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    Review of multi-modal medical image segmentation based on deep learning
    Meng DOU, Zhebin CHEN, Xin WANG, Jitao ZHOU, Yu YAO
    Journal of Computer Applications    2023, 43 (11): 3385-3395.   DOI: 10.11772/j.issn.1001-9081.2022101636
    Abstract1802)   HTML73)    PDF (3904KB)(1824)       Save

    Multi-modal medical images can provide clinicians with rich information of target areas (such as tumors, organs or tissues). However, effective fusion and segmentation of multi-modal images is still a challenging problem due to the independence and complementarity of multi-modal images. Traditional image fusion methods have difficulty in addressing this problem, leading to widespread research on deep learning-based multi-modal medical image segmentation algorithms. The multi-modal medical image segmentation task based on deep learning was reviewed in terms of principles, techniques, problems, and prospects. Firstly, the general theory of deep learning and multi-modal medical image segmentation was introduced, including the basic principles and development processes of deep learning and Convolutional Neural Network (CNN), as well as the importance of the multi-modal medical image segmentation task. Secondly, the key concepts of multi-modal medical image segmentation was described, including data dimension, preprocessing, data enhancement, loss function, and post-processing, etc. Thirdly, different multi-modal segmentation networks based on different fusion strategies were summarized and analyzed. Finally, several common problems in medical image segmentation were discussed, the summary and prospects for future research were given.

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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
    Abstract1606)   HTML31)    PDF (2002KB)(1476)       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.

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    Multimodal knowledge graph representation learning: a review
    Chunlei WANG, Xiao WANG, Kai LIU
    Journal of Computer Applications    2024, 44 (1): 1-15.   DOI: 10.11772/j.issn.1001-9081.2023050583
    Abstract1149)   HTML95)    PDF (3449KB)(1320)       Save

    By comprehensively comparing the models of traditional knowledge graph representation learning, including the advantages and disadvantages and the applicable tasks, the analysis shows that the traditional single-modal knowledge graph cannot represent knowledge well. Therefore, how to use multimodal data such as text, image, video, and audio for knowledge graph representation learning has become an important research direction. At the same time, the commonly used multimodal knowledge graph datasets were analyzed in detail to provide data support for relevant researchers. On this basis, the knowledge graph representation learning models under multimodal fusion of text, image, video, and audio were further discussed, and various models were summarized and compared. Finally, the effect of multimodal knowledge graph representation on enhancing classical applications, including knowledge graph completion, question answering system, multimodal generation and recommendation system in practical applications was summarized, and the future research work was prospected.

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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
    Abstract1130)   HTML61)    PDF (1142KB)(985)       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.

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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
    Abstract848)   HTML9)    PDF (1175KB)(746)       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.

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    Few-shot text classification method based on prompt learning
    Bihui YU, Xingye CAI, Jingxuan WEI
    Journal of Computer Applications    2023, 43 (9): 2735-2740.   DOI: 10.11772/j.issn.1001-9081.2022081295
    Abstract828)   HTML60)    PDF (884KB)(815)       Save

    Text classification tasks usually rely on sufficient labeled data. Concerning the over-fitting problem of classification models on samples with small size in low resource scenarios, a few-shot text classification method based on prompt learning called BERT-P-Tuning was proposed. Firstly, the pre-trained model BERT (Bidirectional Encoder Representations from Transformers) was used to learn the optimal prompt template from labeled samples. Then, the prompt template and vacancy were filled in each sample, and the text classification task was transformed into the cloze test task. Finally, the final labels were obtained by predicting the word with the highest probability of the vacant positions and combining the mapping relationship between it and labels. Experimental results on the short text classification tasks of public dataset FewCLUE show that the proposed method have significantly improved the evaluation indicators compared to the BERT fine-tuning based method. In specific, the proposed method has the accuracy and F1 score increased by 25.2 and 26.7 percentage points respectively on the binary classification task, and the proposed method has the accuracy and F1 score increased by 6.6 and 8.0 percentage points respectively on the multi-class classification task. Compared with the PET (Pattern Exploiting Training) method of constructing templates manually, the proposed method has the accuracy increased by 2.9 and 2.8 percentage points respectively on two tasks, and the F1 score increased by 4.4 and 4.2 percentage points respectively on two tasks. The above verifies the effectiveness of applying pre-trained model on few-shot tasks.

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    Dynamic multi-domain adversarial learning method for cross-subject motor imagery EEG signals
    Xuan CAO, Tianjian LUO
    Journal of Computer Applications    2024, 44 (2): 645-653.   DOI: 10.11772/j.issn.1001-9081.2023030286
    Abstract784)   HTML8)    PDF (3364KB)(210)       Save

    Decoding motor imagery EEG (ElectroEncephaloGraphy) signal is one of the crucial techniques for building Brain Computer Interface (BCI) system. Due to EEG signal’s high cost of acquisition, large inter-subject discrepancy, and characteristics of strong time variability and low signal-to-noise ratio, constructing cross-subject pattern recognition methods become the key problem of such study. To solve the existing problem, a cross-subject dynamic multi-domain adversarial learning method was proposed. Firstly, the covariance matrix alignment method was used to align the given EEG samples. Then, a global discriminator was adapted for marginal distribution of different domains, and multiple class-wise local discriminators were adapted to conditional distribution for each class. The self-adaptive adversarial factor for multi-domain discriminator was automatically learned during training iterations. Based on dynamic multi-domain adversarial learning strategy, the Dynamic Multi-Domain Adversarial Network (DMDAN) model could learn deep features with generalization ability between cross-subject domains. Experimental results on public BCI Competition IV 2A and 2B datasets show that, DMDAN model improves the ability of learning domain-invariant features, achieving 1.80 and 2.52 percentage points higher average classification accuracy on dataset 2A and dataset 2B compared with the existing adversarial learning method Deep Representation Domain Adaptation (DRDA). It can be seen that DMDAN model improves the decoding performance of cross-subject motor imagery EEG signals, and has generalization ability on different datasets.

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    Review of object pose estimation in RGB images based on deep learning
    Yi WANG, Jie XIE, Jia CHENG, Liwei DOU
    Journal of Computer Applications    2023, 43 (8): 2546-2555.   DOI: 10.11772/j.issn.1001-9081.2022071022
    Abstract771)   HTML31)    PDF (858KB)(592)       Save

    6 Degree of Freedom (DoF) pose estimation is a key technology in computer vision and robotics, and has become a crucial task in the fields such as robot operation, automatic driving, augmented reality by estimating 6 DoF pose of an object from a given input image, that is, 3 DoF translation and 3 DoF rotation. Firstly, the concept of 6 DoF pose and the problems of traditional methods based on feature point correspondence, template matching, and three-dimensional feature descriptors were introduced. Then, the current mainstream 6 DoF pose estimation algorithms based on deep learning were introduced in detail from different angles of feature correspondence-based, pixel voting-based, regression-based and multi-object instances-oriented, synthesis data-oriented, and category level-oriented. At the same time, the datasets and evaluation indicators commonly used in pose estimation were summarized and sorted out, and some algorithms were evaluated experimentally to show their performance. Finally, the challenges and the key research directions in the future of pose estimation were given.

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    Gradient descent with momentum algorithm based on differential privacy in convolutional neural network
    Yu ZHANG, Ying CAI, Jianyang CUI, Meng ZHANG, Yanfang FAN
    Journal of Computer Applications    2023, 43 (12): 3647-3653.   DOI: 10.11772/j.issn.1001-9081.2022121881
    Abstract711)   HTML118)    PDF (1985KB)(702)       Save

    To address the privacy leakage problem caused by the model parameters memorizing some features of the data during the training process of the Convolutional Neural Network (CNN) models, a Gradient Descent with Momentum algorithm based on Differential Privacy in CNN (DPGDM) was proposed. Firstly, the Gaussian noise meeting differential privacy was added to the gradient in the backpropagation process of model optimization, and the noise-added gradient value was used to participate in the model parameter update process, so as to achieve differential privacy protection for the overall model. Secondly, to reduce the impact of the introduction of differential privacy noise on convergence speed of the model, a learning rate decay strategy was designed and then the gradient descent with momentum algorithm was improved. Finally, to reduce the influence of noise on the accuracy of the model, the value of the noise scale was adjusted dynamically during model optimization, thereby changing the amount of noise that needs to be added to the gradient in each round of iteration. Experimental results show that compared with DP-SGD (Differentially Private Stochastic Gradient Descent) algorithm, the proposed algorithm can improve the accuracy of the model by about 5 and 4 percentage points at privacy budget of 0.3 and 0.5, respectively, proving that by using the proposed algorithm, the model usability is improved and privacy protection of the model is achieved.

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    UAV cluster cooperative combat decision-making method based on deep reinforcement learning
    Lin ZHAO, Ke LYU, Jing GUO, Chen HONG, Xiancai XIANG, Jian XUE, Yong WANG
    Journal of Computer Applications    2023, 43 (11): 3641-3646.   DOI: 10.11772/j.issn.1001-9081.2022101511
    Abstract689)   HTML13)    PDF (2944KB)(498)       Save

    When the Unmanned Aerial Vehicle (UAV) cluster attacks ground targets, it will be divided into two formations: a strike UAV cluster that attacks the targets and a auxiliary UAV cluster that pins down the enemy. When auxiliary UAVs choose the action strategy of aggressive attack or saving strength, the mission scenario is similar to a public goods game where the benefits to the cooperator are less than those to the betrayer. Based on this, a decision method for cooperative combat of UAV clusters based on deep reinforcement learning was proposed. First, by building a public goods game based UAV cluster combat model, the interest conflict problem between individual and group in cooperation of intelligent UAV clusters was simulated. Then, Muti-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm was used to solve the most reasonable combat decision of the auxiliary UAV cluster to achieve cluster victory with minimum loss cost. Training and experiments were performed under conditions of different numbers of UAV. The results show that compared to the training effects of two algorithms — IDQN (Independent Deep Q-Network) and ID3QN (Imitative Dueling Double Deep Q-Network), the proposed algorithm has the best convergence, its winning rate can reach 100% with four auxiliary UAVs, and it also significantly outperforms the comparison algorithms with other UAV numbers.

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    Poisoning attack detection scheme based on generative adversarial network for federated learning
    Qian CHEN, Zheng CHAI, Zilong WANG, Jiawei CHEN
    Journal of Computer Applications    2023, 43 (12): 3790-3798.   DOI: 10.11772/j.issn.1001-9081.2022121831
    Abstract688)   HTML34)    PDF (2367KB)(451)       Save

    Federated Learning (FL) emerges as a novel privacy-preserving Machine Learning (ML) paradigm. However, the distributed training structure of FL is more vulnerable to poisoning attack, where adversaries contaminate the global model through uploading poisoning models, resulting in the convergence deceleration and the prediction accuracy degradation of the global model. To solve the above problem, a poisoning attack detection scheme based on Generative Adversarial Network (GAN) was proposed. Firstly, the benign local models were fed into the GAN to output testing samples. Then, the testing samples were used to detect the local models uploaded by the clients. Finally, the poisoning models were eliminated according to the testing metrics. Meanwhile, two test metrics named F1 score loss and accuracy loss were defined to detect the poisoning models and extend the detection scope from one single type of poisoning attacks to all types of poisoning attacks. Besides, a threshold determination method was designed to deal with misjudgment, so that the robust of misjudgment was confirmed. Experimental results on MNIST and Fashion-MNIST datasets show that the proposed scheme can generate high-quality testing samples, and then detect and eliminate poisoning models. Compared with the global models trained with the detection scheme based on directly gathering test data from clients and the detection scheme based on generating test data and using test accuracy as the test metric, the global model trained with the proposed scheme has significant accuracy improvement from 2.7 to 12.2 percentage points.

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    Feature selection method for graph neural network based on network architecture design
    Dapeng XU, Xinmin HOU
    Journal of Computer Applications    2024, 44 (3): 663-670.   DOI: 10.11772/j.issn.1001-9081.2023030353
    Abstract673)   HTML106)    PDF (1001KB)(820)       Save

    In recent years, researchers have proposed many improved model architecture designs for Graph Neural Network (GNN), driving performance improvements in various prediction tasks. But most GNN variants start with the assumption that node features are equally important, which is not the case. To solve this problem, a feature selection method was proposed to improve the existing model and select important feature subsets for the dataset. The proposed method consists of two components, a feature selection layer, and a separate label-feature mapping. Softmax normalizer and feature “soft selector” were used for feature selection in the feature selection layer, and the model structure was designed under the idea of separate label-feature mapping to select the corresponding subsets of related features for different labels, and multiple related feature subsets were performed union operation to obtain an important feature subset of the final dataset. Graph ATtention network (GAT) and GATv2 models were selected as the benchmark models, and the algorithm was applied to the benchmark models to obtain new models. Experimental results show that when the proposed models perform node classification tasks on six datasets, their accuracies are improved by 0.83% - 8.79% compared with the baseline models. The new models also select the corresponding important feature subsets for the six datasets, in which the number of features accounts for 3.94% - 12.86% of the total number of features in their respective datasets. After using the important feature subset as the new input of the benchmark model, the accuracy more than 95% (using all features) is still achieved. That is, the scale of the model is reduced while ensuring the accuracy. It can be seen that the proposed new algorithm can improve the accuracy of node classification, and can effectively select the corresponding important feature subset for the dataset.

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    Survey of data-driven intelligent cloud-edge collaboration
    Pengxin TIAN, Guannan SI, Zhaoliang AN, Jianxin LI, Fengyu ZHOU
    Journal of Computer Applications    2023, 43 (10): 3162-3169.   DOI: 10.11772/j.issn.1001-9081.2022091418
    Abstract672)   HTML39)    PDF (1772KB)(479)       Save

    With the rapid development of Internet of Things (IoT), a large amount of data generated in edge scenarios such as sensors often needs to be transmitted to cloud nodes for processing, which brings huge transmission cost and processing delay. Cloud-edge collaboration provides a solution for these problems. Firstly, on the basis of comprehensive investigation and analysis of the development process of cloud-edge collaboration, combined with the current research ideas and progress of intelligent cloud-edge collaboration, the data acquisition and analysis, computation offloading technology and model-based intelligent optimization technology in cloud edge architecture were analyzed and discussed emphatically. Secondly, the functions and applications of various technologies in intelligent cloud-edge collaboration were analyzed deeply from the edge and the cloud respectively, and the application scenarios of intelligent cloud-edge collaboration technology in reality were discussed. Finally, the current challenges and future development directions of intelligent cloud-edge collaboration were pointed out.

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    Few-shot object detection algorithm based on Siamese network
    Junjian JIANG, Dawei LIU, Yifan LIU, Yougui REN, Zhibin ZHAO
    Journal of Computer Applications    2023, 43 (8): 2325-2329.   DOI: 10.11772/j.issn.1001-9081.2022121865
    Abstract600)   HTML45)    PDF (1472KB)(836)       Save

    Deep learning based algorithms such as YOLO (You Only Look Once) and Faster Region-Convolutional Neural Network (Faster R-CNN) require a huge amount of training data to ensure the precision of the model, and it is difficult to obtain data and the cost of labeling data is high in many scenarios. And due to the lack of massive training data, the detection range is limited. Aiming at the above problems, a few-shot object Detection algorithm based on Siamese Network was proposed, namely SiamDet, with the purpose of training an object detection model with certain generalization ability by using a few annotated images. Firstly, a Siamese network based on depthwise separable convolution was proposed, and a feature extraction network ResNet-DW was designed to solve the overfitting problem caused by insufficient samples. Secondly, an object detection algorithm SiamDet was proposed based on Siamese network, and based on ResNet-DW, Region Proposal Network (RPN) was introduced to locate the interested objects. Thirdly, binary cross entropy loss was introduced for training, and contrast training strategy was used to increase the distinction among categories. Experimental results show that SiamDet has good object detection ability for few-shot objects, and SiamDet improves AP50 by 4.1% on MS-COCO 20-way 2-shot and 2.6% on PASCAL VOC 5-way 5-shot compared with the suboptimal algorithm DeFRCN (Decoupled Faster R-CNN).

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    Survey of incomplete multi-view clustering
    Yao DONG, Yixue FU, Yongfeng DONG, Jin SHI, Chen CHEN
    Journal of Computer Applications    2024, 44 (6): 1673-1682.   DOI: 10.11772/j.issn.1001-9081.2023060813
    Abstract580)   HTML8)    PDF (2050KB)(374)       Save

    Multi-view clustering has recently been a hot topic in graph data mining. However, due to the limitations of data collection technology or human factors, multi-view data often has the problem of missing views or samples. Reducing the impact of incomplete views on clustering performance is a major challenge currently faced by multi-view clustering. In order to better understand the development of Incomplete Multi-view Clustering (IMC) in recent years, a comprehensive review is of great theoretical significance and practical value. Firstly, the missing types of incomplete multi-view data were summarized and analyzed. Secondly, four types of IMC methods, based on Multiple Kernel Learning (MKL), Matrix Factorization (MF) learning, deep learning, and graph learning were compared, and the technical characteristics and differences among the methods were analyzed. Thirdly, from the perspectives of dataset types, the numbers of views and categories, and application fields, twenty-two public incomplete multi-view datasets were summarized. Then, the evaluation metrics were outlined, and the performance of existing incomplete multi-view clustering methods on homogeneous and heterogeneous datasets were evaluated. Finally, the existing problems, future research directions, and existing application fields of incomplete multi-view clustering were discussed.

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    Lightweight image super-resolution reconstruction network based on Transformer-CNN
    Hao CHEN, Zhenping XIA, Cheng CHENG, Xing LIN-LI, Bowen ZHANG
    Journal of Computer Applications    2024, 44 (1): 292-299.   DOI: 10.11772/j.issn.1001-9081.2023010048
    Abstract575)   HTML21)    PDF (1855KB)(349)       Save

    Aiming at the high computational complexity and large memory consumption of the existing super-resolution reconstruction networks, a lightweight image super-resolution reconstruction network based on Transformer-CNN was proposed, which made the super-resolution reconstruction network more suitable to be applied on embedded terminals such as mobile platforms. Firstly, a hybrid block based on Transformer-CNN was proposed, which enhanced the ability of the network to capture local-global depth features. Then, a modified inverted residual block, with special attention to the characteristics of the high-frequency region, was designed, so that the improvement of feature extraction ability and reduction of inference time were realized. Finally, after exploring the best options for activation function, the GELU (Gaussian Error Linear Unit) activation function was adopted to further improve the network performance. Experimental results show that the proposed network can achieve a good balance between image super-resolution performance and network complexity, and reaches inference speed of 91 frame/s on the benchmark dataset Urban100 with scale factor of 4, which is 11 times faster than the excellent network called SwinIR (Image Restoration using Swin transformer), indicates that the proposed network can efficiently reconstruct the textures and details of the image and reduce a significant amount of inference time.

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    Multi-view clustering network with deep fusion
    Ziyi HE, Yan YANG, Yiling ZHANG
    Journal of Computer Applications    2023, 43 (9): 2651-2656.   DOI: 10.11772/j.issn.1001-9081.2022091394
    Abstract560)   HTML51)    PDF (1074KB)(381)       Save

    Current deep multi-view clustering methods have the following shortcomings: 1) When feature extraction is carried out for a single view, only attribute information or structural information of the samples is considered, and these two types of information are not integrated. Thus, the extracted features cannot fully represent latent structure of the original data. 2) Feature extraction and clustering were divided into two separated processes, without establishing the relationship between them, so that the feature extraction process cannot be optimized by the clustering process. To solve these problems, a Deep Fusion based Multi-view Clustering Network (DFMCN) was proposed. Firstly, the embedding space of each view was obtained by combining autoencoder and graph convolution autoencoder to fuse attribute information and structure information of samples. Then, the embedding space of the fusion view was obtained through weighted fusion, and clustering was carried out in this space. And in the process of clustering, the feature extraction process was optimized by a two-layer self-supervision mechanism. Experimental results on FM (Fashion-MNIST), HW (HandWritten numerals), and YTF (YouTube Face) datasets show that the accuracy of DFMCN is higher than those of all comparison methods; and DFMCN has the accuracy increased by 1.80 percentage points compared with the suboptimal CMSC-DCCA (Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis) method on FM dataset, the Normalized Mutual Information (NMI) of DFMCN is increased by 1.26 to 14.84 percentage points compared to all methods except for CMSC-DCCA and DMSC (Deep Multimodal Subspace Clustering networks). Experimental results verify the effectiveness of the proposed method.

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    Survey on combination of computation offloading and blockchain in internet of things
    Rui MEN, Shujia FAN, Axida SHAN, Shaoyu DU, Xiumei FAN
    Journal of Computer Applications    2023, 43 (10): 3008-3016.   DOI: 10.11772/j.issn.1001-9081.2022091466
    Abstract540)   HTML32)    PDF (882KB)(232)       Save

    With the recent development of mobile communication technology and the popularization of smart devices, the computation-intensive tasks of the terminal devices can be offloaded to edge servers to solve the problem of insufficient resources. However, the distributed nature of computation offloading technology exposes terminal devices and edge servers to security risks. And, blockchain technology can provide a safe environment transaction for the computation offloading system. The combination of the above two technologies can solve the insufficient resource and the security problems in internet of things. Therefore, the research results of applications combining computation offloading with blockchain technologies in internet of things were surveyed. Firstly, the application scenarios and system functions in the combination of computation offloading and blockchain technologies were analyzed. Then, the main problems solved by blockchain technology and the key techniques used in this technology were summarized in the computation offloading system. The formulation methods, optimization objectives and optimization algorithms of computation offloading strategies in the blockchain system were classified. Finally, the problems in the combination were provided, and the future directions of development in this area were prospected.

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    Source code vulnerability detection based on hybrid code representation
    Kun ZHANG, Fengyu YANG, Fa ZHONG, Guangdong ZENG, Shijian ZHOU
    Journal of Computer Applications    2023, 43 (8): 2517-2526.   DOI: 10.11772/j.issn.1001-9081.2022071135
    Abstract538)   HTML15)    PDF (1958KB)(251)       Save

    Software vulnerabilities pose a great threat to network and information security, and the root of vulnerabilities lies in software source code. Existing traditional static detection tools and deep learning based detection methods do not fully represent code features, and simply use word embedding method to transform code representation, so that their detection results have low accuracy and high false positive rate or high false negative rate. Therefore, a source code vulnerability detection method based on hybrid code representation was proposed to solve the problem of incomplete code representation and improve detection performance. Firstly, source code was compiled into Intermediate Representation (IR), and the program dependency graph was extracted. Then, structural features were obtained through program slicing based on data flow and control flow analysis. At the same time, unstructural features were obtained by embedding node statements using doc2vec. Next, Graph Neural Network (GNN) was used to learn the hybrid features. Finally, the trained GNN was used for prediction and classification. In order to verify the effectiveness of the proposed method, experimental evaluation was performed on Software Assurance Reference Dataset (SARD) and real-world datasets, and the F1 score of detection results reached 95.3% and 89.6% respectively. Experimental results show that the proposed method has good vulnerability detection ability.

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    Differential privacy clustering algorithm in horizontal federated learning
    Xueran XU, Geng YANG, Yuxian HUANG
    Journal of Computer Applications    2024, 44 (1): 217-222.   DOI: 10.11772/j.issn.1001-9081.2023010019
    Abstract520)   HTML11)    PDF (1418KB)(263)       Save

    Clustering analysis can uncover hidden interconnections between data and segment the data according to multiple indicators, which can facilitate personalized and refined operations. However, data fragmentation and isolation caused by data islands seriously affects the effectiveness of cluster analysis applications. To solve data island problem and protect data privacy, an Equivalent Local differential privacy Federated K-means (ELFedKmeans) algorithm was proposed. A grid-based initial cluster center selection method and a privacy budget allocation scheme were designed for the horizontal federation learning model. To generate same random noise with lower communication cost, all organizations jointly negotiated random seeds, protecting local data privacy. The ELFedKmeans algorithm was demonstrated satisfying differential privacy protection through theoretical analysis, and it was also compared with Local Differential Privacy distributed K-means (LDPKmeans) algorithm and Hybrid Privacy K-means (HPKmeans) algorithm on different datasets. Experimental results show that all three algorithms increase F-measure and decrease SSE (Sum of Squares due to Error) gradually as privacy budget increases. As a whole, the F-measure values of ELFedKmeans algorithm was 1.794 5% to 57.066 3% and 21.245 2% to 132.048 8% higher than those of LDPKmeans and HPKmeans algorithms respectively; the Log(SSE) values of ELFedKmeans algorithm were 1.204 2% to 12.894 6% and 5.617 5% to 27.575 2% less than those of LDPKmeans and HPKmeans algorithms respectively. With the same privacy budget, ELFedKmeans algorithm outperforms the comparison algorithms in terms of clustering quality and utility metric.

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    Review of research on aquaculture counting based on machine vision
    Hanyu ZHANG, Zhenbo LI, Weiran LI, Pu YANG
    Journal of Computer Applications    2023, 43 (9): 2970-2982.   DOI: 10.11772/j.issn.1001-9081.2022081261
    Abstract513)   HTML24)    PDF (1320KB)(294)       Save

    Aquaculture counting is an important part of the aquaculture process, and the counting results provide an important basis for feeding, breeding density adjustment, and economic efficiency estimation of aquatic animals. In response to the traditional manual counting methods, which are time-consuming, labor-intensive, and prone to large errors, a large number of methods and applications based on machine vision have been proposed, thereby greatly promoting the development of non-destructive counting of aquatic products. In order to deeply understand the research on aquaculture counting based on machine vision, the relevant domestic and international literature in the past 30 years was collated and analyzed. Firstly, a review of aquaculture counting was presented in the perspective of data acquisition, and the methods for acquiring the data required for machine vision were summed up. Secondly, the aquaculture counting methods were analyzed and summarized in terms of traditional machine vision and deep learning. Thirdly, the practical applications of counting methods in different farming environments were compared and analyzed. Finally, the difficulties in the development of aquaculture counting research were summarized in terms of data, methods, and applications, and corresponding views were presented for the future trends of aquaculture counting research and equipment applications.

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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
    Abstract503)   HTML12)    PDF (1615KB)(431)       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.

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    Application review of deep models in medical image segmentation: from U-Net to Transformer
    Journal of Computer Applications    DOI: 10.11772/j.issn.1001-9081.2023071059
    Online available: 26 October 2023

    Collaborative recommendation algorithm based on deep graph neural network
    Runchao PAN, Qishan YU, Hongfei XIONG, Zhihui LIU
    Journal of Computer Applications    2023, 43 (9): 2741-2746.   DOI: 10.11772/j.issn.1001-9081.2022091361
    Abstract493)   HTML44)    PDF (1539KB)(384)       Save

    For the problem of over-smoothing in the existing recommendation algorithms based on Graph Neural Network (GNN), a collaborative filtering recommendation algorithm based on deep GCN was proposed, namely Deep NGCF (Deep Neural Graph Collaborative Filtering). In the algorithm, the initial residual connection and identity mapping were introduced into GNN, which avoided GNN from falling into over-smoothing after multiple graph convolution operations. Firstly, the initial embeddings of users and items were obtained through their interaction history. Next, in aggregation and propagation layer, collaborative signals of users and items in different stages were obtained with the use of initial residual connection and identity mapping. Finally, score prediction was performed according to the linear representation of all collaborative signals. In addition, to further improve the flexibility and recommendation performance of the model, the weights were set in the initial residual connection and identity mapping for adjustment. In order to verify the feasibility and effectiveness of Deep NGCF algorithm, experiments were conducted on datasets Gowalla, Yelp-2018 and Amazon-book. The results show that compared with the existing GNN recommendation algorithm such as Graph Convolutional Matrix Completion (GCMC) and Neural Graph Collaborate Filtering (NGCF), Deep NGCF algorithm achieves the best results on recall and Normalized Discounted Cumulative Gain (NDCG), thereby verifying the effectiveness of the proposed algorithm.

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    Acceleration and optimization of quantum computing simulator implemented on new Sunway supercomputer
    Xinmin SHI, Yong LIU, Yaojian CHEN, Jiawei SONG, Xin LIU
    Journal of Computer Applications    2023, 43 (8): 2486-2492.   DOI: 10.11772/j.issn.1001-9081.2022091456
    Abstract493)   HTML59)    PDF (2000KB)(503)       Save

    Two optimization methods for quantum simulator implemented on Sunway supercomputer were proposed aiming at the problems of gradual scaling of quantum hardware and insufficient classical simulation speed. Firstly, the tensor contraction operator library SWTT was reconstructed by improving the tensor transposition strategy and computation strategy, which improved the computing kernel efficiency of partial tensor contraction and reduced redundant memory access. Secondly, the balance between complexity and efficiency of path computation was achieved by the contraction path adjustment method based on data locality optimization. Test results show that the improvement method of operator library can improve the simulation efficiency of the "Sycamore" quantum supremacy circuit by 5.4% and the single-step tensor contraction efficiency by up to 49.7 times; the path adjustment method can improve the floating-point efficiency by about 4 times with the path computational complexity inflated by a factor of 2. The two optimization methods have the efficiencies of single-precision and mixed-precision floating-point operations for the simulation of Google’s 53-bit, 20-layer quantum chip random circuit with a million amplitude sampling improved from 3.98% and 1.69% to 18.48% and 7.42% respectively, and reduce the theoretical estimated simulation time from 470 s to 226 s for single-precision and 304 s to 134 s for mixed-precision, verifying that the two methods significantly improve the quantum computational simulation speed.

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    Hierarchical storyline generation method for hot news events
    Dong LIU, Chuan LIN, Lina REN, Ruizhang HUANG
    Journal of Computer Applications    2023, 43 (8): 2376-2381.   DOI: 10.11772/j.issn.1001-9081.2022091377
    Abstract492)   HTML23)    PDF (1333KB)(307)       Save

    The development of hot news events is very rich, and each stage of the development has its own unique narrative. With the development of events, a trend of hierarchical storyline evolution is presented. Aiming at the problem of poor interpretability and insufficient hierarchy of storyline in the existing storyline generation methods, a Hierarchical Storyline Generation Method (HSGM) for hot news events was proposed. First, an improved hotword algorithm was used to select the main seed events to construct the trunk. Second, the hotwords of branch events were selected to enhance the branch interpretability. Third, in the branch, a storyline coherence selection strategy fusing hotword relevance and dynamic time penalty was used to enhance the connection of parent-child events, so as to build hierarchical hotwords, and then a multi-level storyline was built. In addition, considering the incubation period of hot news events, a hatchery was added during the storyline construction process to solve the problem of neglecting the initial events due to insufficient hotness. Experimental results on two real self-constructed datasets show that in the event tracking process, compared with the methods based on singlePass and k-means respectively, HSGM has the F score increased by 4.51% and 6.41%, 20.71% and 13.01% respectively; in the storyline construction process, HSGM performs well in accuracy, comprehensibility and integrity on two self-constructed datasets compared with Story Forest and Story Graph.

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    Dam surface disease detection algorithm based on improved YOLOv5
    Shengwei DUAN, Xinyu CHENG, Haozhou WANG, Fei WANG
    Journal of Computer Applications    2023, 43 (8): 2619-2629.   DOI: 10.11772/j.issn.1001-9081.2022081207
    Abstract491)   HTML28)    PDF (7862KB)(331)       Save

    For the current water conservancy dams mainly rely on manual on-site inspections, which have high operating costs and low efficiency, an improved detection algorithm based on YOLOv5 was proposed. Firstly, a modified multi-scale visual Transformer structure was used to improve the backbone, and the multi-scale global information associated with the multi-scale Transformer structure and the local information extracted by Convolutional Neural Network (CNN) were used to construct the aggregated features, thereby making full use of the multi-scale semantic information and location information to improve the feature extraction capability of the network. Then, coordinate attention mechanism was added in front of each feature detection layer of the network to encode features in the height and width directions of the image, and long-distance associations of pixels on the feature map were constructed by the encoded features to enhance the target localization ability of the network in complex environments. The sampling algorithm of the positive and negative training samples of the network was improved to help the candidate positive samples to respond to the prior frames of similar shape to themselves by constructing the average fit and difference between the prior frames and the ground-truth frames, so as to make the network converge faster and better, thus improving the overall performance of the network and the network generalization. Finally, the network structure was lightened for application requirements and was optimized by pruning the network structure and structural re-parameterization. Experimental results show that on the current adopted dam disease data, compared with the original YOLOv5s algorithm, the improved network has the mAP (mean Average Precision)@0.5 improved by 10.5 percentage points, the mAP@0.5:0.95 improved by 17.3 percentage points; compared to the network before lightening, the lightweight network has the number of parameters and the FLOPs(FLoating point Operations Per second) reduced by 24% and 13% respectively, and the detection speed improved by 42%, verifying that the network meets the requirements for precision and speed of disease detection in current application scenarios.

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    Overview of cryptocurrency regulatory technologies research
    Jiaxin WANG, Jiaqi YAN, Qian’ang MAO
    Journal of Computer Applications    2023, 43 (10): 2983-2995.   DOI: 10.11772/j.issn.1001-9081.2022111694
    Abstract481)   HTML58)    PDF (911KB)(630)       Save

    With the help of blockchain and other emerging technologies, cryptocurrencies are decentralized, autonomous and cross-border. Research on cryptocurrency regulatory technologies is not only helpful to fight criminal activities based on cryptocurrencies, but also helpful to provide feasible supervision schemes for the expansion of blockchain technologies in other fields. Firstly, based on the application characteristics of cryptocurrency, the Generation, Exchange and Circulation (GEC) cycle theory of cryptocurrency was defined and elaborated. Then, the frequent international and domestic crimes based on cryptocurrencies were analyzed in detail, and the research status of cryptocurrency security supervision technologies in all three stages was investigated and surveyed as key point. Finally, the cryptocurrency regulatory platform ecology systems and current challenges faced by the regulatory technologies were summarized, and the future research directions of cryptocurrency regulatory technologies were prospected in order to provide reference for subsequent research.

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    Knowledge tracing model based on graph neural network blending with forgetting factors and memory gate
    Haodong ZHENG, Hua MA, Yingchao XIE, Wensheng TANG
    Journal of Computer Applications    2023, 43 (9): 2747-2752.   DOI: 10.11772/j.issn.1001-9081.2022081184
    Abstract477)   HTML19)    PDF (1266KB)(318)       Save

    The knowledge tracing task diagnoses a student’s cognitive state in real time based on historical learning data, and predicts the future performance of the student in answering questions. In order to accurately model the forgetting behaviors and the time-series characteristics of the answering sequence in knowledge tracing, a Graph neural network-based Knowledge Tracing blending with Forgetting factors and Memory gate (GKT-FM) model was proposed. Firstly, through the answering record, the correlations of knowledge points were calculated and a knowledge graph was constructed. Then, Graph Neural Network (GNN) was used to model the cognitive state of the student, and seven characteristics that affect forgetting behaviors were considered comprehensively. After that, the memory gate structure was used to model the time-series characteristics in the student’s answering sequence, and the update process of GNN-based knowledge tracing was reconstructed. Finally, the prediction results were obtained by integrating the forgetting factors and the time-series characteristics. Experimental results on public datasets ASSISTments2009 and KDDCup2010 show that compared with GKT (Graph-based Knowledge Tracing) model, GKT-FM model improves the average AUC (Area Under Curve) by 6.9% and 9.5% respectively, and the average ACC (ACCuarcy) by 5.3% and 6.7% respectively, indicating that GKT-FM model can better model students’ forgetting behaviors and trace their cognitive states.

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    Aspect-based sentiment analysis method with integrating prompt knowledge
    Xinyue ZHANG, Rong LIU, Chiyu WEI, Ke FANG
    Journal of Computer Applications    2023, 43 (9): 2753-2759.   DOI: 10.11772/j.issn.1001-9081.2022091347
    Abstract471)   HTML22)    PDF (1699KB)(233)       Save

    Aspect-based sentiment analysis based on pre-trained models generally uses end-to-end frameworks, has the problems of inconsistency between the upstream and downstream tasks, and is difficult to model the relationships between aspect words and context effectively. To address these problems, an aspect-based sentiment analysis method integrating prompt knowledge was proposed. First, in order to capture the semantic relation between aspect words and context effectively and enhance the model’s perception ability for sentiment analysis tasks, based on the Prompt mechanism, a prompt text was constructed and spliced with the original sentence and aspect words, and the obtained results were used as the input of the pre-trained model Bidirectional Encoder Representations from Transformers (BERT). Then, a sentimental label vocabulary was built and integrated into the sentimental verbalizer layer, so as to reduce search space of the model, make the pre-trained model obtain rich semantic knowledge in the label vocabulary, and improve the learning ability of the model. Experimental results on Restaurant and Laptop field datasets of SemEval2014 Task4 dataset as well as ChnSentiCorp dataset show that the F1-score of the proposed method reaches 77.42%, 75.20% and 94.89% respectively, which is increased by 0.65 to 10.71, 1.02 to 9.58 and 0.83 to 6.40 percentage points compared with the mainstream aspect-based sentiment analysis methods such as Glove-TextCNN and P-tuning. The above verifies the effectiveness of the proposed method.

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    Zero-shot relation extraction model via multi-template fusion in Prompt
    Liang XU, Chun ZHANG, Ning ZHANG, Xuetao TIAN
    Journal of Computer Applications    2023, 43 (12): 3668-3675.   DOI: 10.11772/j.issn.1001-9081.2022121869
    Abstract463)   HTML36)    PDF (1768KB)(272)       Save

    Prompt paradigm is widely used to zero-shot Natural Language Processing (NLP) tasks. However, the existing zero-shot Relation Extraction (RE) model based on Prompt paradigm suffers from the difficulty of constructing answer space mappings and dependence on manual template selection, which leads to suboptimal performance. To address these issues, a zero-shot RE model via multi-template fusion in Prompt was proposed. Firstly, the zero-shot RE task was defined as the Masked Language Model (MLM) task, where the construction of answer space mapping was abandoned. Instead, the words output by the template were compared with the relation description text in the word embedding space to determine the relation class. Then, the part of speech of the relation description text was introduced as a feature, and the weight between this feature and each template was learned. Finally, this weight was utilized to fuse the results output by multiple templates, thereby reducing the performance loss caused by the manual selection of Prompt templates. Experimental results on FewRel (Few-shot Relation extraction dataset) and TACRED (Text Analysis Conference Relation Extraction Dataset) show that, the proposed model significantly outperforms the current state-of-the-art model, RelationPrompt, in terms of F1 score under different data resource settings, with an increase of 1.48 to 19.84 percentage points and 15.27 to 15.75 percentage points, respectively. These results convincingly demonstrate the effectiveness of the proposed model for zero-shot RE tasks.

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    Rain detection algorithm based on event camera
    Junyu YANG, Yan DONG, Zhennan LONG, Xin YANG, Bin HAN
    Journal of Computer Applications    2023, 43 (9): 2904-2909.   DOI: 10.11772/j.issn.1001-9081.2022091360
    Abstract462)   HTML11)    PDF (1427KB)(201)       Save

    To reduce the harmful effects of rain for visual tasks, rain removal algorithms are commonly utilized on single frame images or video streams to remove rain. However, since rain falls extremely fast, frame-based cameras cannot capture the temporal continuity of rain, and the fixed exposure time and motion blur further reduce the sharpness of the rain on images, as a result, the traditional image rain removal algorithms cannot detect rain coverage areas accurately. In order to explore the new idea of image rain removal, a rain event generation model was constructed and a rain detection algorithm for event camera based on spatial-temporal relevance was proposed by using the characteristics of event camera: extremely high sampling rate and no motion blur. In this algorithm, the probability of each event generated by rain movement was calculated by analyzing the spatial-temporal relationship between each event recorded by the event camera and adjacent events, so as to achieve rain detection. Experimental results on three rainfall scenes show that when the camera is static, the proposed algorithm can reach more than 95% rain detection true positive rate, and the false positive rate less than 5%, and when the camera moves, the proposed algorithm can still reach more than 95% true positive rate and no more than 20% false positive rate. The above shows that the rain can be detected effectively by the proposed algorithm.

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    Stomach cancer image segmentation method based on EfficientNetV2 and object-contextual representation
    Di ZHOU, Zili ZHANG, Jia CHEN, Xinrong HU, Ruhan HE, Jun ZHANG
    Journal of Computer Applications    2023, 43 (9): 2955-2962.   DOI: 10.11772/j.issn.1001-9081.2022081159
    Abstract446)   HTML22)    PDF (4902KB)(217)       Save

    In view of the problems that the upsampling process of U-Net is easy to lose details, and the datasets of stomach cancer pathological image are generally small, which tends to lead to over-fitting, an automatic segmentation model for pathological images of stomach cancer based on improved U-Net was proposed, namely EOU-Net. In EOU-Net, based on the existing U-Net model, EfficientNetV2 was used as the backbone, thereby enhancing the feature extraction ability of the network encoder. In the decoding stage, the relations between cell pixels were explored on the basis of Object-Contextual Representation (OCR), and the improved OCR module was used to solve the loss problem of the upsampled image details. Then, the post-processing of Test Time Augmentation (TTA) was used to predict the images obtained by rollover and rotations at different angles of the input image respectively, and then the prediction results of these images were combined by feature fusion to further optimize the output results of the network, thereby solving the problem of small medical datasets effectively. Experimental results on datasets SEED, BOT and PASCAL VOC 2012 show that the Mean Intersection over Union (MIoU) of EOU-Net is improved by 1.8, 0.6 and 4.5 percentage points respectively compared with that of OCRNet. It can be seen that EOU-Net can obtain more accurate segmentation results of stomach cancer images.

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    Survey on privacy-preserving technology for blockchain transaction
    Qingqing XIE, Nianmin YANG, Xia FENG
    Journal of Computer Applications    2023, 43 (10): 2996-3007.   DOI: 10.11772/j.issn.1001-9081.2022101555
    Abstract446)   HTML39)    PDF (2911KB)(599)       Save

    Blockchain ledger is open and transparent. Some attackers can obtain sensitive information through analyzing the ledger data. It causes a great threat to users’ privacy preservation of transaction. In view of the importance of blockchain transaction privacy preservation, the causes of the transaction privacy leakage were analyzed at first, and the transaction privacy was divided into two types: the transaction participator’s identity privacy and transaction data privacy. Then, in the perspectives of these two types of transaction privacy, the existing privacy-preserving technologies for blockchain transaction were presented. Next, in view of the contradiction between the transaction identity privacy preservation and supervision, transaction identity privacy preservation schemes considering supervision were introduced. Finally, the future research directions of the privacy-preserving technologies for blockchain transaction were summarized and prospected.

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    Maximal clique searching algorithm for hypergraphs
    Lantian XU, Ronghua LI, Yongheng DAI, Guoren WANG
    Journal of Computer Applications    2023, 43 (8): 2319-2324.   DOI: 10.11772/j.issn.1001-9081.2022091334
    Abstract444)   HTML53)    PDF (1332KB)(318)       Save

    Most of entity relationships in the real world cannot be represented by simple binary relations, and hypergraph can represent the n-ary relations among entities well. Therefore, definitions of hypergraph clique and maximal clique were proposed, and the exact algorithm and approximation algorithm for searching hypergraph maximal clique were given. First, the reason why the existing maximal clique searching algorithms on ordinary graphs cannot be applied to hypergraphs directly was analyzed. Then, based on the characteristics of hypergraph and the definition of maximal clique, a novel data structure for preserving the adjacency relations among hyperpoints was proposed, and an accurate maximal clique searching algorithm on hypergraph was proposed. As the running of the exact algorithm is slow, the pruning idea of pivots was combined with, the number of recursive layers was reduced, and an approximation maximal clique searching algorithm on hypergraph was proposed. Experimental results on multiple real hypergraph datasets show that under the premise finding most maximal cliques, the proposed approximation algorithm improves the search speed. When the number of test hypergraph cliques on 3-uniform hypergraph is 22, the acceleration ratio reaches over 1 000.

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    Multi-robot task allocation algorithm combining genetic algorithm and rolling scheduling
    Fuqin DENG, Huanzhao HUANG, Chaoen TAN, Lanhui FU, Jianmin ZHANG, Tinlun LAM
    Journal of Computer Applications    2023, 43 (12): 3833-3839.   DOI: 10.11772/j.issn.1001-9081.2022121916
    Abstract441)   HTML9)    PDF (2617KB)(253)       Save

    The purpose of research on Multi-Robot Task Allocation (MRTA) is to improve the task completion efficiency of robots in smart factories. Aiming at the deficiency of the existing algorithms in dealing with large-scale multi-constrained MRTA, an MRTA Algorithm Combining Genetic Algorithm and Rolling Scheduling (ACGARS) was proposed. Firstly, the coding method based on Directed Acyclic Graph (DAG) was adopted in genetic algorithm to efficiently deal with the priority constraints among tasks. Then, the prior knowledge was added to the initial population of genetic algorithm to improve the search efficiency of the algorithm. Finally, a rolling scheduling strategy based on task groups was designed to reduce the scale of the problem to be solved, thereby solving large-scale problems efficiently. Experimental results on large-scale problem instances show that compared with the schemes generated by Constructive Heuristic Algorithm (CHA), MinInterfere Algorithm (MIA), and Genetic Algorithm with Penalty Strategy (GAPS), the scheme generated by the proposed algorithm has the average order completion time shortened by 30.02%, 16.86% and 75.65% respectively when the number of task groups is 20, which verifies that the proposed algorithm can effectively shorten the average waiting time of orders and improve the efficiency of multi-robot task allocation.

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    Review of mean field theory for deep neural network
    Mengmei YAN, Dongping YANG
    Journal of Computer Applications    2024, 44 (2): 331-343.   DOI: 10.11772/j.issn.1001-9081.2023020166
    Abstract440)   HTML53)    PDF (1848KB)(320)       Save

    Mean Field Theory (MFT) provides profound insights to understand the operation mechanism of Deep Neural Network (DNN), which can theoretically guide the engineering design of deep learning. In recent years, more and more researchers have started to devote themselves into the theoretical study of DNN, and in particular, a series of works based on mean field theory have attracted a lot of attention. To this end, a review of researches related to mean field theory for deep neural networks was presented to introduce the latest theoretical findings in three basic aspects: initialization, training process, and generalization performance of deep neural networks. Specifically, the concepts, properties and applications of edge of chaos and dynamical isometry for initialization were introduced, the training properties of overparameter networks and their equivalence networks were analyzed, and the generalization performance of various network architectures were theoretically analyzed, reflecting that mean field theory is a very important basic theoretical approach to understand the mechanisms of deep neural networks. Finally, the main challenges and future research directions were summarized for the investigation of mean field theory in the initialization, training and generalization phases of DNN.

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    Review on advances in recognition and classification of cognitive impairment based on EEG signals
    Junpeng ZHANG, Yujie SHI, Rui JANG, Jingjing DONG, Changjian QIU
    Journal of Computer Applications    2023, 43 (10): 3297-3308.   DOI: 10.11772/j.issn.1001-9081.2022101471
    Abstract439)   HTML17)    PDF (1199KB)(439)       Save

    Early detection and timely intervention of cognitive impairment are crucial to slow down the progress of the disease. The ElectroEncephaloGraphy (EEG) signal has become an important tool for the investigation of biomarkers of cognitive diseases due to its high temporal resolution and easy acquisition. Compared with the traditional biomarker recognition method, the machine learning method has higher accuracy and better stability for the recognition and classification of cognitive impairment based on EEG signals. Aiming at the relevant research literature on the recognition and classification of cognitive impairment based on EEG signals in the past three years, firstly, from the perspectives of five categories of EEG features commonly used in the recognition and classification of cognitive impairment, including time domain, frequency domain, combination of time and frequency domains, nonlinear dynamics, functional connectivity and brain network, more representative EEG features were found. Then, the currently commonly used classification methods based on machine learning and deep learning, such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), as well as their performance were summarized. Finally, the current problems in different kinds of studies were analyzed, and the future research directions in this field were prospected, thereby providing reference for the follow-up research on the recognition and classification of cognitive impairment based on EEG signals.

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    Current research status and challenges of blockchain in supply chain applications
    Lina GE, Jingya XU, Zhe WANG, Guifen ZHANG, Liang YAN, Zheng HU
    Journal of Computer Applications    2023, 43 (11): 3315-3326.   DOI: 10.11772/j.issn.1001-9081.2022111758
    Abstract438)      PDF (2371KB)(530)       Save

    The supply chain faces many challenges in the development process, including how to ensure the authenticity and reliability of information as well as the security of the traceability system in the process of product traceability, the security of products in the process of logistics, and the trust management in the financing process of small and medium enterprises. With characteristics of decentralization, immutability and traceability, blockchain provides efficient solutions to supply chain management, but there are some technical challenges in the actual implementation process. To study the applications of blockchain technology in the supply chain, some typical applications were discussed and analyzed. Firstly, the concept of supply chain and the current challenges were briefly introduced. Secondly, problems faced by blockchain in three different supply chain fields of information flow, logistics flow and capital flow were described, and a comparative analysis of related solutions was given. Finally, the technical challenges faced by blockchain in the practical applications of supply chain were summarized, and future applications were prospected.

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    Citation recommendation algorithm fusing knowledge graph and graph attention network
    Haiwei FAN, Xinsiyu LU, Limiao ZHANG, Yisheng AN
    Journal of Computer Applications    2023, 43 (8): 2420-2425.   DOI: 10.11772/j.issn.1001-9081.2022071110
    Abstract434)   HTML35)    PDF (1853KB)(277)       Save

    Aiming at problems of data sparseness and cold start in traditional Collaborative Filtering (CF) and problem that meta-path and random walk algorithms do not fully utilize node information, a Citation Recommendation Algorithm Fusing Knowledge Graph and Graph Attention Network (C-KGAT) was proposed. Firstly, knowledge graph information was mapped into low-dimensional dense vectors by using TransR algorithm to obtain embedded feature representation of the nodes. Secondly, through multi-channel fusion mechanism, graph attention network was used to aggregate neighbor node information to enrich semantics of target nodes and capture high-order connectivity between nodes. Thirdly, without affecting depth or width of network, dynamic convolutional layer was introduced to aggregate information of neighbor nodes dynamically to improve expression ability of the model. Finally, the interaction probabilities of users and citations were calculated through the prediction layer. Experimental results on public datasets AAN (ACL Anthology Network) and DataBase systems and Logic Programming (DBLP) show that the proposed algorithm performs better than all comparison models. The MRR (Mean Reciprocal Rank) of the proposed algorithm is increased by 6.0 and 3.4 percentage points respectively compared with that of the suboptimal model NNSelect, and the P r e c i s i o n and R e c a l l indicators of the proposed algorithm also have different degrees of improvement, which verifies the effectiveness of the algorithm.

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2024 Vol.44 No.7

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