一种融合实体描述和拓扑结构的知识图谱补全方法

  

知识图谱补全旨在预测给定三元组中缺失的实体和关系,以增强知识图谱的完整性和质量。现有的知识图谱补全方法通常只考虑三元组自身的结构信息或者是实体单一的附加信息(如实体的文本描述或拓扑结构信息),而忽略了融合多种附加信息来增强实体的特征信息,从而导致现有方法补全缺失实体时性能不佳。针… …   相似文献

韩道军;李云松;张俊涛;王泽民 《计算机科学》2025,52(5):260-269

EvoTrace:基于非线性数据包遥测和批处理的轻量级带内网络遥测方法

  

带内网络遥测(In-band Network Telemetry,INT)使数据包能够携带网络状态信息,具有较高的测量准确性和精度。然而,这种提升是以增加数据平面开销为代价的。遥测信息的嵌入会导致数据平面的网络开销过大。同时,现有的遥测方法通常对大流的数据包进行大量的测量,忽略了… …   相似文献

王攀祥;崔允贺;申国伟;郭春;陈意;钱清 《计算机科学》2025,52(5):291-298

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ObjectiveSingle-modality medical imaging is often insufficient for providing a comprehensive review of lesion characteristics, including structure, metabolism, and other critical details. Medical images can generally be categorized into anatomical medical imaging and functional medical imaging. Anatomical medical imaging offers rich information on the structure of the body, but it lacks insight into metabolic processes. In contrast, functional medical imaging is the opposite. In clinical applications, doctors use medical imaging from multiple modalities to diagnose diseases, localize lesions, and plan surgeries. However, simultaneously observing multimodal medical images is not intuitive and may not fully capture all the relevant features of the lesion. Therefore, multimodal medical image fusion is commonly employed in practice to integrate and enhance the information from different imaging techniques. How to fully retain the unique features of each modality while effectively integrating the shared features between modalities is a common challenge in medical image fusion. The information interaction of shared modal features in currently used two-branch image coding methods is often underdeveloped, and the process is somewhat inadequate. This condition limits the establishment of feature correlations between multimodal images. A multiscale medical image fusion network is designed to address these issues. This network is based on progressive feature extraction, frequency domain information supplementation, and image reconstruction by Swin Transformer and convolutional neural network（CNN）.MethodFirst, a multiscale feature extraction module guided by gradient information was designed, which can be integrated into a three-branch feature extraction architecture. The left and right branches are responsible for extracting the unique features from each modality of the medical images, while the middle branch extracts the shared features between modalities. The extraction architecture comprises several multiscale feature extraction modules, each based on gradient information guidance. These submodule can simultaneously integrate features from all scale levels. The extraction architecture fully considers the information interaction between modalities and can progressively extract the common and unique features across different modalities. In addition, this extraction architecture effectively integrates multiscale features from multimodal medical images. A progressive fusion module that integrates cross-attention mechanisms was designed to fully utilize the frequency domain information and guide the fusion process at the modal level. This fusion module enhances the interaction of spatial domain information between different modalities and leverages high- and low-frequency positional information from the frequency domain, guiding the model for more targeted multimodal fusion. Finally, a Swin-CNN reconstruction module was designed to determine the relationship between global and local area features of medical images. The reconstruction module uses Swin Transformer to capture global information, such as the overall structure and shape of the image, while simultaneously employing CNN to extract regional features, such as local texture details. The reconstruction module can effectively improve the quality of fused images by integrating the global and local feature information of medical images simultaneously.ResultThe datasets used for the experiments include the MRI-SPECT and MRI-PET fusion datasets from the whole brain database at Harvard Medical School and the GFP-PC fusion dataset from the John Innes Center, respectively. Considering the visual effect of the fused images, the proposed fusion model effectively preserves the structural and functional features of different medical image modalities and improves the quality of the fused images. The advantages of the fused images generated by this model are as follows： 1） The fused image has richer texture details and sharper features such as edges and contours. These images effectively preserve the information-rich regions of each modal image. 2） The fused image also effectively preserves the visual features in all original medical images, which ensures no bias toward preserving information from only one modality of the medical image. 3） The fused image is rendered effectively, with no artifacts affecting the visual effect. In addition, in terms of comparison of quantitative indicators, the model achieves optimization for all eight image fusion evaluation metrics in MRI-SPECT and MRI-PET fusion tasks. Compared to the model with the second-best performance, the mutual information （MI） and discrete cosine transform feature mutual information （FMI_dct） are drastically improved. MI demonstrated an improvement of 4.42% and 17.30%, respectively, and FMI_dct showed improvements of 5.17% and 11%, respectively. In the GFP-PC fusion task, six optimal and two sub-optimal results are achieved. Compared to the model with the second-best performance, MI and visual information fidelity （VIF） are substantially improved by 16.43% and 16.87%, respectively. Ablation experiments were also conducted for the network structure and loss function of the model to effectively analyze the experimental results and evaluate the effectiveness of each part of the model in this paper. Experimental results show that all model components and the loss function enhance the image fusion effect.ConclusionThe proposed fusion model leverages the common and unique features of different medical image modalities and progressively integrates multiscale information using a three-branch architecture. The model also utilizes a progressive fusion module that incorporates cross-attention to fuse high- and low-frequency features in a highly targeted manner. Furthermore, the model focuses on the global and local attribute information of medical images in the reconstruction process, effectively enhancing the quality of multimodal medical image fusion. The proposed model in this paper performs well in three medical image fusion tasks with good generalization capability. This model can provide multimodal medical fusion images with clear contour structures and rich texture details, aiding doctors in clinical diagnosis and improving diagnostic efficiency and accuracy. Future studies will investigate the constraints or effects of downstream medical semantic segmentation and other tasks on image fusion. The network architecture will also be optimized for specific tasks, ensuring a close integration between tasks such as semantic segmentation and image fusion. This research aims to improve the quality of fused images while enhancing the performance of downstream tasks, thereby expanding the application possibilities of multimodal medical image fusion.… …   相似文献

《中国图象图形学报》2025,30(5):1510-1527

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ObjectiveUltrasound imaging plays a crucial role in medical diagnosis due to its convenience, non-invasive nature, and cost-effectiveness, m… …   相似文献

《中国图象图形学报》2025,30(5):1303-1317

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ObjectiveRegong art, originating from the Longwu River valley in the Tibetan region of Huangnan, Qinghai Province, has flourished in this ar… …   相似文献

《中国图象图形学报》2025,30(5):1377-1388

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ObjectiveColorectal cancer, a high-incidence and extremely harmful disease, represents a serious threat to human health. Statistics show tha… …   相似文献

《中国图象图形学报》2025,30(5):1479-1496

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ObjectiveCeladon is not only a dazzling pearl among the cultural treasures of the Chinese nation but also a cultural messenger in cultural exchanges between China and other countries. It has rich historical and cultural connotations and demonstrates excellent artistic value. Its elegant shape and moist glaze make it an outstanding representative of traditional Chinese craft aesthetics. The production of celadon embodies the wisdom and creativity of ancient craftsmen and is an important carrier for the inheritance of excellent traditional Chinese culture. In the context of cultural digitization, constructing a cross-modal knowledge graph of celadon is one of the key technologies for promoting the protection and inheritance of celadon culture. In this process, matching the same entities across different modalities, which involves aligning the different modal features of equivalent entities, is crucial. However, the inherent structural differences between cross-modal data present challenges for alignment tasks. Traditional methods that rely on manually annotated data can ensure the accuracy of alignment to some extent, but they have problems such as low efficiency and high cost. In addition, coarse-grained annotated data can hardly meet the requirements for fine-grained concepts and for entity recognition when constructing a cross-modal knowledge graph. At present, the vision-language pretraining （VLP） model can effectively capture cross-modal semantic associations by learning rich cross-modal representations from large-scale unmarked image-text pair data. The strong cross-modal understanding ability of the VLP model can provide precise semantic associations and fine-grained entity recognition for aligning entities of different modalities in graph construction. Here, a cross-modal entity alignment method based on the VLP model, which can map multiple features of images, is proposed to maximize the degree of matching between celadon images and text.MethodThe cross-modal entity alignment method proposed in this study, which maps multiple features of images, is initialized with the publicly available VLP model for both the image and the text encoders, and the parameters of the encoders remain unchanged during the training process. The method mainly consists of four parts. First, on the basis of the visual characteristics of celadon images, local features in terms of contour, texture, and color are extracted. Then, a gated multifusion unit is introduced to adaptively assign weights to the image features, and the extracted multiple local image features are used to generate reliable fused features. Furthermore, a multilayer fully connected mapper is designed to learn the mapping of the fused features to an appropriate intermediate representation space by using multiple layers of nonlinear transformations, guiding the text encoder to generate text features that match the image features more closely. Finally, the model is trained and optimized via the information noise contrastive estimation loss function, that is, by optimizing the similarity of positive sample pairs and the difference in negative sample pairs through calculating the cosine similarity between cross-modality features, thereby establishing the connection between image features and text features.ResultThe proposed method was compared with four of the latest benchmark methods in an experimental comparison, namely, contrastive VLP in Chinese （CN-CLIP）, context optimization （CoOp）, conditional context optimization （CoCoOp）, and mapping pictures to words （Pic2Word）. The quantitative evaluation metrics are the recall rates, including R@1, R@5, R@10, and the mean recall （MR）. The experiments were conducted using the ChinaWare dataset, so all methods were trained on this dataset. A data table comparing each method’s performance on recall rate metrics was provided. In terms of the MR metric, the proposed method outperformed zero-shot CN-CLIP_ViT-B/16 by 3.2% in the text-to-image alignment task and by 7.5% in the image-to-text task. CoOp focuses on text features; it also outperforms CoOp by 11.4% and 12.1%, respectively. Moreover, CoCoOp considers image features on the basis of CoOp, and the proposed method outperforms CoCoOp by 8.4% and 9.5%, respectively. Pic2Word also focuses on original image features and does not fully utilize other local image features to improve model performance, and the proposed method outperforms Pic2Word by 5.8% and 5.6%, respectively.ConclusionThe cross-modal entity alignment method proposed in this study can fully explore the effective intermediate representation of image features to reconstruct text features without changing the parameters of the VLP model, thereby improving the cross-modal recognition accuracy of the details of celadon. The experimental results show that this method is superior to several state-of-the-art methods and has improved the performance of alignment. Ultimately, a celadon cross-modal knowledge graph with 8 949 nodes and 18 211 relationships was successfully constructed by applying technologies such as ontology modeling, data mining, and the cross-modal entity alignment method proposed in this study.… …   相似文献

《中国图象图形学报》2025,30(5):1318-1333

面向属性网络社团检测的度修正广义随机块模型

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随机块模型可以拟合各种网络的生成,挖掘网络的隐含结构与潜在联系,在社团检测中具有明显的优势.广义随机块模型GSB是基于链接社团的思想发现广义社团的,但其仅适用于有向无属性网络.针对无向属性网络,对网络拓扑信息建模的同时对节点属性进行建模,提出一种度修正的属性网络广义随机块模型DC… …   相似文献

王笑  戴芳  郭文艳  王军锋 《软件学报》2025,36(5):2308-2320

  

In the 6G extremely large-scale MIMO systems, the transmission range between base stations and users falls within the near-field region, tra… …   相似文献

《通信学报》2025,46(5):177-187

  

To address privacy leakage issues in traditional V2G power transaction authentication, including identity forgery, transaction behavior anal… …   相似文献

《通信学报》2025,46(5):145-158

  

To mitigate the channel estimation challenges induced by hybrid near-far field and beam squint effects in THz ultra-massive MIMO systems, a … …   相似文献

《通信学报》2025,46(5):77-90

大模型时代的混合专家系统优化综述

  

近年来,大模型推动自然语言处理、机器视觉等众多领域取得前所未有的进展. 混合专家（mixture of experts,MoE）凭借在模型参数扩展、计算成本控制和复杂任务处理等方面的独特优势成为大模型的主流架构之一. 然而,随着参数规模的持续增长,系统的执行效率和可扩展能力愈发难… …   相似文献

史宏志;赵健;赵雅倩;李茹杨;魏辉;胡克坤;温东超;金良 《计算机研究与发展》2025,62(5):1164-1189

考虑长度匹配的快速单通量量子电路布线算法

  

由于快速单通量量子 (rapid single-flux-quantum, RSFQ)电路的高频特性,对电路的版图设计构成了巨大挑战. 针对RSFQ电路的高频特性带来的电路时延问题,可以在布线阶段通过使用延时元件如无源传输线来解决. 因为无源传输线的时延与它的长度近似成正比,且传… …   相似文献

刘耿耿;余延涛;周茹平;魏榕山;徐宁 《计算机研究与发展》2025,62(5):1151-1163

Neptune：一种通用网络处理器微结构模拟和性能仿真框架

  

网络包处理是网络设备的基本功能,涉及报文修改、校验和与哈希计算、数据包镜像或过滤、统计限速等多项任务. 作为网络包处理的重要部件,网络处理器（network processor,NP）基于处理器结构,为网络设备提供线速的性能和充分的可编程能力,但其架构多样,可分为单段式架构和多段… …   相似文献

林涵越;吴婧雅;卢文岩;钟浪辉;鄢贵海 《计算机研究与发展》2025,62(5):1091-1107

面向子空间聚类的多视图统一表示学习网络

  

多视图子空间聚类旨在挖掘多视图的丰富信息来指导高维数据聚类,其研究关键在于如何有效地学习多视图统一表示和子空间表示. 近年来,深度聚类方法利用神经网络强大的表征能力取得了优异的性能. 然而,多视图数据固有的多源异构性使得大多数现有方法以单模态编码器实现对各个视图的独立编码,不仅增加了模型参数量,同时限制了模型的泛化能力. 另一方面,低秩子空间表示被证明能够提升聚类性能,传统的核范数正则化优化没有考虑不同奇异值隐含的信息量差异,是矩阵秩的一个有偏估计. 为此,提出了一种面向子空间聚类的多视图统一表示学习网络. 首先,基于Transformer构建编码器,通过共享参数将异构视图以相同的映射规则投影到低维特征空间. 其次,针对每个样本在不同视图中可能具有不同的表现,采用视图内样本加权融合的方法学习多视图统一表示. 最后,引入加权Schatten-p范数对子空间表示矩阵施加低秩约束. 在7个多视图数据集上的广泛实验验证了所提方法的有效性和优越性.… …   相似文献

林毓秀;刘慧;于晓;张彩明 《计算机研究与发展》2025,62(5):1248-1261

结合动态缓冲池和时间递减约束的离线到在线强化学习

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离线到在线强化学习中, 虽然智能体能够通过预先收集的离线数据进行初步策略学习, 但在线微调阶段, 早期过程常常表现出不稳定性, 且微调结束后, 性能提升幅度较小. 针对这一问题, 提出了两种关键设计: 1)模拟退火的动态离线-在线缓冲池; 2)模拟退火的行为约束衰减. 第1种设计… …   相似文献

闫雷鸣  朱永昕  刘健 《计算机系统应用》2025,34(5):14-23

基于不确定性校准的烟雾语义分割

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烟雾检测在早期火灾预警当中非常重要. 现有检测算法基本是基于确定性的卷积神经网络来进行的, 然而确定性的神经网络往往会给出非常自信的预测结果, 即使它完全不知道某些区域当中是否有目标对象, 尤其是烟雾边缘区域有着更加透明的效果, 致使该区域和周围环境极易混淆, 因此检测算法对该区域并不能进行很好的判断, 进而造成大量的假阳性. 因此, 本文提出一种改进的DeepLabV3+算法, 首先, 该算法基于贝叶斯思想优化DeepLabV3+从而输出非确定性的特征编码, 以量化预测图像中不确定性的大小, 校准模型的学习过程. 其次基于预处理思想对特征编码进行预处理, 降低无关干扰特征信息量, 并且强化DeepLabV3+网络中特征融合能力, 充分利用网络提取到的多尺度特征信息. 最后将DeepLabV3+网络中上采样算子优化为CARAFE算子, 降低上采样过程中重要信息的丢失. 模型在公开的SMOKE5K数据集上取得良好的性能, MIoU指标达到了92.41%.… …   相似文献

刘志宏  杨海波  贾军营  卢鑫 《计算机系统应用》2025,34(5):31-40

多注意力融合的TransUNet医学影像分割模型

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精确识别组织器官和病变区域是医学影像分析中最重要的任务之一. 在现有的医学影像语义分割研究中, 基于U-Net结构的模型占据了主导地位. TransUNet结合了CNN和Transformer的优势, 弥补了两者在捕捉长程依赖和提取局部特征方面的不足, 但在提取和复原特征的位置时仍不够准确. 针对此问题, 提出了一种多注意力融合机制的医学影像分割模型MAF-TransUNet. 该模型首先在Transformer层之前增加一个多注意力融合模块(MAF)来增强位置信息的表达; 然后在跳跃连接中再次结合多注意模块(MAF)使位置信息能够有效地传递到解码器一侧; 最后在解码阶段使用深度卷积注意力模块(DCA)保留更多的空间信息. 实验结果显示, MAF-TransUNet相较TransUNet在Synapse多器官分割数据集和ACDC自动心脏诊断数据集上的Dice系数分别提升了3.54%和0.88%.… …   相似文献

赵亮  赵雨祺  金海波 《计算机系统应用》2025,34(5):64-76

多粒度单元格对比的文本和表格数值问答模型

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在文本和表格的数值问答任务中,模型需要在给定的文本和表格下进行数值推理.任务目标是生成一个包含多步数值计算的计算程序,并将计算程序结果作为问题的答案.为了建模文本和表格,当前工作通过模板将表格线性化为一系列单元格句子,再基于文本和单元格句子设计生成器以产生计算程序.然而,这种方法… …   相似文献

琚江舟  毛云麟  吴震  陈宇飞  戴新宇  陈家骏 《软件学报》2025,36(5):2167-2187

基于声感知的移动终端身份认证综述

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随着移动终端的普及和用户隐私数据保护需求的增强,基于移动终端的身份认证研究引起了广泛关注.近年来,移动终端的音频传感器为设计性能优良的新颖身份认证方案提供了更大的灵活性和可拓展性.在调研了大量相关科研文献的基础上,首先按照依赖凭据和感知方法的不同将基于声感知的移动终端身份认证方案… …   相似文献

周满  李向前  王骞  李琦  沈超  周雨庭 《软件学报》2025,36(5):2229-2253