检索结果-内蒙古大学图书馆

11th International Conference on Learning Representations, ICLR 2023

作者： Dong, Zehao Cao, Weidong Zhang, Muhan Tao, Dacheng Chen, Yixin Zhang, Xuan Department of Computer Science & Engineering Washington University St.Louis United States Department of Electrical & Systems Engineering Washington University St.Louis United States Institute for Artificial Intelligence Peking University China School of Computer Science The University of Sydney Australia

The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit *** the past decades, intensive research efforts have mostly been paid to automate the transistor sizing with a given circuit *** recognizing the graph nature of circuits, this paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing based on the encoder-dependent optimization ***, CktGNN encodes circuit graphs using a two-level GNN framework (of nested GNN) where circuits are represented as combinations of subgraphs in a known subgraph *** this way, it significantly improves design efficiency by reducing the number of subgraphs to perform message ***, another critical roadblock to advancing learning-assisted circuit design automation is a lack of public benchmarks to perform canonical assessment and reproducible *** tackle the challenge, we introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains 10K distinct operational amplifiers with carefully-extracted circuit *** is also equipped with communicative circuit generation and evaluation capabilities such that it can help to generalize CktGNN to design various analog circuits by producing corresponding *** on OCB show the extraordinary advantages of CktGNN through representation-based optimization frameworks over other recent powerful GNN baselines and human experts' manual *** work paves the way toward a learning-based open-sourced design automation for analog *** source code is available at https://***/zehao-dong/CktGNN. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Timing circuits

来源：评论

学校读者我要写书评

暂无评论

Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

引用

computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial Enabling Technologies and systems for Industry 5.0: From Foundation Models to Foundation Intelligence

引用

IEEE Transactions on systems, Man, and Cybernetics: systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Multi-Instance Convolutional Neural Network for Disaster Classification From High-Resolution Remote Sensing Images

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 2098-2114页

作者： Li, Chengfan Zhang, Zixuan Liu, Lan Kim, Jung Yoon Sangaiah, Arun Kumar the School of Computer Engineering and Science Shanghai University Shanghai200444 China the Key Laboratory of National Geographic Census and Monitoring Ministry of Natural Resources Wuhan University Wuhan430079 China the Key Laboratory for Digital Land and Resources of Jiangxi Province East China University of Technology Nanchang330013 China the School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China the College of Future Industry Gachon University Seongnam-Si Gyeonggi-do13120 Korea Republic of the International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu64002 Taiwan the Department of Electrical and Computer Engineering Lebanese American University Byblos11020 Lebanon

—The fully supervised deep convolutional neural network (CNN) cannot detect the discriminant local information that is responsible for spatial transformations in high-resolution remote sensing images. To address the various types and missing labels of natural disasters, a new deep multi-instance convolutional neural network (DMCNN) model for disaster classification in high-resolution remote sensing image is presented in this article. Specifically, based on sample enhancement and atrous spatial pyramid pooling, we first extract and integrate the features via the CNN structure to obtain the instance feature of bags in the image. Besides, introducing a prototype learning layer with distance measure, the instance features extracted from pretrained CNN are mapped into a series of prototype instance features with bag-level. Subsequently, all instance features from prototype and bag take part in disaster detection and image classification. Finally, we conduct extensive experiments on xBD dataset and discussions from qualitative and quantitative aspects. Experimental results show that the proposed DMCNN model achieves better classification accuracy of natural disaster from high-resolution remote sensing images compared to traditional CNNs, and effectively improves the disaster classification performance with weakly supervised from high-resolution remote sensing images. © 2023 The Authors.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

RAT: A Refactoring-Aware Tool for Tracking Code History 24

RAT: A Refactoring-Aware Tool for Tracking Code History

引用

46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Niu, Feifei Shao, Junqian Xu, Chaofan Mayr-Dorn, Christoph Assunção, Wesley K. G. Huang, Liguo Li, Chuanyi Ge, Jidong Luo, Bin Egyed, Alexander State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina United States Department of Computer Science and Engineering Southern Methodist University DallasTX United States

ISBN: (纸本)9798400705021

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT-Demo. Demo Video: https://***/VI-xwUaIPp4. © 2024 IEEE computer Society. All rights reserved.

关键词： Rats

来源：评论

学校读者我要写书评

暂无评论

GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract) 40

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Fang, Wei Jiang, Haipeng Lu, Hengyang Sun, Jun Wu, Xiaojun Lin, Jerry Chun-Wei Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and Technology Wuxi214122 China Silesian University of Technology Department of Distributed Systems and IT Devices Poland

ISBN: (纸本)9798350317152

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality. © 2024 IEEE.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Identification Based on Butterfly Optimization and Machine Learning

Heart Disease Identification Based on Butterfly Optimization...

引用

2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Asrar, Manal Bawazir, Joud Khan, Sibghatullah I. Qaisar, Saeed Mian Effat University Computer Science Department Jeddah22332 Saudi Arabia Department of Electronics and Communication Engineering Hyderabad India Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia Cesi Engineering School Lineact Laboratory Villeurbanne69100 France

ISBN: (纸本)9798350347050

This paper aims to make use of The Physionet Challenge 2016 collection of normal and abnormal heart sound recordings that were classified by automated identification of PCG sounds to help detect heart diseases earlier and prevent incidents. People with heart diseases have been increasing and most of them lead to fatalities so the detection of sound signals through PCGs can be applied in machine learning models by extracting features from data. In this study, the dataset's recordings are segmented to be used in variational mode decomposition. Once they are decomposed, those means will be fused together into a set of features given to the Butterfly Optimization Algorithm which will conduct a selection of features. As the features are selected, MATLAB was used to test various machine learning algorithms. Results from Support Vector Machines (SVM) and artificial neural networks were used in this investigation (ANN). The ANN model, which had an accuracy rate of 94.8%, was the most accurate of them. © 2023 IEEE.

关键词： Variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Lattice Design for Multiple Topologically Protected Edge Modes

Lattice Design for Multiple Topologically Protected Edge Mod...

引用

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Gyunghun Kim Joseph Suh Dayeong Lee Namkyoo Park Sunkyu Yu Department of Electrical and Computer Engineering Intelligent Wave Systems Laboratory Seoul National University Seoul Korea Department of Electrical and Computer Engineering Photonic Systems Laboratory Seoul National University Seoul Korea

We propose the lattice design that allows multiple topologically protected edge modes. The scattering between these modes, which is linear, energy preserving, and robust against local disorders, is discussed in terms ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Lattices Scattering Signal processing Lasers and electrooptics Laser modes Electro-optical waveguides

来源：评论

学校读者我要写书评

暂无评论

Retinal disorder diagnosis based on hybrid deep learning models

引用

Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution 41

Hierarchical Neural Operator Transformer with Learnable Freq...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Luo, Xihaier Qian, Xiaoning Yoon, Byung-Jun Computational Science Intiative Brookhaven National Laboratory UptonNY United States Department of Electrical and Computer Engineering Texas A&M University College StationTX United States

In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the resolution of scientific data, which often involves complex challenges such as continuity, multi-scale physics, and the intricacies of high-frequency signals. Grounded in operator learning, the proposed method is resolution-invariant. The core of our model is a hierarchical neural operator that leverages a Galerkin-type self-attention mechanism, enabling efficient learning of mappings between function spaces. Sinc filters are used to facilitate the information transfer across different levels in the hierarchy, thereby ensuring representation equivalence in the proposed neural operator. Additionally, we introduce a learnable prior structure that is derived from the spectral resizing of the input data. This loss prior is model-agnostic and is designed to dynamically adjust the weighting of pixel contributions, thereby balancing gradients effectively across the model. We conduct extensive experiments on diverse datasets from different domains and demonstrate consistent improvements compared to strong baselines, which consist of various state-of-the-art SR methods. Copyright 2024 by the author(s)

关键词： Ground state

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：