检索结果-内蒙古大学图书馆

作者： Zhu, Hu Xu, Guoxia Liu, Lu Deng, Lizhen Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunication Nanjing China Department of Computer Science Norwegian University of Science and Technology Gjovik Norway National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunication Nanjing China

Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low-rank tensor completion via spatial-temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real-world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results. © 2021 John Wiley & Sons, Ltd.

关键词： Smoke

来源：评论

学校读者我要写书评

暂无评论

Modeling Inter-Intra Heterogeneity for Graph Federated Learning 39

Modeling Inter-Intra Heterogeneity for Graph Federated Learn...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Detection Method Toward Yellow Dragon Disease Spreads Using Spark and Deep Learning 3

A Detection Method Toward Yellow Dragon Disease Spreads Usin...

引用

3rd International Conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Xie, Wu Su, Zhenzhao Zhang, Huimin Kang, Ping Qin, Kun Fan, Yong Zhao, Quanyou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541214 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Lingchuan Science and Technology Information Institute Guilin541299 China Guilin Hivision Technology Co. Ltd. Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541214 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before. © 2024 SPIE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential of Large Language Models for Red Teaming in Military Coalition Networks

Exploring the Potential of Large Language Models for Red Tea...

引用

MILCOM, Military Communications Conference

作者： Erik Adler Johannes F. Loevenich Linnet Moxon Tobias Hürten Florian Spelter Johannes Braun Yann Gourlet Thomas Lefeuvre Roberto Rigolin F. Lopes Secure Communications & Information (SIX) Thales Deutschland Ditzingen Germany Department of Computer Science Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department of Mathematics/Computer Science University of Osnabrück Osnabrück Germany Institute for Natural Language Processing (IMS) University of Stuttgart Stuttgart Germany Department of Computer Science IV Rheinische Friedrich Wilhelms Universität Bonn Germany Department of Computer Science University of Stuttgart Stuttgart Germany École Supérieure d’Ingénieurs de Rennes (ESIR) University of Rennes Rennes France

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

This paper reports on an ongoing investigation comparing the performance of large language models (LLMs) in generating penetration test scripts for realistic red agents. The goal is to develop human-level adversaries (red agents) in an automated cyber operations gym environment instrumented to train a team of blue agents. Our methodology defines five approaches for structuring the prompts used to generate Metasploit scripts to exploit vulnerabilities described in Common Vulnerabilities Exposures (CVEs). These approaches have been tested using three LLMs, namely GPT-4o, WhiteRabbitNeo, and Mistral-7b. GPT-4o is used as the baseline for our comparison study. The results suggest that GPT-4o outperforms the other LLMs in all experiments. However, the results also suggest that Mistral-7b can be fine-tuned to achieve acceptable performance while consuming much less computational and memory resources during execution due to a smaller number of parameters: 7 billion parameters for Mistral-7b versus 1.76 trillion for GPT-4o.

关键词： Training Performance evaluation Military computing Large language models Instruments Computational modeling Memory management Reinforcement learning Usability Testing

来源：评论

学校读者我要写书评

暂无评论

An Integrated Web Solution for Planetary Data Visualization and Exploration

An Integrated Web Solution for Planetary Data Visualization ...

引用

Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ananya Maheshwari Love Lakhwani Pariav Naik Abhishek Kumar Jai G. Singla Amitabh Nitant Dube VijayPal Singh Dhaka School of Computing and Intelligent Systems Manipal University Jaipur Dehmi Kalan Jaipur Rajasthan India Department of Computer Engineering Charotar University of Science and Technology Anand Gujarat India Department of Electrical Engineering Indian Institute of Technology Bhubaneshwar Bhubaneshwar Odisha India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad Gujarat India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The exploration and study of planetary bodies have produced vast amounts of geospatial data, creating a need for effective visualization and analysis tools. Many existing solutions are constrained by platform limitations and are resource-intensive, reducing their accessibility for broader use. To overcome these challenges, we developed a lightweight, web-based platform that visualizes interplanetary data and supports multiple geospatial data formats, including shapefiles, KML, and CZML. Our solution includes unique features such as 2D and 2.5D terrain visualization, 3D tiles, and tools for navigating to specific coordinates and measuring distances. These enhancements allow users to explore and interact with geospatial data from various planetary bodies across multiple devices without requiring specialized hardware or software. By modifying the underlying system, we extended its application to planetary data while preserving accessibility and performance across different platforms. This paper discusses the methodology behind these customizations, the challenges we encountered, and the potential applications of our platform in scientific research and education.

关键词： Performance evaluation Three-dimensional displays Coordinate measuring machines Navigation Data visualization Geoscience and remote sensing Extraterrestrial measurements Software Hardware Geospatial analysis

来源：评论

学校读者我要写书评

暂无评论

Evaluation of LoRaWAN for Remote Health Monitoring: A Preliminary Study

Evaluation of LoRaWAN for Remote Health Monitoring: A Prelim...

引用

IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Konstantinos Katzis Nina Evetovic Brian Cloteaux Kamran Sayrafian Dragana Bajić Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus Department of Communications & Signal Processing University of Novi Sad Novi Sad Serbia Currently with the Institute of Measurement science Slovak Academy of Sciences Information Technology Laboratory National Institute of Standards & Technology Maryland USA

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

Lack of sufficient patient monitoring data patterns and scenarios are among the challenges for accurate traffic modeling and network performance optimization when considering IoT medical devices. This paper examines how health data-traffic models can be used to investigate the capability and proper configuration of Long-Range Wide Area Networks (LoRaWAN) for remotely monitoring patients in a hospital environment or at home. For the hospital environment, it is assumed that there are two types of patients at any given time i.e., patients in the intensive care unit (ICU) or surgical wards and those located in the general medical wards. The transmission times of the health data from patients in the ICU/Surgical wards and those in general wards are randomly distributed within one and four-hour periods, respectively. For personal health monitoring devices that people use at their homes, a two-state Markov chain is used to model the generated traffic pattern. Preliminary results indicate that LoRaWAN may be used to support a significant number of nodes with sufficient reliability in both scenarios.

关键词： Wide area networks Patient monitoring Medical devices Hospitals LoRaWAN Surgery Traffic control Reliability Standards Optimization

来源：评论

学校读者我要写书评

暂无评论

Review of MRI brain tumor segmentation and MGMT promoter classification methods on BraTs dataset based on Deep learning

Review of MRI brain tumor segmentation and MGMT promoter cla...

引用

International Conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Feriel Amor Hiba Mzoughi Ines Njeh Mohamed Ben Slima Advanced Technologies for Image and Signal Processing (ATISP Lab) Sfax University National Engineering School of Sfax ATMS LAB Tunisia Gabès university Higher Institute of Computer Science and Multimedia of Gabes Gabes Sfax University National School of Electronics and Telecommunications of Sfax Sfax

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

During clinical decision-making, the segmentation process Is considered an important task because it provides valuable information about the location, size, and characterization of the brain tumor. However, manual human segmentation is error-prone, time-consuming, and requires skilled *** resonance imaging (MRI) could give extremely detailed images for the investigation and diagnosis of glioblastoma brain *** compared evaluated approaches on the BRATS 2021 and BRATS 2022 datasets and found that they outperformed and could compete with state-of-the-art algorithms in comparable *** our research, we focused on two crucial tasks: segmentation and MGMT classification. This study also addresses asn objective evaluation through performance evaluation of cutting-edge DL-based techniques for MR image analysis (Brats 2021-Brats 2022). Based on the findings of the contrasted methods, we can confirm that using a combination of DL techniques will produce more accurate segmentation results than depending on a single, unique methodology. For the second task, five distinct deep learning-based methods were evaluated to predict the methylation state of the MGMT promoter.

关键词： Deep learning Performance evaluation Learning systems image segmentation Accuracy Reviews Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Tendency Coefficient-Based Weighted Distance Measure for Intuitionistic Fuzzy Sets with Applications 12

Tendency Coefficient-Based Weighted Distance Measure for Int...

引用

12th International Conference on Intelligent Control and Information processing, ICICIP 2024

作者： Anum, Manasseh Terna Zhang, Hanyin Ejegwa, Paul Augustine Feng, Yuming Institute of Intelligent Information Processing South China Business College of Guangdong University of Foreign Studies Guangzhou510545 China Olin School of Business Washington University in St. Louis St. Louis United States Joseph Sarwuan Tarka University Department of Mathematics Makurdi Nigeria School of Computer Science and Engineering Chongqing Three Gorges University Chongqing404100 China

ISBN: (纸本)9798350308020

Decision-making (DM) as a critical aspect of every human endeavor has greatly been challenged by the presence of uncertainties and imprecisions. Albeit, the concept of intuitionistic fuzzy sets (IFSs) has been of great help in resolving uncertainties and imprecisions in DM via distance operators. Many authors have constructed sundry distance measuring techniques under IFSs, but none of them have considered the tendency coefficients of the intuitionistic fuzzy parameters and the weights of the elements upon which the IFSs are defined. In this paper, a new distance measure between IFSs is constructed, which we call tendency coefficient-based weighted distance measure (TCWDM). Besides the fact that tendency coefficients and weights are considered, the complete defining parameters of IFSs are also put into account to forestall error of exclusion during the distance computation. After characterizing the TCWDM under IFSs to ensure its alliance with the distance metric, the applications of the new distance operator is discussed in problems pertaining pattern recognition and disease diagnosis. In addition, we present a comparative study of the TCWDM alongside other extant distance measuring techniques under IFSs, and observe that the new approach is consistent with the distance metric, yields more reasonable results, and offers reliable interpretations in real-world applications. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Immune microenvironment-reshaping Au@Bi_(2)Te_(3) nanoparticles for spectral computed tomography/photoacoustic imaging-guided synergetic photo/radio/immunotherapy

引用

Nano Research 2023年第1期16卷 771-781页

作者： Kai Zhu Zede Wu Qiuyu Li Meirong Hou Honglei Hu Shuting Zheng Li Qi Yikai Xu Chenggong Yan Bingxia Zhao Department of Medical Imaging Center Nanfang HospitalSouthern Medical UniversityGuangzhou 510515China Guangzhou Key Laboratory of Tumor Immunology Research Cancer Research InstituteSchool of Basic Medical SciencesSouthern Medical UniversityGuangzhou 510515China Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic TechnologySchool of Biomedical EngineeringSouthern Medical UniversityGuangzhou 510515China Guangdong Provincial Key Laboratory of Shock and Microcirculation Guangzhou 510515China Experimental Education/Administration Center School of Basic Medical ScienceSouthern Medical UniversityGuangzhou 510515China

Radiotherapy(RT)mediated tumor immunogenicity offers an opportunity for simultaneous RT and immunotherapy via immunogenic cell death(ICD),which releases damaged-associated molecular patterns and generates“eat me”signals for the innate immune system to modulate the ***,tumor hypoxia significantly reduces the therapeutic efficacy of RT and hampers its mediation of ICD ***,Au@Bi_(2)Te_(3)-polyethylene glycol(PEG)was rationally constructed as theranostic nanozymes for mild photothermal therapy,tumor hypoxia modulation,and RT adjuvant cancer *** tumor-specific production of oxygen could not only augment the effects of RT by enhanced reactive oxygen species(ROS)generation,but also reduce hypoxia-related cytokines and downregulate programmed cell death-ligand 1(PD-L1)to unleash immune-enhancing T ***,Au@Bi_(2)Te_(3)-PEG could act as an immune-blocking inhibitor by efficient ICD induction with the combination of mild-photothermal therapy+RT to inhibit the tumor immune escape and improve antitumor immune *** amounts of CD^(4+) and CD^(8+) Tcells and elevated levels of cytokines could be observed that eventually led to effective post-medication inhibition of primary and abscopal *** computed tomography/photoacoustic imaging allowed noninvasive and real-time tracking of nanoparticle(NP)accumulation and oxygenation status at tumor ***,Au@Bi_(2)Te_(3)-PEG NPs could serve as effective theranostic nanoregulators with remarkable synergistic mildphotothermal/RT/immunotherapy effects that helped reshape the immune microenvironment and had remarkable molecular imaging properties.

关键词： radioresistance immunotherapy immunogenic cell death hypoxia programmed cell death-ligand 1(PD-L1) spectral computed tomography(CT)

来源：评论

学校读者我要写书评

暂无评论

Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

引用

International Journal of Hybrid Intelligent Systems 2024年第PRE-PRESS期Pre-press卷 1-20页

作者： Azizi, Ridha Sakly, Houneida Wajdi, Abdallah Ahmed Hmida, Alaa Eddinne Ben Bouhlel, Med Salim Research Lab: Smart systems for Engineering and E-health based on Technologies of Image and Telecomunications (SETIT) ISBS Sfax University Tunisia Center for Research on Microelectronics and Nanotechnology (CRMN) Sousse Technopole Sahloul Sousse Tunisia RIADI Laboratory ENSI Manouba University Campus Universitaire de la Manouba La Manouba Tunisia Department IT Higher Institute of Computer Science and Mathematics of Monastir Tunisia Department IT Higher Institute of Computer Science and Mathematics of Monastir Tunisia

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.

关键词： COVID-19 radiomics pyradiomics classifiers segmentation SVM radiogenomics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：