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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 （739）   HTML （22）    PDF （2932KB）（5655）       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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Embedded road crack detection algorithm based on improved YOLOv8 Huantong GENG, Zhenyu LIU, Jun JIANG, Zichen FAN, Jiaxing LI Journal of Computer Applications    2024, 44 (5): 1613-1618.   DOI: 10.11772/j.issn.1001-9081.2023050635 Abstract （2152）   HTML （78）    PDF （2002KB）（3248）       Knowledge map    Save Deploying the YOLOv8L model on edge devices for road crack detection can achieve high accuracy， but it is difficult to guarantee real-time detection. To solve this problem， a target detection algorithm based on the improved YOLOv8 model that can be deployed on the edge computing device Jetson AGX Xavier was proposed. First， the Faster Block structure was designed using partial convolution to replace the Bottleneck structure in the YOLOv8 C2f module， and the improved C2f module was recorded as C2f-Faster； second， an SE （Squeeze-and-Excitation） channel attention layer was connected after each C2f-Faster module in the YOLOv8 backbone network to further improve the detection accuracy. Experimental results on the open source road damage dataset RDD20 （Road Damage Detection 20） show that the average F1 score of the proposed method is 0.573， the number of detection Frames Per Second （FPS） is 47， and the model size is 55.5 MB. Compared with the SOTA （State-Of-The-Art） model of GRDDC2020 （Global Road Damage Detection Challenge 2020）， the F1 score is increased by 0.8 percentage points， the FPS is increased by 291.7%， and the model size is reduced by 41.8%， which realizes the real-time and accurate detection of road cracks on edge devices. 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 （309）   HTML （7）    PDF （1400KB）（2915）       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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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 （567）   HTML （45）    PDF （1427KB）（2721）       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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Survey of code similarity detection technology Xiangjie SUN, Qiang WEI, Yisen WANG, Jiang DU Journal of Computer Applications    2024, 44 (4): 1248-1258.   DOI: 10.11772/j.issn.1001-9081.2023040551 Abstract （634）   HTML （19）    PDF （1868KB）（2627）       Knowledge map    Save Code reuse not only brings convenience to software development， but also introduces security risks， such as accelerating vulnerability propagation and malicious code plagiarism. Code similarity detection technology is to calculate code similarity by analyzing lexical， syntactic， semantic and other information between codes. It is one of the most effective technologies to judge code reuse， and it is also a program security analysis technology that has developed rapidly in recent years. First， the latest technical progress of code similarity detection was systematically reviewed， and the current code similarity detection technology was classified. According to whether the target code was open source， it was divided into source code similarity detection and binary code similarity detection. According to the different programming languages and instruction sets， the second subdivision was carried out. Then， the ideas and research results of each technology were summarized， the successful cases of machine learning technology in the field of code similarity detection were analyzed， and the advantages and disadvantages of existing technologies were discussed. Finally， the development trend of code similarity detection technology was given to provide reference for relevant researchers. Table and Figures | Reference | Related Articles | Metrics

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Review of evolutionary multitasking from the perspective of optimization scenarios Jiawei ZHAO, Xuefeng CHEN, Liang FENG, Yaqing HOU, Zexuan ZHU, Yew‑Soon Ong Journal of Computer Applications    2024, 44 (5): 1325-1337.   DOI: 10.11772/j.issn.1001-9081.2024020208 Abstract （829）   HTML （74）    PDF （1383KB）（2541）       Knowledge map    Save Due to the escalating complexity of optimization problems， traditional evolutionary algorithms increasingly struggle with high computational costs and limited adaptability. Evolutionary MultiTasking Optimization （EMTO） algorithms have emerged as a novel solution， leveraging knowledge transfer to tackle multiple optimization issues concurrently， thereby enhancing evolutionary algorithms’ efficiency in complex scenarios. The current progression of evolutionary multitasking optimization research was summarized， and different research perspectives were explored by reviewing existing literature and highlighting the notable absence of optimization scenario analysis. By focusing on the application scenarios of optimization problems， the scenarios suitable for evolutionary multitasking optimization and their fundamental solution strategies were systematically outlined. This study thus could aid researchers in selecting the appropriate methods based on specific application needs. Moreover， an in-depth discussion on the current challenges and future directions of EMTO were also presented to provide guidance and insights for advancing research in this field. 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 （883）   HTML （15）    PDF （2618KB）（2492）       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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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 （391）   HTML （10）    PDF （2335KB）（2458）       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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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 （587）   HTML （6）    PDF （754KB）（2386）    PDF（mobile） （1807KB）（30）    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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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 （1576）   HTML （108）    PDF （1142KB）（2382）       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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Survey and prospect of large language models Xiaolin QIN, Xu GU, Dicheng LI, Haiwen XU Journal of Computer Applications    2025, 45 (3): 685-696.   DOI: 10.11772/j.issn.1001-9081.2025010128 Abstract （1234）   HTML （93）    PDF （2035KB）（2373）       Knowledge map    Save Large Language Models （LLMs） are a class of language models composed of artificial neural networks with a vast number of parameters （typically billions of weights or more）. They are trained on a large amount of unlabeled text using self-supervised or semi-supervised learning and are the core of current generative Artificial Intelligence （AI） technologies. Compared to traditional language models， LLMs demonstrate stronger language understanding and generation capabilities， supported by substantial computational power， extensive parameters， and large-scale data. They are widely applied in tasks such as machine translation， question answering systems， and dialogue generation with good performance. Most of the existing surveys focus on the theoretical construction and training techniques of LLMs， while systematic exploration of LLMs’ industry-level application practices and evolution of the technological ecosystem remains insufficient. Therefore， based on introducing the foundational architecture， training techniques， and development history of LLMs， the current general key technologies in LLMs and advanced integration technologies with LLMs bases were analyzed. Then， by summarizing the existing research， challenges faced by LLMs in practical applications were further elaborated， including problems such as data bias， model hallucination， and computational resource consumption， and an outlook was provided on the ongoing development trends of LLMs. 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 （748）   HTML （55）    PDF （2858KB）（2310）       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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Few-shot object detection via fusing multi-scale and attention mechanism Hongtian LI, Xinhao SHI, Weiguo PAN, Cheng XU, Bingxin XU, Jiazheng YUAN Journal of Computer Applications    2024, 44 (5): 1437-1444.   DOI: 10.11772/j.issn.1001-9081.2023050699 Abstract （682）   HTML （16）    PDF （2781KB）（2266）       Knowledge map    Save The existing two-stage few-shot object detection methods based on fine-tuning are not sensitive to the features of new classes， which will cause misjudgment of new classes into base classes with high similarity to them， thus affecting the detection performance of the model. To address the above issue， a few-shot object detection algorithm that incorporates multi-scale and attention mechanism was proposed， namely MA-FSOD （Few-Shot Object Detection via fusing Multi-scale and Attention mechanism）. Firstly， grouped convolutions and large convolution kernels were used to extract more class-discriminative features in the backbone network， and Convolutional Block Attention Module （CBAM） was added to achieve adaptive feature augmentation. Then， a modified pyramid network was used to achieve multi-scale feature fusion， which enables Region Proposal Network （RPN） to accurately find Regions of Interest （RoI） and provide more abundant high-quality positive samples from multiple scales to the classification head. Finally， the cosine classification head was used for classification in the fine-tuning stage to reduce the intra-class variance. Compared with the Few-Shot object detection via Contrastive proposal Encoding （FSCE） algorithm on PASCAL-VOC 2007/2012 dataset， the MA-FSOD algorithm improved AP50 for new classes by 5.6 percentage points； and on the more challenging MSCOCO dataset， compared with Meta-Faster-RCNN， the APs corresponding to 10-shot and 30-shot were improved by 0.1 percentage points and 1.6 percentage points， respectively. Experimental results show that MA-FSOD can more effectively alleviate the misclassification problem and achieve higher accuracy in few-shot object detection than some mainstream few-shot object detection algorithms. 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 （245）   HTML （1）    PDF （2186KB）（2236）       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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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 （707）   HTML （9）    PDF （1067KB）（2233）       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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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 （719）   HTML （68）    PDF （1625KB）（2145）       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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Survey of visual object tracking methods based on Transformer Ziwen SUN, Lizhi QIAN, Chuandong YANG, Yibo GAO, Qingyang LU, Guanglin YUAN Journal of Computer Applications    2024, 44 (5): 1644-1654.   DOI: 10.11772/j.issn.1001-9081.2023060796 Abstract （842）   HTML （22）    PDF （1615KB）（2134）       Knowledge map    Save Visual object tracking is one of the important tasks in computer vision， in order to achieve high-performance object tracking， a large number of object tracking methods have been proposed in recent years. Among them， Transformer-based object tracking methods become a hot topic in the field of visual object tracking due to their ability to perform global modeling and capture contextual information. Firstly， existing Transformer-based visual object tracking methods were classified based on their network structures， an overview of the underlying principles and key techniques for model improvement were expounded， and the advantages and disadvantages of different network structures were also summarized. Then， the experimental results of the Transformer-based visual object tracking methods on public datasets were compared to analyze the impact of network structure on performance. in which MixViT-L （ConvMAE） achieved tracking success rates of 73.3% and 86.1% on LaSOT and TrackingNet， respectively， proving that the object tracking methods based on pure Transformer two-stage architecture have better performance and broader development prospects. Finally， the limitations of these methods， such as complex network structure， large number of parameters， high training requirements， and difficulty in deploying on edge devices， were summarized， and the future research focus was outlooked， by combining model compression， self-supervised learning， and Transformer interpretability analysis， more kinds of feasible solutions for Transformer-based visual target tracking could be presented. Table and Figures | Reference | Related Articles | Metrics

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Lightweight large-format tile defect detection algorithm based on improved YOLOv8 Songsen YU, Zhifan LIN, Guopeng XUE, Jianyu XU Journal of Computer Applications    2025, 45 (2): 647-654.   DOI: 10.11772/j.issn.1001-9081.2024020198 Abstract （298）   HTML （26）    PDF （3856KB）（2109）       Knowledge map    Save In view of the problems of current tile defect detection mainly relying on manual detection， such as strong subjectivity， low efficiency， and high labor intensity， an improved lightweight algorithm for detecting small defects in large-format ceramic tile images based on YOLOv8 was proposed. Firstly， the high-resolution large-format image was cropped， and HorBlock was introduced into the backbone network to enhance model’s capture capability. Secondly， Large Separable Kernel Attention （LSKA） was incorporated to improve C2f for improving the detection performance of the model and model’s feature extraction capability was enhanced by introducing SA （Shuffle Attention）. Finally， Omni-Dimensional Dynamic Convolution （ODConv） was introduced to further enhance model’s capability to handle with small defects. Experimental results on Alibaba Tianchi tile defect detection dataset show that the improved model not only has lower parameters than the original YOLOv8n， but also has an increase of 8.2 percentage points in mAP@0.5 and an increase of 7 percentage points in F1 score compared to the original YOLOv8n. It can be seen that the improved model can identify and process small surface defects of large-format tiles more accurately， and improve the detection effect significantly while maintaining lightweight. Table and Figures | Reference | Related Articles | Metrics

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Research review on explainable artificial intelligence in internet of things applications Xiaoyang ZHAO, Xinzheng XU, Zhongnian LI Journal of Computer Applications    2025, 45 (7): 2169-2179.   DOI: 10.11772/j.issn.1001-9081.2024070927 Abstract （293）   HTML （8）    PDF （2756KB）（2080）       Knowledge map    Save In the era of Internet of Things （IoT）， the integration of Artificial Intelligence （AI） and IoT has become a significant trend driving technological advancements and application innovations. With the exponential growth in the number of connected devices， enhancing end-users’ trust in intelligent systems has become especially critical. Explainable Artificial Intelligence （XAI） refers to AI systems capable of providing their decision-making processes and outcome explanations. The emergence of XAI has propelled the development of AI technology and increased users’ trust in AI systems. Therefore， a research review on XAI in IoT applications was performed. Firstly， the background and significance of IoT and XAI were discussed. Secondly， the definition and key technologies of XAI were presented. Thirdly， the recent progress in traditional AI-driven IoT applications as well as XAI-driven IoT applications were introduced. Finally， the future development directions of XAI in IoT applications were prospected and the associated challenges were summarized. 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 （346）   HTML （11）    PDF （1391KB）（2026）       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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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 （560）   HTML （6）    PDF （3942KB）（2004）       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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Adaptive hybrid network for affective computing in student classroom Yan RONG, Jiawen LIU, Xinlei LI Journal of Computer Applications    2024, 44 (9): 2919-2930.   DOI: 10.11772/j.issn.1001-9081.2023091303 Abstract （585）   HTML （5）    PDF （4730KB）（1967）       Knowledge map    Save Affective computing can provide a better teaching effectiveness and learning experience for intelligent education. Current research on affective computing in classroom domain still suffers from limited adaptability and weak perception on complex scenarios. To address these challenges， a novel hybrid architecture was proposed， namely SC-ACNet， aiming at accurate affective computing for students in classroom. In the architecture， the followings were included： a multi-scale student face detection module capable of adapting to small targets， an affective computing module with an adaptive spatial structure that can adapt to different facial postures to recognize five emotions （calm， confused， jolly， sleepy， and surprised） of students in classroom， and a self-attention module that visualized the regions of the model contributing most to the results. In addition， a new student classroom dataset， SC-ACD， was constructed to alleviate the lack of face emotion image datasets in classroom. Experimental results on SC-ACD dataset show that SC-ACNet improves the mean Average Precision （mAP） by 4.2 percentage points and the accuracy of affective computing by 9.1 percentage points compared with the baseline method YOLOv7. Furthermore， SC-ACNet has the accuracies of 0.972 and 0.994 on common sentiment datasets， namely KDEF and RaFD， validating the viability of the proposed method as a promising solution to elevate the quality of teaching and learning in intelligent classroom. 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 （933）   HTML （11）    PDF （1545KB）（1951）       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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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 （372）   HTML （5）    PDF （2750KB）（1948）       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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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 （636）   HTML （17）    PDF （1378KB）（1934）       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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Probability-driven dynamic multiobjective evolutionary optimization for multi-agent cooperative scheduling Xiaofang LIU, Jun ZHANG Journal of Computer Applications    2024, 44 (5): 1372-1377.   DOI: 10.11772/j.issn.1001-9081.2023121865 Abstract （494）   HTML （19）    PDF （1353KB）（1885）       Knowledge map    Save In multi-agent systems， there are multiple cooperative tasks that change with time and multiple conflict optimization objective functions. To build a multi-agent system， the dynamic multiobjective multi-agent cooperative scheduling problem becomes one of critical problems. To solve this problem， a probability-driven dynamic prediction strategy was proposed to utilize the probability distributions in historical environments to predict the ones in new environments， thus generating new solutions and realizing the fast response to environmental changes. In detail， an element-based representation for probability distributions was designed to represent the adaptability of elements in dynamic environments， and the probability distributions were gradually updated towards real distributions according to the best solutions found by optimization algorithms in each iteration. Taking into account continuity and relevance of environmental changes， a fusion-based prediction mechanism was built to predict the probability distributions and to provide a priori knowledge of new environments by fusing historical probability distributions when the environment changes. A new heuristic-based sampling mechanism was also proposed by combining probability distributions and heuristic information to generate new solutions for updating out-of-date populations. The proposed probability-driven dynamic prediction strategy can be inserted into any multiobjective evolutionary algorithms， resulting in probability-driven dynamic multiobjective evolutionary algorithms. Experimental results on 10 dynamic multiobjective multi-agent cooperative scheduling problem instances show that the proposed algorithms outperform the competing algorithms in terms of solution optimality and diversity， and the proposed probability-driven dynamic prediction strategy can improve the performance of multiobjective evolutionary algorithms in dynamic environments. 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 （684）   HTML （7）    PDF （1975KB）（1856）       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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Dynamic UAV path planning based on modified whale optimization algorithm Xingwang WANG, Qingyang ZHANG, Shouyong JIANG, Yongquan DONG Journal of Computer Applications    2025, 45 (3): 928-936.   DOI: 10.11772/j.issn.1001-9081.2024030370 Abstract （382）   HTML （8）    PDF （7205KB）（1846）       Knowledge map    Save A dynamic Unmanned Aerial Vehicle （UAV） path planning method based on Modified Whale Optimization Algorithm （MWOA） was proposed for the problem of UAV path planning in environments with complex terrains. Firstly， by analyzing the mountain terrain， dynamic targets， and threat zones， a three-dimensional dynamic environment and a UAV route model were established. Secondly， an adaptive step size Gaussian walk strategy was proposed to balance the algorithm’s abilities of global exploration and local exploitation. Finally， a supplementary correction strategy was proposed to correct the optimal individual in the population， and combined with differential evolution strategy， the population was avoided from falling into local optimum while improving convergence accuracy of the algorithm. To verify the effectiveness of MWOA， MWOA and intelligent algorithms such as Whale Optimization Algorithm （WOA）， and Artificial Hummingbird Algorithm （AHA） were used to solve the CEC2022 test functions， and validated in designed UAV dynamic environment model. The comparative analysis of simulation results shows that compared with the traditional WOA， MWOA improves the convergence accuracy by 6.1%， and reduces the standard deviation by 44.7%. The above proves that the proposed MWOA has faster convergence and higher accuracy， and can handle UAV path planning problems effectively. Table and Figures | Reference | Related Articles | Metrics

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Development and key system analysis of intelligent rail transit Jingyu LI, Juxia DING Journal of Computer Applications    0, (): 316-322.   DOI: 10.11772/j.issn.1001-9081.2024020246 Abstract （92）      PDF （1957KB）（1839）       Knowledge map    Save The rail transit with significant features of many lines， large traffic， complex environment， and high system integration is regarded as the main mode of transportation for residents. Currently， Artificial Intelligence （AI） based information processing， system optimization， and control technology have brought new challenges and opportunities for rail transit technology development. Firstly， starting from the recent advancements in intelligent development of rail transit both domestically and internationally， an analysis was conducted on strategic planning and development progress of various countries worldwide in the domain of intelligent rail transit. Then， from the perspectives of intelligent signal system， intelligent operation and maintenance system， intelligent passenger service system， and intelligent monitoring system， the intelligent system for rail transit was summed up. At the same time， development needs and key technological advancements of intelligentialization of this system were outlined. Finally， the future intelligent development of rail transit was summarized and prospected. Reference | Related Articles | Metrics

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Multi-domain spatiotemporal hierarchical graph neural network for air quality prediction Handa MA, Yadong WU Journal of Computer Applications    2025, 45 (2): 444-452.   DOI: 10.11772/j.issn.1001-9081.2024010064 Abstract （438）   HTML （7）    PDF （3113KB）（1810）       Knowledge map    Save In the spatiotemporal hybrid models that integrate meteorological， spatial， and temporal information， the modeling of temporal changes is usually done in one-dimensional space. To solve the problems that one-dimensional sequences are limited in sliding windows and is lack of the flexibility of multi-scale feature extraction， a Multi-domain SpatioTemporal Hierarchical Graph Neural Network （MST-HGNN） model was proposed. Firstly， two levels of hierarchical graphs were constructed， namely， city-wide global scale one and station-level local scale one， so as to perform spatial relationship learning. Secondly， the one-dimensional air quality sequences were transformed into a set of two-dimensional tensors based on multiple periods， and multi-scale convolution in two-dimensional space was used to capture frequency domain features by periodic decoupling. At the same time， Long Short-Term Memory （LSTM） network in one-dimensional space was employed to fit temporal features. Finally， to avoid redundant information aggregation， a gating mechanism fusion module was designed for multi-domain feature fusion of frequency domain and temporal domain features. Experimental results on Urban-Air dataset and the Yangtze River Delta city cluster dataset show that compared with Multi-View Multi-Task Spatiotemporal Graph Convolutional Network model （M2）， the proposed model has lower Mean Absolute Error （MAE） and Root Mean Squared Error （RMSE） than the comparison model in predicting air quality at the 1 h， 3 h， 6 h， and 12 h. It can be seen that MST-HGNN can decouple complex time patterns in the frequency domain， compensate for the limitations of temporal feature modeling using frequency domain information， and predict air quality changes more comprehensively by combining time domain information. Table and Figures | Reference | Related Articles | Metrics