检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Gizaw Tohye, Tewodros Qin, Zhiguang Wake Hundera, Negalign Assefa, Maregu Lejebo Leka, Habte Atandoh, Peter School of Information and Software Engineering University of Electronic Science and Technology of China North Jianshe Road Sichuan Chengdu610054 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China The Zhejiang Institute of Optoelectronics Jinhua China

Background and objectives: Optic disc and Optic cup segmentation is critical in order to identifyglaucoma. But, the nature of fundus images like shape variations of optic disc from person to person ,a lack of contrast and fuzzy borders makes it difficult to easily identify the segmentation region. Veryfew methods have tried to analyze and utilize most important silent features to precisely segment therequired regions from fundus ***: In this study, we introduced CARES-UNet which is based on contour-guided attention toseparate the optic disc and the optic cup regions. The CARES-UNet model’s encoder part is integratingCBAM after each residual block. The model achieves precise boundary detection by incorporatingcontour cues during the segmentation process reducing the likelihood of missing crucial ***, we provide the residual blocks with both the original and contour information to retainthe discriminative regions. Based on the experiments conducted on two publicly available datasetsREFUGUE and DRISHTI-GS .Results: We verify the effectiveness and efficiency of our proposed method on two public *** specifically obtained dice coefficients of 0.98, 0.969 IoU, 0.99 recall, and precision of 0.97 on theREFUGUE dataset. DRISHTI-GS obtain optic disc results with dice coefficients of 0.97, 0.95 IOU,0.99 Recall, and 0.90 ***: These encouraging findings demonstrate the potential of the contour-guided attentionbased RES-UNet model for segmenting the optic disc and the optic cup, enhancing ophthalmologyand enabling more accurate detection and management of eye illnesse © 2023, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

arXiv

引用

arXiv 2023年

作者： Wang, Di Zhang, Jing Du, Bo Xu, Minqiang Liu, Lin Tao, Dacheng Zhang, Liangpei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at SAMRS. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid

Large Capacity Image Watermarking Model using SWT and Laplac...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Baowei Wang Fan Yang Yufeng Wu Changyu Dai Wenjue Huang Xingyuan Zhao Engineering Research Center of Digital Forensics Ministry of Education China Waterford Institute Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. This is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. Therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

引用

International Conference on engineering and Emerging Technologies (ICEET)

作者： Aqib Hassan Vengas Memon Farhan Bashir Shaikh Saad Alahmari Mansoor Iqbal Farooq Azam School of computing and mathematical sciences University of Greenwich London United Kingdom Faculty of Information and Communication Tech. Universiti Tunku Abdul Rahman Perak Malaysia Department of Computer Science Northern Border University Arar Saudi Arabia Department of Computer Science University of Wah Wah Cantt Pakistan Dept. of Computer Software Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability.

关键词： TCP Transport protocols Protocols Force Packet loss Throughput Real-time systems Stability analysis Reliability Low latency communication

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Di...

引用

Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： D. Karthikeyan R. Nivasini Kiruba Thangam Raja D. Madhivadhani B. Sivakumar A.T.R. Krishna Priya Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Guvi Geek Networks IITM Research Park Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electronics and Communication Engineering Kings Engineering College Sriperumbudur Chennai Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari India

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer’s disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

引用

arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement

Machine Learning in Higher Education using RF and SVM for Pe...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models 39

Divide, Conquer and Combine: A Training-Free Framework for H...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Wenbin Ding, Liang Zeng, Minyan Zhou, Xiabin Shen, Li Luo, Yong Yu, Wei Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia Jiangsu University China Shenzhen Campus of Sun Yat-sen University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Multimodal large language models (MLLMs) have experienced significant advancements recently, but still struggle to recognize and interpret intricate details in high-resolution (HR) images effectively. While state-of-the-art (SOTA) MLLMs claim to process images at 4K resolution, existing MLLM benchmarks only support up to 2K, leaving the capabilities of SOTA models on true HR images largely untested. Furthermore, existing methods for enhancing HR image perception in MLLMs rely on computationally expensive visual instruction tuning. To address these limitations, we introduce HR-Bench, the first deliberately designed benchmark to rigorously evaluate MLLM performance on 4K&8K images. Through extensive experiments, we demonstrate that while downsampling HR images leads to vision information loss, leveraging complementary modalities, e.g., text, can effectively compensate for this loss. Building upon this insight, we propose Divide, Conquer and Combine, a novel training-free framework for enhancing MLLM perception of HR images. Our method follows a three-staged approach: 1) Divide: recursively partitioning the HR image into patches and merging similar patches to minimize computational overhead, 2) Conquer: leveraging the MLLM to generate accurate textual descriptions for each image patch, and 3) Combine: utilizing the generated text descriptions to enhance the MLLM's understanding of the overall HR image. Extensive experiments show that: 1) the SOTA MLLM achieves 63% accuracy, which is markedly lower than the 87% accuracy achieved by humans on HR-Bench;2) our method brings consistent and significant improvements (a relative increase of +6% on HR-Bench and +8% on general multimodal benchmarks). Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

Defense Against Adversarial Faces at the Source: Strengthened Faces Based on Hidden Disturbances

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Li, Shuangliang Wang, Jinwei Wu, Hao Zhang, Jiawei Cheng, Xin Luo, Xiangyang Ma, Bin School of Software Nanjing Universityof Information Science and Technology Nanjing210044 China College of Cryptology and Cyber Science Nankai University Tianjing300073 China State Key Laboratory of Mathemat-ical Engineering and Advanced Computing Zhengzhou450002 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Cyber Security Qilu University of Technology Shandong Academy of Sciences Shandong250353 China

Face Recognition (FR) systems, while widely used across various sectors, are vulnerable to adversarial attacks, particularly those based on deep neural networks. Despite existing efforts to enhance the robustness of FR models, they still face the risk of secondary adversarial attacks. To address this, we propose a novel approach employing "strengthened face" with preemptive defensive perturbations. Strengthened face ensures original recognition accuracy while safeguarding FR systems against secondary attacks. In the white-box scenario, the strengthened face utilizes gradient-based and optimization-based methods to minimize feature representation differences between face pairs. For the black-box scenario, we propose Shielded Gradient Sign Descent (SGSD) to optimize the gradient update direction of strengthened faces, ensuring the transferability and effectiveness against unknown adversarial attacks. Experimental results demonstrate the efficacy of strengthened faces in defending against adversarial faces without compromising the performance of FR models or face image visual quality. Moreover, SGSD outperforms conventional methods, achieving an average performance improvement of 4% in transferability across different attack intensities. © 2020 IEEE.

关键词： adversarial attacks face recognition facial protection

来源：评论

学校读者我要写书评

暂无评论

SAMRS: scaling-up remote sensing segmentation dataset with segment anything model 23

SAMRS: scaling-up remote sensing segmentation dataset with s...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Di Wang Jing Zhang Bo Du Minqiang Xu Lin Liu Dacheng Tao Liangpei Zhang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

关键词：

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：