检索结果-内蒙古大学图书馆

4th International Conference on Computing and Data Science, CONF-CDS 2022

作者： Zheng, Wentao Liu, Kan Che, Fuqi Zheng, Yichen School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Optics and Photonics Beijing Institute of Technology Beijing100089 China School of Software Dalian University of Technology Liaoning Dalian116620 China Dulwich International High School Jiangsu Suzhou215021 China

There is a lot of room for research in how to detect breast cancer metastases in whole slide images more quickly and precisely. This work contrasts several machine learning and deep learning models on the breast cancer metastases detection problem and experiments the impacts of feature extraction, dimension reduction and the sizes of the datasets on these models. This work also establishes a breast cancer metastases detection pipeline based on the most accurate model in the experiments. Among the models, Dense Model using features trained from DenseNet201 with weights trained by ImageNet is of the best performance. This work develops a computer aided method to examine the cancerous regions in the whole slide images of breast cancer more quickly and precisely than an experienced doctor does. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Rolling Bearing Intelligent Faults Diagnosis Based on Deep Convolutional Neural Network

Rolling Bearing Intelligent Faults Diagnosis Based on Deep C...

引用

2022 International Conference on Signal Processing, computer Networks, and Communications, SPCNC 2022

作者： Wang, Wenjie Lv, Rongxin Ding, Haonan Li, Tengfeng Li, Tingyu Wang, Xin School of Computer Science Shenyang Aerospace University Shenyang China School of Innovation Shenyang Aerospace University Shenyang China School of Electrical Engineering Henan University of Technology Henan Zhengzhou China School of Resource and Security Engineering Henan Institute of Engineering Henan Zhengzhou China School of software Zhongyuan University of Technology Henan Zhengzhou China School of Information Science and Technology Zhengzhou Normal University Henan Zhengzhou China

ISBN: (纸本)9781510664616

In the processing, manufacturing, and production of modern fields, rolling bearings, the most basic module of most mechanical equipment, have a key role that cannot be ignored. This paper proposes three fault diagnosis model architectures for rolling bearings based on the deep convolutional neural network. Three models were tested on the industry-common Case Western Reserve University Dataset (CWRU). The original vibration signal acquisition and processing module mainly uses the vibration signal window translation method to complete the segmentation of overlapping signals and uses the Inception network to efficiently complete one-dimensional signal preprocessing. Finally, we use t-distributed stochastic neighbor embedding (t-SNE) to reduce the dimension and visualize the learned fault data distribution. © 2023 SPIE.

关键词： Roller bearings

来源：评论

学校读者我要写书评

暂无评论

A systematic literature review to implement large language model in higher education: issues and solutions

引用

Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

来源：评论

学校读者我要写书评

暂无评论

Timo: In-memory temporal query processing for big temporal data

Timo: In-memory temporal query processing for big temporal d...

引用

作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes 35

Understanding and Detecting Privacy Leakage Vulnerabilities ...

引用

35th IEEE International Symposium on software Reliability engineering, ISSRE 2024

作者： Chen, Ziming Li, Yue Gao, Jianbo Zhang, Jiashuo Wang, Ke Hu, Jianbin Guan, Zhi Chen, Zhong Peking University School of Computer Science Beijing China Taiyuan University of Technology College of Computer Science and Technology Taiyuan China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350353884

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China National Vulnerability Database. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Partially Hidden Policy Attribute-Based Multi-Keyword Searchable Encryption with Verification and Revocation

引用

IEEE Transactions on Mobile Computing 2025年

作者： Chen, Liqing Xu, Shiguo Jin, Chunhua Zhang, Hao Weng, Jian Huaiyin Institute of Technology Faculty of Computer and Software Engineering Jiangsu Huai'an223003 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Jinan University College of Information Science and Technology Guangdong Guangzhou510632 China

Data security is becoming increasingly critical as outsourced data services flourish. An effective solution for ensuring data confidentiality in the cloud is attribute-based searchable encryption (ABSE). However, most extant ABSE solutions have not been able to simultaneously achieve rich expressive queries, result validation, attribute revocation, and policy hiding, and there are problems such as inflexibility in query methods and access control capabilities, insufficient scalability and practicality, and privacy leakage. In particular, it should be noted that most ABSE schemes fail to promise the privacy security of access policies and are susceptible to offline keyword guessing attack. Hidden policy-ABSE (HP-ABSE) has been proposed to tackle the issue of explicit attribute values in access policies potentially compromising privacy. Nevertheless, in most existing HP-ABSE schemes, an adversary may initiate attribute values guessing attack to speculate on the values of attribute within access strategy. To this end, this paper proposes partially hidden policy attribute-based multi-keyword searchable encryption with verification and revocation (PHP-ABMKSE-VR). PHP-ABMKSE-VR scheme is practical, secure, as well as effective, making it appropriate for real-world scenarios like smart healthcare, as demonstrated by the experimental outcomes and safety analysis. © 2002-2012 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 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

Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Efficient Cache Retrieving Scheme in VANET via Online Reinforcement Learning

引用

IEEE Transactions on Vehicular technology 2025年

作者： Tan, Zhenhao Chen, Bin Li, Ruidong Yang, Lei Xu, Xiaohua University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Kanazawa University Institute of Science and Engineering Ishikawa920-1192 Japan South China University of Technology School of Software Engineering Guangdong510640 China

The implementation of intelligent driving technology is inseparable from reliable wireless communication and efficient cache scheduling in Vehicle Ad-hoc Networks (VANETs). The purpose of cache scheduling in VANETs is to provide efficient and fast data services to vehicles. In response to difficult to handle wireless communication interference and high requirements for communication delay and energy consumption optimization in vehicle-road cooperation, we propose a new communication model of "Retrieving-Cast"to simplify the cache scheduling process oriented by data requirements. In V2I mode, consider the uplink of all roadside units(RSUs) retrieving data from vehicles, and prioritize data retrieving based on factors such as the number, density, and urgency of vehicles within the communication coverage range of RSUs. We hope that as many high-priority RSUs as possible to complete cached data retrieving as soon as possible under wireless communication interference. To overcome the limitations of traditional heuristic algorithms with low scheduling efficiency and deep learning methods requiring a large amount of training data, we propose a double-online reinforcement learning method, which efficiently solves the cache retrieval scheduling problem. Through experiments and analysis, our method achieved better scheduling results compared to traditional approximation algorithms and single greedy Deep Q-learning methods. The designed data transformation method also addresses the issue of requiring a large amount of training data, enabling rapid computation of scheduling requests in the network at an extremely low training cost, meeting the low-latency computation requirements in VANETs. © 1967-2012 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Bayesian Wasserstein Repulsive Gaussian Mixture Models

arXiv

引用

arXiv 2025年

作者： Huang, Weipeng Ng, Tin Lok James School of Computer Science and Software Engineering Shenzhen Institute of Information Technology Guangdong Shenzhen China School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

We develop the Bayesian Wasserstein repulsive Gaussian mixture model that promotes well-separated clusters. Unlike existing repulsive mixture approaches that focus on separating the component means, our method encourages separation between mixture components based on the Wasserstein distance. We establish posterior contraction rates within the framework of nonparametric density estimation. Posterior sampling is performed using a blocked-collapsed Gibbs sampler. Through simulation studies and real data applications, we demonstrate the effectiveness of the proposed model. Copyright © 2025, The Authors. All rights reserved.

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：