Journal of Cloud Computing-Advances Systems and Applications最新文献_第8页

Machine Learning-based EEG Signal Classification of Parkinson’s Disease 基于机器学习的帕金森病脑电信号分类

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00078

Hao Wu, Jun Qi, Yong Yue

{"title":"Machine Learning-based EEG Signal Classification of Parkinson’s Disease","authors":"Hao Wu, Jun Qi, Yong Yue","doi":"10.1109/CSCloud-EdgeCom58631.2023.00078","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00078","url":null,"abstract":"As the second most common neurodegenerative disease in the world, Parkinson’s disease continues to affect the normal and healthy life of patients. In recent years, considerable progress has been made in studying the EEG of patients with Parkinson’s disease. Many EEG data of patients with Parkinson’s disease can be published, and more filtering algorithms and classification models suitable for EEG signals of Parkinson’s disease have been proposed. However, studying channel redundancy of EEG signals in Parkinson’s disease still faces challenges. The pathogenesis of Parkinson’s disease is still uncertain in medicine, and it is difficult to propose a channel selection scheme suitable for all patients with Parkinson’s disease. In this paper, the open UNM data set is used to extract multi-scale features based on the fourth-order Butterworth IIR filter and Wavelet Packet Transform. The channel selection is carried out by using single-channel verification. 12 and 25 channels with the relative best R2 scores were selected for the feature data set generated based on these two methods. The classification performance of data sets with and without channel selection was compared between the open and closed-eye data sets. The negative effect of open eye status on EEG classification of Parkinson’s disease was found, and the channel selection was used to improve the AUC by 1% in the same data set. Results showed that the proposed channel selection scheme can alleviate the overfitting phenomenon that occurred in the training set in the testing set while maintaining the classification accuracy.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"3 1","pages":"423-428"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89681333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Risk Assessment Model for Social High-Risk Individuals Based on Graph Attention Network 基于图注意网络的社会高危人群风险评估模型研究

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00024

Yan Li, Xin Su, Xin Liu, He Yi Mu, Y. Zheng, Shuping Wang

引用次数: 0

5G Spectrum Research 5G频谱研究

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00035

Peiyuan Zhu, Lijun Xiao, Shu Tan, Jiahong Cai, Yingzi Huo, Ronglin Zhang

引用次数: 0

Federated Learning-Based Intrusion Detection System for IoT Environments with Locally Adapted Model 基于局部适应模型的物联网环境下联邦学习入侵检测系统

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00043

Souradip Roy, Juan Li, Yan Bai

引用次数: 0

A Blockchain-empowered Federated Learning Framework Supprting GDPR-compliance 支持gdpr合规性的区块链授权联邦学习框架

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00074

Lijun Xiao, Dezhi Han, Sisi Zhou, Nengxiang Xu, Lin Chen, Siqi Xie

{"title":"A Blockchain-empowered Federated Learning Framework Supprting GDPR-compliance","authors":"Lijun Xiao, Dezhi Han, Sisi Zhou, Nengxiang Xu, Lin Chen, Siqi Xie","doi":"10.1109/CSCloud-EdgeCom58631.2023.00074","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00074","url":null,"abstract":"In recent years, data privacy security has been widely and highly valued by countries around the world. In the context of European Union’s General Data Protection Regulation (GDPR), the regulatory requirements of laws and regulations are becoming increasingly strict, bringing huge impacts and challenges to enterprises with user’s personal data such as internet services and financial technology. Up to a point, federal learning ensures data privacy by storing and processing personal data locally. However, due to malicious clients or central servers being able to launch attacks on global models or user privacy data, the security of federated learning is questioned, introducing blockchain into the federated learning framework is a feasible solution to address these data security issues. In this work, the concept of Blockchain (BC), Federated Learning (FL), GDPR and other similar data protection laws are presented, where a Blockchain-empowered Federated Learning (BC-empowered FL) framework is introduced. The challenges on complying with the GDPR are described, and the solutions or principles for improving the GDPR-compliance of BC-empowered FL systems are analyzed, sorting out the differences and connections among the GDPR-compliance methods yet laying a foundation to design legal and compliant applications for different domains and scenarios which need touch upon the user’s personal data.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"40 1","pages":"399-404"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79663109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Traceable Location Privacy Preserving Scheme for Data Collection in Vehicular Fog Computing 一种用于车辆雾计算数据采集的可追踪位置隐私保护方案

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00020

Qinyang Chen, Keming Wang, Tao Xie

引用次数: 0

BContext2Name: Naming Functions in Stripped Binaries with Multi-Label Learning and Neural Networks BContext2Name:基于多标签学习和神经网络的剥离二进制文件命名函数

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00037

Bing Xia, Yunxiang Ge, Ruinan Yang, Jiabin Yin, Jianmin Pang, Chongjun Tang

{"title":"BContext2Name: Naming Functions in Stripped Binaries with Multi-Label Learning and Neural Networks","authors":"Bing Xia, Yunxiang Ge, Ruinan Yang, Jiabin Yin, Jianmin Pang, Chongjun Tang","doi":"10.1109/CSCloud-EdgeCom58631.2023.00037","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00037","url":null,"abstract":"Conducting binary function naming helps reverse engineers understand the internal workings of the code and perform malicious code analysis without accessing the source code. However, the loss of debugging information poses the challenge of insufficient high-level semantic information description for stripping binary code function naming. Meanwhile, the existing binary function naming scheme has one function label for only one sample. The long-tail effect of function labels for a single sample makes the machine learning-based prediction models face the challenge. To obtain a function correlation label and improve the propensity score of uncommon tail labels, we propose a multi-label learning-based binary function naming model BContext2Name. This model automatically generates relevant labels for binary function naming by function context information with the help of PfastreXML model. The experimental results show that BContext2Name can enrich function labels and alleviate the long-tail effect that exists for a single sample class. To obtain high-level semantics of binary functions, we align pseudocode and basic blocks based on disassembly and decompilation, identify concrete or abstract values of API parameters by variable tracking, and construct API-enhanced control flow graphs. Finally, a seq2seq neural network translation model with attention mechanism is constructed between function multi-label learning and enhanced control flow graphs. Experiments on the dataset reveal that the F1 values of the BContext2Name model improve by 3.55% and 15.23% over the state-of-the-art XFL and Nero, respectively. This indicates that function multi-label learning can provide accurate labels for binary functions and can help reverse analysts understand the inner working mechanism of binary code. Code and data for this evaluation are available at https://github.com/CSecurityZhongYuan/BContext2Name.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"22 1","pages":"167-172"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85100602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A One-Dimensional Residual Network and Physical Fitness-Based Exercise Prescription Recommendation Method 一维残差网络及基于体能的运动处方推荐方法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00046

Runqing Fan, Zhenlian Peng, Buqing Cao, Jianxun Liu, Peng Che, Tieping Chen

{"title":"A One-Dimensional Residual Network and Physical Fitness-Based Exercise Prescription Recommendation Method","authors":"Runqing Fan, Zhenlian Peng, Buqing Cao, Jianxun Liu, Peng Che, Tieping Chen","doi":"10.1109/CSCloud-EdgeCom58631.2023.00046","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00046","url":null,"abstract":"In the context of the national big data strategy, physical fitness test data has become one of the main influencing factors in guiding and promoting the participation of the population in sports and fitness. Recommending exercise prescriptions based on national physical fitness test data has become an important research topic. However, currently, there is little research on how to accurately use computer data processing technology to recommend exercise prescriptions based on physical fitness test data. In this study, we propose a ResNet-based Exercise Prescription (ResNet-EP) method that utilizes one-dimensional residual neural network technology to recommend exercise prescriptions based on physical fitness testing data. This method comprehensively analyzes physical fitness testing data and exercise prescription data and realizes the automatic recommendation of exercise prescriptions. Experimental results on a real dataset demonstrate that the ResNet-EP model outperforms other comparison models in terms of precision (79.98%), recall (83.73%), and F1 score (81.81%). This study provides novel insights into the combination of physical fitness testing and exercise.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"28 1","pages":"223-228"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81607808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Secret and Traceable Approach for Cloud Data Sharing 云数据共享的秘密和可追踪方法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00038

Chenying Xu, Yanfei Yin, Yingwen Chen

{"title":"A Secret and Traceable Approach for Cloud Data Sharing","authors":"Chenying Xu, Yanfei Yin, Yingwen Chen","doi":"10.1109/CSCloud-EdgeCom58631.2023.00038","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00038","url":null,"abstract":"With the development of cloud computing, data owners generally use cloud services to reduce storage and computing overhead. However, data stored in cloud servers is out of the direct control of the data owners, causing serious security issues. Access control mechanism based on cryptography can effectively protect the security of cloud data and prevent unauthorized access to it. Nevertheless, users may redistribute data to other users after obtaining it, which can harm the copyright interests of the data owner. To address this issue, this paper proposes a secret and traceable approach for cloud data sharing. We combine the lattice-based proxy re-encryption with digital watermarking technology for redistribution tracking in cloud data sharing scenario. The lattice cipher used in this scheme is an encryption algorithm with homomorphic property based on the Ring-LWE problem. It has the advantage of reducing the ciphertext length and improving the efficiency of encryption. This paper demonstrates the feasibility and effectiveness of this scheme from a series of experiments. The encryption of an image of size 1024*1024 pixels takes only 9.78 seconds on average. The computational performance of this scheme is roughly 91.434% better than the existing data sharing schemes supporting redistribution tracking.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"19 1","pages":"173-180"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77990304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Aggregation Protocol Resisting Collusion Attacks in the Internet of Vehicles Environment 车联网环境下抗合谋攻击的聚合协议

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00014

Zisang Xu, Ruirui Zhang, Peng Huang, Jianbo Xu

引用次数: 0