism, and the correlation information of time series was extracted fully; by using Multi-scale Convolution Module (MCM), the multi-scale features of the series were extracted through Convolutional Neural Networks (CNNs) with different kernel sizes; by using Attention Module (AM), the channel information was fused to obtain the importance of features and assign attention weights, which enabled the network to focus on important time series features. Experimental results on 65 univariate time series datasets of UCR archive show that compared with the state-of-the-art time series classification methods: Unsupervised Scalable Representation Learning-FordA (USRL-FordA), Unsupervised Scalable Representation Learning-Combined (1-Nearest Neighbor) (USRL-Combined (1-NN)), Omni-Scale Convolutional Neural Network (OS-CNN), Inception-Time and Robust Temporal Feature Network for time series classification (RTFN),MCA-LSTM has the Mean Error (ME) reduced by 7.48, 9.92, 2.43, 2.09 and 0.82 percentage points, respectively; and achieved the highest Arithmetic Mean Rank (AMR) and Geometric Mean Rank (GMR), which are 2.14 and 3.23 respectively. These results fully demonstrate the effectiveness of MCA-LSTM in the classification of univariate time series.

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    Review of image classification algorithms based on convolutional neural network
    Changqing JI, Zhiyong GAO, Jing QIN, Zumin WANG
    Journal of Computer Applications    2022, 42 (4): 1044-1049.   DOI: 10.11772/j.issn.1001-9081.2021071273
    Abstract341)   HTML37)    PDF (605KB)(245)       Save

    Convolutional Neural Network (CNN) is one of the important research directions in the field of computer vision based on deep learning at present. It performs well in applications such as image classification and segmentation, target detection. Its powerful feature learning and feature representation capability are admired by researchers increasingly. However, CNN still has problems such as incomplete feature extraction and overfitting of sample training. Aiming at these issues, the development of CNN, classical CNN network models and their components were introduced, and the methods to solve the above issues were provided. By reviewing the current status of research on CNN models in image classification, the suggestions were provided for further development and research directions of CNN.

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    Image segmentation algorithm with adaptive attention mechanism based on Deeplab V3 Plus
    Zhen YANG, Xiaobao PENG, Qiangqiang ZHU, Zhijian YIN
    Journal of Computer Applications    2022, 42 (1): 230-238.   DOI: 10.11772/j.issn.1001-9081.2021010137
    Abstract334)   HTML20)    PDF (1160KB)(337)       Save

    In order to solve the problem that image details and small target information are lost prematurely in the subsampling operations of Deeplab V3 Plus, an adaptive attention mechanism image semantic segmentation algorithm based on Deeplab V3 Plus network architecture was proposed. Firstly, attention mechanism modules were embedded in the input layer, middle layer and output layer of Deeplab V3 Plus backbone network, and a weight value was introduced to be multiplied with each attention mechanism module to achieve the purpose of constraining the attention mechanism modules. Secondly, the Deeplab V3 Plus embedded with the attention modules was trained on the PASCAL VOC2012 common segmentation dataset to obtain the weight values (empirical values) of the attention mechanism modules manually. Then, various fusion methods of attention mechanism modules in the input layer, the middle layer and the output layer were explored. Finally, the weight value of the attention mechanism module was automatically updated by back propagation and the optimal weight value and optimal segmentation model of the attention mechanism module were obtained. Experimental results show that, compared with the original Deeplab V3 Plus network structure, the Deeplab V3 Plus network structure with adaptive attention mechanism has the Mean Intersection over Union (MIOU) increased by 1.4 percentage points and 0.7 percentage points on the PASCAL VOC2012 common segmentation dataset and the plant pest dataset, respectively.

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    Performance interference analysis and prediction for distributed machine learning jobs
    Hongliang LI, Nong ZHANG, Ting SUN, Xiang LI
    Journal of Computer Applications    2022, 42 (6): 1649-1655.   DOI: 10.11772/j.issn.1001-9081.2021061404
    Abstract333)   HTML77)    PDF (1121KB)(352)       Save

    By analyzing the problem of job performance interference in distributed machine learning, it is found that performance interference is caused by the uneven allocation of GPU resources such as memory overload and bandwidth competition, and to this end, a mechanism for quickly predicting performance interference between jobs was designed and implemented, which can adaptively predict the degree of job interference according to the given GPU parameters and job types. First, the GPU parameters and interference rates during the operation of distributed machine learning jobs were obtained through experiments, and the influences of various parameters on performance interference were analyzed. Second, some GPU parameter-interference rate models were established by using multiple prediction technologies to analyze the job interference rate errors. Finally, an adaptive job interference rate prediction algorithm was proposed to automatically select the prediction model with the smallest error for a given equipment environment and job set to predict the job interference rates quickly and accurately. By selecting five commonly used neural network tasks, experiments were designed on two GPU devices and the results were analyzed. The results show that the proposed Adaptive Interference Prediction (AIP) mechanism can quickly complete the selection of prediction model and the performance interference prediction without providing any pre-assumed information, it has comsumption time less than 300 s and achieves prediction error rate in the range of 2% to 13%, which can be applied to scenarios such as job scheduling and load balancing.

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    Stock trend prediction method based on temporal hypergraph convolutional neural network
    Xiaojie LI, Chaoran CUI, Guangle SONG, Yaxi SU, Tianze WU, Chunyun ZHANG
    Journal of Computer Applications    2022, 42 (3): 797-803.   DOI: 10.11772/j.issn.1001-9081.2021050748
    Abstract324)   HTML17)    PDF (742KB)(186)       Save

    Traditional stock prediction methods are mostly based on time-series models, which ignore the complex relations among stocks, and the relations often exceed pairwise connections, such as stocks in the same industry or multiple stocks held by the same fund. To solve this problem, a stock trend prediction method based on temporal HyperGraph Convolutional neural Network (HGCN) was proposed, and a hypergraph model based on financial investment facts was constructed to fit multiple relations among stocks. The model was composed of two major components: Gated Recurrent Unit (GRU) network and HGCN. GRU network was used for performing time-series modeling on historical data to capture long-term dependencies. HGCN was used to model high-order relations among stocks to learn intrinsic relation attributes, and introduce the multiple relation information among stocks into traditional time-series modeling for end-to-end trend prediction. Experiments on real dataset of China A-share market show that compared with existing stock prediction methods, the proposed model improves prediction performance, e.g. compared with the GRU network, the proposed model achieves the relative increases in ACC and F1_score of 9.74% and 8.13%, respectively, and is more stable. In addition, the simulation back-testing results show that the trading strategy based on the proposed model is more profitable, with an annual return of 11.30%, which is 5 percentage points higher than that of Long Short-Term Memory (LSTM) network.

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    Network representation learning model based on node attribute bipartite graph
    Le ZHOU, Tingting DAI, Chun LI, Jun XIE, Boce CHU, Feng LI, Junyi ZHANG, Qiao LIU
    Journal of Computer Applications    2022, 42 (8): 2311-2318.   DOI: 10.11772/j.issn.1001-9081.2021060972
    Abstract321)   HTML94)    PDF (843KB)(291)       Save

    It is an important task to carry out reasoning and calculation on graph structure data. The main challenge of this task is how to represent graph-structured knowledge so that machines can easily understand and use graph structure data. After comparing the existing representation learning models, it is found that the models based on random walk methods are likely to ignore the special effect of attributes on the association between nodes. Therefore, a hybrid random walk method based on node adjacency and attribute association was proposed. Firstly the attribute weights were calculated through the common attribute distribution among adjacent nodes, and the sampling probability from the node to each attribute was obtained. Then, the network information was extracted from adjacent nodes and non-adjacent nodes with common attributes respectively. Finally, the network representation learning model based on node attribute bipartite graph was constructed, and the node vector representations were obtained through the above sampling sequence learning. Experimental results on Flickr, BlogCatalog and Cora public datasets show that the Micro-F1 average accuracy of node classification by the node vector representations obtained by the proposed model is 89.38%, which is 2.02 percentage points higher than that of GraphRNA (Graph Recurrent Networks with Attributed random walk) and 21.12 percentage points higher than that of classical work DeepWalk. At the same time, by comparing different random walk methods, it is found that increasing the sampling probabilities of attributes that promote node association can improve the information contained in the sampling sequence.

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    Sparrow search algorithm based on Sobol sequence and crisscross strategy
    Yuxian DUAN, Changyun LIU
    Journal of Computer Applications    2022, 42 (1): 36-43.   DOI: 10.11772/j.issn.1001-9081.2021010187
    Abstract308)   HTML17)    PDF (771KB)(163)       Save

    For the shortcomings of falling into the local optimum easily and slow convergence in Sparrow Search Algorithm (SSA), a Sparrow Search Algorithm based on Sobol sequence and Crisscross strategy (SSASC) was proposed. Firstly, the Sobol sequence was introduced in the initialization stage to enhance the diversity and ergodicity of the population. Secondly, the nonlinear inertia weight in exponential form was proposed to improve the convergence efficiency of the algorithm. Finally, the crisscross strategy was applied to improve the algorithm. In specific, the horizontal crossover was used to enhance the global search ability, while the vertical crossover was used to maintain the diversity of the population and avoid the algorithm from trapping into the local optimum. Thirteen benchmark functions were selected for simulation experiments, and the performance of the algorithm was evaluated by Wilcoxon rank sum test and Friedman test. In comparison experiments with other metaheuristic algorithms, the mean and standard deviation generated by SSASC are always better than other algorithms when the benchmark functions extending from 10 dimensions to 100 dimensions. Experimental results show that SSASC achieves certain superiority in both convergence speed and solution accuracy.

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    News recommendation model with deep feature fusion injecting attention mechanism
    Yuxi LIU, Yuqi LIU, Zonglin ZHANG, Zhihua WEI, Ran MIAO
    Journal of Computer Applications    2022, 42 (2): 426-432.   DOI: 10.11772/j.issn.1001-9081.2021050907
    Abstract296)   HTML45)    PDF (755KB)(176)       Save

    When mining news features and user features, the existing news recommendation models often lack comprehensiveness since they often fail to consider the relationship between the browsed news, the change of time series, and the importance of different news to users. At the same time, the existing models also have shortcomings in more fine-grained content feature mining. Therefore, a news recommendation model with deep feature fusion injecting attention mechanism was constructed, which can comprehensively and non-redundantly conduct user characterization and extract the features of more fine-grained news fragments. Firstly, a deep learning-based method was used to deeply extract the feature matrix of news text through the Convolutional Neural Network (CNN) injecting attention mechanism. By adding time series prediction to the news that users had browsed and injecting multi-head self-attention mechanism, the interest characteristics of users were extracted. Finally, a real Chinese dataset and English dataset were used to carry out experiments with convergence time, Mean Reciprocal Rank (MRR) and normalized Discounted Cumulative Gain (nDCG) as indicators. Compared with Neural news Recommendation with Multi-head Self-attention (NRMS) and other models, on the Chinese dataset, the proposed model has the average improvement rate of nDCG from -0.22% to 4.91% and MRR from -0.82% to 3.48%. Compared with the only model with negative improvement rate, the proposed model has the convergence time reduced by 7.63%. on the English dataset, the proposed model has the improvement rates reached 0.07% to 1.75% and 0.03% to 1.30% respectively on nDCG and MRR; At the same time this model always has fast convergence speed. Results of ablation experiments show that adding attention mechanism and time series prediction module is effective.

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    Multiscale residual UNet based on attention mechanism to realize breast cancer lesion segmentation
    Shengqin LUO, Jinyi CHEN, Hongjun LI
    Journal of Computer Applications    2022, 42 (3): 818-824.   DOI: 10.11772/j.issn.1001-9081.2021040948
    Abstract288)   HTML21)    PDF (1860KB)(75)       Save

    Concerning the characteristics of breast cancer in Magnetic Resonance Imaging (MRI), such as different shapes and sizes, and fuzzy boundaries, an algorithm based on multiscale residual U Network (UNet) with attention mechanism was proposed in order to avoid error segmentation and improve segmentation accuracy. Firstly, the multiscale residual units were used to replace two adjacent convolution blocks in the down-sampling process of UNet, so that the network could pay more attention to the difference of shape and size. Then, in the up-sampling stage, layer-crossed attention was used to guide the network to focus on the key regions, avoiding the error segmentation of healthy tissues. Finally, in order to enhance the ability of representing the lesions, the atrous spatial pyramid pooling was introduced as a bridging module to the network. Compared with UNet, the proposed algorithm improved the Dice coefficient, Intersection over Union (IoU), SPecificity (SP) and ACCuracy (ACC) by 2.26, 2.11, 4.16 and 0.05 percentage points, respectively. The experimental results show that the algorithm can improve the segmentation accuracy of lesions and effectively reduce the false positive rate of imaging diagnosis.

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    Machine reading comprehension model based on event representation
    Yuanlong WANG, Xiaomin LIU, Hu ZHANG
    Journal of Computer Applications    2022, 42 (7): 1979-1984.   DOI: 10.11772/j.issn.1001-9081.2021050719
    Abstract282)   HTML66)    PDF (916KB)(258)       Save

    In order to truly understand a piece of text, it is very important to grasp the main clues of the original text in the process of reading comprehension. Aiming at the questions of main clues in machine reading comprehension, a machine reading comprehension method based on event representation was proposed. Firstly, the textual event graph including the representation of events, the extraction of event elements and the extraction of event relations was extracted from the reading material by clue phrases. Secondly, after considering the time elements, emotional elements of events and the importance of each word in the document, the TextRank algorithm was used to select the events related to the clues. Finally, the answers of the questions were constructed based on the selected clue events. Experimental results show that on the test set composed of the collected 339 questions of clues, the proposed method is better than the sentence ranking method based on TextRank algorithm on BiLingual Evaluation Understudy (BLEU) and Consensus-based Image Description Evaluation (CIDEr) evaluation indexes. In specific, BLEU-4 index is increased by 4.1 percentage points and CIDEr index is increased by 9 percentage points.

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    Lightweight object detection algorithm based on improved YOLOv4
    Zhifeng ZHONG, Yifan XIA, Dongping ZHOU, Yangtian YAN
    Journal of Computer Applications    2022, 42 (7): 2201-2209.   DOI: 10.11772/j.issn.1001-9081.2021050734
    Abstract279)   HTML6)    PDF (5719KB)(260)       Save

    YOLOv4 (You Only Look Once version 4) object detection network has complex structure, many parameters, high configuration required for training and low Frames Per Second (FPS) for real-time detection. In order to solve the above problems, a lightweight object detection algorithm based on YOLOv4, named ML-YOLO (MobileNetv3Lite-YOLO), was proposed. Firstly, MobileNetv3 was used to replace the backbone feature extraction network of YOLOv4, which greatly reduced the amount of backbone network parameters through the depthwise separable convolution in MobileNetv3. Then, a simplified weighted Bi-directional Feature Pyramid Network (Bi-FPN) structure was used to replace the feature fusion network of YOLOv4. Therefore, the object detection accuracy was optimized by the attention mechanism in Bi-FPN. Finally, the final prediction box was generated through the YOLOv4 decoding algorithm, and the object detection was realized. Experimental results on VOC (Visual Object Classes) 2007 dataset show that the mean Average Precision (mAP) of the ML-YOLO algorithm reaches 80.22%, which is 3.42 percentage points lower than that of the YOLOv4 algorithm, and 2.82 percentage points higher than that of the YOLOv5m algorithm; at the same time, the model size of the ML-YOLO algorithm is only 44.75 MB, compared with the YOLOv4 algorithm, it is reduced by 199.54 MB, and compared with the YOLOv5m algorithm, it is only 2.85 MB larger. Experimental results prove that the proposed ML-YOLO model greatly reduces the size of the model compared with the YOLOv4 model while maintaining a higher detection accuracy, indicating that the proposed algorithm can meet the lightweight and accuracy requirements of mobile or embedded devices for object detection.

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    Research progress of blockchain-based federated learning

    Journal of Computer Applications    DOI: 10.11772/j.issn.1001-9081.2021111934
    Accepted: 19 January 2022

    Efficient storage scheme for deadline aware distributed matrix multiplication
    Yongzhu ZHAO, Weidong LI, Bin TANG, Feng MEI, Wenda LU
    Journal of Computer Applications    2020, 40 (2): 311-315.   DOI: 10.11772/j.issn.1001-9081.2019091640
    Abstract273)   HTML11)    PDF (742KB)(450)       Save

    Distributed matrix multiplication is a fundamental operation in many distributed machine learning and scientific computing applications, but its performance is greatly influenced by the stragglers commonly existed in the systems. Recently, researchers have proposed a fountain code based coded matrix multiplication method, which can effectively mitigate the effect of stragglers by fully exploiting the partial results of stragglers. However, it lacks the consideration of the storage cost of worker nodes. By considering the tradeoff relationship between the storage cost and the finish time of computation, the computational deadline-aware storage optimization problem for heterogeneous worker nodes was proposed firstly. Then, through the theoretical analysis, the solution based on expectation approximation was presented, and the problem was transformed into a convex optimization problem by relaxation for efficient solution. Simulation results show that in the case of ensuring a large task success rate, the storage overhead of the proposed scheme will rapidly decrease as the task duration is relaxed, and the scheme can greatly reduce the storage overhead brought by encoding. In other words, the proposed scheme can significantly reduce the extra storage overhead while guaranteeing that the whole computation can be finished before the deadline with high probability.

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    Defect target detection for printed matter based on Siamese-YOLOv4
    Haojie LOU, Yuanlin ZHENG, Kaiyang LIAO, Hao LEI, Jia LI
    Journal of Computer Applications    2021, 41 (11): 3206-3212.   DOI: 10.11772/j.issn.1001-9081.2020121958
    Abstract266)   HTML18)    PDF (1573KB)(121)       Save

    In the production of printing industry, using You Only Look Once version 4 (YOLOv4) directly to detect printing defect targets has low accuracy and requires a large number of training samples. In order to solve the problems, a defect target detection method for printed matter based on Siamese-YOLOv4 was proposed. Firstly, a strategy of image segmentation and random parameter change was used to enhance the dataset. Then, the Siamese similarity detection network was added to the backbone network, and the Mish activation function was introduced into the similarity detection network to calculate the similarity of image blocks. After that, the regions with similarity below the threshold were regarded as the defect candidate regions. Finally, the candidate region images were trained to achieve the precise positioning and classification of defect targets. Experimental results show that, the detection precision of the proposed Siamese-YOLOv4 model is better than those of the mainstream target detection models. On the printing defect dataset, the Siamese-YOLOv4 network has the detection precision for satellite ink droplet defect of 98.6%, the detection precision for dirty spot of 97.8%, the detection precision for print lack of 93.9%; and the mean Average Precision (mAP) reaches 96.8%, which is 6.5 percentage points,6.4 percentage points, 14.9 percentage points and 10.6 percentage points higher respectively than the YOLOv4 algorithm, the Faster Regional Convolutional Neural Network (Faster R-CNN) algorithm, the Single Shot multibox Detector (SSD) algorithm and the EfficientDet algorithm. The proposed Siamese-YOLOv4 model has low false positive rate and miss rate in the defect detection of printed matter, and improves the detection precision by calculating similarity of the image blocks through the similarity detection network, proving that the proposed defect detection method can be applied to the printing quality inspection and therefore improve the defect detection level of printing enterprises.

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    Popular science text classification model enhanced by knowledge graph
    Wangjing TANG, Bin XU, Meihan TONG, Meihuan HAN, Liming WANG, Qi ZHONG
    Journal of Computer Applications    2022, 42 (4): 1072-1078.   DOI: 10.11772/j.issn.1001-9081.2021071278
    Abstract264)   HTML26)    PDF (1056KB)(150)       Save

    Popular science text classification aims to classify the popular science articles according to the popular science classification system. Concerning the problem that the length of popular science articles often exceeds 1 000 words, which leads to the model hard to focus on key points and causes poor classification performance of the traditional models, a model for long text classification combining knowledge graph to perform two-level screening was proposed to reduce the interference of topic-irrelevant information and improve the performance of model classification. First, a four-step method was used to construct a knowledge graph for the domains of popular science. Then, this knowledge graph was used as a distance monitor to filter out irrelevant information through training sentence filters. Finally, the attention mechanism was used to further filter the information of the filtered sentence set, and the attention-based topic classification model was completed. Experimental results on the constructed Popular Science Classification Dataset (PSCD) show that the text classification algorithm model based on the domain knowledge graph information enhancement has higher F1-Score. Compared with the TextCNN model and the BERT (Bidirectional Encoder Representations from Transformers) model, the proposed model has the F1-Score increased by 2.88 percentage points and 1.88 percentage points respectively, verifying the effectiveness of knowledge graph to long text information screening.

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    Multi-head attention memory network for short text sentiment classification
    Yu DENG, Xiaoyu LI, Jian CUI, Qi LIU
    Journal of Computer Applications    2021, 41 (11): 3132-3138.   DOI: 10.11772/j.issn.1001-9081.2021010040
    Abstract262)   HTML22)    PDF (681KB)(161)       Save

    With the development of social networks, it has important social value to analyze the sentiments of massive texts in the social networks. Different from ordinary text classification, short text sentiment classification needs to mine the implicit sentiment semantic features, so it is very difficult and challenging. In order to obtain short text sentiment semantic features at a higher level, a new Multi-head Attention Memory Network (MAMN) was proposed for sentiment classification of short texts. Firstly, n-gram feature information and Ordered Neurons Long Short-Term Memory (ON-LSTM) network were used to improve the multi-head self-attention mechanism to fully extract the internal relationship of the text context, so that the model was able obtain richer text feature information. Secondly, multi-head attention mechanism was adopted to optimize the multi-hop memory network structure, so as to expand the depth of the model and mine higher level contextual internal semantic relations at the same time. A large number of experiments were carried out on Movie Review dataset (MR), Stanford Sentiment Treebank (SST)-1 and SST-2 datasets. The experimental results show that compared with the baseline models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) structure and some latest works, the proposed MAMN achieves the better classification results, and the importance of multi-hop structure in performance improvement is verified.

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    Network embedding method based on multi-granularity community information
    Jun HU, Zhengkang XU, Li LIU, Fujin ZHONG
    Journal of Computer Applications    2022, 42 (3): 663-670.   DOI: 10.11772/j.issn.1001-9081.2021040790
    Abstract261)   HTML54)    PDF (758KB)(233)       Save

    Most of the existing network embedding methods only preserve the local structure information of the network, while they ignore other potential information in the network. In order to preserve the community information of the network and reflect the multi-granularity characteristics of the network community structure, a network Embedding method based on Multi-Granularity Community information (EMGC) was proposed. Firstly, the network’s multi-granularity community structure was obtained, the node embedding and the community embedding were initialized. Then, according to the node embedding at previous level of granularity and the community structure at this level of granularity, the community embedding was updated, and the corresponding node embedding was adjusted. Finally, the node embeddings under different community granularities were spliced to obtain the network embedding that fused the community information of different granularities. Experiments on four real network datasets were carried out. Compared with the methods that do not consider community information (DeepWalk, node2vec) and the methods that consider single-granularity community information (ComE, GEMSEC), EMGC’s AUC value on link prediction and F1 score on node classification are generally better than those of the comparison methods. The experimental results show that EMGC can effectively improve the accuracy of subsequent link prediction and node classification.

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    Multi-modal deep fusion for false information detection
    Jie MENG, Li WANG, Yanjie YANG, Biao LIAN
    Journal of Computer Applications    2022, 42 (2): 419-425.   DOI: 10.11772/j.issn.1001-9081.2021071184
    Abstract252)   HTML36)    PDF (1079KB)(155)       Save

    Concerning the problem of insufficient image feature extraction and ignorance of single-modal internal relations and the interactions between single-modal and multi-modal, a text and image information based Multi-Modal Deep Fusion (MMDF) model was proposed. Firstly, the Bi-Gated Recurrent Unit (Bi-GRU) was used to extract the rich semantic features of the text, and the multi-branch Convolutional-Recurrent Neural Network (CNN-RNN) was used to extract the multi-level features of the image. Then the inter-modal and intra-modal attention mechanisms were established to capture the high-level interaction between the fields of language and vision, and the multi-modal joint representation was obtained. Finally, the original representation of each modal and the fused multi-modal joint representation were re-fused according to their attention weights to strengthen the role of the original information. Compared with the Multimodal Variational AutoEncoder (MVAE) model, the proposed model has the accuracy improved by 1.9 percentage points and 2.4 percentage points on the China Computer Federation (CCF) competition and the Weibo datasets respectively. Experimental results show that the proposed model can fully fuse multi-modal information and effectively improve the accuracy of false information detection.

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    Named entity recognition method of elementary mathematical text based on BERT
    Yi ZHANG, Shuangsheng WANG, Bin HE, Peiming YE, Keqiang LI
    Journal of Computer Applications    2022, 42 (2): 433-439.   DOI: 10.11772/j.issn.1001-9081.2021020334
    Abstract251)   HTML27)    PDF (689KB)(265)       Save

    In Named Entity Recognition (NER) of elementary mathematics, aiming at the problems that the word embedding of the traditional NER method cannot represent the polysemy of a word and some local features are ignored in the feature extraction process of the method, a Bidirectional Encoder Representation from Transformers (BERT) based NER method for elementary mathematical text named BERT-BiLSTM-IDCNN-CRF (BERT-Bidirectional Long Short-Term Memory-Iterated Dilated Convolutional Neural Network-Conditional Random Field) was proposed. Firstly, BERT was used for pre-training. Then, the word vectors obtained by training were input into BiLSTM and IDCNN to extract features, after that, the output features of the two neural networks were merged. Finally, the output was obtained through the correction of CRF. Experimental results show that the F1 score of BERT-BiLSTM-IDCNN-CRF is 93.91% on the dataset of test questions of elementary mathematics, which is 4.29 percentage points higher than that of BiLSTM-CRF benchmark model, and 1.23 percentage points higher than that of BERT-BiLSTM-CRF model. And the F1 scores of the proposed method to line, angle, plane, sequence and other entities are all higher than 91%, which verifies the effectiveness of the proposed method on elementary mathematical entity recognition. In addition, after adding attention mechanism to the proposed model, the recall of the model decreases by 0.67 percentage points, but the accuracy of the model increases by 0.75 percentage points, which means the introduction of attention mechanism has little effect on the recognition effect of the proposed method.

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    Feature construction and preliminary analysis of uncertainty for meta-learning
    Yan LI, Jie GUO, Bin FAN
    Journal of Computer Applications    2022, 42 (2): 343-348.   DOI: 10.11772/j.issn.1001-9081.2021071198
    Abstract251)   HTML65)    PDF (483KB)(154)       Save

    Meta-learning is the learning process of applying machine learning methods (meta-algorithms) to seek the mapping between features of a problem (meta-features) and relative performance measures of the algorithm, thereby forming the learning process of meta-knowledge. How to construct and extract meta-features is an important research content. Concerning the problem that most of meta-features used in the existing related researches are statistical features of data, uncertainty modeling was proposed and the impact of uncertainty on learning system was studied. Based on inconsistency of data, complexity of boundary, uncertainty of model output, linear capability to be classified, degree of attribute overlap, and uncertainty of feature space, six kinds of uncertainty meta-features were established for data or models. At the same time,the uncertainty size of the learning problem itself was measured from different perspectives, and specific definitions were given. The correlations between these meta-features were analyzed on artificial datasets and real datasets of a large number of classification problems, and multiple classification algorithms such as K-Nearest Neighbor (KNN) were used to conduct a preliminary analysis of the correlation between meta-features and test accuracy. Results show that the average degree of correlation is about 0.8, indicating that these meta-features have a significant impact on learning performance.

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    New computing power network architecture and application case analysis
    Zheng DI, Yifan CAO, Chao QIU, Tao LUO, Xiaofei WANG
    Journal of Computer Applications    2022, 42 (6): 1656-1661.   DOI: 10.11772/j.issn.1001-9081.2021061497
    Abstract246)   HTML26)    PDF (1584KB)(120)       Save

    With the proliferation of Artificial Intelligence (AI) computing power to the edge of the network and even to terminal devices, the computing power network of end-edge-supercloud collaboration has become the best computing solution. The emerging new opportunities have spawned the deep integration between end-edge-supercloud computing and the network. However, the complete development of the integrated system is unsolved, including adaptability, flexibility, and valuability. Therefore, a computing power network for ubiquitous AI named ACPN was proposed with the assistance of blockchain. In ACPN, the end-edge-supercloud collaboration provides infrastructure for the framework, and the computing power resource pool formed by the infrastructure provides safe and reliable computing power for the users, the network satisfies users’ demands by scheduling resources, and the neural network and execution platform in the framework provide interfaces for AI task execution. At the same time, the blockchain guarantees the reliability of resource transaction and encourage more computing power contributors to join the platform. This framework provides adaptability for users of computing power network, flexibility for resource scheduling of networking computing power, and valuability for computing power providers. A clear description of this new computing power network architecture was given through a case.

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    Recommendation model for user attribute preference modeling based on convolutional neural network interaction
    Renzhi PAN, Fulan QIAN, Shu ZHAO, Yanping ZHANG
    Journal of Computer Applications    2022, 42 (2): 404-411.   DOI: 10.11772/j.issn.1001-9081.2021041070
    Abstract246)   HTML32)    PDF (633KB)(168)       Save

    Latent Factor Model (LFM) have been widely used in recommendation field due to their excellent performance. In addition to interactive data, auxiliary information is also introduced to solve the problem of data sparsity, thereby improving the performance of recommendations. However, most LFMs still have some problems. First, when modeling users by LFM, how users make decisions on items based on their feature preferences is ignored. Second, the feature interaction using inner product assumes that the feature dimensions are independent to each other, without considering the correlation between the feature dimensions. In order to solve the above problems, a recommendation model for User Attribute preference Modeling based on Convolutional Neural Network (CNN) interaction (UAMC) was proposed. In this model, the general preferences of users, user attributes and item embeddings were firstly obtained, and then the user attributes and item embeddings were interacted to explore the preferences of different attributes of users to different items. After that, the interacted user preference attributes were sent to the CNN layer to explore the correlation between different dimensions of different preference attributes and thus obtain the users’ attribute preference vectors. Next, the attention mechanism was used to combine the general preferences of the users with the attribute preferences obtained from CNN layer to obtain the vector representations of the users. Finally, the dot product was used to calculate the users’ ratings of the items. Experiments were conducted on three real datasets: Movielens-100K, Movielens-1M and Book-crossing. The results show that the proposed algorithm decreases the Root Mean Square Error (RMSE) by 1.75%, 2.78% and 0.25% respectively compared with the model of Neural Factorization Machine for sparse predictive analytics (NFM), which verifies the effectiveness of UAMC model in improving the accuracy of recommendation in the rating prediction recommendation of LFM.

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    Derivative-free few-shot learning based performance optimization method of pre-trained models with convolution structure
    Yaming LI, Kai XING, Hongwu DENG, Zhiyong WANG, Xuan HU
    Journal of Computer Applications    2022, 42 (2): 365-374.   DOI: 10.11772/j.issn.1001-9081.2021020230
    Abstract244)   HTML44)    PDF (841KB)(301)       Save

    Deep learning model with convolution structure has poor generalization performance in few-shot learning scenarios. Therefore, with AlexNet and ResNet as examples, a derivative-free few-shot learning based performance optimization method of convolution structured pre-trained models was proposed. Firstly, the sample data were modulated to generate the series data from the non-series data based on causal intervention, and the pre-trained model was pruned directly based on the co-integration test from the perspective of data distribution stability. Then, based on Capital Asset Pricing Model (CAPM) and optimal transmission theory, in the intermediate output process of the pre-trained model, the forward learning without gradient propagation was carried out, and a new structure was constructed, thereby generating the representation vectors with clear inter-class distinguishability in the distribution space. Finally, the generated effective features were adaptively weighted based on the self-attention mechanism, and the features were aggregated in the fully connected layer to generate the embedding vectors with weak correlation. Experimental results indicate that the proposed method can increase the Top-1 accuracies of the AlexNet and ResNet convolution structured pre-trained models on 100 classes of images in ImageNet 2012 dataset from 58.82%, 78.51% to 68.50%, 85.72%, respectively. Therefore, the proposed method can effectively improve the performance of convolution structured pre-trained models based on few-shot training data.

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    Several novel intelligent optimization algorithms for solving constrained engineering problems and their prospects
    Mengjian ZHANG, Deguang WANG, Min WANG, Jing YANG
    Journal of Computer Applications    2022, 42 (2): 534-541.   DOI: 10.11772/j.issn.1001-9081.2021020265
    Abstract240)   HTML29)    PDF (849KB)(220)       Save

    To study the performance and application prospects of novel intelligent optimization algorithms, six bionic intelligent optimization algorithms proposed in the past few years were analyzed, concluding Harris Hawks Optimization (HHO) algorithm, Equilibrium Optimizer (EO), Marine Predators Algorithm (MPA), Political Optimizer (PO), Slime Mould Algorithm (SMA), and Heap-Based Optimizer (HBO). Their performance and applications in different constrained engineering optimization problems were compared and analyzed. Firstly, the basic principles of six optimization algorithms were introduced. Secondly, the optimization tests were performed on ten standard benchmark functions for six optimization algorithms. Thirdly, six optimization algorithms were applied to solve three engineering optimization problems with constraints. Experimental results show that the convergence accuracy of PO is the best for the optimization of unimodal and multimodal test functions and can reach the theoretical optimal value zero many times. The EO and MPA are better for solving constrained engineering problems with fast optimization speed, high stability and standard deviation of a small order of magnitude. Finally, the improvement methods and development potentials of six optimization algorithms were analyzed.

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    Adaptive deep graph convolution using initial residual and decoupling operations
    Jijie ZHANG, Yan YANG, Yong LIU
    Journal of Computer Applications    2022, 42 (1): 9-15.   DOI: 10.11772/j.issn.1001-9081.2021071289
    Abstract230)   HTML36)    PDF (648KB)(174)       Save

    The traditional Graph Convolutional Network (GCN) and many of its variants achieve the best effect in the shallow layers, and do not make full use of higher-order neighbor information of nodes in the graph. The subsequent deep graph convolution models can solve the above problem, but inevitably generate the problem of over-smoothing, which makes the models impossible to effectively distinguish different types of nodes in the graph. To address this problem, an adaptive deep graph convolution model using initial residual and decoupling operations, named ID-AGCN (model using Initial residual and Decoupled Adaptive Graph Convolutional Network), was proposed. Firstly, the node’s representation transformation as well as feature propagation was decoupled. Then, the initial residual was added to the node’s feature propagation process. Finally, the node representations obtained from different propagation layers were combined adaptively, appropriate local and global information was selected for each node to obtain node representations containing rich information, and a small number of labeled nodes were used for supervised training to generate the final node representations. Experimental result on three datasets Cora, CiteSeer and PubMed indicate that the classification accuracy of ID-AGCN is improved by about 3.4 percentage points, 2.3 percentage points and 1.9 percentage points respectively, compared with GCN. The proposed model has superiority in alleviating over-smoothing.

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    Global-scale radar data restoration algorithm based on total variation and low-rank group sparsity
    Chenyu GE, Liang DONG, Yikun XU, Yi CHANG, Hongming ZHANG
    Journal of Computer Applications    2021, 41 (11): 3353-3361.   DOI: 10.11772/j.issn.1001-9081.2020122047
    Abstract226)   HTML9)    PDF (3343KB)(186)       Save

    The mixed noise formed by a large number of spikes, speckles and multi-directional stripe errors in Shuttle Radar Terrain Mission (SRTM) will cause serious interference to the subsequent applications. In order to solve the problem, a Low-Rank Group Sparsity_Total Variation (LRGS_TV) algorithm was proposed. Firstly, the uniqueness of the data in the local range low-rank direction was used to regularize the global multi-directional stripe error structure, and the variational idea was used to perform unidirectional constraints. Secondly, the non-local self-similarity of the weighted kernel norm was used to eliminate the random noise, and the Total Variation (TV) regularity was combined to constrain the data gradient, so as to reduce the difference of local range changes. Finally, the low-rank group sparse model was solved by the alternating direction multiplier optimization to ensure the convergence of model. Quantitative evaluation shows that, compared with four algorithms such as TV, Unidirectional Total Variation (UTV), Low-Rank-based Single-Image Decomposition (LRSID) and Low-Rank Group Sparsity (LRGS) model, the proposed LRGS_TV has the Peak Signal-to-Noise Ratio (PSNR) of 38.53 dB and the Structural SIMilarity (SSIM) of 0.97, which are both better than the comparison algorithms. At the same time, the slope and aspect results show that after LRGS_TV processing, the subsequent applications of the data can be significantly improved. The experimental results show that, the proposed LRGS_TV can repair the original data better while ensuring that the terrain contour features are basically unchanged, and can provide important support to the reliability improvement and subsequent applications of SRTM.

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    Centered kernel alignment based multiple kernel one-class support vector machine
    Xiangzhou QI, Hongjie XING
    Journal of Computer Applications    2022, 42 (2): 349-356.   DOI: 10.11772/j.issn.1001-9081.2021071230
    Abstract225)   HTML46)    PDF (608KB)(164)       Save

    In comparison with single kernel learning, Multiple Kernel Learning (MKL) methods obtain better performance in the tasks of classification and regression. However, all the traditional MKL methods are used for tackling two-class or multi-class classification problems. To make MKL methods fit for dealing with the problems of One-Class Classification (OCC), a Centered Kernel Alignment (CKA) based multiple kernel One-Class Support Vector Machine (OCSVM) was proposed. Firstly,CKA was utilized to calculate the weight of each kernel matrix, and the obtained weights were used as the linear combination coefficients to linearly combine different types of kernel functions to construct the combination kernel function and introduce them into the traditional OCSVM to replace the single kernel function. The proposed method can not only avoid the selection of kernel function, but also improve the generalization and anti-noise performances. In comparison with other five related methods including OCSVM,Localized Multiple Kernel OCSVM (LMKOCSVM) and Kernel-Target Alignment based Multiple Kernel OCSVM (KTA-MKOCSVM) on 20 UCI benchmark datasets, the geometric mean (g-mean) values of the proposed algorithm were higher than those of the comparison methods on 13 datasets. At the time, the traditional single kernel OCSVM obtained better results on 2 datasets,LMKOCSVM and KTA-MKOCSVM achieved better classification effects on 5 datasets. Therefore, the effectiveness of the proposed method was sufficiently verified by experimental comparisons.

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2022 Vol.42 No.9

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Honorary Editor-in-Chief: ZHANG Jingzhong
Editor-in-Chief: XU Zongben
Associate Editor: SHEN Hengtao XIA Zhaohui
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