检索结果-内蒙古大学图书馆

10th IEEE Smart World Congress, SWC 2024

作者： Wang, Shuai Liu, Shenghao Fan, Xiaoxuan Wang, Haijun Zhou, Yinxin Gao, Hanjun Lu, Hongwei Deng, Xianjun Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Hunan Technical College of Railway High-speed China Nanjing University of Posts and Telecommunications China China National Nuclear Power Co Ltd China

ISBN: (纸本)9798331520861

intelligent Cyber-Physical systems typically utilize sensors to gather a significant amount of raw data which often possesses temporal and high-dimensional characteristics. Ensuring the data reliability is crucial for the normal operation of the system. While most existing methods are capable of identifying anomalies in high-dimensional time series data from sensors, they often lack adequate accuracy and fail to adequately consider the correlation between sensor nodes. To address these limitations, we propose an unsupervised framework which is a combination of variational autoencoder and graph neural network(VG-Net). By jointly optimizing the reconstruction module and the prediction module, VG-Net effectively models the correlation between sensor nodes while simultaneously detecting anomalies. The reconstruction module is responsible for modeling the latent representation space and reconstructing the time series data through variational autoencoder. The prediction module leverages bidirectional long short term memory networks and neural tensor network to learn the relationships between sensor nodes, and uses graph neural network to predict the future behavior of the data. Experiments on eight real and synthetic datasets demonstrate that, compared with existing methods, VG-Net can accurately model the relationship between sensor nodes and enhance the anomaly detection performance of high-dimensional time series data. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Low-repair array codes based on nested structures

Low-repair array codes based on nested structures

引用

Artificial Intelligence and intelligent Manufacturing (AIIM), International Symposium on

作者： Juan Huang Jinghua Tang Dan Tang Hongliang Cai Hongzhou Yu School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

To address the problem of low repair efficiency of single- and double-disk of localized horizontal array code, this paper retains the original advantages of horizontal array code, transforms it on the basis of RDP 2D array code, and proposes a 2D array code model based on nested structure - NRDP (Nested Rows Diagonal Parity Code). The overall layout of the NRDP code is the same as that of the RDP code. However, when deploying data columns, the NRDP code deploys the RDP code of one prime number level smaller as a whole in the data columns of the NRDP code, forming a nested relationship. The increased redundant data reduce the repair overhead and error recovery time. Through theoretical analysis, NRDP has good compiled code complexity, improves the problem of uneven reading and writing during disk repair, and reduces the total amount of data that needs to be read during single- and double-disk failures, improving the success rate of single- and double-disk repair. The experimental results show that compared with RDP, LRRDP, and DRDP, NRDP can save $7.03 \%-\mathbf{3 0. 0 1 \%}$ of the single-disk failure repair time and $14.79 \%-26.51 \%$ of the dual-disk repair time.

关键词： Codes Layout Distributed databases Transforms Maintenance engineering Writing data models Manufacturing Arrays Periodic structures

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Enhanced Federated Learning for Secure and intelligent Consumer Electronics : An Overview

引用

IEEE Consumer Electronics Magazine 2025年

作者： Zhu, Chenlu Wang, Shuilong Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao He, Yuanyuan Wu, Celimuge Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China The University of Electro-Communications China

With the rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT), consumer electronics have become essential in daily life. However, these devices generate and store vast amounts of personal data, posing significant security threats. Federated learning, a distributed machine learning method, protects user privacy by training models collaboratively without sharing raw data. However, it still faces numerous challenges. To further enhance data security, the integration of blockchain technology is crucial. Blockchain's decentralized and immutable characteristics enable secure data sharing and privacy protection in consumer electronics. This paper analyzes the application of federated learning in consumer electronics, examines how blockchain can enhance federated learning, and discusses future research directions. The primary contributions of this paper include a detailed application of blockchain-enhanced federated learning in consumer electronics, intended to serve as a reference for future research. © 2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Security Compressed Sensing Image Encryption Algorithm Based on Elliptic Curve 9th

Security Compressed Sensing Image Encryption Algorithm Based...

引用

9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Jin, Anan Li, Xiang Xiong, Qingzhi School of Information Engineering East China University of Technology Jiangxi Nanchang330013 China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Jiangxi Nanchang330013 China Jiangxi Key Laboratory of Cybersecurity Intelligent Perception East China University of Technology Jiangxi Nanchang330013 China

ISBN: (纸本)9789819959679

Aiming to address the problems of high costs associated with the storage and transmission of environmental monitoring images, as well as potential security risks, this paper proposes a security compressed sensing image encryption algorithm based on elliptic curve cryptography. The algorithm introduces elliptic curve encryption technology within a compressed sensing framework, using elliptic curve cryptography to encrypt the matrix during the compression perception acquisition process. This enables secure acquisition of environmental monitoring encrypted images. Therefore, this paper presents a security compressed sensing framework to address security gaps in the compression perception reconstruction process and improve the security of environmental monitoring images. Experimental results show that the proposed algorithm effectively encrypts and reconstructs environmental monitoring images, with high security and resistance to cracking. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

引用

2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

引用

IEEE Transactions on intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

引用

2nd International Conference on Robotics, Automation and intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (system on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Cell softness reveals tumorigenic potential via ITGB8/AKT/glycolysis signaling in a mice model of orthotopic bladder cancer

引用

Chinese Medical Journal 2024年第2期137卷 209-221页

作者： Shi Qiu Yaqi Qiu Linghui Deng Ling Nie Liming Ge Xiaonan Zheng Di Jin Kun Jin Xianghong Zhou Xingyang Su Boyu Cai Jiakun Li Xiang Tu Lina Gong Liangren Liu Zhenhua Liu Yige Bao Jianzhong Ai Tianhai Lin Lu Yang Qiang Wei Department of Urology Institute of UrologyNational Clinical Research Center for Geriatrics and Center of Biomedical Big DataWest China Hospital of Sichuan UniversityChengduSichuan 610041China Department of Molecular Oncology Institute of Oncology Research(IOR)Oncology Institute of Southern Switzerland(IOSI)Bellinzona 6500Switzerland Department of Science and Drug Technology University of TurinTurinItaly National Clinical Research Center of Geriatrics The Center of Gerontology and GeriatricsWest China HospitalSichuan UniversityChengduSichuan 610000China Department of Pathology West China HospitalSichuan UniversityChengduSichuan 610041China Department of Pharmaceutics and Bioengineering School of Chemical EngineeringSichuan UniversityChengduSichuan 610000China

Background:Bladder cancer,characterized by a high potential of tumor recurrence,has high lifelong monitoring and treatment *** date,tumor cells with intrinsic softness have been identified to function as cancer stem cells in several cancer ***,the existence of soft tumor cells in bladder tumors remains ***,our study aimed to develop a microbarrier microfluidic chip to efficiently isolate deformable tumor cells from distinct types of bladder cancer ***:The stiffness of bladder cancer cells was determined by atomic force microscopy(AFM).The modified microfluidic chip was utilized to separate soft cells,and the 3D Matrigel culture system was to maintain the softness of tumor *** patterns of integrinβ8(ITGB8),protein kinase B(AKT),and mammalian target of rapamycin(mTOR)were determined by Western *** immunostaining was conducted to examine the interaction between F-actin and tripartite motif containing 59(TRIM59).The stem-cell-like characteristics of soft cells were explored by colony formation assay and in vivo studies upon xenografted tumor ***:Using our newly designed microfluidic approach,we identified a small fraction of soft tumor cells in bladder cancer *** importantly,the existence of soft tumor cells was confirmed in clinical human bladder cancer specimens,in which the number of soft tumor cells was associated with tumor ***,we demonstrated that the biomechanical stimuli arising from 3D Matrigel activated the F-actin/ITGB8/TRIM59/AKT/mTOR/glycolysis pathways to enhance the softness and tumorigenic capacity of tumor ***,we detected a remarkable up-regulation in ITGB8,TRIM59,and phospho-AKT in clinical bladder recurrent tumors compared with their non-recurrent ***:The ITGB8/TRIM59/AKT/mTOR/glycolysis axis plays a crucial role in modulating tumor softness and ***,the soft tumor cells become more sensitive to chemother

关键词： Soft tumor cells 3D Matrigel Stemness Non-muscle invasive bladder neoplasms Microfluidic analytical techniques

来源：评论

学校读者我要写书评

暂无评论

Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

引用

8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

引用

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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：