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Aiming at non-perfect eavesdropping channel state information (CSI), a joint-assisted multi-user secure transmission scheme using intelligent transmissive surfaces (ITS) and intelligent reflective surfaces (IRS) is proposed. Firstly, an optimization problem is formulated to maximize the weighted sum secure rate (WSSR) of the system. Secondly, a robust beamforming algorithm is developed to maximize the WSSR, which is used to alternately design the power allocation factors for multiple users, the ITS beamforming vector, and the IRS phase shift vector. Furthermore, to evaluate the security performance and energy efficiency of the system, a secure energy efficiency (SEE) optimization problem is constructed, and a robust beamforming algorithm is proposed to maximize the SEE. Finally, the convergence and computational complexity of the proposed algorithm are analyzed. Simulation results demonstrate that the proposed algorithms exhibit good security and robust performance.

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针对非完美窃听信道状态信息（CSI）的情况，提出一种智能透射面（ITS）与智能反射面（IRS）联合辅助的多用户安全传输方案。首先，构造最大化系统加权和安全速率（WSSR）优化问题；其次，设计一个WSSR最大化鲁棒波束赋形算法，用以交替设计多用户功率分配因子、ITS波束赋形向量和IRS相位偏移向量；再次，为了综合评估该系统的安全性能与能量效率，构造系统安全能量效率（SEE）优化问题，并设计一个SEE最大化鲁棒波束赋形算法；最后，分析所提算法的收敛性和计算复杂度。仿真结果表明，所提算法具有较好的安全性和鲁棒性。

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杨少川（1989—），男，博士生，主要研究方向为物理层安全。E-mail：

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郑州航空工业管理学院
移动互联网安全技术国家工程研究中心
国防科技大学
宁波诺丁汉大学
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杨少川（1989—），男，博士生，主要研究方向为物理层安全。E-mail：yangsc@zua.edu.cn

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杨少川（1989—），男，博士生，主要研究方向为物理层安全。E-mail：yangsc@zua.edu.cn

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To address the problems of high complexity and low accuracy associated with existing time-domain direction of arrival (DOA) estimation schemes, a novel approach based on virtual multi-channel in time-varying metasurface (TVM) is proposed. The harmonic properties of TVM are exploited in this method, with the incident signal being modulated by a periodic modulation function. This modulation maps the single-channel received signal to multiple harmonics, effectively equivalent to multi-channel reception by distributing the single-channel signal across multiple harmonic orders. Subsequently, an orthogonal pattern is designed to receive the signal, resulting in the acquisition of mutually independent virtual multi-channel signals. The autocorrelation matrix is then estimated using the Fourier coefficient matrix related to the modulation function, and the multi-signal classification (MUSIC) algorithm is employed for DOA estimation. Numeric simulations have demonstrated the validity of the virtual multi-channel scheme. The performance of this approach closely approximates that of DOA estimation based on multi-channel arrays.

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针对现有时间维度波达方向（DOA）估计方案复杂度高和精度低的问题，提出一种基于虚拟多通道的时间调制超表面（TVM）DOA估计方案。该方案首先利用TVM的谐波效应，设计周期调制函数对入射信号进行调制，将单通道接收信号映射到多阶谐波之上，以此等价多通道接收信号；其次，设计正交方向图对信号进行接收，获得相互独立的虚拟多通道信号；最后，获得通过与调制函数相关的傅里叶系数矩阵估计出自相关矩阵，利用多信号分类（MUSIC）算法进行DOA估计。仿真结果表明，设计的DOA估计方案可以较好地实现单通道的DOA估计，能够最大程度逼近基于多通道阵列的DOA算法估计性能。

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陈 明（1999—），男，硕士生，主要研究方向为超表面与无线通信。E-mail：

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陈 明（1999—），男，硕士生，主要研究方向为超表面与无线通信。E-mail：522938824@qq.com

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Affected by network equipment update and communication protocol upgrade, the distribution, category and attribute of network data have unpredictable drift characteristics, subsequently impairing the classification precision of machine learning-based network data classification models. To solve this problem, a class concept drift network data detection and classification algorithm based on prototype learning is proposed. The network data is addressed from the time series perspective, harnessing a network equipped with an attention mechanism to distill spatiotemporal features from the data. Drawing on the principles of prototype learning, the distances between samples and prototypes are utilized for classification purposes. In instances of class concept drift, a suitable threshold is established to identify novel classes, and the mean values are employed to refresh the prototype matrix. Experiment result shows that the utilization of prototype matching for classification not only yields higher accuracy than traditional softmax classifiers, but also can effectively detect the drift when the data has class concept drift, and has better classification performance on the drift data.

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受网络设备更新、通信协议升级等影响，网络数据的分布、类别和属性发生不可预知的漂移特性，导致基于机器学习的网络数据分类模型的分类精度下降。针对此问题，提出一种采用原型学习的类概念漂移网络数据检测与分类算法。该算法从时间序列的角度处理网络数据，利用带有注意力机制的网络提取数据的时空特征。借鉴原型学习思想，使用样本与原型之间的距离进行分类。当发生类概念漂移时，设定合适的阈值以区分新类，并使用其均值更新原型矩阵。实验结果表明，使用原型匹配分类不仅比传统的softmax分类器准确率高，且当数据发生类概念漂移时，所提算法能够有效检测漂移，并在漂移数据上能够表现出较好的分类性能。

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陈 坤（1993—），男，硕士生，主要研究方向为协议分析。E-mail：

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陈 坤（1993—），男，硕士生，主要研究方向为协议分析。E-mail：514239948@qq.com

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陈 坤（1993—），男，硕士生，主要研究方向为协议分析。E-mail：514239948@qq.com

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Synthetic aperture radar automatic target recognition (SAR ATR) technology is widely utilized in military target detection. However, the difficulty in obtaining labeled SAR samples restricts the utilization of existing recognition techniques. A SAR target recognition method, which combines a wasserstein GAN with gradient penalty (WGAN-GP) with a pre-trained model, is proposed. After augmentation of small training datasets using WGAN-GP, the data is then fed into a convolutional neural network (CNN) model pretrained on the large-scale remote sensing image scene classification (RESISC) dataset for training, ultimately yielding SAR target recognition results. The algorithm’s capabilities are evaluated using the moving and stationary target acquisition and recognition (MSTAR) dataset. Experimental results indicate WGAN-GP, when utilized, outperforms other generative adversarial networks in SAR sample enhancement. Furthermore, the selection of the RESISC45 dataset is found to effectively enhance the classifier’s pretraining ability. Compared to existing research findings, the approach exhibits advantages in improving SAR target recognition accuracy and CNN model convergence speed.

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合成孔径雷达自动目标识别（SAR ATR）技术广泛应用于目标监测等军事领域，但标记SAR样本难以获得限制了现有识别技术的使用。提出一种基于带梯度惩罚的生成对抗网络（WGAN-GP）和预训练模型相结合的SAR目标识别方法，利用WGAN-GP对小样本训练数据集进行扩充后，输入到大规模遥感图像场景（RESISC）分类数据集预训练后的卷积神经网络（CNN）模型中进行训练，最终得到SAR目标识别结果。利用运动和静止目标获取识别（MSTAR）数据集检测算法能力，实验结果表明，所提方法所使用的WGAN-GP对比其他生成对抗网络在SAR样本增强上具备性能优势，而RESISC45数据集的选取能有效提升分类器预训练的能力。与现有研究成果相比，所提方法在提高SAR目标识别精度和CNN模型收敛速度上具备优势。

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周明康（1994—），男，硕士生，主要研究方向为智能化态势认知。E-mail：

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周明康（1994—），男，硕士生，主要研究方向为智能化态势认知。E-mail：1012743815@qq.com

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周明康（1994—），男，硕士生，主要研究方向为智能化态势认知。E-mail：1012743815@qq.com

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樊争光（1997—），男，硕士生，主要研究方向为数字芯片验证。E-mail：

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樊争光（1997—），男，硕士生，主要研究方向为数字芯片验证。E-mail：13526843660@163.com

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樊争光（1997—），男，硕士生，主要研究方向为数字芯片验证。E-mail：13526843660@163.com

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Three dimensional (3D) reconstruction of unmanned aerial vehicle (UAV) aerial images is an important means to obtain high quality 3D models. In the existing UAV aerial photography task planning methods, generate simple the routes are generated simply and the aerial photography angle is fixed, which lead to insufficient coverage of the image, thus restricting 3D modeling quality. To obtain high quality 3D model, a shortest route planning method based on view reconstructability constraint is proposed. The candidate viewpoint set is firstly generated based on the prior model by using the methed. Then the viewpoint is optimized by using the downhill simplex method constrained by reconstructability. Finally the shortest route istransformed into the traveling salesman problem to be solved. The experimental results show that this route planning method can improve the coverage degree of the image to the scene and significantly improve the reconstruction effect of the target scene. The quality of 3D image reconstruction can be improved by designing the shortest route of UAV with the constraint of reconstructability.

","authors":null,"authorsList":"Zhenqi LI, Xuefeng CAO","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":null,"magXml":null,"pdfUrl":null,"pdf":null,"pdfFileSize":null,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":null,"abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":null,"mapNumber":null},"CN":{"id":"1160183243090878764","articleId":"1160183241505431826","tenantId":"1045748351789510663","journalId":"1155139928303341809","language":"CN","title":"基于视点可重建性约束的最短航线规划方法","columnId":"1210243555671781473","journalTitle":"信息工程大学学报","columnName":"计算机科学与技术","runingTitle":null,"highlight":null,"articleAbstract":"

无人机航拍图像三维重建是目前获取高质量三维模型的重要手段。现有无人机航拍任务规划方法生成的航线简单、航拍视角固定导致图像对景物覆盖程度不够高，制约三维模型建模质量。为了得到高质量三维模型，提出一种基于视点可重建性约束的最短航线规划方法。该方法首先基于先验模型生成候选视点集合，其次以可重建度为约束采用下山单纯形法优化视点，最后将最短航线问题转化为旅行商问题进行求解。实验结果表明，该航线规划方法能够提高图像对景物的覆盖程度，显著改善目标场景的重建效果。以可重建性为约束条件设计无人机最短航线，可以提高影像三维重建质量。

","authors":"

李振琦（1998—），男，硕士生，主要研究方向为面向图像三维重建的无人机路径规划。E-mail：

","authorsList":"李振琦, 曹雪峰","authorCompany":"信息工程大学
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李振琦（1998—），男，硕士生，主要研究方向为面向图像三维重建的无人机路径规划。E-mail：li13083833858@126.com

\"}","bioImg":null,"bioContent":"

李振琦（1998—），男，硕士生，主要研究方向为面向图像三维重建的无人机路径规划。E-mail：li13083833858@126.com

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Feature Dimensionality Augmentation Analysis Method for CNN","columnId":null,"journalTitle":"Journal of Information Engineering University","columnName":null,"runingTitle":null,"highlight":null,"articleAbstract":"

To address the difficulty in distinguishing feature importance due to the close coupling between convolutional neural networks models and input data, a feature dimensionality augmentation method to analyze the importance of input features from the output results of the network model is proposed. Firstly, a standard orthogonal basis is sequentially assigned to the sample features of the input network model in a high-dimensional Euclidean space, and the input sample features are represented with dimensionality augmentation. Secondly, the convolutional neural networks is computationally extended in high-dimensional Euclidean space, and the features represented by the dimensionality augmentation are computed. Finally, in the calculation results, the corresponding relationship between the standard orthogonal basis and the input sample features can be analyzed to determine the influence weights of each input sample feature in the output results. Experiment shows that the weights analyzed in this method can effectively reflect the influence of input features on convolutional neural networks.

","authors":null,"authorsList":"Yonghao PAN, Ranran ZHANG, Hongtao YU, Ruiyang HUANG, Kejun JIN","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":null,"magXml":null,"pdfUrl":null,"pdf":null,"pdfFileSize":null,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":null,"abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":null,"mapNumber":null},"CN":{"id":"1160183237965439225","articleId":"1160183236036059353","tenantId":"1045748351789510663","journalId":"1155139928303341809","language":"CN","title":"一种针对卷积神经网络的特征升维分析方法","columnId":"1210243555671781473","journalTitle":"信息工程大学学报","columnName":"计算机科学与技术","runingTitle":null,"highlight":null,"articleAbstract":"

针对卷积神经网络模型与输入数据紧密耦合的特性导致特征重要性难以区分的问题，提出一种从网络模型的输出结果中分析输入特征重要性的特征升维分析方法。首先，在高维欧氏空间中对输入网络模型的样本特征依次分配一个标准正交基，对输入样本特征进行升维表示；其次，在高维欧氏空间中对卷积神经网络进行计算扩展，并对升维表示的特征进行计算；最后，在计算结果中可由标准正交基与输入样本特征的对应关系，分析得出各个输入样本特征在输出结果中的影响权值。实验表明该方法分析得出的权重能够有效反映输入特征对卷积神经网络的影响力。

","authors":"

潘永昊（1992—），男，工程师，博士生，主要研究方向为人工智能安全。E-mail：

","authorsList":"潘永昊, 张苒苒, 于洪涛, 黄瑞阳, 金柯君","authorCompany":"信息工程大学
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潘永昊（1992—），男，工程师，博士生，主要研究方向为人工智能安全。E-mail：panyonghao2016@163.com

\"}","bioImg":null,"bioContent":"

潘永昊（1992—），男，工程师，博士生，主要研究方向为人工智能安全。E-mail：panyonghao2016@163.com

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随着量子比特规模的扩大，人工标定量子比特成本巨大且效率低下，因此量子测控的自动化工作势在必行。针对此问题，构建一个自动化量子测控的系统框架，包括测控实验、数据处理和决策判断等3个模块，以及测控实验库、数据处理函数库、测控经验库和测控数据库等4个数据库，并基于此完成单比特性质的自动化标定。对24个量子比特进行读取门和单比特门的标定，总共耗时2.7 h，平均每比特耗时6.75 min，效果上达到了平均单比特门保真度99.28%、读取保真度78.6%的水平。

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范智强（1998—），男，硕士生，主要研究方向为量子计算。E-mail：

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范智强（1998—），男，硕士生，主要研究方向为量子计算。E-mail：zhiqiang-fan@foxmail.com

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范智强（1998—），男，硕士生，主要研究方向为量子计算。E-mail：zhiqiang-fan@foxmail.com

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The traditional network defense technologies, which rely on prior knowledge, are thus limited in effectiveness against complex, covert, and unknown threats.An unknown threat detection method is proposed, based on endogenous security theory and the dynamic heterogeneous redundancy (DHR) architecture, to improve the accuracy of anomaly detection in traffic within power monitoring systems. In this method, the advantages of multiple classifiers are integrated and a dynamically heterogeneous and redundant learning framework is adopted. Ensemble learning, discriminative models, and other heterogeneous learning techniques are leveraged to enhance the robustness and accuracy of the model. The core technology involved consists of multi-classifier voting and feedback mechanisms, which are utilized to iteratively optimize and adjust sample distribution for continuous improvement in detection performance.The advantages of the method include reducing the generalization issue of single models, mitigating the risk of falling into local minima, and expanding the representation space to enhance adaptability. Experimental results showed that high accuracy in detecting unknown threats in power monitoring systems is achieved by using the proposed method.

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传统网络防御技术依赖先验知识，面对复杂且隐蔽的未知威胁时，防御能力有限。为应对这一挑战，提出一种基于内生安全理论和动态异构冗余（DHR）构造的未知威胁检测方法，旨在提升电力监控系统中的异常流量感知精度。该方法融合多种分类器优势，采用动态、异构、冗余的学习框架，通过集成学习、判别式模型等异构学习方法，增强模型的鲁棒性与精度。核心技术包括多分类器表决与反馈机制，通过迭代优化调整样本分布，持续提升检测性能。该方法有降低单一模型的泛化问题、减少陷入局部极小的风险以及扩展表示空间提升适应性等优点。实验结果表明，该方法在电力监控系统中检测未知威胁的高准确性。

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苏 杨（1982—），男，教授级高级工程师，硕士，主要研究方向为电力监控系统网络安全、智能电网通信。E-mail：

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紫金山实验室内生安全研究中心
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An action prediction and path optimization method based on individual soldier movement trajectory is proposed in this paper. First, the intelligent individual soldier multi-hop relay communication system is used as the front-end to obtain individual solider trajectory data, which is then retured to the command and control background in real time. Second, in the back-end, data storage, trajectory pre-processing, marking task points, clustering point analysis, trajectory segmentation are comprehensively used to realize trajectory data tube. Finally, similarity measurement algorithm is employed to deduce the optimal path. Based on the test data of a certain area, the verification results show that the trajectory map drawn is consistent with the actual action, and the optimized path has the characteristics of short distance and large signal coverage, which has practical application value.

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基于单兵移动轨迹进行行动预测和路径优化的方法被提出。以智能化单兵多跳中继通信系统为前端，获取单兵的轨迹数据并实时传回到指挥控制后台，在后端综合运用数据存储、轨迹预处理、标记任务点、聚类点分析、轨迹分段等实现轨迹数据管理，最后，采用相似性度量算法等开展了最优路径求解计算。结合地试验数据，经验证，表明绘制的轨迹图与实际行动相符，优化后的路径具有距离短、信号覆盖率大等特点，有实际应用价值。

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张乐飞（1980—），男，高级工程师，博士生，主要研究方向为军事通信学。E-mail：

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湖南国天电子科技有限公司
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鹤壁天海电子信息系统有限公司
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张乐飞（1980—），男，高级工程师，博士生，主要研究方向为军事通信学。E-mail：13810353281@139.com

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张乐飞（1980—），男，高级工程师，博士生，主要研究方向为军事通信学。E-mail：13810353281@139.com

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Evaluation Against Differential Cryptanalysis for A New CLEFIA-Like Cryptographic Structure","columnId":null,"journalTitle":"Journal of Information Engineering University","columnName":null,"runingTitle":null,"highlight":null,"articleAbstract":"

A new CLEFIA-like cryptographic structure is proposed, and its security evaluation results, particularly its resistance to differential analysis, are presented. It is proven that when all round functions are bijective, the u-round active index (the number of active round functions) is u for u = 6k (k ≥ 1) or 6k + 1 (k ≥ 3) while the u-round active index for the CLEFIA-like cryptographic structure is (u-1) in other cases. Furthermore, the lower bound of the active index established in this study is confirmed to be reachable.

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提出一种新的类CLEFIA密码结构，并给出这种结构抵抗差分分析的安全性评估结果。在轮函数都是双射的条件下，当迭代轮数u=6k(k≥1)或6k+1(k≥3)时，证明u轮活动指标（活动轮函数个数）至少为u；当迭代轮数u为其他值时，证明u轮活动指标至少为（u-1）。此外，给出的活动指标的下界是可达的。

","authors":"

王念平（1973—），男，教授，博士，主要研究方向为信息安全与密码学。E-mail：

","authorsList":"王念平, 倪亮","authorCompany":"信息工程大学
中原工学院计算机学院
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王念平（1973—），男，教授，博士，主要研究方向为信息安全与密码学。E-mail：wwnnpp@126.com

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王念平（1973—），男，教授，博士，主要研究方向为信息安全与密码学。E-mail：wwnnpp@126.com

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Feature extraction for transaction data relies on manual design, which fails to adequately capture the evolving behavioral intent of contracts. Bytecode feature extraction, which focuses on the differing frequency distributions of opcodes between Ponzi and non-Ponzi smart contracts, overlooks the structural features of contract code. In the proposed model, features of code structure and temporal behavioral features are extracted to enhance the detection performance of Ponzi contracts. Specifically, in the module for code structure feature representation, the contract control flow graph is obtained by parsing the contract bytecode, and the features of the code structure are derived using graph embedding techniques. In the module for time-behavior feature representation, a sequence of subgraphs is constructed based on contract transactions, with each subgraph being embedded and represented, and then processed using by the long and short-term memory network to obtain the time-behavior features. Finally, the code structure and time-behavioral features are combined to identify Ethereum Ponzi contracts.

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交易数据特征提取依赖人工设计，不能较好地在时间演化中刻画合约的行为意图；字节码特征提取依靠庞氏和非庞氏智能合约操作码频率分布不同，忽略了合约代码在结构上的特征。提出一个能提取代码结构特征和时间行为特征的模型来提高庞氏合约检测性能。首先，在代码结构特征表示模块中，通过解析合约字节码得到合约控制流图，利用图嵌入技术得到代码结构特征；其次，在时间行为特征表示模块中，根据合约交易构造子图序列，并对每个子图进行嵌入表示，再经过长短时记忆网络处理得到时间行为特征；最后，结合代码结构和时间行为特征来识别以太坊庞氏合约。

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秦 鉴（1997—），男，硕士生，主要研究方向为区块链交易异常检测。E-mail：

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秦 鉴（1997—），男，硕士生，主要研究方向为区块链交易异常检测。E-mail：qinjian3141@163.com

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秦 鉴（1997—），男，硕士生，主要研究方向为区块链交易异常检测。E-mail：qinjian3141@163.com

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When nodes in the unmanned aerial vehicle (UAV) ad hoc network enter or leave the network, the routing needs to be recalculated immediately. If the location information cannot be obtained sufficiently during the routing establishment process, it is easy to lead to mistakes in routing decisions. To solve the above problems, a netgrid model-based dynamic routing (NDR) for UAV swarm is proposed. Firstly, the flight area 1 000 m×1 000 m×1 000 m above the ground is divided into netgrids. The UAV node adopts the routing expression form of the netgrid ID. The routing table is dynamically updated to make routing decisions during the flight. Secondly, the netgrid-based probabilistic routing method is demonstrated in detail. The netgrid to be forwarded is selected according to the NDR strategy, and all nodes in the netgrid have a certain probability to be selected as relays, instead of selecting the gateway in advance, which can effectively reduce routing holes, and avoid local optimization and packet delivery failures. Simulation experiments demonstrate that less energy is consumed by using the proposed method of UAV ad hoc network nodes, the network delay and signaling overhead are significantly reduced compared with those of other routing protocols, and the success rate of packet delivery is also increased by nearly 100%.

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无人机自组织网络节点进入或离开网络需要立即重新计算路由，如果在路由建立过程中不能充分获得位置信息，容易导致路由决策失误。针对此问题，提出一种栅格化无人机集群动态路由（NDR）方法。首先，将地面以上1 000 m×1 000 m×1 000 m飞行区域进行栅格划分，无人机节点采用基于栅格ID的路由表达形式，在飞行过程中动态更新路由表进行路由决策。其次，论证基于栅格的概率路由方法，节点根据NDR策略选择待转发栅格，栅格中所有节点均有一定概率会被选做中继节点，代替事先选取网关的方法，有效减少路由空洞的发生概率，避免局部优化和数据包交付失败等问题。仿真实验证明，所提方法的无人机自组织网络节点能量消耗较慢，延迟与信令开销相比其他路由协议有明显降低，包投递成功率也提升近一倍。

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张敬棋（1993—），女，工程师，硕士，主要研究方向为自组织网络。E-mail：

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张敬棋（1993—），女，工程师，硕士，主要研究方向为自组织网络。E-mail：654432770@qq.com

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张敬棋（1993—），女，工程师，硕士，主要研究方向为自组织网络。E-mail：654432770@qq.com

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A security protocol logic vulnerability detection method based on black-box fuzz testing is proposed to address potential logical errors in the implementations of security protocols. In this method,abnormal protocol interaction behavior is modeled as a deterministic finite automaton (DFA). Abstract execution sequences are extracted from fuzz testing interaction data, and their acceptance by the DFA is determined for detecting target logical errors and abnormal behavior under black-box conditions. The generation of testing sequences is driven by DFA state information, guiding fuzz testing to explore deeper protocol state spaces and enhancing the probability of discovering errors and abnormalities. Based on this approach, a general black-box protocol fuzz testing framework, named BDFuzz, and a specific testing module for the IPsec protocol are implemented. Experimental results, conducted on multiple widely-used implementations of the IPsec protocol, reveal various abnormal behaviors violating protocol specifications and vulnerabilities capable of causing the target program to crash.

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针对安全协议实现中可能存在的逻辑错误，提出一种基于黑盒模糊测试的安全协议逻辑漏洞检测方法。将异常的协议交互行为建模为确定有限自动机（DFA），从模糊测试交互数据中提取抽象执行序列并判断其能否被DFA接受，以实现在黑盒条件下检测目标逻辑错误和异常行为的功能；基于DFA状态信息驱动测试序列生成，引导模糊测试探索深层协议状态空间，以提高模糊测试发现错误和异常的概率。基于该方法实现通用的黑盒协议模糊测试框架BDFuzz以及特定于IPsec协议的测试模块，在多个广泛应用的IPsec协议实现上进行实验，发现多个违反协议规范的异常行为和能够导致目标程序崩溃的漏洞。

","authors":"

赵栋梁（2000—），男，硕士生，主要研究方向为安全协议脆弱性分析。E-mail：

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赵栋梁（2000—），男，硕士生，主要研究方向为安全协议脆弱性分析。E-mail：xiao__xiang@163.com

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赵栋梁（2000—），男，硕士生，主要研究方向为安全协议脆弱性分析。E-mail：xiao__xiang@163.com

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The system emulation environment for embedded firmware serves as the foundation for dynamic analysis of embedded devices. However, there are issues like high labor costs and low fidelity in peripheral model code generation within current system emulation environments. Therefore, an embedded firmware system emulation technology based on retrieval-augmented generation is proposed in this study. Through preprocessing of the data manual, the memory map table and text descriptions of peripheral register fields are extracted. Subsequently, a large language model is employed to generate summaries from raw data and construct a vector knowledge base using embedded texts. Utilizing a multi-vector retriever, the contexts related to the target peripheral are identified and peripheral model codes are automatically generated to establish the desired firmware emulation environment. Experimental results demonstrate the memory maps, peripheral register names, and descriptions of peripheral behavior from the data manual can be successfully extraced and generate. Using the method the emulation environment effectively guides. And the system initialization can be effutively and peripheral accesses in the firmware with accuracy by the emulation euvirenment. Compared to firmware rehosting based on symbolic execution, the generated emulation environment enables more state-checking code execution with higher code coverage.

","authors":null,"authorsList":"Haonan SUN, Jing JING, Xin LYU","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":null,"magXml":null,"pdfUrl":null,"pdf":null,"pdfFileSize":null,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":null,"abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":null,"mapNumber":null},"CN":{"id":"1160183244441444665","articleId":"1160183242218463515","tenantId":"1045748351789510663","journalId":"1155139928303341809","language":"CN","title":"检索增强生成在嵌入式固件系统仿真中的应用方法","columnId":"1210243469386559580","journalTitle":"信息工程大学学报","columnName":"网络空间安全","runingTitle":null,"highlight":null,"articleAbstract":"

嵌入式固件的系统仿真环境构建是面向嵌入式设备固件动态分析的基础，针对当前固件系统仿真环境构建中外设模型代码生成人工成本高且保真度低等问题，提出一种基于检索增强生成的嵌入式固件系统仿真技术。首先，通过数据手册预处理提取仿真环境构建所需的内存映射表和外设寄存器字段文本描述等信息；其次，利用大语言模型的归纳总结能力生成文本描述概要，并将生成的概要文本嵌入后构建向量知识库；最后，通过多向量检索器查找知识库中与目标外设相关的上下文并自动生成外设模型代码，实现目标固件的仿真环境构建。实验结果表明，该方法能成功提取并生成内存映射表、外设寄存器名和外设行为等描述，并能够正确引导系统初始化、响应固件中的外设访问等。与基于符号执行的固件托管技术相比，生成的仿真环境能使固件通过更多的状态检查代码，具有更高的代码覆盖率。

","authors":"

孙浩楠（1999—），男，硕士生，主要研究方向为嵌入式设备安全、固件仿真。E-mail：

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孙浩楠（1999—），男，硕士生，主要研究方向为嵌入式设备安全、固件仿真。E-mail：sunhnsun@foxmail.com

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孙浩楠（1999—），男，硕士生，主要研究方向为嵌入式设备安全、固件仿真。E-mail：sunhnsun@foxmail.com

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孙亚洲（1995—），男，助理工程师，硕士，主要研究方向为网络安全、信息处理。E-mail：

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孙亚洲（1995—），男，助理工程师，硕士，主要研究方向为网络安全、信息处理。E-mail：sxfyycsyz@163.com

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孙亚洲（1995—），男，助理工程师，硕士，主要研究方向为网络安全、信息处理。E-mail：sxfyycsyz@163.com

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To reduce the dependence on the prior information, improve the signal detection ability under the condition of low signal-to-noise ratio, and effectively describe the time-frequency relationship of the signal, the strong immunity of Chua’s circuit to noise and its sensitivity to the initial value of the signal are utilized to construct Chua’s circuit group, and to design the corresponding signal detection method. Drawing on the idea of calculus, the complex modulated signal to be detected is divided into multi-segment short-time linear signals, and Chua’s circuit group with five synchronous circuits is constructed. The efficient detection of the signal and the accurate measurement of the frequency are realized. Through simulation experiments, the effectiveness of the proposed algorithm is verified, and the simulation circuit with engineering practical significance is given.

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为降低对先验信息的依赖性，提升在低信噪比条件下的信号检测能力，同时有效刻画出信号的时频关系，利用蔡氏电路对噪声的强免疫性以及对信号初值的敏感性，构建蔡氏电路组并设计对应的信号检测方法。借鉴微积分思想，将复杂调制的待检测信号分割为多段短时线性信号，构建具有5个同步电路的蔡氏电路组，实现信号的高效检测和频率的精确测量。通过仿真实验，验证所提算法的有效性，并给出具有工程实践意义的仿真电路。

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刘 斌（1991—），男，讲师，博士，主要研究方向为信号处理和电磁频谱作战。E-mail：

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刘 斌（1991—），男，讲师，博士，主要研究方向为信号处理和电磁频谱作战。E-mail：277306879@qq.com

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刘 斌（1991—），男，讲师，博士，主要研究方向为信号处理和电磁频谱作战。E-mail：277306879@qq.com

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The inductive link prediction model of knowledge graphs requires both positive and negative triplets during the training process. However, the current random negative sampling method tends to produce low-quality negative triplets, which affects the feature learning ability of the model. To address this problem, a similarity-based negative sampling method is proposed. Firstly, the set of k-hop neighbor nodes around the replaced entity is obtained. Secondly, the entities with high similarity are selected from the set to replace the head or tail entities in the original triplet, so as to generate a negative triplet. Finally, the negative sampling method is used in the inductive link prediction model, and the inductive link prediction experiments are carried out on the WN18RR and FB15K-237 datasets. Experimental results demonstrate that compared with other models, the MRR metric is increased by up to 10.47 percentage point, and the Hits@10 metric is increased by up to 16.02 percentage point. Furthermore, the negative sample quality analysis illustrates that high-quality negative triplets are generated by using the negative sampling method, which improves the performance of the model.

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知识图谱归纳链接预测模型在训练过程中需要使用正例三元组和负例三元组，而当前的随机负采样方法容易产生低质量的负例三元组，影响模型的特征学习能力。针对该问题，提出一种基于相似性的负采样方法。首先，获取被替换实体周围的k跳邻居节点集合；其次，从该集合中挑选相似度高的实体替换原三元组中头实体或者尾实体，从而生成负例三元组；最后，将该方法应用在归纳链接预测模型中，并在WN18RR和FB15K-237数据集上进行归纳链接预测实验。实验结果表明，相比其他模型，该方法在MRR指标最高提升10.47个百分点，在Hits@10指标最高提升16.02个百分点。通过负样本质量分析，进一步说明该负采样方法生成高质量的负例三元组，提升模型的性能。

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刘洪波（1981—），副教授，博士生，主要研究方向为大数据分析、知识图谱表示。E-mail：

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刘洪波（1981—），副教授，博士生，主要研究方向为大数据分析、知识图谱表示。E-mail：lhb921@163.com

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刘洪波（1981—），副教授，博士生，主要研究方向为大数据分析、知识图谱表示。E-mail：lhb921@163.com

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To provide data collection and communication relay services for the construction of kill webs in future battlefields without preset communication facilities, a multi-unmanned-nodes edge service coverage method based on resource distribution is studied. Firstly, the problem of providing service coverage for resources via unmanned nodes is analyzed, and a corresponding model is established. Secondly, the situation of unmanned nodes required under different resource distributions is analyzed, and an approximate coverage algorithm considering resource distribution is designed, with foa on isolated points identification maximum, clique searching, unmanned nodes deployment location determination, and soon. Finally, the algorithm is tested and validated by asing test cases. The results obtained indicate that, in cases of highly dispersed resource distribution, the optimal results can be achieved by asing this proposed method. When resource intensity is high, the results are comparable to those of regional coverage. In comparison, the average number of unmanned nodes required in the same situations reduce by 14% to 39%, proving the feasibility and effectiveness of the method.

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赵锦明（1991—），男，助理研究员，博士生，主要研究方向为军事运筹、指挥控制。E-mail：

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赵锦明（1991—），男，助理研究员，博士生，主要研究方向为军事运筹、指挥控制。E-mail：634082615@qq.com

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Aiming at the problems of complex battlefield environment and excessive reliance on subjective experience in traditional evaluation methods in modern war, an improved YOLOv8 algorithm for building damage effect evaluation is proposed. Firstly, the Mosaic-9 is used for image preprocessing at the sample input to improve the generalization ability of the network model. Secondly, the efficient multi-scale attention (EMA) mechanism is introduced into the backbone network, and the newly constructed fast cross stage partial network fusion (FC2f) is used to improve the feature extraction ability and operation efficiency of the network model. Finally, the Scylla intersection over union loss (SIoU Loss) function is used to optimize the network loss function to further improve the detection and evaluation accuracy and operation efficiency of the network model. Experimental results show that this algorithm has high detection and evaluation accuracy, fast operational speed, and certain military application value.

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针对现代战争中战场环境复杂和传统评估方法过于依赖主观经验的问题，提出一种改进YOLOv8的建筑物毁伤效果评估算法。首先，在样本输入端采用Mosaic-9进行图像预处理，提升网络模型的泛化能力。其次，在骨干网络中引入高效多尺度注意力机制（EMA），同时利用新构造的快速跨阶段局部网络融合模块（FC2f）模块，提升网络模型的特征提取能力和运行效率。最后，采用Scylla交并比损失函数（SIoU Loss）对网络损失函数进行优化，进一步提升网络模型的检测评估精度和运行效率。实验结果表明，该算法检测评估精度较高，运算速度较快，具有一定的军事应用价值。

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An improved pied kingfisher optimizer (IPKO) with multiple strategies is proposed to address the issues of reduced population diversity, slower convergence speed, and the tendency to get stuck in local optima during the search process of the basic pied kingfisher optimizer (PKO). Firstly, the Latin hypercube sampling method is employed to initialize the population, thereby enhancing its diversity. Secondly, a nonlinear inertia weight is introduced in both the global search and local development stages to expedite the algorithm’s convergence speed. Then, by adopting the post-consolidation strategy from the artemisinin optimization algorithm, the algorithm’s capability to avoid local optima is enhanced. An optimization performance analysis is conducted on the IPKO algorithm using 12 benchmark test functions, and it is compared with particle swarm optimization (PSO), sine cosine algorithm (SCA), sparrow search algorithm (SSA), grey wolf optimizer (GWO), and sooty tern optimization algorithm (STOA), and PKO. The experimental results indicate that the IPKO algorithm exhibits superior convergence accuracy and speed compared to the other algorithms. Additionally, the IPKO algorithm is applied to the optimization problem of three-bar truss design, and the results demonstrate that its accuracy in solving practical engineering problems is better than that of PKO, thereby verifying the effectiveness and robustness of the algorithm.

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针对基本花斑翠鸟优化算法（PKO）搜索过程中存在种群多样性降低、收敛速度变慢、陷入局部最优解等问题，提出多策略改进花斑翠鸟优化算法（IPKO）。首先，采用拉丁超立方抽样方法对种群个体进行初始化，丰富其多样性。其次，在全局搜索和局部开发阶段引入非线性惯性权重，加快算法在搜索空间的收敛速度。然后，借鉴青蒿素优化算法的后巩固策略，提高算法跳出局部最优解的能力。将IPKO算法对12个基准测试函数进行寻优性能分析，而后与粒子群算法（PSO）、正余弦优化算法（SCA）、麻雀搜索算法（SSA）、灰狼优化算法（GWO）、乌燕鸥优化算法（STOA）和PKO进行比较。实验结果表明该算法相较于其他算法寻优精度和收敛速度更为优秀。此外，将该算法应用于三杆桁架设计优化问题，结果表明该算法在解决实际工程问题时寻优效果优于PKO，验证了该算法的有效性和鲁棒性。

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：panboyang@126.com

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：panboyang@126.com

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A classroom attention measurement scheme is proposed to address false and missed detections in head pose estimation within seminar or large-depth lecture classrooms. Firstly, a cascaded neural network is employed to detect heads in classroom videos in order to accurately locate and count student heads. Secondly, a global face detector and head pose classifier are developed to precisely determine gaze and non-gaze states by combining face detection and pose classification. Subsequently by introducing active parameters are introduced, and a head position matching algorithm is used to calculate head pose changes between frames. A new formula for head-up rate is derived based on gaze state changes. Finally, an attention model is analyzed to obtain an overall numerical value, the attention K-value, which combines head-up rate and active parameters. Experimental results indicate that the accuracy of the head detedor exceeds 95.6% in multi-scale classrooms, and the precision of the head classifier reaches 94.6%. Verification in real classrooms shows that the scheme accurately reflects student attention, providing valuable insights for teaching reflection and evaluation.

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针对研讨型或纵深较大的讲授型教室中头部姿态估计方法存在的误检、漏检问题，提出一种课堂注意力测量方案。首先，采用级联神经网络结构对课堂视频进行头部检测，精确获取学生头部的位置和数量。其次，构建全局人脸检测器和头部姿态分类器，结合人脸检测与姿态分类，准确判定凝视与非凝视状态。接着，引入活跃参数，通过前后帧头部位置匹配算法计算头部姿态变化次数，并结合凝视状态改变次数表征抬头率，得到新的抬头率计算公式。最后，结合抬头率和活跃参数，通过注意力模型分析得出总体表征课堂注意力的数值——注意力K值。实验结果显示，头部检测器在多尺度课堂中的准确率超95.6%，头部姿态分类器分类精确率达94.6%。实际课堂验证表明，该方案能真实反映学生注意力情况，为教学反思和教学能力评价提供重要参考。

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王慧敏（1988—），男，讲师，硕士，主要研究方向为武器运用工程。E-mail：

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王慧敏（1988—），男，讲师，硕士，主要研究方向为武器运用工程。E-mail：h_m_wang@126.com

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王慧敏（1988—），男，讲师，硕士，主要研究方向为武器运用工程。E-mail：h_m_wang@126.com

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针对现有对抗性人脸检测方法在不同数据集间迁移性不足的问题，提出一种基于图像变分辨率敏感度不一致性的对抗性人脸检测方法。通过分析超分辨率、去噪等图像变换过程中的敏感度变化，利用余弦相似度量化变换前后人脸图像特征的差异，检测对抗性人脸攻击。在人脸数据库（LFW）、CelebA-HQ等数据集上的实验证明，该方法在6种对抗攻击条件下均达到90%以上的检测准确率。

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刘宏基（1990—），男，硕士生，主要研究方向为图像识别。E-mail：

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刘宏基（1990—），男，硕士生，主要研究方向为图像识别。E-mail：307008503@qq.com

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刘宏基（1990—），男，硕士生，主要研究方向为图像识别。E-mail：307008503@qq.com

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Instruction candidate set generation is an essential component of extended instruction set processor design, but it is also recognized as a computationally intensive task. To improve the efficiency of candidate set generation, a bidirectional search algorithm with post-fusion is proposed. First, an efficient search tree construction algorithm for connected subgraphs is developed based on the adjacency list of the data flow graph. Second, during the traversal of the search tree, a bidirectional parallel search approach is systematically implemented to enhance search efficiency. Additionally, a multi-constraint pruning optimization technique is applied to subgraphs composed of different tree nodes to further accelerate the process. Experimental results demonstrate that the proposed algorithm not only adapts to multiple constraints but also has one to two the performance of existing algorithms.

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指令候选集生成是扩展指令集处理器设计中必不可缺的一部分，但该过程也是一种计算密集型任务。为提升候选集生成效率，提出一种双向搜索后融合的算法。首先，基于数据流图的邻接表提出一种高效的连通子图搜索树建立算法；其次，在搜索树遍历过程中整体采用双向并行搜索的思路来提升搜索效率，针对由不同树节点构成的子图，应用多约束裁剪优化技术来提升搜索速度。实验结果表明，所提算法能够适应多种约束条件，且性能为已有算法的1~2倍。

","authors":"

范旺（1998—），男，硕士生，主要研究方向为拟态计算。E-mail：

","authorsList":"范旺, 刘勤让, 赵博, 高彦钊, 祁晓峰","authorCompany":"信息工程大学
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范旺（1998—），男，硕士生，主要研究方向为拟态计算。E-mail：isfanw@163.com

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范旺（1998—），男，硕士生，主要研究方向为拟态计算。E-mail：isfanw@163.com

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of Multi-layer Network Knowledge Graph for Port Emergency Events","columnId":null,"journalTitle":"Journal of Information Engineering University","columnName":null,"runingTitle":null,"highlight":null,"articleAbstract":"

To address the limitations of hierarchical structure oversight and interconnected element neglect in conventional research, a multi-layer network-based methodology for constructing port emergency knowledge graphs is proposed. Event and port ontologies are systematically constructed through conceptual framework development and entity element mapping, with the 5W1H analytical method and web ontology language (OWL) being applied. Based on the ontologies, inter-entity relationships are modeled using a kind of multi-layer network architecture. The Tianjin port explosion incident is examined as a case study, with the SPARQL protocol and RDF query language (SPARQL) being implemented for experimental query analysis. It is demonstrated that this approach enables efficient relationship-driven data retrieval while mitigating circular result generation in multi-hop queries, and achieve enhanced query accuracy. The method will help to gain to a deeper understanding of cross-domain impacts of port emergencies on multidimensional port elements.

","authors":null,"authorsList":"Xiuteng YIN, Xiang LI, Guohui LIU, Pengfei LU, Weidong ZHANG","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":null,"magXml":null,"pdfUrl":null,"pdf":null,"pdfFileSize":null,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":null,"abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":null,"mapNumber":null},"CN":{"id":"1160183238552641793","articleId":"1160183236791034080","tenantId":"1045748351789510663","journalId":"1155139928303341809","language":"CN","title":"面向港口突发事件的多层网络知识图谱构建","columnId":"1210243555671781473","journalTitle":"信息工程大学学报","columnName":"计算机科学与技术","runingTitle":null,"highlight":null,"articleAbstract":"

为克服传统研究中忽视相关要素层次性和关联性的问题，提出一种基于多层网络的港口突发事件知识图谱构建方法。该方法通过概念框架构建和实体要素映射，使用5W1H分析法和网络本体语言（OWL）构建事件本体和港口本体，并基于一种多层网络模型对实体间关系进行建模。以天津港爆炸事故为例，应用协议和资源描述框架查询语言（SPARQL）进行查询实验分析。结果表明，该方法能够实现高效的基于关系的数据查询，减少多跳查询中的循环结果问题，提升查询结果的准确性，有助于深入理解港口突发事件对港口多维度要素的跨领域影响。

","authors":"

尹秀腾（1996—），男，硕士生，主要研究方向为多层网络建模理论与方法。E-mail：

","authorsList":"尹秀腾, 李响, 刘国辉, 芦鹏飞, 张卫东","authorCompany":"信息工程大学
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To address the issue that traditional zero-shot stance detection (ZSSD) methods rely on a large amount of labeled data for model fine-tuning, a method based on in-context learning and semantic retrieval enhancement is proposed. The stance reasoning ability of large language models (LLMs) for unseen targets is improved without the need to tune model parameters. By retrieving semantically similar examples from existing labeled data and following the in-context learning paradigm of LLMs, relevant examples and test texts are formatted into input prompts with task descriptions, which enables models to perform stance classification in a more informative context. It is demonstrated through experiments that the ZSSD performance of the Flan-T5 model is improved by using this method, and the original model is significantly outperformed on the fine-grained task of the SEM16 dataset. The results indicate that the LLMs, understanding of the task can be deepened through this method, and relevant knowledge in examples is referenced to encourage LLM to gain better comprehension of test texts, thereby enabling more accurate inference of stances toward unseen targets.

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针对传统零样本立场检测方法需要依赖大量标注数据对模型微调的问题，提出一种基于上下文学习与语义检索增强的方法，以在不修改模型参数的条件下，提高大模型对未见对象的立场推理能力。通过语义相似度检索，从已有标注数据中筛选与待测文本相关的样例，并基于大模型的上下文学习范式，使用任务描述把相关样例和待测文本格式化为输入提示，以驱动大模型在更丰富的上下文语境下进行立场分类。实验结果表明，该方法在原始基础上提升了Flan-T5模型的零样本立场检测性能，并在SEM16数据集的细粒度零样本立场检测中显著优于原始模型。通过应用该方法，可以深化大语言模型对任务的了解，并激励模型参考相关样例中的关联知识以理解待测文本，从而更准确地推理关于未见对象的立场。

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卢银鹏（1997—），男，硕士生，主要研究方向为自然语言处理和数据挖掘。E-mail：

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卢银鹏（1997—），男，硕士生，主要研究方向为自然语言处理和数据挖掘。E-mail：sevenzard@163.com

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卢银鹏（1997—），男，硕士生，主要研究方向为自然语言处理和数据挖掘。E-mail：sevenzard@163.com

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Existing research methods for analyzing the importance of node features in graph neural networks are primarily based on structural features, making the analysis of each node feature’s importance difficult. To address this issue, a node feature dimension augmentation analysis method for graph neural networks is proposed. Firstly, node features are represented in a high-dimensional space, and a data structure adapted for the sparse characteristics of high-dimensional node feature data is constructed. Then, the computational rules of graph neural networks are extended and defined in high-dimensional space, with high-dimensional node features being calculated accordingly. Finally, the calculation results in high-dimensional space are analyzed, and the weights of each node feature in the graph neural network calculation results are obtained. In experiments, it is found that node features with large weights have the greatest impact on model accuracy, accounting for 62.88%, which verifies that the weights can effectively reflect the importance of input node features.

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现有针对图神经网络节点特征重要性的研究方法主要基于图结构特征，难以针对每个节点特征的重要性进行分析。为解决这一问题，提出一种面向图神经网络的节点特征升维分析方法。首先，在高维空间中对节点特征进行升维表示，并针对高维节点特征数据稀疏的特点构建适配数据结构；然后，扩展定义高维空间图神经网络计算规则并对高维节点特征进行计算；最后，对高维空间中的计算结果分析得出各个节点特征在图神经网络计算结果中的权重。实验中，大权重节点特征对模型准确率影响最大为62.88%，验证了该权重能够有效反应输入节点特征的重要性。

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潘永昊（1992—），男，工程师，博士生，主要研究方向为人工智能安全。E-mail：

","authorsList":"潘永昊, 张苒苒, 于洪涛, 黄瑞阳","authorCompany":"信息工程大学
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潘永昊（1992—），男，工程师，博士生，主要研究方向为人工智能安全。E-mail：panyonghao2016@163.com

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潘永昊（1992—），男，工程师，博士生，主要研究方向为人工智能安全。E-mail：panyonghao2016@163.com

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An online adversarial distillation method, based on multi-layer knowledge mutual learning, is proposed to address the issues of limited improvement in robust accuracy, which are caused by the declining reliability of static teacher knowledge and the underutilization of knowledge in existing adversarial distillation methods. A parameter fusion model (PFM) is constructed by weightedly fusing the model parameters of a group of students, with the adversarial loss of PFM being minimized to assist students in finding flatter loss minima. To ensure mutual learning of reliable soft label knowledge at the output layer, students are encouraged to mimic the average clean predictions of all models, in terms of inter-class ranking relationships within the predictive distribution of adversarial examples. Subsequently, to utilize the richer structured knowledge of the middle layer for supplementing the limited information in soft labels, the similarity of sample relationships at the feature channel level among students is maximized. Experimental results demonstrate that, compared to mutual adversarial training (MAT) ResNet-18 network trained by the method exhibits an improvement in test accuracy against adversarial examples generated by projected gradient descent (PGD) by 2.05 and 2.19 percentage point, respectively.

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针对对抗蒸馏中静态教师知识的可靠性逐渐下降和知识单一导致的鲁棒精度提升有限的问题，提出一种基于多层知识互学习的在线对抗蒸馏方法。首先加权融合一组对抗训练中的学生的模型参数构建参数融合模型（PFM），最小化PFM的对抗损失以帮助学生寻找更平坦的损失最小值。其次，鼓励学生在对抗样本的预测分布上的类间排名关系模仿所有模型的平均原始预测，以确保输出层可靠的软标签知识的互学习。最后，最大化学生之间基于特征通道级别的样本间关系的相似性，以利用中间层更丰富的特征结构化知识补充单一的软标签知识中的有限信息。实验结果表明，相较于互对抗训练（MAT），所提方法训练的ResNet-18网络在防御由投影梯度下降法（PGD）生成的对抗样本上的测试准确率分别提升2.05和2.19个百分点。

","authors":"

李舒意（1994—），女，博士生，主要研究方向为人工智能安全。E-mail：

","authorsList":"李舒意, 扈红超, 杨晓晗, 程国振, 郭威","authorCompany":"信息工程大学
网络空间安全教育部重点实验室
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李舒意（1994—），女，博士生，主要研究方向为人工智能安全。E-mail：hhc19820523@163.com

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With the rapid development of internet technology, cybersecurity information has been surging dramatically, and cybercrimes have been occurring frequently, thereby posing significant threats to personal privacy and enterprise security. To effectively analyze and utilize cybersecurity data, event extraction technology has emerged. Various methods currently employed for event extraction in cybersecurity have been systematically summarized and compared in this review, with an in-depth analysis provided on the data sources these methods rely on, the detection techniques adopted, and their respective strengths and limitations. Relevant concepts of event extraction have been defined first, and commonly used datasets such as CASIE and CySecED have been introduced. Subsequently, event detection methods based on triggered and triggerless approaches, as well as sentence-level and document-level event argument extraction methods, have been explored in detail. Finally, the application prospects of large models in cybersecurity event extraction have been looked ahead to, the challenges faced by current research have been highlighted, and future directions have been outlined, offering a new perspective for event extraction research in the cybersecurity field.

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随着互联网技术的高速发展，网络安全信息急剧增长，网络犯罪频发，对个人隐私和企业安全构成巨大威胁。为有效分析和利用网络安全数据，事件抽取技术应运而生。系统地总结和比较了当前用于网络安全事件抽取的多种方法，深入分析这些方法所依赖的数据来源、采用的检测技术及其各自的优势与局限。首先界定事件抽取的相关定义，并介绍CASIE和CySecED等常用数据集。接着，详细探讨基于有触发词和无触发词的事件检测方法，以及句子级和篇章级的事件论元抽取方法。最后，展望大模型在网络安全事件抽取中的应用前景，指出当前研究面临的挑战和未来发展方向，为网络安全领域的事件抽取研究提供新视角。

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汤萌萌（1989—），女，博士生，主要研究方向为网络安全领域的自然语言处理。E-mail：

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汤萌萌（1989—），女，博士生，主要研究方向为网络安全领域的自然语言处理。E-mail：Tang2022_05@126.com

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汤萌萌（1989—），女，博士生，主要研究方向为网络安全领域的自然语言处理。E-mail：Tang2022_05@126.com

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The majority of APT detection studies currently in existence concentrate on data from partial APT attack phases, and contextual correlation analysis of all APT attack phases is absent. To address the above issue, a multivariate time-series dataset including all APT phases is created by combining host-side and network-side data. An APT attack sequence detection method based on feature selection and the two-tower Transformer model is proposed. Firstly, a feature optimization module is employed to select critical feature subsets as the input. Secondly, a two-tower structure is utilized to capture associated information between states at two adjacent time points of APT attack sequences from the time dimension, and to explore implicit relationships between feature variables from the feature dimension. Finally, the gate structure is introduced to connect and merge the weights of the two-tower, and the implicit information of APT attack sequence in time and feature dimensions is adaptively fused to achieve the purpose of improving the detection performance. Experimental results demonstrate that compared with recurrent neural networks (RNN), long short-term memory (LSTM) and Transformer models, superior performance is achieved by using the proposed method, with a detection accuracy of 95.42%.

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现有APT检测研究多针对APT攻击部分阶段数据，缺少对完整APT攻击阶段的上下文关联分析。为解决上述挑战，结合主机侧和网络侧数据，构建包含APT完整阶段的多变量时序数据集，提出一种基于特征选择和双塔Transformer模型的APT攻击序列检测方法。首先，利用特征优选模块，筛选出重要特征子集作为输入；其次，采用双塔结构，从时间维度上捕获APT攻击序列前后时刻状态间的关联信息，从特征维度上挖掘特征变量间的隐含关系；最后，引入门控结构，连接合并双塔的权重，自适应地融合APT攻击序列在时间维度和特征维度的隐含信息，以达到提升检测性能的目的。实验结果表明，与循环神经网络（RNN）、长短时记忆网络（LSTM）和Transformer模型相比，该方法表现更好，检测准确率达到95.42%。

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汪一帆（1996—），男，硕士生，主要研究方向为网络攻击检测。E-mail：

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汪一帆（1996—），男，硕士生，主要研究方向为网络攻击检测。E-mail：785694915@qq.com

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Addressing the issues of low accuracy and weak generalization in field semantic inference during binary protocol reverse engineering, an automatic inference method based on the field semantic inference model for softmax classification model (FSISC) is proposed. Firstly, the collected protocol data are divided into sessions according to IP addresses and port numbers. Secondly, three kinds of gated recurrent unit (GRU) are used to extract features for known and unknown protocol field, field column context and multiple sequence row context. Thirdly, the semantic descriptions of known protocol fields are converted into embedding vectors, the cosine similarity between these vectors is calculated, and the k-means++ algorithm is used to cluster according to the semantic similarity of field descriptions. Finally, the softmax classification model is employed to map the extracted features and the aggregated semantic categories to realize the automated semantic inference of unknown protocols. Experimental results demonstrate that the generalization ability of unknown protocols is effectively improved by using the proposed method, achieving semantic inference for four protocols. Compared with the automated field semantics inference method for binary protocol reverse engineering (FSIBP), the average accuracy of semantic inference is improved.

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针对二进制协议逆向工程中字段语义推断准确性低且泛化能力弱的问题，提出一种基于softmax分类模型的字段语义推断模型（FSISC）的自动推断方法。首先，将收集到的协议数据，根据IP地址、端口号进行会话分组；其次，针对已知和未知协议字段本身、字段列上下文以及多序列行上下文3类特征，采用3种门控循环单元（GRU）进行特征提取；再次，将已知协议字段语义描述转换为嵌入向量，计算向量之间的余弦相似度，并根据字段描述的语义相似度使用k-means++算法进行聚类；最后，利用softmax分类模型对提取的特征和聚合后的语义类别进行类别映射，实现未知协议的自动化语义推断。实验结果显示，所提方法可有效提升对未知协议的泛化能力，实现4种协议的语义推断，与二进制协议逆向工程的自动字段语义推理方法（FSIBP）相比，语义推理准确率有所提升。

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董姝岐（1997—），女，硕士生，主要研究方向为物联网安全、协议逆向。E-mail：

","authorsList":"董姝岐, 黄辑贤, 粘镇泓, 井靖","authorCompany":"信息工程大学
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董姝岐（1997—），女，硕士生，主要研究方向为物联网安全、协议逆向。E-mail：shuqi_dong0211@163.com

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董姝岐（1997—），女，硕士生，主要研究方向为物联网安全、协议逆向。E-mail：shuqi_dong0211@163.com

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Software-defined wireless sensor networks (SDWSN) are considered to better address the issue of routing data aggregation and network topology control. However, it still faces many challenges such as resource limitation and node heterogeneity how to integrate this centralized control mode with the distributed deployment of WSN. To solve the above problems, an SDWSN deployment scheme based on bacterial foraging optimization algorithm is proposed, which is utilized to realize the deployment of control nodes and dynamic adaptive clustering of sensor node network. Three factors, namely energy, distance and load balancing, are incorporated into the adaptive function. The collaboration among nodes and the adaptive adjustment are controlled by using the function to enhance the network resilience and operational efficiency. Simulation results indicate that compared with genetic mutation-based whale optimization algorithm (GM-WOA) and non-linear weight particle swarm optimization algorithm (NWPSO), superior performance is achieved by using the proposed scheme in energy consumption, network efficiency and network lifetime.

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软件定义无线传感器网络（SDWSN）能够更好地解决路由数据的聚合和网络拓扑控制问题。然而，如何将这种集中控制模式与WSN的分布式结构部署相结合仍然面临着资源限制、节点异构性等诸多挑战。针对以上问题，提出一种基于细菌觅食优化算法的SDWSN部署方案，用于实现控制节点的部署和传感器节点网络动态自适应聚类。该自适应函数包含能量、距离和负载均衡3个因素，通过控制节点间的协作和自适应调整来提升网络弹性和运行效率。仿真结果表明，所提方案在能量消耗、网络效率和网络寿命3个方面均优于基于基因突变的鲸鱼优化算法（GM-WOA）和非线性加权粒子群优化算法（NWPSO）等对比方案。

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毛明（1987—），男，工程师，博士，主要研究方向为网络安全、新型网络体系结构。E-mail：

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毛明（1987—），男，工程师，博士，主要研究方向为网络安全、新型网络体系结构。E-mail：maoming12345@163.com

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毛明（1987—），男，工程师，博士，主要研究方向为网络安全、新型网络体系结构。E-mail：maoming12345@163.com

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In response to the limited applicability and insufficient estimation accuracy of existing traditional modulation index estimation algorithms, a hybrid neural network-based approach is proposed for modulation index estimation of continuous phase modulation (CPM) signals. Leveraging the structural characteristics of CPM signals, redundant information and frequency offset effects are mitigated by employing the signal’s instantaneous frequency as input to the neural network model. Guided by signal denoising theory and the memory properties inherent in CPM signals, soft thresholding and contextual self-attention mechanisms are incorporated into the conventional deep residual network architecture, forming the contextual residual shrinkage network (CRSNet). This enhancement improves the network model’s noise reduction capabilities and long-range information extraction performance. The CRSNet model is subsequently utilized for modulation index estimation. Simulation analyses reveal that, under a 10 dB signal-to-noise ratio condition, the proposed algorithm achieves an estimation accuracy of 10⁻⁴. When compared to conventional algorithms, a performance improvement exceeding 10 dB is observed, with broad applicability to various CPM signal types without requiring a priori information. This method offers parametric support for addressing blind demodulation challenges in non-cooperative CPM signal reception scenarios, demonstrating significant practical application value.

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针对传统调制指数估计算法适用范围有限、估计精度不足的问题，提出一种基于混合神经网络的连续相位调制（CPM）信号调制指数估计方法。该方法从CPM信号结构特点出发，通过将信号瞬时频率作为网络模型输入来减少冗余信息和频偏影响。同时，根据信号降噪理论和CPM信号有记忆性的特点，该方法在经典的深度残差网络的基础上引入软阈值化和上下文自注意力机制构成上下文残差收缩网络（CRSNet），来增强网络模型的降噪能力和长距离信息提取能力，并利用CRSNet模型估计得到调制指数信息。仿真结果表明，在信噪比为10 dB条件下，所提算法估计精度能够达到10-4。与传统算法相比，所提算法有超过10 dB的性能增益，并且广泛适用多种类型CPM信号，不要求先验信息，为解决非合作接收场景下CPM信号盲解调问题提供参数支撑，具有实际应用价值。

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杨开元（1996—），男，助理工程师，硕士生，主要研究方向为通信信号处理。E-mail：1334223739@qq.com

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杨开元（1996—），男，助理工程师，硕士生，主要研究方向为通信信号处理。E-mail：1334223739@qq.com

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For the problem of waveform design for radar range tracking decoy targets, an intelligent interference waveform design method based on an improved genetic algorithm is proposed. A square-root cubature Kalman filter residual constraint function is designed, along with an effectiveness evaluation index for interference. The interference pulse number minimization genetic algorithm (IPMGA) is proposed to deceive range tracking radars effectively. Nonlinear and irregular random towing distance increments are generated by the IPMGA interference algorithm, meeting the requirements under certain towing probability conditions and effectively avoiding suppression by radar anti-interference methods. Towing interference is achieved more rapidly by the IPMGA compared to uniform acceleration towing, with the radar tracking lock being lost in just 8.576 s. The IPMGA is considered a rapid and effective method for decoy targets to deceive radar range tracking and is deemed to have significant theoretical and engineering implications.

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针对雷达距离跟踪的拖引假目标波形设计问题，提出一种基于改进遗传算法的智能化干扰波形设计方法。该方法设计均方根容积卡尔曼滤波残差约束函数和干扰有效性评价指标，并提出干扰脉冲数最小化遗传算法（IPMGA），实现对距离跟踪雷达的有效欺骗干扰。IPMGA能够产生非线性、无规律的随机拖引距离增量，满足一定拖引概率条件下的要求，有效避免雷达抗干扰方法的抑制。相较于匀加速拖引，IPMGA能更快地实现拖引干扰，仅用8.576 s便实现雷达跟踪失锁。实验证明，IPMGA是一种快速、有效的雷达距离跟踪假目标欺骗方法，具有一定的理论和工程指导意义。

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王海军（1990—），男，助理研究员，博士生，主要研究方向为雷达智能化对抗技术。E-mail：

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王海军（1990—），男，助理研究员，博士生，主要研究方向为雷达智能化对抗技术。E-mail：whjhra88@163.com

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王海军（1990—），男，助理研究员，博士生，主要研究方向为雷达智能化对抗技术。E-mail：whjhra88@163.com

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When individuals perform visual tasks, eye movements occur. These movements contain rich visual information, revealing their internal states in visual attention, perception, resource allocation and cognition. Currently, analysis based on eye movement information has brought various application scenarios in many fields of visual interaction. To update researchers on the latest research findings, after reviewing eye movement data acquisition techniques for visual interaction, it is focused on three widely used visual interaction technologies which are eye movement-based visual computing enhancement, visual interaction control, and gaze state assessment. Additionally, challenges and research directions in eye movement-based visual interaction technologies are further explored. This provides a useful reference for advancing technology and application innovation in related fields of visual interaction.

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个体执行视觉任务时眼球运动随之产生，其蕴含着丰富的视觉信息，能够揭示眼球在视觉注意、感知、资源分配及认知方面的内在状态。目前，基于眼动信息的分析已为视觉交互多个领域带来丰富的应用场景。为增强研究者对该领域最新研究成果的了解，在梳理面向视觉交互的眼动数据采集技术的基础上，重点讨论基于眼动的视觉计算增强、视觉交互控制和人员注视状态评估这3个目前应用最广泛的视觉交互技术。此外，还对基于眼动的视觉交互技术的挑战和研究方向进行探讨，为推动眼动在视觉交互相关领域的技术进步和应用创新提供有益的参考。

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胡易东（2000—），男，硕士生，主要研究方向为成像与智能信息处理。E-mail：

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胡易东（2000—），男，硕士生，主要研究方向为成像与智能信息处理。E-mail：hyd0211jsh@163.com

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胡易东（2000—），男，硕士生，主要研究方向为成像与智能信息处理。E-mail：hyd0211jsh@163.com

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A multi-strategy hiking optimization algorithm (MSHOA) is proposed in this paper to address the limitations of slow convergence speed and imbalanced global-local search capabilities in the original hiking optimization algorithm (HOA). Initially, Chebyshev chaotic mapping is employed to enhance population initialization quality. Subsequently, an adaptive expansion factor strategy is incorporated to accelerate convergence speed. Thirdly, a partial dimension recombination and mutation strategy is introduced to strengthen the algorithm's ability to escape local optima. Comprehensive simulation experiments are conducted on 12 benchmark functions comparing MSHOA with particle swarm optimization (PSO), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and moth-flame optimization (MFO). Results demonstrate that MSHOA achieves superior optimization accuracy and faster convergence compared to other algorithms. Finally, engineering validation through speed reducer design and welded beam design applications confirms that MSHOA exhibits significant performance advantages over standard HOA, proving its effectiveness in solving real-world engineering problems.

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针对徒步旅行优化算法（HOA）存在收敛速度慢、全局搜索和局部开发能力不平衡等问题，提出一种多策略徒步旅行优化算法（MSHOA）。首先，采用Chebyshev混沌映射初始化提高种群质量；其次，融入自适应扩张因子策略提高算法收敛速度；再次，引入部分维度重组与突变策略增强算法跳出局部极值的能力。将MSHOA与粒子群算法（PSO）、鲸鱼优化算法（WOA）、哈里斯鹰优化算法（HHO）、飞蛾扑火优化算法（MFO）在12个基准测试函数上进行仿真实验。结果表明，MSHOA相较于其他优化算法寻优精度更高、收敛速度更快。最后，将MSHOA应用于减速器设计和焊接梁设计问题。实验结果表明，该算法相较于标准HOA具有显著优势，验证了其在求解实际应用问题中的可行性。

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：panboyang@126.com

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：panboyang@126.com

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针对近年来固件托管技术的相关研究成果存在仿真执行效率不高、执行路径单一等问题，提出一种自动化多状态嵌入式设备固件托管方法。该方法基于静态特征自动定位启动完成位置，设计距离优先的选路算法，实现仿真设备的快速启动自检；设计基于异常规避的混合执行方法，生成具有多路径执行能力的动态执行模型，实现执行路径的多样化。通过面向两类平台3类架构下19款固件的实验测试，验证该方法能够成功为所有嵌入式设备固件创建静态启动自检模型和动态混合执行模型，实现设备固件高效多状态的仿真运行。

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吕 鑫（1990—），男，硕士生，主要研究方向为嵌入式固件仿真。E-mail：jason_bourne_1949@163.com

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To address the system threat state removal under resource-constrained executors, a limited executer arbitration and scheduling algorithm (LEAS) is proposed. The phased scheduling strategy is developed by integrating random scheduling, probabilistic scheduling and inferential scheduling, achieving balance between scheduling overhead and abnormal state removal efficiency. Experimental results demonstrate that compared with other two commonly used scheduling algorithms in similar scenarios, the proposed LEAS algorithm exhibits the shortest average anomaly scheduling cycle, suggesting superior efficiency in eliminating system threat states.

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针对执行体资源受限场景下的系统威胁状态清除问题，提出一种异构资源受限的拟态裁决调度算法（LEAS）。该算法采用分阶段调度策略，结合随机调度、概率调度及推理调度3种方式，平衡调度开销与异常状态清除效率。实验结果表明，与同类应用场景下的两种常用调度算法相比，LEAS算法的平均异常调度周期在三者中最低，在清除系统威胁状态上具有更高的效率。

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针对现有基于超图表示学习的有向事件异常检测方法无法充分捕获异常模式和使用受限的问题，提出一种利用多尺度对比学习的有向事件异常检测方法。首先，将事件建模为有向超图；其次，设计多尺度对比学习算法，捕获有向事件在实体对、实体集、头尾集和事件方向等方面的异常模式；最后，设计异常事件评分函数完成异常事件检测。在不同领域的3个真实数据集上的实验表明，相比基线方法，该方法在精确率和曲线下面积（AUC）两个指标上均取得最优，精确率平均提升11.84%，AUC平均提升15.22%。

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李英乐（1985—），男，副研究员，博士生，主要研究方向为通信网安全、网络异常检测、超图表示学习。E-mail：

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李英乐（1985—），男，副研究员，博士生，主要研究方向为通信网安全、网络异常检测、超图表示学习。E-mail：lyl7225@163.com

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李英乐（1985—），男，副研究员，博士生，主要研究方向为通信网安全、网络异常检测、超图表示学习。E-mail：lyl7225@163.com

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A vulnerability analysis method for multi-layer networks with uneven layer impacts is proposed based on DomiRank centrality, aiming to address the limitations of existing algorithms that neglect uneven layer impacts and exhibit inadequate performance. First, multi-layer weighted factors are introduced to construct a network model characterizing uneven layer impacts. Subsequently, an improved multi-layer DomiRank centrality (MDRC) metric is designed using a weighted supra-adjacency matrix. Finally, node attacks are implemented by sequentially removing all nodes sorted in descending order of MDRC values, through which network vulnerability is evaluated by performance degradation under different node removal ratios. Experimental results have demonstrated that the proposed method outperforms benchmark methods, with the multi-layer weighted factors being verified to effectively discriminate vulnerability variations caused by distinct layer characteristics.

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针对现有多层网络脆弱性分析算法未考虑层影响不均特性对全局脆弱性的影响以及算法性能不足的问题，提出一种基于DomiRank中心性的层影响不均多层网络脆弱性分析方法。首先，引入多层加权因子，构建表征层影响不均特性的多层网络模型；其次，基于加权超邻接矩阵设计一种改进的多层DomiRank中心性（MDRC）指标；最后，按照MDRC大小降序排列多层网络所有节点，开展节点攻击并依序删除网络节点，通过观察不同节点删除比例下多层网络性能变化实现对网络脆弱性的分析。实验结果表明，所提方法性能优于基准方法，多层加权因子能有效区分不同分层特性对脆弱性的影响。

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李海涛（1982—），男，副研究员，博士生，主要研究方向为通信网安全、复杂网络。E-mail：

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李海涛（1982—），男，副研究员，博士生，主要研究方向为通信网安全、复杂网络。E-mail：ca_lht@126.com

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李海涛（1982—），男，副研究员，博士生，主要研究方向为通信网安全、复杂网络。E-mail：ca_lht@126.com

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To address the absence of a systematic method for effectively guiding practical attacks on small dq CRT-RSA, a practical attack method based on parameter optimization, fitting and binary search is proposed. Initially, the parameters τp and τq in the small dq attack are optimized. Subsequently, guided by the optimized values of τp and τq, along with experimental statistics on the length of the reduced lattice basis, an estimation method for an attackable upper bound is proposed through parameter fitting, which aligns with experimental results. Guided by the method, the attackable upper bound of small dq given the parameters is explored. Furthermore, the most significant bits (MSBs) guess strategy for the prime factor of the modulus, which is proposed in previous explorations of practical attacks on small RSA decryption exponents, is introduced. Based on the “multivalued phenomenon” observed in experiments, a binary search attack method suitable for parallel implementation is proposed, effectively enhancing the upper bound of practical attacks on small dq under the given parameters. Additionally, the lattice proposed by Takayasu et al. is optimized, and the performance of helpful polynomials in practical attacks is explored. The practical attack exploration presented here can offer inspiration and assistance for the future research on small dq attacks against CRT-RSA.

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针对当前缺乏系统的方法来有效指导CRT-RSA小dq实际攻击的问题，提出一种基于参数优化拟合和二分搜索的CRT-RSA小dq实际攻击方法。首先对小dq攻击参数τp、τq进行优化，基于优化的τp、τq值和约化格基长度的实验统计，给出一种基于参数拟合的可攻击上界的估计方法，其估计结果与实验结果吻合；在该方法的指导下，对给定参数条件下的小dq 实际攻击上界进行探索；随后加入之前RSA小解密指数实际攻击探索时所提的模数素因子的高比特猜测策略，基于实验中的“多值现象”，提出适合并行实现的二分搜索攻击方法，该方法能有效提升给定参数下小dq实际可攻击的上界。此外，对Takayasu等的格进行优化，并对其提出的helpful多项式在实际攻击中的效果进行探索，可为后续CRT-RSA的小dq攻击研究带来启发和帮助。

","authors":"

李强（1990—），男，讲师，博士生，主要研究方向为基于格的公钥密码分析。E-mail：

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李强（1990—），男，讲师，博士生，主要研究方向为基于格的公钥密码分析。E-mail：hnuliqiang@163.com

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李强（1990—），男，讲师，博士生，主要研究方向为基于格的公钥密码分析。E-mail：hnuliqiang@163.com

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Aiming at the problem that existing social media fake news detection methods overlook the deep semantic dependency structure in posts, a knowledge-enhanced heterogeneous graph attention fake news detection method is proposed. Firstly, through heterogeneous textual graphs, the intrinsic semantic dependencies and extrinsic knowledge associations of different semantic units in the posts are depicted in detail. Secondly, a dual-layer graph attention mechanism is designed to learn the pattern features of the posts and the knowledge-enhanced entity semantic representations, thereby enhancing the model’s fine-grained semantic perception of the post content and its ability to capture fake news clues from multiple perspectives. The experimental results on two public datasets show that the overall detection performance of this method is superior to the existing text-based content detection methods at present, effectively enhancing the early detection capability of false information on social media.

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针对现有社交媒体虚假信息检测方法忽视帖文中深层次的语义依赖结构的问题，提出一种知识增强的异质图注意力虚假信息检测方法。首先，通过异质文本图，精细化描绘帖文不同语义单元的内在语义依存和外在知识关联；其次，设计双层图注意力机制，学习帖文的模式特征和知识增强的实体语义表征，以增强模型对帖文的内容细粒度语义感知和多视角捕捉虚假线索的能力。在两个公开数据集的实验结果表明，所提方法的总体检测性能优于当前已有的基于文本内容检测方法，可有效提升社交媒体虚假信息早期检测的性能。

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陈 静（1990—），女，讲师，博士生，主要研究方向为数据挖掘、自然语言处理、社交媒体分析。E-mail：

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陈 静（1990—），女，讲师，博士生，主要研究方向为数据挖掘、自然语言处理、社交媒体分析。E-mail：cathysilense@126.com

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陈 静（1990—），女，讲师，博士生，主要研究方向为数据挖掘、自然语言处理、社交媒体分析。E-mail：cathysilense@126.com

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To address the security and the collaborative scheduling issues of multiple decision scheduling dielets (DSDs) in the endogenous security structure of software-defined system on wafer (SDSoW), a multi-level distributed dynamic heterogeneous redundancy (DHR) architecture (MD2HR) is proposed. Firstly, by interconnecting multiple local heterogeneous DSDs through a software-defined network on wafer, a dynamic multimode decision model (DMDM) is formed. Secondly, a dynamic decision switching algorithm based on trust and optimal delay is proposed to solve the optimization problem of dynamic switching among DSDs. Finally, a traffic game-based collaborative scheduling algorithm is designed to achieve collaboration of the output decision information from heterogeneous DSDs. A test system is constructed, and an on-chip secure interconnect dielets that simultaneously support multiple heterogeneous attributes and decision scheduling functions is realized. Experimental analysis shows that, compared to traditional single and multiple decision structures, the proposed architecture and model can defend against the differential mode attack targeting any DSDs with a controllable delay overhead of 5.11% to 47.40%.

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针对软件定义晶上系统内生安全构造中裁决芯粒的安全问题和协同调度问题，提出一种多级分布式动态异构冗余（DHR）架构。首先，通过软件定义晶上互连网络将多区域异构裁决芯粒互连，形成动态多模裁决模型；其次，设计一种基于信任度和延迟最优的裁决动态切换算法，解决裁决调度模块动态切换的最优化问题；最后，设计一种基于交通博弈的裁决协同调度算法，解决异构裁决芯粒裁决信息的协同调度。构建测试系统，实现一种同时支持多异构属性和裁决调度功能的晶上安全互连芯粒。测试结果表明，相比传统单一裁决和多裁决结构，所提架构和模型可以防御针对任何裁决芯粒的差模攻击，延迟开销在5.11%~47.40%的可控范围内。

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李沛杰（1990—），男，助理研究员，博士生，主要研究方向为软件定义晶上系统、内生安全。E-mail：

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李沛杰（1990—），男，助理研究员，博士生，主要研究方向为软件定义晶上系统、内生安全。E-mail：lipeijie@csivo.com

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李沛杰（1990—），男，助理研究员，博士生，主要研究方向为软件定义晶上系统、内生安全。E-mail：lipeijie@csivo.com

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许含意（1996—），女，硕士生，主要研究方向为云计算安全。E-mail：xuhanyi_1221@163.com

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To address the issues of insufficient automation and program coverage in existing programmable logic controller (PLC) protocol fuzz testing, a black-box fuzz testing method is proposed. Firstly, the characteristics of PLC protocol, which are characterized by a “request-response” pattern with function code as the core, are analyzed to demonstrate its adaptability to niche genetic algorithms. Secondly, a method of dividing niches based on function codes and an evolution strategy of “subpopulation selection and full population evolution” are proposed to improve the niche genetic algorithm. Then, a fitness calculation method combining individual similarity and the response status of the device under test is designed, aiming to automatically guide test cases to more comprehensively explore the program space. Experiments show that compared with Peach and the method using a simple genetic algorithm, the generated test cases trigger a higher proportion of abnormal responses and have stronger vulnerability discovery capabilities. Faults are triggered in three PLCs from Schneider, Mitsubishi, and General Electric (GE), and one undisclosed vulnerability is found.

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针对现有可编程逻辑控制器（PLC）协议模糊测试自动化程度与程序覆盖率不足的问题，提出一种基于改进小生境遗传算法的黑盒测试方法。首先，通过分析PLC协议以功能码为核心的“请求—响应”特征，论证其与小生境遗传算法的适配性；其次，提出以功能码划分小生境和“子种群选优、全种群进化”的进化策略，改进小生境遗传算法；最后，结合个体相似度与被测设备响应状态的适应度计算方法，最终实现自动引导测试用例更全面探索程序空间的目的。实验结果表明，相比Peach及基本遗传算法的方法，该方法生成的测试用例触发异常响应占比更多，漏洞发现能力更强，在施耐德、三菱、通用电气（GE）的3款PLC中均触发故障，发现1个未公开漏洞。

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马航（1980—），男，硕士生，主要研究方向为物联网安全。E-mail：

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马航（1980—），男，硕士生，主要研究方向为物联网安全。E-mail：b.meteor@163.com

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马航（1980—），男，硕士生，主要研究方向为物联网安全。E-mail：b.meteor@163.com

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针对现有加密流量分类方法因流量特征表示单一和细粒度分析不足导致关键业务流量处理效果不佳的问题，提出一种基于流量映射矩阵（TMM）的细粒度分类方法。通过构建融合报文序列特征、统计特征及拓扑结构特征的TMM，形成包含时空关联信息的复合特征表征。基于Vision Transformer架构设计自监督分类器，采用预训练范式对TMM进行特征学习，建立应用行为分类模型（ABC-Model）。在公开数据集和私有数据集上的实验表明，该方法可实现细粒度的应用行为分类，最高准确率达96.73%。研究证实多维特征融合的TMM能有效表征加密流量行为特性，为关键业务流量识别提供了新的解决方案。

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To address the issue of low quantum state utilization in star-type quantum secret sharing protocols, a high-efficient multi-party quantum secret sharing scheme is proposed based on two-photon polarization-spatial mode hyperentangled states. In the new scheme, secrets are split and subsequently grouped for encryption, enabling one bit of secret information to be shared among multiple agents through one qubit based on the method of secure direct communication. When compared with existing star-type quantum secret sharing protocols, the new scheme significantly enhances the utilization rate of quantum states and can be easily implemented under current conditions, as it does not require quantum storage. Moreover, cheating attacks launched by dishonest agents can be effectively resisted through the verifiable algorithm incorporated in the new scheme.

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针对星型量子秘密共享协议的量子态利用率较低的问题，基于双光子偏振—空间模超纠缠态提出一个高效多方量子秘密共享方案。该方案将秘密拆分后再分组加密，基于安全直接通信方法实现了多个代理成员间通过一个量子比特共享一比特秘密信息的目的。相比现有的星型量子秘密共享协议，新方案大幅提升了量子态利用率，且不需要量子存储，在现有条件下容易实现。另外，新方案采用可验证算法能够有效地抵抗不诚实成员的欺骗攻击。

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杜宇韬（1985—），男，讲师，博士生，主要研究方向为量子密码协议。E-mail：

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杜宇韬（1985—），男，讲师，博士生，主要研究方向为量子密码协议。E-mail：dyt2008xd@163.com

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杜宇韬（1985—），男，讲师，博士生，主要研究方向为量子密码协议。E-mail：dyt2008xd@163.com

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Remote sensing images contain rich feature information, and the use of intelligent semantic understanding methods for remote sensing images can realize the automated analysis and description of remote sensing images, and quickly generate a natural language that describes the image content or scene. In recent years, the intelligent semantic understanding method of images based on machine learning has been widely used in the rapid analysis of remote sensing images and gradually formed an independent technical system. The intelligent semantic understanding methods for remote sensing images are summarized by collecting and organizing the domestic and international literature in the past 9 years, which provides a reference value for the subsequent research. Firstly, remote sensing image semantic understanding methods are introduced, including template and retrieval-based framework, encoder-decoder-based framework, and two-stage combined-based framework. Secondly, common datasets and evaluation indexes for remote sensing image semantic understanding are introduced. Finally, the challenges faced by the current remote sensing image semantic understanding methods as well as the direction of future research are discussed.

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遥感图像包含丰富的地物信息，利用图像智能语义理解方法能够实现遥感图像的自动化分析和描述。近年来，基于机器学习的图像智能语义理解方法被广泛应用于遥感图像的快速分析，逐渐形成独立的技术体系。通过收集整理近9年的国内外文献，对遥感图像智能语义理解方法进行总结，为后续研究提供参考价值。首先，介绍遥感图像语义理解方法，包括基于模板和检索的框架、基于编码器—解码器框架以及基于两阶段的组合式框架；其次，介绍遥感图像语义理解常用数据集和评价指标；最后，探讨当前遥感图像语义理解方法面临的挑战以及未来研究的方向。

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周得伟（1999—），男，硕士生，主要研究方向为地理空间情报。E-mail：

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周得伟（1999—），男，硕士生，主要研究方向为地理空间情报。E-mail：1443327039@qq.com

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周得伟（1999—），男，硕士生，主要研究方向为地理空间情报。E-mail：1443327039@qq.com

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Unsupervised Change Detection Method for Remote Sensing Images","columnId":null,"journalTitle":"Journal of Information Engineering University","columnName":null,"runingTitle":null,"highlight":null,"articleAbstract":"

Inspired by vision foundation models, a semantic-driven unsupervised change detection method is proposed to deal with the low accuracy of unsupervised change detection in remote sensing images. Firstly, the segment anything model (SAM) is used to perform unsupervised segmentation of remote sensing images to obtain different object regions. Secondly, the large language and vision assistant (LLaVA) is used to generate corresponding text descriptions for the object regions in two temporal remote sensing images. Thirdly, the word embedding model is further utilized to convert text descriptions into feature vectors, and the semantic similarity of the same object area in two temporal remote sensing images is calculated. According to the semantic differences of the same segmentation object area in different temporal remote sensing images, the threshold segmentation method is used to identify changing regions. Finally, the change results obtained by using remote sensing images from different temporal images as reference images are integrated to further improve the accuracy of change detection. The effectiveness of the proposed method is validated through extensive change detection experiments on two benchmark datasets, LEVIR-CD and WHU-CD.

","authors":null,"authorsList":"Yingying LIU, Gang ZHOU","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":null,"magXml":null,"pdfUrl":null,"pdf":null,"pdfFileSize":null,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":null,"abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":null,"mapNumber":null},"CN":{"id":"1160183224396865669","articleId":"1160183223306346615","tenantId":"1045748351789510663","journalId":"1155139928303341809","language":"CN","title":"语义驱动的遥感影像无监督变化检测方法","columnId":"1210243631342080935","journalTitle":"信息工程大学学报","columnName":"测绘科学与技术","runingTitle":null,"highlight":null,"articleAbstract":"

针对遥感影像无监督变化检测精度较低问题，受视觉基础模型技术启发，提出一种语义驱动的遥感影像无监督变化检测方法。首先，使用分割一切模型（SAM）对遥感影像进行无监督分割获得不同的对象区域。其次，使用大型语言和视觉助手（LLaVA）为两个时相遥感影像中的对象区域生成相应的文本描述。再次，进一步利用文本特征嵌入模型将文本描述转换为特征向量，并计算两个时相遥感影像同一对象区域的语义相似性；根据同一分割对象区域在不同时相遥感影像上的语义差别，使用阈值分割的方法识别变化区域。最后，融合利用不同时相的遥感影像作为分割基准影像获得的变化结果，以进一步提升变化检测精度。在LEVIR-CD和WHU-CD两个基准测试数据集上的大量变化检测实验验证了所提方法的有效性。

","authors":"

刘莺迎（1985—），女，讲师，博士生，主要研究方向为图像处理。E-mail：

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刘莺迎（1985—），女，讲师，博士生，主要研究方向为图像处理。E-mail：liuying85815@163.com

\"}","bioImg":null,"bioContent":"

刘莺迎（1985—），女，讲师，博士生，主要研究方向为图像处理。E-mail：liuying85815@163.com

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Cross-Domain Speaker Diarization Method Based on Domain-Adversarial Neural Network Adaptation","columnId":null,"journalTitle":"Journal of Information Engineering University","columnName":null,"runingTitle":null,"highlight":null,"articleAbstract":"

To address the problem of poor generalization performance of end-to-end speaker diarization systems due to insufficient labeled data, a cross-domain speaker diarization method based on domain-adversarial neural network adaptation is proposed. Firstly, a data domain discrimination model containing a temporal pooling layer is added to the speaker diarization. Secondly, the gradient reversal layer is used to realize the adversarial training of the speaker diarization classification task and the data domain discrimination task. Finally, the adaptation in the data domain is completed. Experiments are carried out to compare the performance of different models in real-world scenarios, demonstrating that the overall performance of the proposed method outperforms that of other models. Compared with the baseline model, when the data domain does not match, the relative improvement is 4.91% for the two-speaker scenarios and 5.41% for the three-speaker scenarios. When the data domain is matched, the relative increase is 3.81% and 5.14%, respectively. Experimental results indicate that the proposed method can effectively enhance the system’s cross-domain generalization ability by reducing the sensitivity of features to domain information.

","authors":null,"authorsList":"Tong NIU, Xiaolin JIAO, Dan QU","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":null,"magXml":null,"pdfUrl":null,"pdf":null,"pdfFileSize":null,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":null,"abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":null,"mapNumber":null},"CN":{"id":"1222590875412014037","articleId":"1222590872266285796","tenantId":"1045748351789510663","journalId":"1155139928303341809","language":"CN","title":"域对抗神经网络自适应的跨域说话人日志方法","columnId":"1210243556649914417","journalTitle":"信息工程大学学报","columnName":"信息与通信工程","runingTitle":null,"highlight":null,"articleAbstract":"

针对端到端说话人日志系统因真实标注数据不足导致泛化性能差的问题，提出一种基于域对抗神经网络自适应的跨域说话人日志方法。首先，通过在说话人日志中增加包含时间池化层的数据域判别模型；其次，利用梯度反转层实现说话人日志分类任务与数据域判别任务的对抗训练；最后，完成在数据域上的自适应。实验对比不同模型在真实场景下的性能，所提模型整体性能优于其他模型。相较于基线模型，数据域不匹配时，两个说话人场景相对提升4.91%，3个说话人场景相对提升5.41%；数据域匹配时，分别相对提升3.81%和5.14%。实验结果表明，所提方法通过降低特征对域信息的敏感性有效提升系统跨域泛化能力。

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牛 铜（1983—），男，副教授，博士生，主要研究方向为智能信息处理、远场语音识别。E-mail：

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牛 铜（1983—），男，副教授，博士生，主要研究方向为智能信息处理、远场语音识别。E-mail：jerry_newton@sina.com

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牛 铜（1983—），男，副教授，博士生，主要研究方向为智能信息处理、远场语音识别。E-mail：jerry_newton@sina.com

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Aiming at the problems of traditional information symbol recognition methods relying on a series of signal processing at the receiver backend and the difficulties in obtaining training data in real time and the high cost of computing resources in deep learning based the information symbol recognition methods, a single-stream information symbol recognition method based on a programmable diffraction deep neural network (PD2NN) is proposed. Firstly, the problem of information symbol recognition is transformed into the recognition of electromagnetic waves at the receiving antenna, and a single-stream information symbol recognition model based on PD2NN is proposed. Secondly, a PD2NN parameter training method based on adaptive moment estimation (ADAM) is designed. Finally, the PD2NN network is used to recognize the single stream information symbol by judging the type of information symbol with the smallest distance from the received symbol. Simulation results show that the bit error rate of the proposed method is comparable to or even better than that of the traditional receiver method, and the symbol recognition of single-stream information under the condition of lower training cost can be achieved.

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针对传统信息符号识别方法依赖于接收机后端的一系列信号处理和基于深度学习的信息符号识别方法中存在的训练数据实时获取难、计算资源成本高等问题，提出一种基于可编程衍射神经网络（PD2NN）的单流信息符号识别方法。首先，将信息符号的识别问题转换为对接收天线处电磁波的识别，提出基于PD2NN的单流信息符号识别模型；其次，设计一种基于自适应矩估计（ADAM）的PD2NN参数训练方法；最后，PD2NN网络通过判断与接收符号距离最小的信息符号类别，实现对单流信息符号的识别。仿真结果表明，所提方法可以实现与传统接收机方法相当甚至更好的误码率性能，并且能够在较低的训练成本条件下完成单流信息符号识别。

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张朕尧（2001—），男，硕士生，主要研究方向为超表面与无线通信。E-mail：

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张朕尧（2001—），男，硕士生，主要研究方向为超表面与无线通信。E-mail：zhangzhenyao0206@163.com

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张朕尧（2001—），男，硕士生，主要研究方向为超表面与无线通信。E-mail：zhangzhenyao0206@163.com

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To address the issues of ambiguous cluster numbers and random cluster center initialization in traditional clustering algorithms for underdetermined mixing matrix estimation, a novel estimation method based on multimodal fusion recognition is proposed. By optimizing the clustering process through multimodal feature fusion in this method, the limitations of conventional clustering algorithms are overcome. Experimental validation under a two-channel four-signal configuration demonstrates that the proposed method achieves a normalized mean square error of -66.034 5 dB for the estimated matrix. Compared with traditional clustering methods, this technique significantly improves mixing matrix estimation accuracy, providing a robust solution for practical applications.

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针对欠定混合矩阵估计中传统聚类算法存在簇数目不明确、簇中心随机选择等问题，提出一种基于多模态融合识别的混合矩阵估计方法。通过多模态融合识别优化聚类过程，克服传统聚类算法的不足。在两通道四信号的实验设置下，采用该方法进行混合矩阵估计。实验结果表明，估计矩阵的归一化均方误差达到-66.034 5 dB，相比传统聚类算法，该方法能有效提高混合矩阵的估计精度，为混合矩阵估计提供了可靠方法。

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张海龙（1986—），男，硕士生，主要研究方向为信号识别与分离。E-mail：

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张海龙（1986—），男，硕士生，主要研究方向为信号识别与分离。E-mail：zhang6438@163.com

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To address the significant performance degradation of domain adversarial neural network (DANN)-based specific emitter identification (SEI) algorithms under variable modulation scenarios, a VAE-DANN algorithm is proposed by reconstructing subtle features of radar signals through deep variational auto-encoder (VAE) optimization. Firstly, a deep VAE module is incorporated after the feature extraction module of DANN to construct the VAE-DANN architecture. Secondly, the cosine annealing strategy is applied to dynamically optimize weighting factors of the total loss function during adversarial training, where network parameters are adjusted by minimizing the total loss function to obtain the optimal model. Finally, SEI implementation for target-domain radar signals is accomplished under variable modulation conditions using the optimized VAE-DANN model. Experimental results demonstrated that the proposed algorithm achieved an average recognition accuracy exceeding 94% at 10 dB signal-to-noise ratio in variable modulation scenarios.

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针对领域对抗神经网络（DANN）模型的雷达辐射源个体识别（SEI）算法在变调制场景下识别性能显著下降的问题，利用深层变分自编码器（VAE）对雷达信号细微特征进行重构优化，提出一种基于VAE-DANN的变调制雷达SEI算法。首先，在DANN模型特征提取模块后增加深层VAE模块，构建VAE-DANN网络；其次，在VAE-DANN网络对抗训练过程中，利用余弦退火思想自适应动态优化总损失函数权重因子，通过最小化总损失函数逐步调整网络参数获得最优VAE-DANN模型；最后，利用最优VAE-DANN模型完成变调制场景下目标域雷达信号的SEI。实验结果表明，在信噪比为10 dB时，所提算法在变调制场景中的雷达SEI平均识别准确率达到94%以上。

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庞启龙（2003—），男，学士，主要研究方向为电子对抗。E-mail：

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庞启龙（2003—），男，学士，主要研究方向为电子对抗。E-mail：19567177631@163.com

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庞启龙（2003—），男，学士，主要研究方向为电子对抗。E-mail：19567177631@163.com

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Sparse compressed sensing algorithms cannot estimate the direction of arrival (DOA) of coherent signals, and the accuracy estimation of polarization parameter is greatly affected by amplitude and phase errors. Traditional algorithms, which are mostly restricted to the research of one-dimensional signals, fail to effectively solve the above problems. Lissajous figure is used to obtain higher dimensional information. Firstly, the spatial relationship between the Lissajous figure formed by the electric field vector of the signal in space and the DOA is analyzed, and the off-grid joint-sparse (OGJS) algorithm is used for DOA parameter estimation, which reduces the accuracy loss caused by the flexibility expansion of the original algorithm. When the signal-to-noise ratio (SNR) is 5 dB, the absolute average error of DOA estimation is reduced from 3.83° to 0.27°. Then, the β-variational auto-encoder (β-VAE) is employed to learn the simulation dataset of Lissajous figure in the form of grayscale images, so as to reconstruct the perturbed data and eliminate the amplitude and phase errors. Compared with the processing of one-dimensional signals by convolutional autoencoder networks, when the SNR is 10 dB and the polarization difference resolution is 1.5°, after 100 rounds of small sample learning training, the matching rate is improved from 80.4% to 99.2%.

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稀疏压缩感知类算法无法对相干信号进行波达方向（DOA）估计，极化参数精度估计受幅相误差影响严重。传统算法拘束研究在一维信号层面，对上述问题解决效果不佳，所以尝试利用Lissajous图形获取更高维度的信息。首先，分析信号在空间的电场矢量形成的Lissajous图形与DOA空间关系，结合离网压缩感知（OGJS）算法进行DOA参数估计，改善原算法因灵活度拓展带来的精度损失，信噪比为5 dB时DOA估计绝对平均误差由3.83°降至0.27°；然后，利用调参变分自编码器（β-VAE）对灰度图形式的Lissajous图形仿真数据集进行学习，实现对扰动数据的重构，消除幅相误差。对比卷积自编码器网络处理一维信号，实现信噪比为10 dB、极化相差分辨率在1.5°时，小样本100轮学习训练下，匹配率由80.4%提升至99.2%。

","authors":"

李金朋（1999—），男，硕士生，主要研究方向为目标信息获取与处理。E-mail：

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李金朋（1999—），男，硕士生，主要研究方向为目标信息获取与处理。E-mail：ljp196@qq.com

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李金朋（1999—），男，硕士生，主要研究方向为目标信息获取与处理。E-mail：ljp196@qq.com

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Leveraging the results of auxiliary tasks such as image classification as prior information can facilitate the generation of high-quality descriptions for remote sensing images. However, the existing methods that employ feature fusion techniques often fail to capture the complex interactions between features and are unable to fully describe the content within remote sensing images. To address these limitations, a remote sensing image captioning method based on multimodal semantic feature fusion is proposed. The image region features are extracted firstly by using a pre-trained ResNet50 network. Then, the semantic attributes of the image are predicted through a multilayer perceptron network. Subsequently, attribute-guided and text-guided cross-attention submodules are employed to enable the interaction and fusion of image, attribute, and text features. Finally, the fused features are fed into a decoder to generate the target image description. Experimental results demonstrate that the proposed method outperforms baseline methods in various evaluation metrics, yielding more accurate and coherent descriptions.

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利用图像分类等辅助任务的结果作为先验信息可以为遥感图像生成高质量的描述，然而这些方法采用的特征融合方式难以捕捉特征之间的复杂交互关系，无法充分描述遥感图像中的内容。为解决此问题，提出一种基于多模态语义特征融合的遥感图像描述生成方法。该方法首先利用预训练的ResNet50网络提取图像区域特征；其次，基于多层感知机网络预测图像的语义属性；再次，通过属性引导的交叉注意力子模块和文本引导的交叉注意力子模块，实现图像、属性和文本特征的交互与融合；最后，将融合后的特征输入解码器，生成目标图像的描述。实验结果表明，该方法在各项评价指标上与基线方法相比均有性能提升，能够生成更加准确的描述。

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周得伟（1999—），男，硕士生，主要研究方向为遥感图像描述生成。E-mail：

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周得伟（1999—），男，硕士生，主要研究方向为遥感图像描述生成。E-mail：ld023619@163.com

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周得伟（1999—），男，硕士生，主要研究方向为遥感图像描述生成。E-mail：ld023619@163.com

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To address the issue of insufficient robustness and generalization ability in unsupervised neural machine translation when handling significant grammatical structure and lexical differences between source and target languages, an unsupervised neural machine translation method based on an adaptive noise insertion strategy is proposed. By analyzing the grammatical structure, lexical complexity and syntactic difference between the source language and the target language, the noise insertion position and intensity can be dynamically adjusted, so as to better adapt to the complexity of different language pairs. For simpler sentences, less noise is inserted to preserve core semantics, while for more complex sentences, more intricate noise is introduced to enhance the model’s ability to learn complex language structures. This ensures that the model can retain important semantic information while improving its generalization ability and robustness. Experimental results show that compared with the baseline model, the bilingual evaluation understudy (BLEU) value is significantly improved by using the proposed method in eight benchmark translation tasks.

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针对无监督神经机器翻译在处理源语言和目标语言之间存在显著语法结构和词汇差异时鲁棒性与泛化能力不足的问题，提出一种基于自适应噪声插入策略的无监督神经机器翻译方法。该方法通过对源语言和目标语言在语法结构、词汇复杂度及句法差异性的分析，能够动态调整噪声的插入位置和强度，从而更好地适应不同语言对的复杂性。在结构简单的句子中，插入较少噪声以保护核心语义，而在复杂句子中，插入较为复杂的噪声以增强模型对复杂语言的学习能力，确保模型在保留重要语义信息的同时，能够增强模型的泛化能力和鲁棒性。实验结果表明，与基线模型相比，该方法在8个基准翻译任务中，显著提升双语评估替补（BLEU）值。

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张传财（1997—），男，硕士生，主要研究方向为自然语言处理。E-mail：

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张传财（1997—），男，硕士生，主要研究方向为自然语言处理。E-mail：zhangchuancai_xd@163.com

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张传财（1997—），男，硕士生，主要研究方向为自然语言处理。E-mail：zhangchuancai_xd@163.com

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Aiming at the problems of low prediction accuracy of remaining useful life and difficulty in extracting degenerative features, a remaining useful life prediction method based on spatio-temporal feature fusion is proposed. Firstly, a dual-channel deep learning network is constructed, the long short-term memory neural network based on channel attention mechanism is used to extract the degraded information in the time dimension in channel 1, and the semantic segmentation network is used to mine the degraded information in the spatial dimension in channel 2. Secondly, the cross attention mechanism is used to fuse the spatiotemporal features. Finally, the regression layer is used to realize the remaining useful life prediction. Experimental verification on the simulation dataset of commercial modular aviation propulsion system shows that compared with other deep learning methods, the value of root mean square error and score in the FD004 test set are reduced by 16.21% and 14.97%, and the remaining useful life is predicted with high accuracy.

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针对剩余寿命预测精度低、退化特征难提取等问题，提出一种基于时空特征融合的剩余寿命预测方法。首先，构造双通道深度学习网络，通道1采用基于通道注意力机制的长短期记忆神经网络提取时间维度的退化信息，通道2采用语义分割网络挖掘空间维度的退化信息；其次，利用交叉注意力机制融合时空特征；最后，采用回归层实现剩余寿命预测。在商用模块化航空推进系统仿真数据集进行实验验证，结果表明，与其他深度学习方法相比，该方法在FD004测试集中均方根误差降低了16.21%，评分函数值降低了14.97%，实现剩余寿命的高精度预测。

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龚永奇（1992—），男，助理工程师，博士生，主要研究方向为装备保障。E-mail：

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龚永奇（1992—），男，助理工程师，博士生，主要研究方向为装备保障。E-mail：David_gyq123@163.com

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龚永奇（1992—），男，助理工程师，博士生，主要研究方向为装备保障。E-mail：David_gyq123@163.com

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To address the limitations of traditional manual video surveillance, such as insufficient real-time performance and inefficient detection of transient targets, an intelligent surveillance detection algorithm based on lightweight YOLOv10 is proposed. Firstly, depthwise separable convolutions are employed to replace standard convolutions, reducing network parameters while accelerating detection speed. Secondly, the cross-stage partial bottleneck structure with dual convolution fusion in the backbone network is substituted with an efficient multi-scale attention module, enhancing the network’s sensitivity to target scale variations. Finally, an auxiliary bounding box optimization loss is integrated to enrich supervision signals and improve small-target detection performance. Experimental evaluations on the UA-DETRAC traffic surveillance dataset demonstrate that the proposed algorithm achieves a higher mean average precision (mAP) by 13.5, 10.9, 1.7, and 0.4 percentage points compared to these of Faster R-CNN, EfficientDet-D5, YOLOv8, and YOLOv11, respectively. With a detection speed of 112 FPS and merely 2.2×106 parameters, this algorithm provides robust technical support for object detection tasks in intelligent surveillance systems.

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为解决传统人工视频监控实时性不足，无法高效检测场景中短暂出现目标的问题，提出一种基于轻量化YOLOv10的智能监控检测算法。首先，利用深度可分离卷积替换标准卷积，减少网络参数，提高检测速度。其次，将主干网络中跨阶段部分双卷积融合瓶颈结构替换为高效多尺度注意力模块，提高网络对目标成像尺寸变化的感知能力。最后，使用辅助边界框优化损失，丰富监督信息，增强网络对小目标的检测性能。在交通监控数据集UA-DETRAC中的实验结果表明：该算法平均精度均值较Faster R-CNN、EfficientDet-D5、YOLOv8及YOLOv11分别高出13.5、10.9、1.7、0.4个百分点；检测速度达112 FPS，参数量仅2.2×106，为智能监控下目标检测任务提供技术支持。

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刘春友（1979—），男，副教授，硕士，主要研究方向为软件开发与深度学习。E-mail：

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刘春友（1979—），男，副教授，硕士，主要研究方向为软件开发与深度学习。E-mail：541913386@qq.com

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刘春友（1979—），男，副教授，硕士，主要研究方向为软件开发与深度学习。E-mail：541913386@qq.com

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To address the challenges of communication infrastructure damage and paralysis faced by military personnel during emergency rescue operations, a three-dimensional spatial deployment optimization model for unmanned aerial vehicle (UAV) base stations is established. The fitness function is defined to maximize system capacity and the number of covered users. The electric eel foraging optimization (EEFO) algorithm is employed to optimize the 3D deployment positions of UAV base stations, with the optimal solution obtained through iterative optimization. Simulation results demonstrate that, under given constraints, the proposed algorithm exhibits significant advantages in communication network coverage compared to the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), particle swarm optimization (PSO), coot optimization algorithm (COOT), and arctic puffin optimization (APO).

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针对部队执行抢险救援行动面临通信基础设施损毁或瘫痪等问题，以最大化系统容量和覆盖用户数量为适应度函数，建立无人机基站三维空间部署位置优化模型。使用电鳗觅食优化算法（EEFO）对无人机基站三维空间部署位置进行优化，通过迭代获取最优解。仿真结果表明，该算法在满足约束条件的前提下，相较于鲸鱼优化算法（WOA）、哈里斯鹰算法（HHO）、粒子群算法（PSO）、白骨顶鸡优化算法（COOT）、北极海雀优化算法（APO），在通信网络覆盖方面具有明显优势。

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：panboyang@126.com

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潘博阳（1987—），男，讲师，硕士，主要研究方向为智能优化算法。E-mail：panboyang@126.com

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Using Flajolet’s theory of combinatorial species, the study of the continued fraction expansion of the generating function for general combinatorial numbers naturally leads to the examination of other combinatorial numbers that have algebraic connections with Motzkin numbers. Based on the study of Motzkin and Catalan lattice paths, as well as the combinatorial models for Schröder numbers and Delannoy numbers, a transformation relationship between two different types of Catalan lattice paths is discovered. This leads to the idea of establishing appropriate lattice path transformations for other combinatorial numbers. By utilizing the equivalence theorem between the generating functions of certain labeled lattice paths on a plane and the Stieltjes-Jacobi type continued fractions, the continued fraction expressions for large Schröder paths, little Schröder paths are derived, and some algebraic conclusions related to Delannoy paths are obtained.

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通过Flajolet的连分式组合学理论，研究一般组合数母函数的连分式展开式，一种自然的想法是考察与Motzkin数有代数联系的其他组合数，基于Motzkin和Catalan格路的研究以及对Schröder 数和Delannoy数组合模型的研究，发现Catalan格路两种不同格路径的转化关系，进而引出对其他组合数建立适当格路转化的思路，通过利用平面上某些带标签的格路径的母函数与Stieltjes-Jacobi型连分式等价定理，得出大Schröder路、小Schröder路的连分式表达式以及Delannoy路的连分式在代数方面的一些相关结论。

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高 冰（1993—），男，硕士，主要研究方向为组合数学。E-mail：

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高 冰（1993—），男，硕士，主要研究方向为组合数学。E-mail：27159137@alu.cqu.edu.cn

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Adversarial training, as an important technique for enhancing model robustness, faces problems of high training costs and inability to defend against multiple adversarial attacks. An improved adversarial training approach based on meta-learning is proposed. By integrating pre-training fine-tuning and diffusion model data generation strategies, a dual-branch training architecture is designed. One branch is fine-tuned on an l∞ robust model to improve its l∞ robustness, and the other branch trains against composite adversarialattacks to enhance the model’s defense capabilities against non-lp norm attacks. During training, the weights of both branches are fused through a mixed model and periodically reinitialized, enabling the final model to simultaneously resist both l∞ attacks and composite adversarial attacks. Experimental results show that the proposed approach maintains l∞ robustness while achieving superior defensive performance against composite adversarial attacks on the composite adversarial robustness benchmark (CARBEN).

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作为提高模型鲁棒性的重要技术，对抗训练存在训练成本高昂、无法抵御多种对抗攻击的问题。提出一种基于元学习的对抗训练改进方法，通过结合预训练微调和扩散模型数据生成策略，设计了双分支训练架构：一个分支在l∞鲁棒模型上进行微调以提高l∞鲁棒性；另一个分支针对组合对抗攻击训练，增强模型对非lp 范数攻击的防御能力。训练中，两个分支的权重通过混合模型进行融合，并以定期重新初始化，使最终模型同时具备抵御l∞攻击和组合对抗攻击的能力。实验结果表明，所提方法在保持l∞鲁棒性的同时，在组合对抗鲁棒性基准（CARBEN）上对组合对抗攻击的防御达到了优异水平。

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刘文钊（1984—），男，助理研究员，博士生，主要研究方向为深度学习和大数据安全。E-mail：

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2.电子信息系统复杂电磁环境效应国家重点实验室，河南 洛阳 471003","bio":"{\"content\":\"

刘文钊（1984—），男，助理研究员，博士生，主要研究方向为深度学习和大数据安全。E-mail：ibestwish@qq.com

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刘文钊（1984—），男，助理研究员，博士生，主要研究方向为深度学习和大数据安全。E-mail：ibestwish@qq.com

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To address performance bottlenecks in cross-architecture binary code representation learning caused by compiler optimizations, architectural variations, and code obfuscation, while mitigating data scarcity constraints in binary vulnerability detection, the DeepClap framework is proposed. Key innovations include the following aspects. A quantized DeepSeek model for code explanation generation. A lightweight residual alignment network to reduce training costs with enhanced representation fidelity. A natural language processing (NLP) -bridged vulnerability detection method linking target binary code is constructed to source code vulnerability datasets. Experimental results demonstrate the improvement of 14.8% in the baseline model's area under the receiver operating characteristic curve (AUC) for binary code similarity analysis. Zero-shot evaluation shows 7.1 percentage point accuracy enhancement, while cross-modal vulnerability retrieval achieves mean reciprocal rank (MRR) of 0.76 and recall@1 of 0.73. The framework is verified to significantly improve cross-architecture code representation quality and vulnerability detection capability, exhibiting particular effectiveness in data-scarce and zero-shot scenarios.

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针对跨架构二进制代码表示学习中编译器优化、架构差异及代码混淆导致的性能瓶颈问题，以及二进制漏洞数据稀缺对检测模型发展的限制，提出DeepClap框架。该框架通过本地部署量化DeepSeek模型生成代码解释，设计轻量级残差对齐网络降低训练成本并提高表示质量，构建基于自然语言桥梁的漏洞跨模态检测方法，将目标二进制代码关联至源代码漏洞数据集缓解数据稀缺。实验结果表明：在二进制代码相似性分析任务中，该框架使基础模型的受试者工作特征曲线下面积（AUC）最高提升14.8个百分点；在零样本评估中，基础模型准确率最高提升7.1个百分点；在跨模态漏洞搜索任务中达到平均倒数排名（MRR）0.76与召回率@1（Recall@1）0.73。研究表明，该方法有效改善了跨架构条件下的代码表示质量与漏洞检测能力，在数据稀缺和零样本场景中具有显著优势。

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杜 江（1990—），男，博士生，主要研究方向为二进制代码相似性分析。E-mail：

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杜 江（1990—），男，博士生，主要研究方向为二进制代码相似性分析。E-mail：dujiang1101@126.com

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杜 江（1990—），男，博士生，主要研究方向为二进制代码相似性分析。E-mail：dujiang1101@126.com

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刘硕硕（1994—），男，硕士生，主要研究方向为信息作战。E-mail：625414352@qq.com

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刘硕硕（1994—），男，硕士生，主要研究方向为信息作战。E-mail：625414352@qq.com

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In response to the jamming issue of linear frequency modulation-multiple input multiple output (LFM-MIMO) radar, a periodic alternating non-uniform intermittent sampling repeater jamming (ISRJ) method is proposed. In this method, the sampling duration is periodically changed. The pulse signal of the LFM-MIMO radar is subjected to short-time sampling, followed by multiple forwarding, and this process is repeated until the end of the pulse. First, the basic principles of ISRJ in uniform and non-uniform modes are elaborated. Then, the non-uniform ISRJ signal model for the LFM-MIMO radar signals is derived, and the causes of false targets generated by this model are analyzed. Through simulation experiments, the impacts of parameters such as sampling pulse width, sampling method, and forwarding times on the jamming effect are analyzed. The results indicate that a group of false targets with non-regular distribution can be generated by using the proposed method, and a certain degree of suppression jamming effect can be achieved on the basis of deception jamming when a higher number of forwarding times is adopted.

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针对线性调频多输入多输出（LFM-MIMO）雷达的干扰问题，提出一种周期交替式非均匀间歇采样转发干扰（ISRJ）方法。该方法通过周期式地改变采样时长，在对LFM-MIMO雷达的脉冲信号进行短时采样后多次转发，并重复该过程至脉冲结束。首先，阐述均匀、非均匀两种模式下ISRJ干扰的基本原理；然后，推导面向LFM-MIMO雷达信号的非均匀ISRJ干扰信号模型，并分析该模型产生假目标的成因。通过仿真实验分析采样脉宽、采样方式与转发次数等参数对干扰效果的影响，结果表明该方法可生成非规律性分布的假目标群，且采用较高转发次数时可在欺骗干扰的基础上实现一定的压制干扰效果。

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吕品品（1985—），男，副教授，博士生，主要研究方向为雷达信号处理、目标定位等。E-mail：15903676771@163.com

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吕品品（1985—），男，副教授，博士生，主要研究方向为雷达信号处理、目标定位等。E-mail：15903676771@163.com

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To address modulation recognition of multi-source time-frequency overlapped signals in non-cooperative wideband reception scenarios this issue, a multi-feature fusion-based modulation recognition method is proposed. Firstly, an improved DeepLabV3+ architecture is adopted, where the core innovation is embedding deformable atrous convolution into the atrous spatial pyramid pooling module, thereby enhancing dense focusing capability on signal energy core regions. Secondly, in the network multi-modal features are integrated simultaneously from both time-frequency and time-domain representations, improving recognition robustness through cross-modal feature complementarity. Finally, an end-to-end joint optimization framework is constructed to achieve efficient collaborative learning of multiple features. Experimental results demonstrate that under conditions of high aliasing and low signal-to-noise ratio, the average recognition accuracy of this method for 9 types of single signals and 21 types of aliase dsignals reaches 97.4%, which is 15.1 percentage points higher than that of the single-modal method, which validates the superiority of the multi-modal fusionstrategy.

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针对非协作宽带接收场景下多源时频混叠信号的调制识别问题，提出一种多特征融合的时频混叠信号调制识别方法。首先，采用改进的DeepLabV3+架构，其核心创新在于将可变形空洞卷积嵌入空洞空间金字塔池化模块，增强了对信号能量核心区的高密度聚焦能力；其次，网络同时融合时频图与时域图的多模态特征，通过跨模态特征互补提升识别鲁棒性；最后，构建了端到端的联合优化框架，实现多特征的高效协同学习。实验表明，在高混叠度与低信噪比条件下，该方法对9类单信号及21类混叠信号的平均识别精度达到97.4%，较单模态方法提升15.1个百分点，验证了多模态融合策略的优越性。

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To address the issue of category concept drift in network protocol data, a ViT-KANs-based dual-head algorithm for communication network protocol data category concept drift detection and classification is proposed. The global perception capability of vision transformer (ViT) and the flexible function approximation ability of kolmogorov-arnold networks (KANs) are integrated to construct an efficient feature extraction network in this algorithm. A dual-head parallel output structure is adopted to handle the classification of old-class data and the detection of category concept drift, respectively. Furthermore, the validation set data is utilized to adaptively compute the confidence threshold, which effectively alleviates the lack of concept drift samples during the training phase. Experiments are conducted on three datasets, namely the Moore dataset, the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset 2017 (CICIDS2017), and the improved version of the Network Security Lab (NSL)-Knowledge Discovery and Data Mining Competition Dataset (NSL-KDD). The results show that the detection error rate of the proposed method is reduced significantly, compared to those of models and out-of-distribution detection methods, while superior classification accuracy category is maintained.

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针对网络协议数据中的类别概念漂移问题，提出一种ViT-KANs的双头通信网络数据协议类别概念漂移检测分类算法。该算法通过集成Vision Transformer（ViT）的全局感知能力与Kolmogorov-Arnold Networks（KANs）的灵活函数逼近能力，构建高效的特征提取网络；并采用双头并行输出结构，分别处理旧类数据的分类与类别概念漂移的检测。此外，进一步利用验证集数据自适应计算置信度阈值，有效缓解训练阶段概念漂移样本缺失的问题。在Moore数据集、加拿大网络安全研究所2017年入侵检测评估数据集（CICIDS2017）和加拿大网络安全研究所对知识发现与数据挖掘竞赛数据集的改进版（NSL-KDD） 3个数据集上的实验表明，所提方法较基线模型及分布外检测方法显著降低了检测错误率，同时保持优越的分类精度。

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王润泽（1988—），男，硕士生，主要研究方向为数据分析。E-mail：

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王润泽（1988—），男，硕士生，主要研究方向为数据分析。E-mail：wrzbleach@126.com

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王润泽（1988—），男，硕士生，主要研究方向为数据分析。E-mail：wrzbleach@126.com

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面向复杂装备体系的全景可视化，设计可视要素多维关联模型，对各类装备资源进行统一建模，完成可视化数据的归一化采集、清洗、规整、关联和应用。首先，根据不同可视数据及其展示的特点，完成资源数据可视化的过程规范，提出基于空间统计的态势可视、装备板块层级可视等多样化布局方法，实现数据域向图形域的可视化映射；其次，通过对不同用户群及用户类别的使用偏好建模构筑用户偏好规则网，实现基于角色模型的个性化页面推荐；最后，实现装备、信号、环境、任务等多维可视化视图，支撑复杂装备体系全流程全维运行监视与维护，解决多种类、高维度、复杂关联数据资源的有效展示难题。

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虞 静（1984—），男，工程师，硕士生，主要研究方向为装备研究和管理。E-mail：81307703@qq.com

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Aiming at the problems of slow convergence speed and easy to fall into local optimum in the basic bitterling fish optimization (BFO) algorithm, a bitterling fish optimization based on hybrid strategy (HSBFO) algorithm is proposed. Firstly, the Tent-Logistic-Cosine chaotic mapping is used to initialize the population quality. Secondly, the double-sided mirror reflection theory is introduced to deal with the cross-border individuals and solve the problem of uneven population distribution. Finally, the Gaussian-Cauchy difference strategy is utilized to enhance the algorithm’s ability to escape from local optima. The HSBFO algorithm is compared with particle swarm optimization (PSO) algorithm, whale optimization algorithm (WOA), seagull optimization algorithm (STOA), sine cosine algorithm (SCA) and basic BFO algorithm to optimize nine benchmark test functions. The experimental results show that the HSBFO algorithm has better optimization accuracy than the other four optimization algorithms. The HSBFO algorithm is applied to the cantilever beam design problem, and the experimental results show that the performance of the HSBFO algorithm is better than the basic BFO algorithm in engineering optimization, which verifies the feasibility of the HSBFO algorithm in dealing with practical engineering problems.

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针对基本苦鱼优化（BFO）算法存在收敛速度较慢和易于陷入局部最优等问题，提出基于混合策略的苦鱼优化（HSBFO）算法。首先，利用Tent-Logistic-Cosine混沌映射初始化提高种群质量；其次，引入双面镜反射理论处理越界个体，解决种群分布不均匀问题；最后，利用高斯柯西差分策略提升算法跳出局部最优解的能力。将HSBFO算法与粒子群优化（PSO）算法、鲸鱼优化算法（WOA）、乌燕鸥优化算法（STOA）、正弦余弦算法（SCA）和基本BFO算法对9个基准测试函数进行寻优实验，实验结果表明HSBFO算法相较于其他4种优化算法寻优精度更好。将HSBFO算法应用于悬臂梁设计问题，实验结果表明HSBFO算法在工程优化方面性能优于基本BFO算法，验证HSBFO算法在处理实际工程问题的可行性。

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In response to the problems of traditional intrusion detection methods for high-dimensional traffic data, such as data imbalance, low feature extraction efficiency, and difficulty in convergence during training, an intrusion detection method based on deep convolutional neural networks combined with data balancing is proposed. Firstly, the CICIDS-2018 network traffic dataset is transformed into grayscale images with integer values, which are then input into a conditional generative adversarial network. Secondly, the trained generator is used to produce attack data for the minority class, which is added to the original dataset to balance it. Finally, the performance of intrusion detection is enhanced by using deep convolutional neural network. Experimental results show that the accuracy rate in multi-classification task of this method is 96.58%, which is superior to that of traditional detection methods, with the classification accuracy rates for the two least frequent attack traffic data Botnet and SQL increased by 5.83% and 32.18%, respectively, compared to those before balancing.

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针对传统入侵检测方法在处理高维流量数据时存在的数据不平衡、特征提取效率低以及训练不易收敛等问题，提出基于深度卷积神经网络结合数据平衡的入侵检测方法。首先，将网络流量数据集CICIDS-2018转化成数值为整数类型的灰度图像；其次，输入至条件生成对抗网路，利用训练的生成器生成少数类的攻击数据，补充到原始数据中以平衡数据集；最后，运用深度卷积神经网络提高入侵检测的性能。实验结果表明，该方法在多分类任务中的准确率为96.58%，优于传统的检测方法，其中两类样本最少的攻击流量数据Botnet和SQL的分类准确率较平衡前分别提升5.83%和32.18%。

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连 盛（2002—），男，硕士生，主要研究方向为入侵检测。E-mail：958292805@qq.com

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Addressing the alert delay issue in existing advanced persistent threat (APT) detection methods based on provenance graphs, a real-time detection scheme named StreamTGN is proposed, which relies on dynamic tracking of system entity states. An operational-level provenance graph with higher information hierarchy is first constructed. Subsequently, the rationality of system activities is dynamically analyzed through the state evolution of entities during system operation. Finally, abnormal behaviors potentially related to APT attacks are detected using dynamically set anomaly thresholds. Experimental results demonstrate that StreamTGN effectively addresses the “low-and-slow” behavioral characteristics of APT attacks while exhibiting stronger detection stability and robustness compared to existing approaches.

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针对现有基于溯源图的高级持续性威胁（APT）检测方法存在的报警延迟问题，提出一种基于系统实体状态动态追踪的实时检测方案StreamTGN。首先，构造信息层级更高的操作级溯源图；其次，通过实体在系统运行过程中的状态演变，动态分析系统活动的合理性；最终，通过动态设置的异常阈值，检测与APT攻击潜在相关的异常行为。实验结果表明，该方案可有效应对APT攻击“低频率，长周期”的行为特征，且相较现有研究表现出更强的检测稳定性和鲁棒性。

","authors":"

陈明豪（1996—），男，博士生，主要研究方向为基于溯源图的APT攻击检测。E-mail：

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陈明豪（1996—），男，博士生，主要研究方向为基于溯源图的APT攻击检测。E-mail：malwaretraffic@163.com

\"}","bioImg":null,"bioContent":"

陈明豪（1996—），男，博士生，主要研究方向为基于溯源图的APT攻击检测。E-mail：malwaretraffic@163.com

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Adaptation Enhancement Method for Network Traffic Detection Based on kNN Retrieval","columnId":null,"journalTitle":"Journal of Information Engineering University","columnName":null,"runingTitle":null,"highlight":null,"articleAbstract":"

To address the issue that the insufficient generalization ability of the model due to the data distribution difference between the target domain and the source domain in network traffic detection leads to declined detection performance, a non-parametric traffic detection method with enhanced domain adaptation ability is proposed. Specifically, during the model fine-tuning stage, a dictionary is constructed by integrating the deep representations of training samples with their labels. In the detection phase, the dictionary is queried to correct the prediction results of the parameterized detection model based on the probability distribution of the k most similar samples to the sample under test. This approach achieves stable and robust detection results without increasing training sample size or model training overhead and possesses high interpretability. Extensive experiments on cross-domain scenarios with imbalanced distributions (e.g., USTC-TFC2016 dataset) demonstrate that the model’s adaptation ability and detection performance are significantly enhanced by the method, and the effectiveness and robustness of the method are further verified under different new domain data distributions.

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针对网络流量检测中目标域与源域之间数据分布差异导致的模型泛化能力不足从而使检测性能下降的问题，提出一种增强域适应能力的非参数化流量检测方法。该方法在模型微调阶段，将训练样本的深度表示与标签构建成一个字典。在检测阶段，同时检索该字典，依据与待检测样本最相似的k个样本的概率分布，对参数化检测模型的预测结果进行校正。在不增加训练样本量以及模型训练开销的前提下，实现稳定且鲁棒的检测结果，且该方法具备高可解释性。实验结果表明，在由USTC-TFC2016等数据集构成的具有不平衡分布的跨域场景中，该方法显著提升了模型的适应能力与检测性能，并且在不同新域数据分布下验证了其有效性与鲁棒性。

","authors":"

孙剑文（1988—），女，工程师，博士生，主要研究方向为流量异常检测、机器学习等。E-mail：

","authorsList":"孙剑文, 张斌, 张昊","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":"pvxV5FGU4sABq6aENV4sQQ==","magXml":"4amdtHq5YotaS6jKXUjjXA==","pdfUrl":null,"pdf":"2OL5UtQIzGqA2xj6GQnSkg==","pdfFileSize":4018509,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":"E6Na2yZNXIhgI9U9sCyyrg==","abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":"EmqUx/X6LRnzN3+DHE2Hkw==","mapNumber":null}},"authors":[{"id":"1222590884668596697","tenantId":"1045748351789510663","journalId":"1155139928303341809","articleId":"1222590882269454572","orderNo":0,"firstName":null,"middleName":null,"lastName":null,"nameCn":null,"orcid":null,"stid":null,"country":null,"authorPic":null,"dead":0,"email":"jianwensun_edu@163.com","emailSecond":null,"emailThird":null,"correspondingAuthor":0,"authorType":1,"ext":{"EN":{"id":1222590884769260003,"tenantId":1045748351789510663,"journalId":1155139928303341809,"articleId":1222590882269454572,"authorId":1222590884668596697,"language":"EN","stringName":"Jianwen SUN","firstName":"Jianwen","middleName":null,"lastName":"SUN","prefix":null,"suffix":null,"authorComment":null,"nameInitials":null,"affiliation":null,"department":null,"xref":"1, 2","address":"1.Information Engineering University, Zhengzhou 450001, China
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2.32708部队，北京 102300","bio":"{\"content\":\"

孙剑文（1988—），女，工程师，博士生，主要研究方向为流量异常检测、机器学习等。E-mail：jianwensun_edu@163.com

\"}","bioImg":null,"bioContent":"

孙剑文（1988—），女，工程师，博士生，主要研究方向为流量异常检测、机器学习等。E-mail：jianwensun_edu@163.com

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Format inference technology is the core part of the unknown protocol reverse analysis framework, and its division accuracy directly determines the reliability of protocol reverse and security detection, so it has become a hot issue in the field of protocol reverse analysis in recent years. Firstly, the classification of unknown protocol format inference methods is given from two aspects: different analysis methods and different message formats. Secondly, the core ideas of various unknown protocol format inference methods are systematically sorted out from five dimensions of sequence alignment, probability statistics, frequent item mining, deep learning and fusion, and the advantages and limitations of various methods are deeply analyzed. Finally, the challenges and future development directions of unknown protocol format inference technology are prospected from two aspects of technical innovation and application expansion.

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李远博（1988—），男，讲师，博士生，主要研究方向为网络拟态防御。E-mail：

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李远博（1988—），男，讲师，博士生，主要研究方向为网络拟态防御。E-mail：200900501775@lit.edu.cn

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李远博（1988—），男，讲师，博士生，主要研究方向为网络拟态防御。E-mail：200900501775@lit.edu.cn

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Aiming at the problem that some static ontologies cannot meet the dynamic update requirements and systematic representation of domain knowledge, a semantic primitives-driven method for ontology model construction and update is proposed. Firstly, by using universal information extraction (UIE) technology and dependency syntactic analysis technology, the event relation set and the component triplet set are obtained respectively. Secondly, by using the methods of part-of-speech tagging and name similarity calculation, the part-of-speech and name similarity of specific elements in the two sets is judged to obtain the matching set of trigger words, the matching set of event arguments and the unmatched set. Then, the similarity of semantic primitives is calculated to extract the common semantic primitives of candidate words and auxiliary word sets within the set. These primitives are then sorted through degree centrality calculation, and the most suitable ones are selected as the preselected new ontology. Finally, with the aid of the Protégé ontology construction tool, dynamic updates are carried out by taking two different ontology models of urban information and geographical points of interest as examples, verifying the effectiveness, robustness and universality of the method.

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针对部分静态本体不能满足领域知识的动态更新需求与系统化表示的问题，提出一种义原驱动的本体模型构建与更新方法。首先，运用通用信息抽取（UIE）技术与依存句法分析技术，分别获取事件集合与成分三元组集合；其次，采用词性标注与名称相似性计算方法，对两组集合中特定元素判断词性与名称相似性，得到触发词匹配集合、事件论元匹配集合与未匹配集合；再次，义原相似度被计算以提取集合中候选词与辅助词集的公共义原，通过度中心性计算对义原排序，选择最合适义原作为预选的新增本体；最后，借助Protégé本体构建工具，以城市信息和地理兴趣点两个不同本体模型为例进行动态更新，验证了方法的有效性、鲁棒性与通用性。

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马式纪（2000—），男，硕士生，主要研究方向为本体映射与知识图谱构建。E-mail：

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马式纪（2000—），男，硕士生，主要研究方向为本体映射与知识图谱构建。E-mail：mashiji2000@foxmail.com

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马式纪（2000—），男，硕士生，主要研究方向为本体映射与知识图谱构建。E-mail：mashiji2000@foxmail.com

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To address the issues that the traditional algorithms have achieved low accuracy, and research on solving track association tasks from the perspective of interactive analysis is still lacking, a interactive analysis approach for ship target track association and fusion under multi-source detection is proposed. Firstly, the nearest neighbor distance track association algorithm is improved for interrupted track association and multi-source track association. Secondly, the visual analysis advantages of “human in the loop”are fully utilized, and the weight of the sensor in the trajectory fusion process is calculated by evaluating its stability. Finally, the interrupted trajectory stitching method based on interpolation fitting and the multi-source track association method based on weighted average are used for trajectory fusion. The effectiveness of the proposed algorithm in enhancing the correct rate of interrupted track association and reducing the error rate of multi-source track association is demonstrated throught the results of experiments. The designed interactive analysis system can verify the effectiveness of the fusion algorithm. Through interactive analysis and improved correlation fusion algorithm, the task of interrupted track and multi-source track association can be completed more accurately.

","authors":null,"authorsList":"Xiaohui CHEN, Jianxiang LIU, Cong ZHANG, Bing ZHANG, Yunpeng ZHAO","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":null,"magXml":null,"pdfUrl":null,"pdf":null,"pdfFileSize":null,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":null,"abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":null,"mapNumber":null},"CN":{"id":"1222590887877239467","articleId":"1222590883917816190","tenantId":"1045748351789510663","journalId":"1155139928303341809","language":"CN","title":"基于交互式分析的多源航迹关联融合方法","columnId":"1210243631342080935","journalTitle":"信息工程大学学报","columnName":"测绘科学与技术","runingTitle":null,"highlight":null,"articleAbstract":"

针对传统航迹关联算法正确率不高、交互分析解决航迹关联融合任务研究较少等问题，提出一种基于交互式分析的多源船舶目标航迹关联融合方法。首先改进最近邻距离的航迹关联算法进行中断航迹关联和多源航迹关联，其次发挥“人在回路”的交互式分析优势，通过评估传感器稳定性计算其在航迹融合过程中的权重，最后采用基于插值拟合的中断航迹拼接方法和基于加权平均的多源航迹融合方法实现航迹融合。实验结果表明，所提出的航迹关联算法能够有效提高中断航迹关联的关联正确率，降低多源航迹关联的关联错误率，设计的交互式分析系统能够验证融合算法的有效性，通过交互式分析和改进的关联融合算法，能够更准确地完成中断航迹和多源航迹的关联任务。

","authors":"

陈晓慧（1983—），女，副教授，博士，主要研究方向为时空数据可视化。E-mail：

","authorsList":"陈晓慧, 刘建湘, 张匆, 张兵, 赵云鹏","authorCompany":null,"correspAuthors":null,"authorNote":null,"correspAuthorsNote":null,"copyrightStatement":null,"copyrightOwner":null,"extLink":null,"articleAbsUrl":null,"sourceXml":"+dTZsmcAxyLUjyV9OwBS5Q==","magXml":"ZukpgzhUTt40bF3A86p+QQ==","pdfUrl":null,"pdf":"i4+j3o6CiMa66wZMbJRP0A==","pdfFileSize":4026303,"pdfExtLink":null,"richHtmlUrl":null,"mobilePdfUrl":null,"reviewReport":null,"pdfFirstPage":null,"abstractGraph":"0Q1E5b0q8zyfgMkygbWhjw==","abstractGraphContent":null,"abstractVideo":null,"citation":null,"cebUrl":null,"magXmlContent":"MC/pgLefdrtn1krDnOtRlg==","mapNumber":null}},"authors":[{"id":"1222590888653185743","tenantId":"1045748351789510663","journalId":"1155139928303341809","articleId":"1222590883917816190","orderNo":0,"firstName":null,"middleName":null,"lastName":null,"nameCn":null,"orcid":null,"stid":null,"country":null,"authorPic":null,"dead":0,"email":"cxh_vrlab@163.com","emailSecond":null,"emailThird":null,"correspondingAuthor":0,"authorType":1,"ext":{"EN":{"id":1222590888716100306,"tenantId":1045748351789510663,"journalId":1155139928303341809,"articleId":1222590883917816190,"authorId":1222590888653185743,"language":"EN","stringName":"Xiaohui CHEN","firstName":"Xiaohui","middleName":null,"lastName":"CHEN","prefix":null,"suffix":null,"authorComment":null,"nameInitials":null,"affiliation":null,"department":null,"xref":"1","address":"1.Information Engineering University, Zhengzhou 450001, China","bio":null,"bioImg":null,"bioContent":null,"aboutCorrespAuthor":null},"CN":{"id":1222590888762237652,"tenantId":1045748351789510663,"journalId":1155139928303341809,"articleId":1222590883917816190,"authorId":1222590888653185743,"language":"CN","stringName":"陈晓慧","firstName":null,"middleName":null,"lastName":null,"prefix":null,"suffix":null,"authorComment":null,"nameInitials":null,"affiliation":null,"department":null,"xref":"1","address":"1.信息工程大学，河南 郑州 450001","bio":"{\"content\":\"

陈晓慧（1983—），女，副教授，博士，主要研究方向为时空数据可视化。E-mail：cxh_vrlab@163.com

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陈晓慧（1983—），女，副教授，博士，主要研究方向为时空数据可视化。E-mail：cxh_vrlab@163.com

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To address issues such as low efficiency in multi-source feature fusion and poor characterization of soil boundary features during soil element extraction in non-close-range areas, a military geological soil classification method based on spatial-frequency feature adaptive fusion network (SFAM-Net) is proposed. The spatial and spectral attention mechanisms are introduced in the encoder-decoder architecture, and a dual-branch feature extraction module is constructed to capture both spatial and spectral characteristics of soil in imagery. Features are extracted through the fusion of convolutional block attention modules, and an adaptive fusion mechanism is adopted to optimize the distribution of feature weights. Experimental results demonstrate that SFAM-Net can effectively distinguish three typical soil types, namely rocky soil, hard soil and ordinary soil, and their boundary information. The mean intersection over union (mIoU) and mean pixel accuracy (mPA) are 71.16% and 84.60% respectively, which are 5.46 percentage points and 8.11 percentage points higher than the best result of the baseline model. It is verified that the accuracy of military geological soil classification are improved by using the method.

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针对非抵近地区土体要素提取中多源特征融合效率低和土体边界特征表征差等问题，提出采用空谱特征自适应融合网络的军事地质土体分类方法（SFAM-Net）。在编码器—解码器结构上，引入空间和光谱注意力机制，构建双分支特征提取模块来提取影像中土体的空间、光谱特征；通过卷积块注意力模块融合提取特征，并采用自适应融合机制优化特征权重分配。实验结果显示，SFAM-Net能对夹石土、硬土和普通土这3种典型土体类型及其边界信息进行有效区分，平均交并比（mIoU）、平均像素精度（mPA）分别为71.16%和84.60%，较基线模型最佳结果分别提升5.46个百分点和8.11个百分点，验证该方法能够提高军事地质土体分类精准度。

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李雪嫚（2000—），女，硕士生，主要研究方向为地图制图学与地理信息工程。E-mail：

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李雪嫚（2000—），女，硕士生，主要研究方向为地图制图学与地理信息工程。E-mail：17836965726@163.com

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李雪嫚（2000—），女，硕士生，主要研究方向为地图制图学与地理信息工程。E-mail：17836965726@163.com

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To address the performance degradation of adaptive beamforming algorithms for uniform circular arrays (UCA) in multipath coherent environments, a robust adaptive beamforming algorithm with high array gain is proposed. Firstly, the UCA is transformed into a virtual uniform linear array (VULA) through modal domain transformation, followed by a spatial smoothing technique to achieve decorrelation. Secondly, the interference steering vector is estimated using the minimum power projection method, while the steering vector of the signal of interest (SOI) is obtained via a convex optimization approach that maximizes the output power. Then, the powers of the interference and noise are estimated using the orthogonality of subspace eigenvectors. Finally, the interference-plus-noise covariance matrix (INCM) is reconstructed based on the estimated parameters, and the beamforming weight vector is computed accordingly. Theoretical analysis and simulation results demonstrate that the proposed algorithm achieves favorable output SINR under various SNR conditions, verifying its effectiveness. Furthermore, its robust performance under different model mismatch scenarios confirms the algorithm’s robustness.

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针对多径相干环境导致的均匀圆形阵列（UCA）下自适应波束形成算法失效问题，提出一种高阵列增益的均匀圆形阵列稳健自适应波束形成算法。首先，通过模态空间变换将UCA转换为虚拟均匀线阵（VULA），随之采用平滑的方法达到UCA去相干的目的；其次，通过最小功率投影估计干扰导向矢量，期望信号（SOI）的导向矢量则通过最大输出功率的凸优化方法获得；再次，利用子空间特征向量正交的方法估计干扰信号和噪声的功率；最后，根据估计的参数重构干扰加噪声协方差矩阵（INCM）并计算权向量。理论分析和仿真实验表明：所提算法在不同信噪比（SNR）下的良好输出信干噪比（SINR）证明了其有效性，不同误差条件下的良好输出结果证明了所提算法的鲁棒性。

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田果林（2000—），男，硕士生，主要研究方向为阵列信号处理。E-mail：

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田果林（2000—），男，硕士生，主要研究方向为阵列信号处理。E-mail：13526406712@163.com

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A cross-regional visual encoding method based on multi-gate mixture-of-experts (MMoE) is proposed to address the problem that functional magnetic resonance imaging (fMRI) signals of various visual regions in the human brain are heterogeneous, and existing single-region modeling methods ignore the influence of other brain regions and fail to effectively predict fMRI signals. First, the encoding of adjacent visual cortices is converted into a multi-task learning problem to simulate cross-regional interactions. Second, a parallel expert network is constructed to capture cross-regional shared features and region-specific information. Finally, an adaptive gating function is designed to dynamically fuse the outputs of experts, further to simulate the cross-regional interaction mechanism of the biological visual system. Experimental results demonstrate that the prediction accuracy of the model in each subregion of V1~V3 is significantly higher than that of the single-region baseline model, with the proportion of dominant voxels in V1v and V1d reaching 81.96% and 92.38%, respectively, validating the effectiveness of the visual encoding method integrating multi-region information.

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针对人脑各视觉区功能磁共振成像（fMRI）信号具有异质性，且现有单区域建模方法忽略其他脑区影响、无法有效预测fMRI信号的问题，提出了一种基于多门混合专家（MMoE）的跨区域视觉编码方法。首先，该方法将相邻视觉皮层编码转换为多任务学习问题来模拟跨区域交互；其次，构建并行专家网络，通过该网络捕获跨区域共性特征与区域特异性信息；最后，设计自适应的门控函数，利用该函数动态融合专家输出，进而模拟生物视觉系统的跨区域交互机制。实验结果表明，模型在V1~V3各子区域预测准确率较单区域基准模型显著提升（V1v、V1d的优势体素占比达81.96%、92.38%），验证了融合多区信息的视觉编码方法的有效性。

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赵未辰（2000—），男，硕士生，主要研究方向为认知域信息处理。E-mail：Zhaoweichenzeyu@163com

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赵未辰（2000—），男，硕士生，主要研究方向为认知域信息处理。E-mail：Zhaoweichenzeyu@163com

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赵未辰（2000—），男，硕士生，主要研究方向为认知域信息处理。E-mail：Zhaoweichenzeyu@163com

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针对复杂电磁环境下传统调制识别方法鲁棒性差，及现有ResNet-CNN-BiLSTM模型计算复杂度高、训练收敛慢的问题，提出一种多维度协同优化的改进模型，实现信号调制识别高精度、轻量化与实时性协同。通过多尺度核与深度可分离卷积适配时频特征，自适应时序池化保障时序完整性，输入降采样与门控合并精简BiLSTM结构；引入分层正则化与信噪比（SNR）感知动态学习率增强噪声鲁棒性，改进多维度置信度计算提升决策可靠性。实验表明，优化后模型验证准确率达98.17%，参数量降至1.5×106（减少51.6%），训练时间48 min20 s，单样本推理时间为8 ms，低SNR环境误判率降低4.2%~6.8%，为算力受限场景提供高效方案。

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Multi-protocol SerDes is a key circuit module for software-defined interconnect chips. To meet the requirements of various protocol specifications and multiple rates for SerDes circuits in such chips, a multi-protocol SerDes timing constraints method for software-defined interconnect chips is designed and implemented. Firstly, the rate and clock requirements of SerDes for different protocols are clarified through analysis of the clock structure of multi-protocol SerDes. Secondly, internal and external clock constraints with protocol tags of SerDes are designed for the clock requirements of four protocols, and a single packetized constraint file is generated. Finally, timing verification and tape-out testing demonstrate that all timing paths meet the timing requirements, with the timing constraints design being confirmed as correct and complete.

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多协议SerDes是软件定义互连芯片的关键电路模块，为满足软件定义互连芯片中多种协议规范及多种速率对SerDes电路的需求，设计并实现一种面向软件定义互连芯片的多协议SerDes时序约束方法。首先，通过分析多协议SerDes时钟结构，明确了不同协议对SerDes的速率和时钟需求；然后，针对4种协议的时钟需求，设计了SerDes内外部带协议标签的时钟约束，生成了单一分组化约束文件。最后，时序验证及流片测试表明，所有时序路径均满足时序要求，时序约束设计正确、完备。

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张 丽（1982—），女，副研究员，硕士，主要研究方向为软件定义互连。E-mail：

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张 丽（1982—），女，副研究员，硕士，主要研究方向为软件定义互连。E-mail：yezizhang2003@126.com

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张 丽（1982—），女，副研究员，硕士，主要研究方向为软件定义互连。E-mail：yezizhang2003@126.com

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Electroencephalogram (EEG) large models have emerged as a significant research direction in EEG analysis due to their exceptional representation learning capabilities. To systematically review progress in this field, the core theories and key technologies underlying these models are first analyzed, examining their theoretical foundations and design principles. Subsequently, a comprehensive comparison of mainstream EEG large models is conducted across dimensions including model concepts, architectural designs, feature extraction methods, learning paradigms, and model scales. A particular emphasis is placed on comparing and analyzing the binary classification performance of the datasets and downstream tasks used by these models. Finally, based on the analytical results, future development directions for EEG large models are discussed. A knowledge framework of EEG large models is established to serve as a reference for subsequent research and promote in-depth development in the field.

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脑电大模型凭借其卓越的表征学习能力，已成为脑电领域的重要研究方向。为系统梳理该领域研究进展，首先，从核心理论与关键技术出发，深入剖析脑电大模型的理论根基与设计原理；其次，围绕模型概念、架构设计、特征提取、学习范式与模型规模等维度，对主流脑电大模型的研究现状与技术特点进行全面对比介绍，并重点对模型使用数据库和下游任务中的二分类性能表现进行对比分析；最后，结合研究现状展望脑电大模型未来发展方向。通过构建脑电大模型知识框架，旨在为后续研究提供参考，推动该领域的深入发展。

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何中阳（1985—），男，讲师，博士生，主要研究方向为脑机接口技术。E-mail：zhongyanghe@foxmail.com

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何中阳（1985—），男，讲师，博士生，主要研究方向为脑机接口技术。E-mail：zhongyanghe@foxmail.com

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Corpus is the starting point for lip reading research. The lack of a universal Chinese lip reading corpus has become one of the key factors restricting the development of Chinese lip reading technology. In this review, the research advances of Chinese lip reading corpus are systematically and comprehensively summarized. First, two types of lip reading technologies are outlined. Then, existing major Chinese lip reading corpus are focused on, sorted out and summarized, with a comparative analysis conducted in-depth across multiple dimensions, including granularity, data scale, acquisition conditions, modality types, and recognition performance. Finally, the current challenges faced by Chinese lip reading corpus and potential countermeasures are discussed, and the possible application values of lip reading are prospected. References are provided for subsequent researchers, and the construction of a data ecosystem for Chinese lip reading is promoted.

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语料库是唇语识别研究的出发点，通用中文唇语语料库的缺乏已成为制约中文唇语识别技术发展的关键因素之一。系统梳理了中文唇语语料库研究进展。首先，简述唇语识别的两种技术。其次，重点梳理总结现有的主要中文唇语语料库，并从颗粒度、数据规模、采集条件、模态类型、识别效果等多个维度进行深入对比分析。最后，探讨中文唇语语料库当前面临的挑战和可能的应对措施，展望唇语识别可能的应用价值，为后续研究者提供有益参考，促进中文唇语识别数据生态的构建。

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何杰（1984—），男，副教授，博士，主要研究方向为网络安全、人工智能。E-mail：

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何杰（1984—），男，副教授，博士，主要研究方向为网络安全、人工智能。E-mail：jack.237@163.com

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何杰（1984—），男，副教授，博士，主要研究方向为网络安全、人工智能。E-mail：jack.237@163.com

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识别技术特征提取时序建模数据依赖适应性准确率
传统技术

人工设计，

如几何、纹理等特征

HMM、DTW等浅层模型

适用于小规模

语料库

对光照、姿态敏感

单词级约60%~80%，

句子级效果差

深度学习技术

自动学习，

如时空联合特征等

LSTM、GRU、Transformer等深度模型

依赖大规模

标注数据

支持多视角、

复杂背景

单词级可达85%以上，

句子级错误率显著降低

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To explore the support patterns of aircraft spare parts, an unsupervised machine learning-driven classification method for aircraft spare parts is proposed. Firstly, the maximal information coefficient is used to detect the correlation between various dimensions of aircraft spare parts support data. Secondly, kernel principal component analysis (KPCA) based on the Gaussian kernel function is adopted to preprocess the aircraft spare parts support data with low correlation. Finally, the Newton-Raphson-based optimizer (NRBO) and dynamic fuzzy parameters are applied to find the optimal position of the fuzzy C-means (FCM) clustering centroid for aircraft spare parts, and the optimal result of aircraft spare parts classification is generated in an adaptive iterative manner. Experimental results show that, under the condition of the same principal component data for aircraft spare parts support, compared with 3 traditional methods, FCM clustering, genetic algorithm-optimized FCM clustering, and particle swarm optimization-based FCM clustering, this method exhibits superior performance in rapid convergence exploration and the ability to escape local optimal solutions. It can achieve better aircraft spare parts classification results and provide a scientific basis for support decisions related to aircraft spare parts, such as procurement, inventory management, and maintenance.

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为挖掘飞机备件保障规律，提出一种无监督机器学习驱动的飞机备件分类方法。通过最大信息系数检测飞机备件保障数据各维度相关性，采用基于高斯核函数的核主成分分析（KPCA）预处理相关性低的飞机备件保障数据，应用牛顿-拉夫逊优化算法（NRBO）和动态模糊参数寻找飞机备件模糊C均值（FCM）聚类质心最佳位置，自适应迭代生成飞机备件分类最优结果。实验结果表明，在相同飞机备件保障主成分数据条件下，相较于传统模糊C均值聚类、遗传算法优化模糊C均值聚类、粒子群优化模糊C均值聚类3种方法，该方法拥有更优越的快速探寻收敛性能和跳出局部最优解能力，可实现更佳效果的飞机备件分类，为飞机备件采购、库存、修理等保障决策提供科学依据。

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朱 臣（1995—），男，讲师，硕士，主要研究方向为航材保障决策与信息化。E-mail：

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朱 臣（1995—），男，讲师，硕士，主要研究方向为航材保障决策与信息化。E-mail：jason19950203@qq.com

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朱 臣（1995—），男，讲师，硕士，主要研究方向为航材保障决策与信息化。E-mail：jason19950203@qq.com

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Regarding the issue of being unable to simultaneously achieve high detection accuracy for both resource-consuming high-traffic attacks and stealth-based dormant attacks, a spatiotemporal network traffic detection framework (CLA+SubModule) tailored for asymmetric attack scenarios is proposed, which is designed to enhance detection accuracy for both attack types. Firstly, issues related to data quality and scale differences are addressed through data preprocessing, thereby minimizing the impact of class imbalance on model performance. Secondly, spatiotemporal features of network traffic are jointly learned at multiple levels through the integration of CNN, LSTM networks, and AM. Additionally, a small-sample detection branch (SubModule) based on adaptive weighting is constructed to enhance the model’s focus on small-sample attacks. Finally, model performance is validated using a test set. Experimental results show that the proposed framework is shown to effectively capture spatiotemporal features of attack sources, achieving detection accuracies exceeding 99.85% for resource-consuming attacks and 92.72% for stealth dormant attacks, significantly outperforming traditional machine learning methods and mainstream deep learning models.

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针对资源消耗型大流量攻击和隐蔽为主的蛰伏型攻击检测准确率不能兼顾的问题，提出一种面向非对称攻击场景网络流的时空检测技术（CLA+SubModule）架构。首先，通过数据预处理解决数据质量、量纲差异问题，最小化类别不平衡对模型的性能影响；其次，通过融合卷积神经网络（CNN）、长短期记忆（LSTM）网络以及注意力机制（AM）模型，在多层次联合学习网络流量时空特征的同时，构建基于自适应权重的小样本检测分支（SubModule），进一步提高模型对小样本攻击的关注能力；最后，通过测试集进行模型性能检验。实验结果表明，技术架构能够有效聚焦攻击源的时空特征，资源消耗型、隐蔽蛰伏型攻击的检测准确率分别在99.85%和92.72%以上，模型性能显著优于传统机器学习方法和主流深度学习模型。

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王文江（1986—），男，工程师，硕士生，主要研究方向为网络空间安全。E-mail：

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王文江（1986—），男，工程师，硕士生，主要研究方向为网络空间安全。E-mail：13752001729@163.com

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王文江（1986—），男，工程师，硕士生，主要研究方向为网络空间安全。E-mail：13752001729@163.com

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To address the challenge of identifying IPSec products, a deep learning method based on data equilibrium combining Transformer and bidirectional long short-term memory network (DETB) is proposed. The method is divided into 3 stages: preprocessing, feature extraction, and feature fusion. In the preprocessing stage, raw traffic is divided into session-level bidirectional flows, and a data equilibrium strategy is introduced. In the feature extraction stage, global and spatio-temporal feature extraction are performed. In the feature fusion stage, global and spatio-temporal features are weighted and fused to achieve product classification. Experiments are conducted on the public dataset ISCXVPN-2016 and a self-built dataset IKEv1. The results show that F1 scores of 95.88% and 97.15% are achieved by using DETB, respectively. Compared with existing mainstream methods, significant advantages in classification performance and generalization capability are shown by using DETB.

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针对IPSec产品难以识别的问题，提出一种基于数据均衡的结合Transformer和双向长短期记忆网络的深度学习方法（DETB）。该方法分为预处理、特征提取和特征融合3个阶段，其中：预处理阶段将原始流量划分为会话级双向流，并引入数据均衡策略；特征提取阶段包含全局特征提取和时空特征提取；特征融合阶段则对全局和时空两类特征进行加权融合，实现产品分类。实验基于公开数据集ISCXVPN-2016与自建数据集IKEv1，结果表明，DETB的F1分数分别达到95.88%、97.15%。相比现有主流方法，DETB在分类性能与泛化能力方面展现出显著优势。

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To address the data imbalance issue faced by traditional graph neural network (GNN) based social bot detection methods in real-world scenarios, a multi-modal social bot detection framework specifically designed for imbalanced data is proposed. Firstly, user semantic and behavioral metadata are integrated to form a comprehensive user representation. Secondly, a heterogeneous graph neural network (HGNN) with a local-global dual aggregation mechanism is designed to effectively capture structural and semantic information, enabling deep characterization of user behavior patterns. Finally, an adaptive cost-sensitive learning module is introduced to dynamically adjust the loss weights of different class samples during training, thereby mitigating model bias caused by data imbalance and significantly improving the detection performance for minority classes. Experimental results show that the proposed method outperforms existing approaches on multiple real-world datasets, providing an effective solution to the class imbalance problem in bot detection.

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针对基于传统图神经网络（GNN）的社交机器人检测方法在现实场景下面临数据不平衡问题，提出一种面向数据不平衡的多模态社交机器人检测框架。首先，整合用户语义与行为元数据等多模态特征，形成全面的用户表示。其次，设计具备局部-全局双重聚合机制的异质图神经网络（HGNN），有效捕捉结构与语义信息，实现对用户行为模式的深层刻画。最后，引入自适应代价敏感学习模块，通过动态调整训练过程中不同类别样本的损失权重，以缓解数据不平衡带来的模型偏差，显著提升对少数类的识别性能。实验证明，所提方法在多个真实数据集上优于现有方法，为机器人检测中的类别不平衡问题提供有效解决方案。

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张诗博（1999—），女，博士生，主要研究方向为数据挖掘、异常检测。E-mail：

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张诗博（1999—），女，博士生，主要研究方向为数据挖掘、异常检测。E-mail：suzzyndsc@163.com

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Aiming at the challenges in long-term ship trajectory prediction, including high complexity of navigation behavior, insufficient intention mining, and difficulties in modeling long-term temporal dependencies, a ship trajectory prediction method based on a causal long-sequence transformer-LSTM hybrid model（CLT-TLSTM）is proposed. Using origin-destination voyage trajectory data from loading and unloading operations as the core, trajectory purification is achieved through a three-level data processing mechanism, and feature representation is enhanced by integrating positional triangular features with sine-cosine encoding of heading. A cascaded architecture of“Transformer encoder + dual-layer LSTM”is adopted, in which a causal self-attention mechanism treats historical trajectory sequences as the“cause”and future trajectory sequences as the“effect”, enabling the prediction of 4-hour future trajectories based on historical data in 3 hours. Experimental results show that the proposed method achieves excellent performance. Compared to those of the TrAISformer model, the mean absolute error reduced by 10%, the root mean square error reduced by 6%, and the coefficient of determination reaches 0.996 226, demonstrating significant improvement in long-term prediction accuracy and robustness.

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针对船舶长期轨迹预测中存在的航行行为复杂性高、意图挖掘不足及长时序依赖建模困难等问题，提出一种基于因果长时序混合模型（CLT-TLSTM）的船舶轨迹长期预测方法。该方法以装卸货作业的起讫点航次轨迹数据为核心，通过三级数据处理机制实现轨迹清洗，并采用位置三角特征与航向正余弦编码增强特征表征。模型采用“Transformer编码器+双层LSTM”级联架构，其中引入的因果自注意力机制将历史轨迹序列作为“因”，未来轨迹序列作为“果”，实现了基于3 h历史轨迹的未来4 h轨迹预测。实验结果表明，所提方法表现优异，与TrAISformer模型相比，平均绝对误差降低约10%、均方根误差降低约6%，决定系数达0.996 226，长期预测精度与鲁棒性得到显著提升。

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韩子堯（2004—），男，本科，主要研究方向为时空数据挖掘。E-mail：

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陈晓慧（1983—），女，副教授，博士，主要研究方向为时空数据可视化。E-mail：cxh_vrlab@163.com
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In response to the continuous escalation of armed conflicts on the Myanmar side of China-Myanmar border, methods such as kernel density and grid analysis are introduced to construct an armed conflicts intensity index model. The differentiation of armed conflicts in seven towns located in the central region from 2011 to 2024 is analyzed at the micro scale. It is found that: the frequency and intensity of armed conflicts are both high and overall increasing, with high and higher value areas forming a“pincer”pattern around Dehong Prefecture in China.With time going on, the intensity of armed conflicts first increases and then decreases, and the overall trend is significantly improved. At the township scale, the center of gravity of armed conflicts intensity shifts northwestward. On the grid scale, low armed conflicts intensity units are the main ones but their proportion decreases. The main distribution area spreads from the border of Shan and Kachin states to the north and east.There are significant differences in the intensity of armed conflicts between different research periods, with the“Democratic League”stage being relatively gentle and the“Coup”stage being the most intense.The occurrence and changes of armed conflicts in the research area have brought multiple impacts on the security, development, and public order in the southwestern border areas of China adjacent to it.

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针对中缅边境缅甸侧武装冲突持续加剧问题，引入核密度与格网分析等方法构建武装冲突强度指数模型，从微观尺度对地处其中部的7个镇域2011—2024年间武装冲突分异态势进行研究。发现：武装冲突发生频数和烈度均较高且整体上升，高与较高值区呈“钳状”围绕中国德宏州布局；随时间演进，武装冲突强度先升后降整体显著提升，镇域尺度上，武装冲突强度重心向西北偏移，格网尺度上，以低武装冲突强度单元为主但占比下降，主要分布区由掸、克钦二邦交界处向北部和东部蔓延；不同研究时段间武装冲突强度差异显著，“民盟阶段”较平缓而“政变阶段”最剧烈；研究区武装冲突的发生和变动给与其接壤的中国西南边境地区带来安全、发展和治安等多重影响。

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张磊（1990—），男，讲师，博士，主要研究方向为土地利用与地缘政治。E-mail：

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李正升（1979—），男，教授，博士，主要研究方向为地缘生态与风险评估。E-mail：zhangleitsu@sina.com
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张磊（1990—），男，讲师，博士，主要研究方向为土地利用与地缘政治。E-mail：1291009063@qq.com

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张磊（1990—），男，讲师，博士，主要研究方向为土地利用与地缘政治。E-mail：1291009063@qq.com

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