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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Cao, Yuanyang Chen, Jian Xue, Zhentao Zhang, Zichao Hu, Gui College of Engineering China Agricultural University Beijing China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijng China

ISBN: (纸本)9789811966125

Aiming at the problems that the classification of remote sensing image datasets is not balanced so that the accuracy of semantic segmentation is not high and the segmentation effect of scarce samples is poor. Transformer encoder with multihead self-attention mechanism is integrated into Deeplabv3+ network, and the attention mechanism is used to enhance the ability to capture global information, so as to improve the precision requirement of scarce categories in remote sensing semantic segmentation task. The algorithm firstly uniformly crops high-resolution remote sensing images into low-resolution image information for batch training and reducing information loss. Secondly, MobileNetV2, a lightweight backbone feature extraction network, is used to replace Xception network of Deeplabv3+. Finally, Transformer encoder based on multihead self-attention mechanism is connected in series with atrous spatial pyramid pooling (ASPP) of Deeplabv3+ encoder region to increase its segmentation precision by enhancing feature learning of scarce category samples in deep feature information. The experimental result shows that the proposed model called TransDeeplabv3+ gets 67.67% mIoU and 81.86% mPA on GID remote sensing dataset. Compared with Deeplabv3+ model, mIoU and mPA are improved by 9.46% and 8.52% respectively. TransDeeplabv3+ can effectively increase the precision because of the ability of increasing attention to the scarce species samples and solve the problem of the decrease of segmentation accuracy due to unbalanced data categories. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Judgment Model of Cock Reproductive Performance based on Vison Transformer 5

Judgment Model of Cock Reproductive Performance based on Vis...

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5th International Conference on Sensors, Signal and Image Processing, SSIP 2022

作者： Lin, Xuhong Yan, Qian Wu, Caicong Chen, Yifei College of Information and Electrical Engineering China Agricultural University China College of Information and Electrical Engineering China Agricultural University Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications China

ISBN: (纸本)9781450397124

With the improvement of people's living standards, the demand for poultry has further increased. The screening of cock reproductive performance according to semen quality has become one of the attention directions. It is time-consuming to screen the breeding performance of cocks based on human vision, and there will be recognition errors. In this study, combined with the hypothesis that there is a correlation between cockscomb characteristics and semen quality, a scheme based on computer vision is proposed to avoid this problem. The collected cockscomb data are input into our model, which can automatically judge the breeding performance of cocks. We use transfer learning and change the weight ratio of the latest fine-grained visual classification algorithm Transfg at different depths to improve the accuracy of our data set. The average accuracy of the model in the test set is 44.6% (the data set contains 2053 pictures in the training set and 505 pictures in the test set), which is 1% better than the original vision transformer and 1.3% better than the convolution network model. © 2022 ACM.

关键词： Statistical tests

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Research on knowledge Graph Construction for Emergency Resource Management 12

Research on Knowledge Graph Construction for Emergency Resou...

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12th International Symposium on Project Management, ISPM 2024

作者： Zhang, Kehong Wang, Min Zhou, Chenjie School of Information Engineering and Artificial Intelligence Lanzhou University of Finance and Economics Gansu Lanzhou730030 China Gansu Provincial Key Laboratory of Intelligent Business China "Belt and Road" Intellectual Property Protection and Data Utilization Industrial Research Institute China

ISBN: (纸本)9798331302344

Emergency resource management is a very important part of the public affairs emergency management system, once the emergence of public emergencies requires timely and efficient emergency rescue response, and rapid emergency rescue capabilities must rely on scientific and reasonable emergency resource management scheduling. Therefore, this paper addresses the problem of emergency resource management scheduling, the application of knowledge mapping technology to build an emergency resource decision-making system, combining the government, enterprises and other social forces, collaborative cooperation, to ensure a rapid response in the event of emergencies, and improve the efficiency of emergency rescue. In this paper, we obtain the original text from the emergency management website through web crawlers and label the entity relationship types to complete the construction of the dataset;on this basis, the entity relationships are objectively displayed through the Neo4j graph database to complete the creation of the knowledge graph of emergency resource scheduling. The constructed knowledge graph is reliable and can provide a scientific program for emergency management scheduling. © 2024 ISPM. All Rights Reserved.

关键词： knowledge graph

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An optimal differentially private data release mechanism with constrained error

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Frontiers of Computer Science 2022年第1期16卷 187-189页

作者： Hao WANG Zhengquan XU Xiaoshan ZHANG Xiao PENG Kaiju LI Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismCollege of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing400065China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan430079China College of Computer Science Chongqing UniversityChongqing400044China

1 Introduction and main contributions In data-driven applications,such as location based services(LBSs),disease surveillance and social networks,etc.,information fusion is necessary for data owners to obtain better *** the aggregated data may contain individuaFs sensi-tive ***,privacy preserving data fusion has become a substantial issue in data aggregating and mining[1,2].

关键词： LBS release optimal

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CCIS-DIFF: A Generative Model with Stable Diffusion Prior for Controlled Colonoscopy Image Synthesis 22

CCIS-DIFF: A Generative Model with Stable Diffusion Prior fo...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Xie, Yifan Wang, Jingge Feng, Tao Ma, Fei Li, Yang School of Software Engineering Xi'an Jiaotong University Xi'an China Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen Key Laboratory of Ubiquitous Data Enabling Shenzhen China

ISBN: (纸本)9798331520526

Colonoscopy is crucial for identifying adenomatous polyps and preventing colorectal cancer. However, developing robust models for polyp detection is challenging by the limited size and accessibility of existing colonoscopy datasets. While previous efforts have attempted to synthesize colonoscopy images, current methods suffer from instability and insufficient data diversity. Moreover, these approaches lack precise control over the generation process, resulting in images that fail to meet clinical quality standards. To address these challenges, we propose CCIS-DIFF, a Controlled generative model for high-quality Colonoscopy Image Synthesis based on a Diffusion architecture. Our method offers precise control over both the spatial attributes (polyp location and shape) and clinical characteristics of polyps that align with clinical descriptions. Specifically, we introduce a blur mask weighting strategy to seamlessly blend synthesized polyps with the colonic mucosa, and a text-aware attention mechanism to guide the generated images to reflect clinical characteristics. Notably, to achieve this, we construct a new multi-modal colonoscopy dataset that integrates images, mask annotations, and corresponding clinical text descriptions. Experimental results demonstrate that our method generates high-quality, diverse colonoscopy images with fine control over both spatial constraints and clinical consistency, offering valuable support for downstream segmentation and diagnostic tasks. © 2025 IEEE.

关键词： Image segmentation

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NcoVis: A Visual Analysis Framework for Exploring Academic Collaboration Networks under New Collaborative Relationships 25

NcoVis: A Visual Analysis Framework for Exploring Academic C...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education

arXiv

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arXiv 2024年

作者： Shao, Pengyang Yang, Yonghui Gao, Chen Chen, Lei Zhang, Kun Zhuang, Chenyi Wu, Le Li, Yong Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group Zhejiang Hangzhou China

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students’ proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence Criterion. Finally, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD. © 2024, CC BY.

关键词： knowledge graph

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A Highly Selective Wideband Filtering Stacked DDPA 4

A Highly Selective Wideband Filtering Stacked DDPA

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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Tian, Kang Zhang, JiaSheng La, Dongsheng Northeastern University at Qinhuangdao Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Qinhuangdao066000 China An Hui Ming Bian Electronic Science & Technology Co. Ltd. China

ISBN: (纸本)9798350363586

In this paper, a compact and highly selective stacked filtering dense dielectric patch (DDP) antenna (DDPA) is proposed. By placing a pair of thin DDP with high dielectric constant along the y axis on the DDP with low dielectric constant, the resonant frequency of the TE12 mode is adjusted to approach low frequency and form a passband with the TE11 mode. By loading a U-shaped microstrip feeding line on the microstrip feeding line, the bandwidth can be further broadened and the TE11, TE11 + δ and TE12 modes can be successfully excited in the passband. The radiation nulls are generated by the U-shaped microstrip line and non-radiative TE12 mode, respectively. By adjusting the length of the U-shaped microstrip line and the size of DDP with high dielectric constant, the independent adjustment of radiation nulls can be achieved. Finally, the proposed filtering antenna achieves a relative bandwidth of 11.66 % (4.04-4.54 GHz). The average realized gain and peak gain are 6.89 dBi and 7.1 dBi, respectively. The realized gain in the passband is very flat, and the fluctuation is less than 0.7 dBi. The proposed antenna has the advantages of compactness, high selectivity and flat gain, and it can be applied to modern satellite communication and wireless communication systems. © 2024 IEEE.

关键词： Bandwidth

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A Color Image Encryption Scheme Based on Block Compressive Sensing and Chaotic Systems 2

A Color Image Encryption Scheme Based on Block Compressive S...

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2nd International Conference on Applied Statistics, Computational Mathematics, and Software engineering, ASCMSE 2023

作者： Tian, Junfeng Chen, Keke Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Henan University Kaifeng475004 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

The traditional compressive sensing theory does not take full advantage of the differences in image block information features for sampling, and the measurement matrix does not adequately measure the image based on the image information features. To solve these problems, we design a color image encryption scheme based on block compressive perception and chaotic systems. First, the color image is divided into three channels of RGB and into 8*8 blocks respectively. Then, a zigzag transform is applied to each block so that each pixel value is evenly distributed. Each block is sparsified by the discrete cosine transform, and the measurement matrix generated by the Chen chaotic system is sampled to compress the image blocks to CR times the original block size according to different compression ratios (CR). The resulting chunked compressed samples are further distributed across the pixel values of the image using a cross-channel confusion dislocation method. Finally, the size of the pixel values is changed by a dissimilarity operation. The experimental results show that the scheme can balance the security of image information transmission and the integrity of reception, and has good resistance to various attacks. © 2023 SPIE.

关键词： Cryptography

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Research on a Technology of Mail Anti-virus Gateway for Industrial Internet

Research on a Technology of Mail Anti-virus Gateway for Indu...

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2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Wu, Yuhong Lv, Yunshan Zhang, Ling School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350314670

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system;then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency. © 2023 IEEE.

关键词： Network security

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