检索结果-内蒙古大学图书馆

2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Rahman, Arief Nur Andriana, Dian MacHbub, Carmadi Bandung Inst. of Technol. Res. Center for Telecommunications National Research and Innovation Agency School of Electrical Engineering and Informatics Bandung Indonesia Telkom University Research Center for Data and Information Science National Research and Innovation Agency School of Computing Bandung Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

This paper presents a comparison between Grad-CAM and EigenCAM methods on the YOLOv5 detection model. Explainable Artificial Intelligence (XAI) is a tool to understand and interpret machine learning models. XAI is used to understand which pixel from an image contributes significantly to the model's final output. EigenCAM is quite simple but a very powerful technique for producing visual explanations, but Grad-CAM is better for a low illumination environment. © 2022 IEEE.

关键词： Cams

来源：评论

学校读者我要写书评

暂无评论

Limitations of Advanced Persistent Threat datasets: Insights for Cybersecurity Research

Limitations of Advanced Persistent Threat Datasets: Insights...

引用

Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Abdullah Al Mamun Harith Al-Sahaf Ian Welch Marinho Barcellos Seyit Camtepe School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Cybersecurity Engineering Al-Zahraa University for Women Karbala Iraq School of Computing and Mathematical Sciences University of Waikato Hamilton New Zealand Data61 CSIRO Australia

ISBN: (数字)9798350379112

ISBN: (纸本)9798350379129

Advanced Persistent Threats (APTs) pose a significant and ever-evolving challenge to cybersecurity, necessitating the development of robust and effective detection mechanisms. Central to this endeavor is the availability of high-quality datasets that accurately capture the complexities and nuances of APT activities. This paper presents a comprehensive analysis of four publicly available APT datasets, focusing on their strengths, limitations, and implications for cybersecurity research. A meticulous examination shows that none of these datasets can be used directly without extensive preprocessing. The strengths and limitations of each dataset are explained, enabling researchers to make informed decisions regarding their selection and application. Additionally, common challenges encountered in APT dataset analysis are identified, and data preprocessing techniques tailored to effectively apply machine learning algorithms are proposed. Leveraging these datasets, initial results demonstrating balanced accuracy across various standard machine learning classifiers are provided. We have made our code publicly available to promote reproducibility and further research. By shedding light on the intricacies of APT dataset management and utilization, this study contributes to the broader discourse on enhancing the detection and mitigation of these sophisticated threats.

关键词： Support vector machines Accuracy Prevention and mitigation Soft sensors Focusing Telecommunication traffic Vectors Complexity theory Computer security Standards

来源：评论

学校读者我要写书评

暂无评论

Computable Model-Independent Bounds for Adversarial Quantum Machine Learning

IEEE Transactions on Quantum Engineering

引用

IEEE Transactions on Quantum engineering 2025年

作者： Li, Bacui Alpcan, Tansu Thapa, Chandra Parampalli, Udaya University of Melbourne Department of Electrical and Electronic Engineering ParkvilleVIC3010 Australia CSIRO Data61 Sydney Australia University of Melbourne School of Computing and Information Systems ParkvilleVIC3010 Australia

By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well documented to be vulnerable to malicious manipulations, and this susceptibility extends to the models of QML. This situation necessitates a thorough understanding of QML's resilience against adversarial attacks, particularly in an era where quantum computing capabilities are expanding. In this regard, this paper examines model-independent bounds on adversarial performance for QML. To the best of our knowledge, we introduce the first computation of an approximate lower bound for adversarial error when evaluating model resilience against sophisticated quantum-based adversarial attacks. Experimental results are compared to the computed bound, demonstrating the potential of QML models to achieve high robustness. In the best case, the experimental error is only 10% above the estimated bound, offering evidence of the inherent robustness of quantum models. This work not only advances our theoretical understanding of quantum model resilience but also provides a precise reference bound for the future development of robust QML algorithms. © 2020 IEEE.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

SMART UI: Crop Classification in Karnataka Nippani Using Ensemble Random Forest

SMART UI: Crop Classification in Karnataka Nippani Using Ens...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Jagarlamudi Susanth Soubraylu Sivakumar Selvanayaki Kolandapalayam Shanmugam R. Sivakumar S. S. Sridhar S. Ganesh Kumar Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Mathematics and Computer Science Ashland University OH United States of America Earthquake Research Cell Civil Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

This study helped in enhancing agricultural management, predicting yields, tracking crop health which are all dependent on accurate crop classification. But the conventional methods are labor intensive and time consuming. The goal of this study is to automate crop classification system that uses machine learning and satellite image to accurately classify the crops. Ground Truth data is gathered from *** website and Random Forest classifier is used to train on data. To be more accurate the algorithm is verified using dataset from specific regions. The Random Forest effectively classified crops with an accuracy of $79 \%$. The outcome shows how well crop classification works when used with machine learning and satellite image together. This study provides scalable algorithm for crop classification, which helps in various agricultural related works like crop health. The future studies should investigate alternative use of real-time data for dynamic classification.

关键词： Accuracy Machine learning algorithms Heuristic algorithms Scalability Crops Real-time systems Satellite images Classification algorithms Random forests Monitoring

来源：评论

学校读者我要写书评

暂无评论

FHRDiff: Leveraging Diffusion Models for Conditional Fetal Heart Rate Signal Generation

FHRDiff: Leveraging Diffusion Models for Conditional Fetal H...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoqing Li Yu Lu Kee Yuan Ngiam Zichang Yu Mohammad Shaheryar Furqan College of Big Data and Internet Shenzhen Technology University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Surgery Yong Loo Lin School of Medicine National University of Singapore Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate analysis of Fetal Heart Rate (FHR) signal is often impeded by challenges such as data scarcity and label imbalance, which affect the reliability and robustness of deep learning models. To address these challenges, this study introduces FHRDiff, a novel diffusion model conditioned on Phase-Rectified Signal Averaging (PRSA) spectrograms for the creation of synthetic FHR signals. Our model integrates time encoding, condition generation from PRSA spectrograms, and residual blocks with dilated convolutions to effectively manage temporal dynamics and long-range dependencies. Extensive qualitative and quantitative experiments on FHR signal synthesis demonstrate the feasibility and effectiveness of FHRDiff. Compared to Generative Adversarial Networks (GANs) and image-based diffusion model, our method achieves the highest signal fidelity and distribution similarity, with key measures including 0.067 maximum mean deviation (MMD), 0.492 percent root mean square difference (PRD), 1.763 relative entropy (RE), and 0.160 Frechet distance (FD). Expert validation confirms the model’s capacity to accurately generate data for normal and abnormal FHR signals based on the paired condition. In addition, an ablation study was conducted to highlight that the spectrogram paired condition can guide the diffusion model to produce synthetics FHR signals with greater diversity compared to unconditional models. The results emphasize diffusion models’ potential for broad application in biomedical time series analysis, such as generation, imputation and noise removal.

关键词： Analytical models Fetal heart rate Biological system modeling Time series analysis Diffusion models data models Robustness Signal synthesis Root mean square Spectrogram

来源：评论

学校读者我要写书评

暂无评论

Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices

arXiv

引用

arXiv 2024年

作者： Hemel, Md. Shahriar Khan Reaz, Mamun Bin Ibne Ali, Sawal Hamid Bin Md Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia Department of Electrical and Electronic Engineering Independent University Bangladesh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Arts Science and Technology Wrexham University United Kingdom Faculty of Computing Engineering and Science University of South Wales United Kingdom

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps. © 2024, CC BY.

关键词： Phase locked loops

来源：评论

学校读者我要写书评

暂无评论

LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

引用

30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Strengthening Authentication: The Role of Multi-Level Hashing in OSPA Protocol 5

Strengthening Authentication: The Role of Multi-Level Hashin...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Generating CGAN-based Synthetic Dermoscopy Images and Classifying with Deep Lightweight CNN

Generating CGAN-based Synthetic Dermoscopy Images and Classi...

引用

computing, Networking, Telecommunications & engineering Sciences Applications (CoNTESA), International Conference on

作者： Suriya Sultana Israt Yasmin Muhammed J. A. Patwary Mohammad Arif Sobhan Bhuiyan Iftekhar Salam Mahdi H. Miraz Department of Computer Science and Engineering International Islamic University Chittagong School of Electrical and Electronic Engineering Xiamen University Malaysia Sepang Malaysia School of Computing and Data Science (SCDS) Xiamen University Malaysia Sepang Malaysia

Effective dermatological diagnosis and treatments significantly depend on the accurate categorisation of the skin lesions. It is difficult to classify skin lesions using automated diagnostic techniques due to class-imbalance datasets and the absence of labelled data. The applications of various deep learning methods have demonstrated superior performance in medical diagnosis in the recent past. Unfortunately, training these models requires a substantial quantity of labelled instances. A novel approach, combining Deep Learning convolutional neural networks (CNNs) as well as conditional generative adversarial networks (CGANs), for classifying skin lesion images, has been presented in this study. CGAN is an enhanced version of the GAN, in which the generator takes input images with specific class labels. Applying conditions for minority classes can help to equalise the dataset by increasing the sample size for those minority classes. In this research, CGANs were used to produce synthetic images of minority classes for a skin lesion dataset named PAD UFES-20. The images generated from CGANS were added then to the original dataset to train a lightweight deep-learning CNN network named MobileNetv2. The analysis of the results shows an improvement of 6% average accuracy and 5% recall value, when the synthetic data augmentation was added to the original dataset. This synthetic data augmentation approach can also widely be used in various medical classification applications to reduce the imbalance problems, resulting in enhanced diagnosis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Task groupings regularization: data-free meta-learning with heterogeneous pre-trained models 24

Task groupings regularization: data-free meta-learning with ...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Yongxian Wei Zixuan Hu Li Shen Zhenyi Wang Yu Li Chun Yuan Dacheng Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China and College of Computing & Data Science Nanyang Technological University Singapore School of Cyber Science and Technology Sun Yatsen University China University of Maryland College Park Department of Computer Science and Engineering The Chinese University of Hong Kong China College of Computing & Data Science Nanyang Technological University Singapore

data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen tasks. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. In this paper, we empirically and theoretically identify and analyze the model heterogeneity in DFML. We find that model heterogeneity introduces a heterogeneity-homogeneity trade-off, where homogeneous models reduce task conflicts but also increase the overfitting risk. Balancing this trade-off is crucial for learning shared representations across tasks. Based on our findings, we propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks. Specifically, we embed pre-trained models into a task space to compute dissimilarity, and group heterogeneous models together based on this measure. Then, we introduce implicit gradient regularization within each group to mitigate potential conflicts. By encouraging a gradient direction suitable for all tasks, the meta-model captures shared representations that generalize across tasks. Comprehensive experiments showcase the superiority of our approach in multiple benchmarks, effectively tackling the model heterogeneity in challenging multidomain and multi-architecture scenarios.

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：