检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Li, Xuefei Chen, Mingzhe Liu, Yuchen Zhang, Zhilong Liu, Danpu Mao, Shiwen The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle’s graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

INSPECTING MODEL FAIRNESS IN ULTRASOUND SEGMENTATION TASKS

arXiv

引用

arXiv 2023年

作者： Xu, Zikang Tang, Fenghe Quan, Quan Ding, Jianrui Ning, Chunping Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science And Technology of China Suzhou China Suzhou Institute for Advanced Research University of Science And Technology of China Suzhou China Institute of Computing Technology CAS Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Ultrasound Department The Affiliated Hospital of Qingdao University Qingdao China

With the rapid expansion of machine learning and deep learning (DL), researchers are increasingly employing learning-based algorithms to alleviate diagnostic challenges across diverse medical tasks and applications. While advancements in diagnostic precision are notable, some researchers have identified a concerning trend: their models exhibit biased performance across subgroups characterized by different sensitive attributes. This bias not only infringes upon the rights of patients but also has the potential to lead to life-altering consequences. In this paper, we inspect a series of DL segmentation models using two ultrasound datasets, aiming to assess the presence of model unfairness in these specific tasks. Our findings reveal that even state-of-the-art DL algorithms demonstrate unfair behavior in ultrasound segmentation tasks. These results serve as a crucial warning, underscoring the necessity for careful model evaluation before their deployment in real-world scenarios. Such assessments are imperative to ensure ethical considerations and mitigate the risk of adverse impacts on patient outcomes. © 2023, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

引用

arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

Comprehensive review on DDoS attack prevention in cloud environment

引用

AIP Conference Proceedings 2024年第1期2802卷

作者： K. Radha R. Parameswari Department of Computer Science School of Computing Science Vels Institute of Science Technology &Advanced Studies Pallavaram Chennai6000117 India

This paper proposed to analyze various DDOS ATTACK protection mechanisms for cloud environment. DDOS ATTACK is very big threat for networks and communication. Now a day every sensitive information stored in cloud environment and other types of dual services such as PAAS, SAAS, IAAS were also attacked by DDOS ATTACKS. So cloud environment must be protected from these types of DDOS ATTACKS. In this paper we provide complete outlook for various DDOS protection, such as machine learning methods with various other methods. Different types of methods tested with test data downloaded from various authentic sources in this paper shows structured ATTACK and prevention. Architecture such as test website ATTACKED by various DDOS ATTACK tools and prevention method executed by separate module or integrated with *** website.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

引用

International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

来源：评论

学校读者我要写书评

暂无评论

Pandemics in silico: Scaling Agent-based Simulations on Realistic Social Contact Networks

arXiv

引用

arXiv 2024年

作者： Kitson, Joy Costello, Ian Chen, Jiangzhuo Jiménez, Diego Hoops, Stefan Mortveit, Henning Meneses, Esteban Yeom, Jae-Seung Marathe, Madhav V. Bhatele, Abhinav Department of Computer Science University of Maryland College Park United States Google Inc. Mountain View United States Biocomplexity Institute and Initiative University of Virginia Charlottesville United States Max Planck Computing and Data Facility Garching Germany National Advanced Computing Collaboratory National High Technology Center San José Costa Rica Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore United States

Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of the spread of such diseases in the presence of possible interventions. The computational cost of modeling epidemic diffusion through large social contact networks necessitates the use of parallel algorithms and resources in order to achieve quick turnaround times. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete event simulation to model disease spread, and is implemented on top of Charm++, an asynchronous, many-task runtime that enables over-decomposition and adaptive overlap of computation and communication. We demonstrate that Loimos is able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC. © 2024, CC BY.

关键词： Discrete event simulation

来源：评论

学校读者我要写书评

暂无评论

Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

来源：评论

学校读者我要写书评

暂无评论

Adversarial Medical Image with Hierarchical Feature Hiding

arXiv

引用

arXiv 2023年

作者： Yao, Qingsong He, Zecheng Li, Yuexiang Lin, Yi Ma, Kai Zheng, Yefeng Zhou, S. Kevin School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Institute of Computing Technology Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Science China Jarvis Research Center Ten-cent YouTu Lab Shenzhen China Group Guangxi Key Laboratory for Genomic and Personalized Medicine Guangxi Medical University Nanning China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Meta Reality Labs United States

Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which optimize the classification logits, are easy to distinguish in the feature space, resulting in accurate reactive defenses. To better understand this phenomenon and reassess the reliability of the reactive defenses for medical AEs, we thoroughly investigate the characteristic of conventional medical AEs. Specifically, we first theoretically prove that conventional adversarial attacks change the outputs by continuously optimizing vulnerable features in a fixed direction, thereby leading to outlier representations in the feature space. Then, a stress test is conducted to reveal the vulnerability of medical images, by comparing with natural images. Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs. We then propose a simple-yet-effective hierarchical feature constraint (HFC), a novel add-on to conventional white-box attacks, which assists to hide the adversarial feature in the target feature distribution. The proposed method is evaluated on three medical datasets, both 2D and 3D, with different modalities. The experimental results demonstrate the superiority of HFC, i.e., it bypasses an array of state-of-the-art adversarial medical AE detectors more efficiently than competing adaptive attacks1, which reveals the deficiencies of medical reactive defense and allows to develop more robust defenses in future. © 2023, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Tumor Infiltration of Microrobot using Magnetic torque and AI Technique

Tumor Infiltration of Microrobot using Magnetic torque and A...

引用

International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： R. Shalini Laxmi Mishra Athulya S Abhijeet Gajanan Chimankar Sunanda Ratna. Kandavalli Keshav Kumar K R. Senthamil Selvan Department of BCA & IT School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Department of Electronics and Communication Engineering Khwaja Moinuddin Chishti Language University Uttar Pradesh Department of computer science and engineering Faculty of engineering Christ deemed to be University Bangalore Department of B.Sc. Computer Science Chatrapati Shivaji BCA And BCS College Maharashtra Department of Electronics and Communication Engineering Sri Padmavati Mahila Visva Vidyalayam Tirupati Department of Humanities and Mathematics G. Narayanamma Institute of Technology and Science Hyderabad Department of Electronics and Communication Engineering Annamacharya Institute of Technology & Sciences Tirupati

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Contour-Aware Equipotential Learning for Semantic Segmentation

arXiv

引用

arXiv 2022年

作者： Yin, Xu Min, Dongbo Huo, Yuchi Yoon, Sung-Eui The School of Computing Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Faculty of the Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of The State Key Lab of Cad and Cg Zhejiang University China Zhejiang Lab 310058 China The Faculty of School of Computing Korea Advanced Institute of Science and Technology Deajeon34141 Korea Republic of

With increasing demands for high-quality semantic segmentation in the industry, hard-distinguishing semantic boundaries have posed a significant threat to existing solutions. Inspired by real-life experience, i.e., combining varied observations contributes to higher visual recognition confidence, we present the equipotential learning (EPL) method. This novel module transfers the predicted/ground-truth semantic labels to a self-defined potential domain to learn and infer decision boundaries along customized directions. The conversion to the potential domain is implemented via a lightweight differentiable anisotropic convolution without incurring any parameter overhead. Besides, the designed two loss functions, the point loss and the equipotential line loss implement anisotropic field regression and category-level contour learning, respectively, enhancing prediction consistencies in the inter/intra-class boundary areas. More importantly, EPL is agnostic to network architectures, and thus it can be plugged into most existing segmentation models. This paper is the first attempt to address the boundary segmentation problem with field regression and contour learning. Meaningful performance improvements on Pascal Voc 2012 and Cityscapes demonstrate that the proposed EPL module can benefit the off-the-shelf fully convolutional network models when recognizing semantic boundary areas. Besides, intensive comparisons and analysis show the favorable merits of EPL for distinguishing semantically-similar and irregular-shaped categories. © 2022, CC BY-NC-ND.

关键词： Semantic Segmentation

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：