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Review of online education learner knowledge tracing Yajuan ZHAO, Fanjun MENG, Xingjian XU Journal of Computer Applications    2024, 44 (6): 1683-1698.   DOI: 10.11772/j.issn.1001-9081.2023060852 Abstract （324）   HTML （21）    PDF （2932KB）（4029）       Knowledge map    Save Knowledge Tracing （KT） is a fundamental and challenging task in online education， and it involves the establishment of learner knowledge state model based on the learning history； by which learners can better understand their knowledge states， while teachers can better understand the learning situation of learners. The KT research for learners of online education was summarized. Firstly， the main tasks and historical progress of KT were introduced. Subsequently， traditional KT models and deep learning KT models were explained. Furthermore， relevant datasets and evaluation metrics were summarized， alongside a compilation of the applications of KT. In conclusion， the current status of knowledge tracing was summarized， and the limitations and future prospects for KT were discussed. Table and Figures | Reference | Related Articles | Metrics

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Technology application prospects and risk challenges of large language models Yuemei XU, Ling HU, Jiayi ZHAO, Wanze DU, Wenqing WANG Journal of Computer Applications    2024, 44 (6): 1655-1662.   DOI: 10.11772/j.issn.1001-9081.2023060885 Abstract （1291）   HTML （103）    PDF （1142KB）（2308）       Knowledge map    Save In view of the rapid development of Large Language Model （LLM） technology， a comprehensive analysis was conducted on its technical application prospects and risk challenges which has great reference value for the development and governance of Artificial General Intelligence （AGI）. Firstly， with representative language models such as Multi-BERT （Multilingual Bidirectional Encoder Representations from Transformer）， GPT （Generative Pre-trained Transformer） and ChatGPT （Chat Generative Pre-trained Transformer） as examples， the development process， key technologies and evaluation systems of LLM were reviewed. Then， a detailed analysis of LLM on technical limitations and security risks was conducted. Finally， suggestions were put forward for technical improvement and policy follow-up of the LLM. The analysis indicates that at a developing status， the current LLMs still produce non-truthful and biased output， lack real-time autonomous learning ability， require huge computing power， highly rely on data quality and quantity， and tend towards monotonous language style. They have security risks related to data privacy， information security， ethics， and other aspects. Their future developments can continue to improve technically， from “large-scale” to “lightweight”， from “single-modal” to “multi-modal”， from “general-purpose” to “vertical”； for real-time follow-up in policy， their applications and developments should be regulated by targeted regulatory measures. Table and Figures | Reference | Related Articles | Metrics

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Summary of network intrusion detection systems based on deep learning Miaolei DENG, Yupei KAN, Chuanchuan SUN, Haihang XU, Shaojun FAN, Xin ZHOU Journal of Computer Applications    2025, 45 (2): 453-466.   DOI: 10.11772/j.issn.1001-9081.2024020229 Abstract （186）   HTML （19）    PDF （1427KB）（1258）       Knowledge map    Save Security mechanisms such as Intrusion Detection System （IDS） have been used to protect network infrastructure and communication from network attacks. With the continuous progress of deep learning technology， IDSs based on deep learning have become a research hotspot in the field of network security gradually. Through extensive literature research， a detailed introduction to the latest research progress in network intrusion detection using deep learning technology was given. Firstly， a brief overview of several IDSs was performed. Secondly， the commonly used datasets and evaluation metrics in deep learning-based IDSs were introduced. Thirdly， the commonly used deep learning models in network IDSs and their application scenarios were summarized. Finally， the problems faced in the current related research were discussed， and the future development directions were proposed. Table and Figures | Reference | Related Articles | Metrics

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Review of marine ship communication cybersecurity Zhongdai WU, Dezhi HAN, Haibao JIANG, Cheng FENG, Bing HAN, Chongqing CHEN Journal of Computer Applications    2024, 44 (7): 2123-2136.   DOI: 10.11772/j.issn.1001-9081.2023070975 Abstract （310）   HTML （6）    PDF （3942KB）（1173）       Knowledge map    Save Maritime transportation is one of the most important modes of human transportation. Maritime cybersecurity is crucial to avoid financial loss and ensure shipping safety. Due to the obvious weakness of maritime cybersecurity， maritime cyberattacks are frequent. There are a lot of research literatures about maritime cybersecurity at domestic and abroad， but most of them have not been reviewed yet. The structures， risks and countermeasures of the maritime network were systematically organized and comprehensively introduced. On this basis， some suggestions were put forward to deal with the maritime cyberthreats. Table and Figures | Reference | Related Articles | Metrics

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Multivariate time series anomaly detection based on multi-domain feature extraction Pei ZHAO, Yan QIAO, Rongyao HU, Xinyu YUAN, Minyue LI, Benchu ZHANG Journal of Computer Applications    2024, 44 (11): 3419-3426.   DOI: 10.11772/j.issn.1001-9081.2023111636 Abstract （252）   HTML （4）    PDF （754KB）（1127）    PDF（mobile） （1807KB）（23）    Knowledge map    Save Due to the high dimensionality and the complex variable distribution of Multivariate Time Series （MTS） data， the existing anomaly detection models generally suffer from high error rates and training difficulties when dealing with MTS datasets. Moreover， most models only consider the spatial-temporal features of time series samples， which are not sufficient to learn the features of time series. To solve the above problems， a multivariate Time Series anomaly detection model based on Multi-domain Feature Extraction （MFE-TS） was proposed. Firstly， starting from the original data domain， the Long Short-Term Memory （LSTM） network and the Convolutional Neural Network （CNN） were used to extract the temporal correlation and spatial correlation features of the MTS respectively. Secondly， Fourier transform was used to convert the original time series into frequency domain space， and Transformer was used to learn the amplitude and phase features of the data in frequency domain space. Multi-domain feature learning was able to model time series features more comprehensively， thereby improving anomaly detection performance of the model to MTS. In addition， the masking strategy was introduced to further enhance the feature learning ability of the model and make the model have a certain degree of noise resistance. Experimental results show that MFE-TS has superior performance on multiple real MTS datasets， while it still maintain good detection accuracy on datasets with noise. Table and Figures | Reference | Related Articles | Metrics

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Physics-informed neural network based on Lobatto method and Legendre polynomials for solving differential-algebraic equations Shuai LAI, Juan TANG, Kun LIANG, Jiasheng CHEN Journal of Computer Applications    2025, 45 (3): 911-919.   DOI: 10.11772/j.issn.1001-9081.2024030313 Abstract （136）   HTML （1）    PDF （2186KB）（1115）       Knowledge map    Save Current neural network methods solving Differential-Algebraic Equations （DAEs） basically adopt data-driven strategies， and require a large number of datasets. So that， there are problems such as sensitive structure and parameter selection of neural networks， low accuracy of solution， and poor stability. In response to these issues， a Physics-Informed Neural Network based on Lobatto method and Legendre polynomials （LL-PINN） was proposed. Firstly， based on the discrete Physics-Informed Neural Network （PINN） computing framework， combined with the advantages of high accuracy and high stability of Lobatto IIIA method solving DAEs， the physical information of DAEs was embedded in the Lobatto IIIA time iteration format， and PINN was used to solve the approximate numerical value of this time iteration. Secondly， a neural network structure with single hidden layer was utilized， by using the approximation capability of Legendre polynomials， these polynomials were applied as activation functions to simplify the process of adjusting the network model. Finally， a time domain decomposition scheme was employed to construct the network model， which a differential neural network and an algebraic neural network were used for each equally divided sub-time domain one by one， enabling high-precision continuous-time prediction of DAEs. Results of numerical examples demonstrate that the LL-PINN based on Legendre polynomials and the 4th-order Lobatto method achieves high-precision solutions for DAEs. Compared to the Theory of Functional Connections （TFC） trial solution method and PINN model， LL-PINN significantly reduces the absolute error between the predicted and exact solutions of differential variables and algebraic variables， and improves accuracy by one or two orders of magnitude. Therefore， the proposed solution model exhibits good computational accuracy for solving DAE problems， providing a feasible solution for challenging partial DAEs. Table and Figures | Reference | Related Articles | Metrics

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Multi-domain fake news detection model enhanced by APK-CNN and Transformer Jinjin LI, Guoming SANG, Yijia ZHANG Journal of Computer Applications    2024, 44 (9): 2674-2682.   DOI: 10.11772/j.issn.1001-9081.2023091359 Abstract （307）   HTML （17）    PDF （1378KB）（1094）       Knowledge map    Save In order to solve the problems of domain shifting and incomplete domain labeling in social media news， as well as to explore more efficient multi-domain news feature extraction and fusion networks， a multi-domain fake news detection model based on enhancement by APK-CNN （Adaptive Pooling Kernel Convolutional Neural Network） and Transformer was proposed， namely Transm3. Firstly， a three-channel network was designed for feature extraction and representation of semantic， emotional， and stylistic information of the text and view combination of these features using a multi-granularity cross-domain interactor. Secondly， the news domain labels were refined by optimized soft-shared memory networking and domain adapters. Then， Transformer was combined with a multi-granularity cross-domain interactor to dynamically and weighty aggregate the interaction features of different domains. Finally， the fused features were fed into the classifier for true/false news discrimination. Experimental results show that compared with M3FEND （Memory-guided Multi-view Multi-domain FakE News Detection） and EANN （Event Adversarial Neural Networks for multi-modal fake news detection）， Transm3 improves the comprehensive F1 value by 3.68% and 6.46% on Chinese dataset， and 6.75% and 11.93% on English dataset； and the F1 values on sub-domains are also significantly improved. The effectiveness of Transm3 for multi-domain fake news detection is fully validated. Table and Figures | Reference | Related Articles | Metrics

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Encrypted traffic classification method based on Attention-1DCNN-CE Haijun GENG, Yun DONG, Zhiguo HU, Haotian CHI, Jing YANG, Xia YIN Journal of Computer Applications    2025, 45 (3): 872-882.   DOI: 10.11772/j.issn.1001-9081.2024030325 Abstract （91）   HTML （2）    PDF （2750KB）（1005）       Knowledge map    Save To address the problems of low multi-classification accuracy， poor generalization， and easy privacy invasion in traditional encrypted traffic identification methods， a multi-classification deep learning model that combines Attention mechanism （Attention） with one-Dimensional Convolutional Neural Network （1DCNN） was proposed， namely Attention-1DCNN-CE. This model consists of three core components： 1） in the dataset preprocessing stage， the spatial relationship among packets in the original data stream was retained， and a cost-sensitive matrix was constructed on the basis of the sample distribution； 2） based on the preliminary extraction of encrypted traffic features， the Attention and 1DCNN models were used to mine deeply and compress the global and local features of the traffic； 3） in response to the challenge of data imbalance， by combining the cost-sensitive matrix with the Cross Entropy （CE） loss function， the sample classification accuracy of minority class was improved significantly， thereby optimizing the overall performance of the model. Experimental results show that on BOT-IOT and TON-IOT datasets， the overall identification accuracy of this model is higher than 97%. Additionally， on public datasets ISCX-VPN and USTC-TFC， this model performs excellently， and achieves performance similar to that of ET-BERT （Encrypted Traffic BERT） without the need for pre-training. Compared to Payload Encoding Representation from Transformer （PERT） on ISCX-VPN dataset， this model improves the F1 score in application type detection by 29.9 percentage points. The above validates the effectiveness of this model， so that this model provides a solution for encrypted traffic identification and malicious traffic detection. Table and Figures | Reference | Related Articles | Metrics

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Hybrid internet of vehicles intrusion detection system for zero-day attacks Jiepo FANG, Chongben TAO Journal of Computer Applications    2024, 44 (9): 2763-2769.   DOI: 10.11772/j.issn.1001-9081.2023091328 Abstract （327）   HTML （13）    PDF （2618KB）（991）       Knowledge map    Save Existing machine learning methods suffer from over-reliance on sample data and insensitivity to anomalous data when confronted with zero-day attack detection， thus making it difficult for Intrusion Detection System （IDS） to effectively defend against zero-day attacks. Therefore， a hybrid internet of vehicles intrusion detection system based on Transformer and ANFIS （Adaptive-Network-based Fuzzy Inference System） was proposed. Firstly， a data enhancement algorithm was designed and the problem of unbalanced data samples was solved by denoising first and then generating. Secondly， a feature engineering module was designed by introducing non-linear feature interactions into complex feature combinations. Finally， the self-attention mechanism of Transformer and the adaptive learning method of ANFIS were combined， which enhanced the ability of feature representation and reduced the dependence on sample data. The proposed system was compared with other SOTA （State-Of-The-Art） algorithms such as Dual-IDS on CICIDS-2017 and UNSW-NB15 intrusion datasets. Experimental results show that for zero-day attacks， the proposed system achieves 98.64% detection accuracy and 98.31% F1 value on CICIDS-2017 intrusion dataset， and 93.07% detection accuracy and 92.43% F1 value on UNSW-NB15 intrusion dataset， which validates high accuracy and strong generalization ability of the proposed algorithm for zero-day attack detection. Table and Figures | Reference | Related Articles | Metrics

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Mobile robot 3D space path planning method based on deep reinforcement learning Tian MA, Runtao XI, Jiahao LYU, Yijie ZENG, Jiayi YANG, Jiehui ZHANG Journal of Computer Applications    2024, 44 (7): 2055-2064.   DOI: 10.11772/j.issn.1001-9081.2023060749 Abstract （420）   HTML （29）    PDF （5732KB）（940）       Knowledge map    Save Aiming at the problems of high complexity and uncertainty in 3D unknown environment， a mobile robot 3D path planning method based on deep reinforcement learning was proposed， under a limited observation space optimization strategy. First， the depth map information was used as the agent’s input in the limited observation space， which could simulate complex 3D space environments with limited and unknown movement conditions. Second， a two-stage action selection policy in discrete action space was designed， including directional actions and movement actions， which could reduce the searching steps and time. Finally， based on the Proximal Policy Optimization （PPO） algorithm， the Gated Recurrent Unit （GRU） was added to combine the historical state information， to enhance the policy stability in unknown environments， so that the accuracy and smoothness of the planned path could be improved. The experimental results show that， compared with Advantage Actor-Critic （A2C）， the average search time is reduced by 49.07% and the average planned path length is reduced by 1.04%. Meanwhile， the proposed method can complete the multi-objective path planning tasks under linear sequential logic constraints. Table and Figures | Reference | Related Articles | Metrics

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Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG Journal of Computer Applications    2024, 44 (8): 2493-2499.   DOI: 10.11772/j.issn.1001-9081.2023081112 Abstract （382）   HTML （2）    PDF （1194KB）（932）       Knowledge map    Save Network traffic anomaly detection is a network security defense method that involves analyzing and determining network traffic to identify potential attacks. A new approach was proposed to address the issue of low detection accuracy and high false positive rate caused by imbalanced high-dimensional network traffic data and different attack categories. One Dimensional Convolutional Neural Network（1D-CNN） and Bidirectional Gated Recurrent Unit （BiGRU） were combined to construct a model for traffic anomaly detection. For class-imbalanced data， balanced processing was performed by using an improved Synthetic Minority Oversampling TEchnique （SMOTE）， namely Borderline-SMOTE， and an undersampling clustering technique based on Gaussian Mixture Model （GMM）. Subsequently， a one-dimensional CNN was utilized to extract local features in the data， and BiGRU was used to better extract the time series features in the data. Finally， the proposed model was evaluated on the UNSW-NB15 dataset， achieving an accuracy of 98.12% and a false positive rate of 1.28%. The experimental results demonstrate that the proposed model outperforms other classic machine learning and deep learning models， it improves the recognition rate for minority attacks and achieves higher detection accuracy. Table and Figures | Reference | Related Articles | Metrics

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Clustering federated learning algorithm for heterogeneous data Qingli CHEN, Yuanbo GUO, Chen FANG Journal of Computer Applications    2025, 45 (4): 1086-1094.   DOI: 10.11772/j.issn.1001-9081.2024010132 Abstract （122）   HTML （7）    PDF （2335KB）（931）       Knowledge map    Save Federated Learning （FL） is a new machine learning model construction paradigm with great potential in privacy preservation and communication efficiency， but in real Internet of Things （IoT） scenarios， there is data heterogeneity between client nodes， and learning a unified global model will lead to a decrease in model accuracy. To solve this problem， a Clustering Federated Learning based on Feature Distribution （CFLFD） algorithm was proposed. In this algorithm， the results obtained through Principal Component Analysis （PCA） of the features extracted from the model by each client node were clustered in order to cluster client nodes with similar data distribution to collaborate with each other， so as to achieve higher model accuracy. In order to demonstrate the effectiveness of the algorithm， extensive experiments were conducted on three datasets and four benchmark algorithms. The results show that the algorithm improves model accuracy by 1.12 and 3.76 percentage points respectively compared to the FedProx on CIFAR10 dataset and Office-Caltech10 dataset. Table and Figures | Reference | Related Articles | Metrics

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Federated learning-based statistical prediction and differential privacy protection method for location big data Yan YAN, Xingying QIAN, Pengbin YAN, Jie YANG Journal of Computer Applications    2025, 45 (1): 127-135.   DOI: 10.11772/j.issn.1001-9081.2024010068 Abstract （151）   HTML （3）    PDF （4957KB）（930）       Knowledge map    Save To address the information silo problem and the risk of location privacy leakage caused by distributed location big data collection， a statistical prediction and privacy protection method for location big data was proposed on the basis of federated learning. Firstly， a horizontal federated learning-based statistical prediction release framework was constructed for location big data. The framework allowed data collectors in each administrative region to keep their raw data， and multiple participants to collaborate to complete the prediction model’s training task by exchanging training parameters. Secondly， PVTv2-CBAM was developed to improve the accuracy of prediction results at clients， aiming for the problem of statistical prediction location big data density with spatiotemporal sequence characteristics. Finally， combined with the MMA （Modified Moments Accountant） mechanism， a dynamic allocation and adjustment algorithm for differential privacy budget was proposed to achieve diffirential privacy protection of the client models. Experimental results show that compared to models such as Convolutional Neural Network （CNN）， Long Short-Term Memory （LSTM）， and Convolutional LSTM （ConvLSTM）the proposed PVTv2-CBAM improves the prediction accuracy by 0 to 62% on the Yellow_tripdata dataset and by 39% to 44% on the T-Driver trajectory dataset；the proposed differential privacy budget dynamic allocation and adjustment algorithm enhances the model prediction accuracy by about 5% and 6% at adjustment thresholds of 0.3 and 0.7， respectively， compared with no dynamic adjustment. The above validates the feasibility and effectiveness of the proposed method. Table and Figures | Reference | Related Articles | Metrics

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Review of YOLO algorithm and its applications to object detection in autonomous driving scenes Yaping DENG, Yingjiang LI Journal of Computer Applications    2024, 44 (6): 1949-1958.   DOI: 10.11772/j.issn.1001-9081.2023060889 Abstract （1014）   HTML （42）    PDF （1175KB）（882）       Knowledge map    Save Object detection in autonomous driving scenes is one of the important research directions in computer vision. The researches focus on ensuring real-time and accurate object detection of objects by autonomous vehicles. Recently， a rapid development in deep learning technology had been witnessed， and its wide application in the field of autonomous driving had prompted substantial progress in this field. An analysis was conducted on the research status of object detection by YOLO （You Only Look Once） algorithms in the field of autonomous driving from the following four aspects. Firstly， the ideas and improvement methods of the single-stage YOLO series of detection algorithms were summarized， and the advantages and disadvantages of the YOLO series of algorithms were analyzed. Secondly， the YOLO algorithm-based object detection applications in autonomous driving scenes were introduced， the research status and applications for the detection and recognition of traffic vehicles， pedestrians， and traffic signals were expounded and summarized respectively. Additionally， the commonly used evaluation indicators in object detection， as well as the object detection datasets and automatic driving scene datasets， were summarized. Lastly， the problems and future development directions of object detection were discussed. Table and Figures | Reference | Related Articles | Metrics

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Overview of research and application of knowledge graph in equipment fault diagnosis Jie WU, Ansi ZHANG, Maodong WU, Yizong ZHANG, Congbao WANG Journal of Computer Applications    2024, 44 (9): 2651-2659.   DOI: 10.11772/j.issn.1001-9081.2023091280 Abstract （539）   HTML （52）    PDF （2858KB）（880）       Knowledge map    Save Useful knowledge can be extracted from equipment fault diagnosis data for construction of a knowledge graph， which can effectively manage complex equipment fault diagnosis information in the form of triples （entity， relationship， entity）. This enables the rapid diagnosis of equipment faults. Firstly， the related concepts of knowledge graph for equipment fault diagnosis were introduced， and the framework of knowledge graph for equipment fault diagnosis domain was analyzed. Secondly， the research status at home and abroad about several key technologies， such as knowledge extraction， knowledge fusion and knowledge reasoning for equipment fault diagnosis knowledge graph， was summarized. Finally， the applications of knowledge graph in equipment fault diagnosis were summarized， some shortcomings and challenges in the construction of knowledge graph in this field were proposed， and some new ideas were provided for the field of equipment fault diagnosis in the future. Table and Figures | Reference | Related Articles | Metrics

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Enterprise ESG indicator prediction model based on richness coordination technology Yan LI, Guanhua YE, Yawen LI, Meiyu LIANG Journal of Computer Applications    2025, 45 (2): 670-676.   DOI: 10.11772/j.issn.1001-9081.2024030262 Abstract （100）   HTML （5）    PDF （1400KB）（879）       Knowledge map    Save Environmental， Social， and Governance （ESG） indicator is a critical indicator for assessing the sustainability of enterprises. The existing ESG assessment systems face challenges such as narrow coverage， strong subjectivity， and poor timeliness. Thus， there is an urgent need for research on prediction models that can forecast ESG indicator accurately using enterprise data. Addressing the issue of inconsistent information richness among ESG-related features in enterprise data， a prediction model RCT （Richness Coordination Transformer） was proposed for enterprise ESG indicator prediction based on richness coordination technology. In this model， an auto-encoder was used in the upstream richness coordination module to coordinate features with heterogeneous information richness， thereby enhancing the ESG indicator prediction performance of the downstream module. Experimental results on real datasets demonstrate that on various prediction indicators， RCT model outperforms multiple models including Temporal Convolutional Network （TCN）， Long Short-Term Memory （LSTM） network， Self-Attention Model （Transformer）， eXtreme Gradient Boosting （XGBoost）， and Light Gradient Boosting Machine （LightGBM）. The above verifies that the effectiveness and superiority of RCT model in ESG indicator prediction. Table and Figures | Reference | Related Articles | Metrics

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Multivariate time series prediction model based on decoupled attention mechanism Liting LI, Bei HUA, Ruozhou HE, Kuang XU Journal of Computer Applications    2024, 44 (9): 2732-2738.   DOI: 10.11772/j.issn.1001-9081.2023091301 Abstract （312）   HTML （11）    PDF （1545KB）（858）       Knowledge map    Save Aiming at the problem that it is difficult to fully utilize the sequence contextual semantic information and the implicit correlation information among variables in multivariate time-series prediction， a model based on decoupled attention mechanism — Decformer was proposed for multivariate time-series prediction. Firstly， a novel decoupled attention mechanism was proposed to fully utilize the embedded semantic information， thereby improving the accuracy of attention weight allocation. Secondly， a pattern correlation mining method without relying on explicit variable relationships was proposed to mine and utilize implicit pattern correlation information among variables. On three different types of real datasets （TTV， ECL and PeMS-Bay）， including traffic volume of call， electricity consumption and traffic， Decformer achieves the highest prediction accuracy over all prediction time lengths compared with excellent open-source multivariate time-series prediction models such as Long- and Short-term Time-series Network （LSTNet）， Transformer and FEDformer. Compared with LSTNet， Decformer has the Mean Absolute Error （MAE） reduced by 17.73%-27.32%， 10.89%-17.01%， and 13.03%-19.64% on TTV， ECL and PeMS-Bay datasets， respectively， and the Mean Squared Error （MSE） reduced by 23.53%-58.96%， 16.36%-23.56% and 15.91%-26.30% on TTV， ECL and PeMS-Bay datasets， respectively. Experimental results indicate that Decformer can enhance the accuracy of multivariate time series prediction significantly. Table and Figures | Reference | Related Articles | Metrics

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Time series classification method based on multi-scale cross-attention fusion in time-frequency domain Mei WANG, Xuesong SU, Jia LIU, Ruonan YIN, Shan HUANG Journal of Computer Applications    2024, 44 (6): 1842-1847.   DOI: 10.11772/j.issn.1001-9081.2023060731 Abstract （461）   HTML （11）    PDF （2511KB）（851）       Knowledge map    Save To address the problem of low classification accuracy caused by insufficient potential information interaction between time series subsequences， a time series classification method based on multi-scale cross-attention fusion in time-frequency domain called TFFormer （Time-Frequency Transformer） was proposed. First， time and frequency spectrums of the original time series were divided into subsequences with the same length respectively， and the point-value coupling problem was solved by adding positional embedding after linear projection. Then， the long-term time series dependency problem was solved because the model was made to focus on more important time series features by Improved Multi-Head self-Attention （IMHA） mechanism. Finally， a multi-scale Cross-Modality Attention （CMA） module was proposed to enhance the interaction between the time domain and frequency domain， so that the model could further mine the frequency information of the time series. The experimental results show that compared with Fully Convolutional Network （FCN）， the classification accuracy of the proposed method on Trace， StarLightCurves and UWaveGestureLibraryAll datasets increased by 0.3， 0.9 and 1.4 percentage points. It is proved that by enhancing the information interaction between time domain and frequency domain of the time series， the model convergence speed and classification accuracy can be improved. Table and Figures | Reference | Related Articles | Metrics

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Review of radar automatic target recognition based on ensemble learning Zirong HONG, Guangqing BAO Journal of Computer Applications    2025, 45 (2): 371-382.   DOI: 10.11772/j.issn.1001-9081.2024020179 Abstract （129）   HTML （8）    PDF （1391KB）（835）       Knowledge map    Save Radar Automatic Target Recognition （RATR） has widespread applications in both domains of military and civilian. Due to the robustness caused by that ensemble learning improves model classification performance by integrating the existing machine learning models， ensemble learning has been applied in the field of radar target detection and recognition increasingly. The research progress of ensemble learning in RATR was discussed in detail through systematic sorting and refining the existing relevant literature. Firstly， the concept， framework， and development process of ensemble learning were introduced， ensemble learning was compared with traditional machine learning and deep learning methods， and the advantages， limitations， and main focuses of research of ensemble learning theory and common ensemble learning methods were summarized. Secondly， the concept of RATR was described briefly. Thirdly， the applications of ensemble learning in different radar image classification features were focused on， with a detailed discussion on target detection and recognition methods based on Synthetic Aperture Radar （SAR） and High-Resolution Range Profile （HRRP）， and the research progress and application effect of these methods were summed up. Finally， the challenges faced by RATR and ensemble learning were discussed， and the applications of ensemble learning in the field of radar target recognition were prospected. Table and Figures | Reference | Related Articles | Metrics

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Time series prediction algorithm based on multi-scale gated dilated convolutional network Yu ZENG, Yang ZHANG, Shang ZENG, Maoli FU, Qixue HE, Linlong ZENG Journal of Computer Applications    2024, 44 (11): 3427-3434.   DOI: 10.11772/j.issn.1001-9081.2023111583 Abstract （243）   HTML （5）    PDF （803KB）（826）       Knowledge map    Save Addressing challenges in time series prediction tasks， such as high-dimensional features， large-scale data， and the demand for high prediction accuracy， a multi-scale trend-period decomposition model based on a multi-head gated dilated convolutional network was proposed. A multi-scale decomposition approach was employed to decompose the original covariate sequence and the prediction variable sequence into their respective periodic terms and trend terms， thereby enabling independent prediction. For the periodic terms， the multi-head gated dilated convolutional network encoder was introduced to extract respective periodic information； in the decoder stage， channel information interaction and fusion were performed through the utilization of a cross-attention mechanism， and after sampling and aligning the periodic information of the prediction variables， the periodic prediction was performed through time attention and channel fusion information. The trend terms prediction was executed by using an autoregressive approach. Finally， the prediction sequence was obtained by incorporating the trend prediction results with the periodic prediction results. Compared with multiple mainstream benchmark models such as Long Short-Term Memory （LSTM） and Informer， on five datasets including ETTm1 and ETTh1， a reduction in Mean Squared Error （MSE） is observed， ranging from 19.2% to 52.8% on average， a decrease in Mean Absolute Error （MAE） is noted， ranging from 12.1% to 33.8% on average. Ablation experiments confirm that the proposed multi-scale decomposition module， multi-head gated dilation convolution， and time attention module can enhance the accuracy of time series prediction. Table and Figures | Reference | Related Articles | Metrics

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Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG Journal of Computer Applications    2024, 44 (9): 2970-2974.   DOI: 10.11772/j.issn.1001-9081.2023091371 Abstract （249）   HTML （8）    PDF （1067KB）（825）       Knowledge map    Save The diagnosis of major depressive disorder predominantly relies on subjective methods， including physician consultations and scale assessments， which may lead to misdiagnosis. EEG （ElectroEncephaloGraphy） offers advantages such as high temporal resolution， low cost， ease of setup， and non-invasiveness， making it a potential quantitative measurement tool for psychiatric disorders， including depressive disorder. Recently， deep learning algorithms have been diversely applied to EEG signals， notably in the diagnosis and classification of depressive disorder. Due to significant redundancy is observed when processing EEG signals through a self-attention mechanism， a convolutional neural network leveraging a Probabilistic sparse Self-Attention mechanism （PSANet） was proposed. Firstly， a limited number of pivotal attention points were chosen in the self-attention mechanism based on the sampling factor， addressing the high computational cost and facilitating its application to extensive EEG data sequences； concurrently， EEG data was amalgamated with patients’ physiological scales for a comprehensive diagnosis. Experiments were executed on a dataset encompassing both depressive disorder patients and a healthy control group. Experimental results show that PSANet exhibits superior classification accuracy and a reduced number of parameters relative to alternative methodologies such as EEGNet. Table and Figures | Reference | Related Articles | Metrics

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Medical image segmentation network integrating multi-scale semantics and parallel double-branch Baohua YUAN, Jialu CHEN, Huan WANG Journal of Computer Applications    2025, 45 (3): 988-995.   DOI: 10.11772/j.issn.1001-9081.2024030358 Abstract （99）   HTML （1）    PDF （2085KB）（821）       Knowledge map    Save In medical image segmentation networks， Convolutional Neural Network （CNN） can extract rich local feature details， but has the problem of insufficient capture of long-range information， and Transformer can capture long-range global feature dependencies， but destroys local feature details. To make full use of the complementarity of characteristics of the two networks， a parallel fusion network of CNN and Transformer for medical image segmentation was proposed， named PFNet. In the parallel fusion module of this network， a pair of interdependent parallel branches based on CNN and Transformer were used to learn both local and global discriminative features efficiently， and fuse local features and long-distance feature dependencies interactively. At the same time， to recover the spatial information lost during downsampling to enhance detail retention， a Multi-Scale Interaction （MSI） module was proposed to extract the local context of multi-scale features generated by hierarchical CNN branches for long-range dependency modeling. Experimental results show that PFNet outperforms other advanced methods such as MISSFormer （Medical Image Segmentation tranSFormer） and UCTransNet （U-Net with Channel Transformer module）. On Synapse and ACDC （Automated Cardiac Diagnosis Challenge） datasets， compared to the optimal baseline method MISSFormer， PFNet increases the average Dice Similarity Coefficient （DSC） by 1.27% and 0.81%， respectively. It can be seen that PFNet can realize more accurate medical image segmentation. Table and Figures | Reference | Related Articles | Metrics

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Federated learning method based on adaptive differential privacy and client selection optimization Chao XU, Shufen ZHANG, Haitian CHEN, Lulu PENG, Shuaihua ZHANG Journal of Computer Applications    2025, 45 (2): 482-489.   DOI: 10.11772/j.issn.1001-9081.2024020162 Abstract （146）   HTML （2）    PDF （2308KB）（809）       Knowledge map    Save The method of applying differential privacy to federated learning has been one of the key techniques for protecting the privacy of training data. Addressing the issue that most previous works do not consider the heterogeneity of parameters， resulting in pruning training parameters uniformly， leading to uniform noise addition in each round， thus affecting model convergence and the quality of training parameters， an adaptive noise addition scheme based on gradient clipping was proposed. Considering the heterogeneity of gradients， adaptive gradient clipping was executed for different clients in different rounds， thereby allowing for the adaptive adjustment of noise magnitude. At the same time， to further improve model performance， different from traditional client random sampling methods， a client sampling method that combines roulette and elite preservation was proposed. Combining the aforementioned two methods， a Client Selection and Adaptive Gradient Clipping Differential Privacy_Federated Learning （CS&AGC DP_FL） was proposed. Experimental results demonstrate that， when the privacy budget is 0.5， compared to the Federated Learning method based on Adaptive Differential Privacy （Adapt DP_FL）， the proposed method improves the final model’s classification accuracy by 4.9 percentage points under the same level of privacy constraints. Additionally， in terms of convergence speed， the proposed method requires 4 to 10 fewer rounds to reach convergence compared to the methods to be compared. Table and Figures | Reference | Related Articles | Metrics

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Intelligent joint power and channel allocation algorithm for Wi-Fi7 multi-link integrated communication and sensing Jing WANG, Xuming FANG Journal of Computer Applications    2025, 45 (2): 563-570.   DOI: 10.11772/j.issn.1001-9081.2024020191 Abstract （85）   HTML （0）    PDF （2623KB）（800）       Knowledge map    Save To solve the problem of joint power and channel resource allocation for integrated communication and sensing in multi-link transmission of next-generation Wi-Fi7 devices， a multi-link multi-agent reinforcement learning algorithm based on QMIX（Q-learning Mixing Network） for Joint Power Control and channel allocation （JPCQMIX） was proposed on the basis of special upper and lower Media Access Control （MAC） layer structure of Multi-Link Device （MLD）. In the algorithm， each lower-layer MAC， i.e.， each link， was regarded as an agent， and mixing network was set up in the upper-layer MAC to process all the local value functions of lower-layer MACs， thereby achieving the effect of centralized training. After the training， each lower-layer MAC entered the distributed execution mode and interacted with its local environment independently to perform power control and channel allocation decision making. Simulation results show that the proposed algorithm improves the communication throughput performance by 20.51% and 29.10% respectively compared with Multi-Agent Deep Q Network （MADQN） algorithm and the traditional heuristic Particle Swarm Optimization （PSO） algorithm. Meanwhile， the proposed algorithm demonstrates better robustness when facing with different sensing accuracy thresholds and different link minimum Signal-to-Interference-plus-Noise Ratio （SINR）. It can be seen that JPCQMIX enhances the system’s communication throughput under the condition of satisfying the sensing accuracy effectively. Table and Figures | Reference | Related Articles | Metrics

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Text-to-SQL model based on semantic enhanced schema linking Xianglan WU, Yang XIAO, Mengying LIU, Mingming LIU Journal of Computer Applications    2024, 44 (9): 2689-2695.   DOI: 10.11772/j.issn.1001-9081.2023091360 Abstract （260）   HTML （24）    PDF （739KB）（798）       Knowledge map    Save To optimize Text-to-SQL generation performance based on heterogeneous graph encoder， SELSQL model was proposed. Firstly， an end-to-end learning framework was employed by the model， and the Poincaré distance metric in hyperbolic space was used instead of the Euclidean distance metric to optimize semantically enhanced schema linking graph constructed by the pre-trained language model using probe technology. Secondly， K-head weighted cosine similarity and graph regularization method were used to learn the similarity metric graph so that the initial schema linking graph was iteratively optimized during training. Finally， the improved Relational Graph ATtention network （RGAT） graph encoder and multi-head attention mechanism were used to encode the joint semantic schema linking graphs of the two modules， and Structured Query Language （SQL） statement decoding was solved using a grammar-based neural semantic decoder and a predefined structured language. Experimental results on Spider dataset show that when using ELECTRA-large pre-training model， the accuracy of SELSQL model is increased by 2.5 percentage points compared with the best baseline model， which has a great improvement effect on the generation of complex SQL statements. Table and Figures | Reference | Related Articles | Metrics

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Lightweight human pose estimation based on decoupled attention and ghost convolution Junying CHEN, Shijie GUO, Lingling CHEN Journal of Computer Applications    2025, 45 (1): 223-233.   DOI: 10.11772/j.issn.1001-9081.2024010099 Abstract （141）   HTML （4）    PDF （3442KB）（779）       Knowledge map    Save With the development of lightweight networks， human pose estimation tasks can be performed on devices with limited computational resources. However， improving accuracy has become more challenging. These challenges mainly led by the contradiction between network complexity and computational resources， resulting in the sacrifice of representation capabilities when simplifying the model. To address these issues， a Decoupled attention and Ghost convolution based Lightweight human pose estimation Network （DGLNet） was proposed. Specifically， in DGLNet， with Small High-Resolution Network （Small HRNet） model as basic architecture， by introducing a decoupled attention mechanism， DFDbottleneck module was constructed. The basic modules were redesigned with shuffleblock structure， in which computationally-intensive point convolutions were replaced with lightweight ghost convolutions， and the decoupled attention mechanism was utilized to enhance module performance， leading to the creation of DGBblock module. Additionally， the original transition layer modules were replaced with redesigned depthwise separable convolution modules that incorporated ghost convolution and decoupled attention， resulting in the construction of GSCtransition module. This modification further reduced computational complexity while enhancing feature interaction and performance. Experimental results on COCO validation set show that DGLNet outperforms the state-of-the-art Lite-High-Resolution Network （Lite-HRNet） model， achieving the maximum accuracy of 71.9% without increasing computational complexity or the number of parameters. Compared to common lightweight pose estimation networks such as MobileNetV2 and ShuffleNetV2， DGLNet achieves the precision improvement of 4.6 and 8.3 percentage points respectively， while only utilizing 21.2% and 25.0% of their computational resources. Furthermore， under the AP50 evaluation criterion， DGLNet surpasses the large High-Resolution Network （HRNet） while having significantly less computational and parameters. Table and Figures | Reference | Related Articles | Metrics

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Random validation blockchain construction for federated learning Tingwei CHEN, Jiacheng ZHANG, Junlu WANG Journal of Computer Applications    2024, 44 (9): 2770-2776.   DOI: 10.11772/j.issn.1001-9081.2023091254 Abstract （219）   HTML （6）    PDF （1975KB）（777）       Knowledge map    Save A random verification blockchain construction and privacy protection method for federated learning was proposed to address the issues such as local device model gradient leakage， the ability of centralized server devices to exit at will， and the inability of global models to resist malicious user attacks in existing federated learning models. Firstly， blockchain leadership nodes were elected randomly by introducing verifiable hash functions， thereby ensuring the fairness of voting a node to create block. Secondly， a verification node cross detection mechanism was designed to defend against malicious node attacks. Finally， based on differential privacy technology， blockchain nodes were trained， and incentive rules were constructed on the basis of the contribution of nodes to the model to enhance the training accuracy of the federated learning model. Experimental results show that the proposed method achieves 80% accuracy for malicious node poisoning attacks with 20% malicious nodes， which is 61 percentage points higher than that of Google FL， and the gradient matching loss of the proposed method is 14 percentage points higher than that of Google FL when the noise variance is 10-3. It can be seen that compared to the federated learning methods such as Google FL， the proposed method can ensure good accuracy while improving the security of the model， and has better security and robustness. Table and Figures | Reference | Related Articles | Metrics

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Review on bimodal emotion recognition based on speech and text Lingmin HAN, Xianhong CHEN, Wenmeng XIONG Journal of Computer Applications    2025, 45 (4): 1025-1034.   DOI: 10.11772/j.issn.1001-9081.2024030319 Abstract （331）   HTML （42）    PDF （1625KB）（775）       Knowledge map    Save Emotion recognition is a technology that allows computers to recognize and understand human emotions. It plays an important role in many fields and is an important development direction in the field of artificial intelligence. Therefore， the research status of bimodal emotion recognition based on speech and text was summarized. Firstly， the representation space of emotion was classified and elaborated. Secondly， the emotion databases were classified according to their emotion representation space， and the common multi-modal emotion databases were summed up. Thirdly， the methods of bimodal emotion recognition based on speech and text were introduced， including feature extraction， modal fusion， and decision classification. Specifically， the modal fusion methods were highlighted and divided into four categories， namely feature level fusion， decision level fusion， model level fusion and multi-level fusion. In addition， results of a series of bimodal emotion recognition methods based on speech and text were compared and analyzed. Finally， the application scenarios， challenges， and future development directions of emotion recognition were introduced. The above aims to analyze and review the work of multi-modal emotion recognition， especially bimodal emotion recognition based on speech and text， providing valuable information for emotion recognition. Table and Figures | Reference | Related Articles | Metrics

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Transfer kernel learning method based on spatial features for motor imagery EEG Siqi YANG, Tianjian LUO, Xuanhui YAN, Guangju YANG Journal of Computer Applications    2024, 44 (11): 3354-3363.   DOI: 10.11772/j.issn.1001-9081.2023111593 Abstract （168）   HTML （4）    PDF （1026KB）（742）       Knowledge map    Save Motor Imagery ElectroEncephaloGram （MI-EEG） signal has gained widespread attention in the construction of non-invasive Brain Computer Interfaces （BCIs） for clinical assisted rehabilitation. Limited by the differences in the distribution of MI-EEG signal samples from different subjects， cross-subject MI-EEG signal feature learning has become the focus of research. However， the existing related methods have problems such as weak domain-invariant feature expression capabilities and high time complexity， and cannot be directly applied to online BCIs. To address this issue， an efficient cross-subject MI-EEG signal classification algorithm， Transfer Kernel Riemannian Tangent Space （TKRTS）， was proposed. Firstly， the MI-EEG signal covariance matrices were projected into the Riemannian space and the covariance matrices of different subjects were aligned in Riemannian space while extracting Riemannian Tangent Space （RTS） features. Subsequently， the domain-invariant kernel matrix on the tangent space feature set was learnt， thereby achieving a complete representation of cross-subject MI?EEG signal features. This matrix was then used to train a Kernel Support Vector Machine （KSVM） for classification. To validate the feasibility and effectiveness of TKRTS method， multi-source domain to single-target domain and single-source domain to single-target domain experiments were conducted on three public datasets， and the average classification accuracy is increased by 0.81 and 0.13 percentage points respectively. Experimental results demonstrate that compared to state-of-the-art methods， TKRTS method improves the average classification accuracy while maintaining similar time complexity. Furthermore， ablation experimental results confirm the completeness and parameter insensitivity of TKRTS method in cross-subject feature expression， making this method suitable for constructing online BCIs. Table and Figures | Reference | Related Articles | Metrics

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Review on security threats and defense measures in federated learning Xuebin CHEN, Zhiqiang REN, Hongyang ZHANG Journal of Computer Applications    2024, 44 (6): 1663-1672.   DOI: 10.11772/j.issn.1001-9081.2023060832 Abstract （356）   HTML （22）    PDF （1072KB）（739）       Knowledge map    Save Federated learning is a distributed learning approach for solving the data sharing problem and privacy protection problem in machine learning， in which multiple parties jointly train a machine learning model and protect the privacy of data. However， there are security threats inherent in federated learning， which makes federated learning face great challenges in practical applications. Therefore， analyzing the attacks faced by federation learning and the corresponding defensive measures are crucial for the development and application of federation learning. First， the definition， process and classification of federated learning were introduced， and the attacker model in federated learning was introduced. Then， the possible attacks in terms of both robustness and privacy of federated learning systems were introduced， and the corresponding defense measures were introduced as well. Furthermore， the shortcomings of the defense schemes were also pointed out. Finally， a secure federated learning system was envisioned. Table and Figures | Reference | Related Articles | Metrics