2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)最新文献_第3页

GAN-based Abnormal Transaction Detection in Bitcoin 基于gan的比特币异常交易检测

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00031

Xiaoqi Zhang, Guangsong Li, Yongjuan Wang

引用次数: 0

Accelerating Sample-based GNN Training by Feature Caching on GPUs gpu特征缓存加速基于样本的GNN训练

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00032

Yuqi He, Zhiquan Lai, Zhejiang Ran, Lizhi Zhang, Dongsheng Li

引用次数: 0

Power Load Curve Clustering based on ISODATA 基于ISODATA的电力负荷曲线聚类

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00022

Zhu Li, Xia Yu

{"title":"Power Load Curve Clustering based on ISODATA","authors":"Zhu Li, Xia Yu","doi":"10.1109/SmartCloud55982.2022.00022","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00022","url":null,"abstract":"Load clustering is the early basis of power grid system planning, load modeling, demand side management, load forecasting and other work. The traditional load classification method based on user types can not meet the needs of power grid services. Iterative Self-Organizing Data Analysis Algorithm (ISODATA) is an unsupervised learning dynamic clustering algorithm based on statistical pattern recognition. In view of the current problems that the initial clustering number of each algorithm is difficult to take and easy to fall into local optimum, the principle and implementation steps of ISODATA are introduced, and this algorithm is applied to the power load curve clustering. The clustering analysis is combined with specific power load curve samples, and the results prove that the clustering effect is better and the time improvement is larger. ISODATA is compared with the traditional clustering method to compare the clustering effect and the time loss of the algorithm. The results of the comparison experiments show that ISODATA has good clustering effect when applied to power load curve clustering.Isodata-based clustering of power load curves can fine distinguish users and provide decision support and scientific basis for the reliable operation of power system.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132514275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Semantic Segmentation Algorithm for Distributed Energy Data Storage Optimization based on Neural Networks 基于神经网络的分布式能源数据存储优化语义分割算法

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00024

Dong Mao, Zhongxu Li, Zuge Chen, Hanyu Rao, Jiuding Zhang, Zehan Liu

引用次数: 0

Performance Impacts of JavaScript-Based Encryption of HTML5 Web Storage for Enhanced Privacy 基于javascript的HTML5 Web存储加密对增强隐私的性能影响

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00037

Michael S. MacFadden, Meikang Qiu

{"title":"Performance Impacts of JavaScript-Based Encryption of HTML5 Web Storage for Enhanced Privacy","authors":"Michael S. MacFadden, Meikang Qiu","doi":"10.1109/SmartCloud55982.2022.00037","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00037","url":null,"abstract":"The HTML5 Web Storage API provides the ability for web applications to store data on client machines. This storage is commonly used for caching, local state tracking, and offline support that allows web applications to work when the web server cannot be contacted. The HTML5 Web Storage is becoming increasingly popular with the majority of new web applications using at least some features provided by this standard. Unfortunately, the local storage provided by HTML5 Web Storage is not entirely secure and does not sufficiently ensure the confidentiality of the user’s data. Encrypting data prior to storage is a common approach to protecting local user data. However, as browser-based applications become more complex and demanding the impact of data encryption may adversely impact application performance. Furthermore, the average web developer is generally not proficient in cryptographic best practices in web applications. First, we provide a simple design approach for encryption of local storage that supports offline web applications. Second, we analyze the impact of various symmetric encryption algorithms and implementations on the performance of the HTML Web Storage API. We show that there are several viable options that will increase the confidentiality and privacy of user data within local storage without imposing significant performance penalties.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114854855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated-Learning-based Hierarchical Diagnosis of Liver Fibrosis 基于联邦学习的肝纤维化分级诊断

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00023

Yueying Zhou, Xinping Ren, Xiaoying Zheng, Yongxin Zhu, Kang Xu, Shijin Song, Li Tian

{"title":"Federated-Learning-based Hierarchical Diagnosis of Liver Fibrosis","authors":"Yueying Zhou, Xinping Ren, Xiaoying Zheng, Yongxin Zhu, Kang Xu, Shijin Song, Li Tian","doi":"10.1109/SmartCloud55982.2022.00023","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00023","url":null,"abstract":"Hepatic fibrosis is an important prognostic factor as severe liver fibrosis may lead to liver cancer or even death. To grade liver fibrosis, ultrasound gray-scale images and ultrasound elastic images are commonly used in clinical diagnosis to judge the severity of liver fibrosis. However, these two diagnoses methods are often vulnerable to disturbances, such as personal experience or instrument differences. Moreover, these individual differences usually lead to conflicting stand-alone machine learning diagnosis models at each hospital whose medical data are not allowed to share in public due to data privacy. To handle the conflicts among diagnosis models, we propose a federated learning based hierarchical diagnosis method of liver fibrosis by utilizing shear wave elasticity pictures of multiple users across hospitals without sharing the original data. Our method is validated with authentic shear wave elasticity pictures of hepatic fibrosis patients in Shanghai, China. Experimental results show that our method is able to preprocess these shear wave elasticity pictures, train local diagnosis models at each hospital and securely consolidate into a shared global diagnosis model whose accuracy is over 70% with only a small dataset containing a few hundreds of labeled pictures. Our method is expected to further improve in its accuracy with more training samples. Our method would be the first practice based on federated learning in liver fibrosis diagnosis.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129417407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of User Electricity Consumption based on Adaptive K-Means Algorithm 基于自适应k均值算法的用户用电量预测

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00026

Li Zhu, Bin Liu

引用次数: 0

TDH: An Efficient One-stop Enterprise-level Big Data Platform TDH:高效一站式企业级大数据平台

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00042

Yuanhao Sun, Cheng Lv, Xi Liu, Tianyang Lei, Zhuoyi Guo, Ning Li, Hongshan Yang

引用次数: 0

Research on 3D Product Service System Based on Spherical Model 基于球面模型的三维产品服务系统研究

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00027

Shufeng He, Dianqi Sun

引用次数: 0

Detecting and Classifying Incoming Traffic in a Secure Cloud Computing Environment Using Machine Learning and Deep Learning System 使用机器学习和深度学习系统在安全云计算环境中检测和分类传入流量

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/smartcloud55982.2022.00010

Geetika Tiwari, Ruchi Jain

{"title":"Detecting and Classifying Incoming Traffic in a Secure Cloud Computing Environment Using Machine Learning and Deep Learning System","authors":"Geetika Tiwari, Ruchi Jain","doi":"10.1109/smartcloud55982.2022.00010","DOIUrl":"https://doi.org/10.1109/smartcloud55982.2022.00010","url":null,"abstract":"Cloud computing has been promoted as one of the most effective methods of hosting and delivering services via the internet. Despite its broad range of applications, cloud security remains a serious worry for cloud computing. Many secure solutions have been developed to safeguard communication in such environments, the majority of which are based on attack signatures. These systems are often ineffective in detecting all forms of threats. A machine learning approach was recently presented. This implies that if the training set lacks sufficient instances in a specific class, the judgment may be incorrect. In this research, we present a novel firewall mechanism for safe cloud computing environments called machine learning and deep learning system. Proposed Methods identifies and classifies incoming traffic packets using a novel combination methodology named most frequent decision, in which the nodes’ one previous decisions are coupled with the machine learning algorithm’s current decision to estimate the final attack category classification. This method improves learning performance as well as system correctness. UNSW-NB-15, a publicly accessible dataset, is utilized to derive our findings. Our data demonstrate that it enhances anomaly detection by 97.68 percent.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127806675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0