Proceedings of the 2023 9th International Conference on Computing and Data Engineering最新文献

An On-Demand Cloud-Native Containerized Storage Design and its Practice of HDFS-on-Kubernetes 按需云原生容器存储设计及其在kubernetes上的hdfs实践

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589846

Jian Lin, Lin Huang, Tao Zhou, Dongming Xie, Bo Yu

{"title":"An On-Demand Cloud-Native Containerized Storage Design and its Practice of HDFS-on-Kubernetes","authors":"Jian Lin, Lin Huang, Tao Zhou, Dongming Xie, Bo Yu","doi":"10.1145/3589845.3589846","DOIUrl":"https://doi.org/10.1145/3589845.3589846","url":null,"abstract":"Cloud-native big data services become popular in recent years. Two pillars of these services are identified: the separation architecture of compute and storage, and the application-specific controller mechanism. In terms of storage for big data on the cloud, current practices focus on managing a single on-premise storage cluster or building independent PaaS storage services. This paper focuses on the cloud-native containerized storage. An on-demand provisioning design is proposed, which extends the mainstream storage architecture and supports the provisioning of storage clusters for multi-tenancy in a dynamic manner. Its instance of HDFS-on-Kubernetes is implemented. With the mechanisms of global endpoint provisioning and dynamic volume provisioning, this provisioner enables the creation and management of multiple on-demand storage clusters with full-stack resources in an automated way. It guarantees the native performance of host network and local storage, which has been validated through experiments and production applications. It is also easy to use because of its high-level abstraction and single-point configuration mechanism. The design as well as the provisioner has served real business in industrial scenarios.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121883807","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

Efficient Face Generation and Clustering Using Generative Adversarial Networks 基于生成对抗网络的高效人脸生成和聚类

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589853

Aashika Varadharajan, Aishwarya Deshpande, Yuni Xia, S. Fang

引用次数: 0

A Closed-loop Detection Algorithm for Online Updating of Bag-Of-Words Model 词袋模型在线更新的闭环检测算法

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589847

Xiuqiang Shen, Lihang Chen, Zhuhua Hu, Yuexin Fu, Hao Qi, Yunfeng Xiang, Jiaqi Wu

引用次数: 0

Prediction of road traffic flow applying Long Short-Term Memory Model considering impact of COVID-19 in Toyota City 考虑新冠肺炎影响的长短期记忆模型预测丰田市道路交通流

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589855

Rui Mu, Yasuhiro Mimura, M. Yamazaki, Yusuke Suzuki, Toshiyasu Takakuwa

{"title":"Prediction of road traffic flow applying Long Short-Term Memory Model considering impact of COVID-19 in Toyota City","authors":"Rui Mu, Yasuhiro Mimura, M. Yamazaki, Yusuke Suzuki, Toshiyasu Takakuwa","doi":"10.1145/3589845.3589855","DOIUrl":"https://doi.org/10.1145/3589845.3589855","url":null,"abstract":"Due to various changes during the COVID-19 pandemic, special changes of road traffic flow are assumed. Changes of detected road traffic flow (DRTF) compared to that of 2019 under the same conditions in Toyota city are analyzed firstly. Generally, the DRTF decrease. Monthly change rate of the DRTF fluctuated during 2020 in 83.6%∼98.3%, however, they keep relatively stable during 2021 in 88.7%∼93.2%. Change rate of one-day-average DRTF for different weekdays, and for three long holidays also have different trends in 2020 and 2021. Moreover, change rate of one-day-average DRTF for different time of state of emergency declarations (SED) have special characteristics. Regarding the analysis above, a Long Short-Term Memory (LSTM) Model which consider impact of COVID-19 is developed to predict one-day DRTF. Sequence-to-sequence (StS) model is introduced, one-to-one and many-to-one models is designed separately to do the prediction. The results demonstrate that MAE, MAPE, and RMSE of one-to-one model is better than many-to-one model, although relationship of DRTF in one week is considered in many-to-one model.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114969664","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

Research on the Prediction Method of Rural Industry Integration Based on Improved RBF Neural Network Model 基于改进RBF神经网络模型的农村产业整合预测方法研究

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589856

Jianhua Zhao, Tao Yan

{"title":"Research on the Prediction Method of Rural Industry Integration Based on Improved RBF Neural Network Model","authors":"Jianhua Zhao, Tao Yan","doi":"10.1145/3589845.3589856","DOIUrl":"https://doi.org/10.1145/3589845.3589856","url":null,"abstract":"The integration development of rural industries can promote the high-quality development of rural commerce, cultural industry and tourism. In this paper, we propose an improved RBF neural network-based rural industry integration prediction method to address the current problem of insufficient accuracy of rural industry integration prediction. Firstly, we use the entropy value method to obtain the influencing factors indexes of rural industry integration, and then use the RBF neural network as the basic prediction model. On the premise that the prediction results of RBF neural network are greatly influenced by the network parameters, this paper innovatively adopts the artificial fish swarm algorithm improved by Lévy flight to optimize the RBF parameters, thus finally obtaining the prediction model of rural industry integration based on the improved RBF neural network. Finally, the integration degree evaluation indexes obtained by entropy weighting method are input into the prediction model for experiments. The experimental results show that the rural industry integration prediction method proposed in this paper can predict the rural industry integration degree more accurately and has better computing efficiency, which is helpful for the study of digital transformation of rural industry in the context of digital economy.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122551094","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

An Anchor Free Car Damage Detection Method 一种无锚车损伤检测方法

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589848

Haoran Jin, Xinkuang Wang, Z. Wu

引用次数: 0

Breast Microcalcification detection in digital mammograms using Deep Transfer learning approaches 使用深度迁移学习方法检测数字乳房x线照片中的乳房微钙化

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589849

Ehtsham Rasool, Muhammad Junaid Anwar, Bilawal Shaker, Muhammad Harris Hashmi, K. Rehman, Yousaf Seed

引用次数: 0

Profiling Cultural Tourists by Using User Generated Big Data from Online Travel Agencies 利用在线旅行社用户生成的大数据分析文化游客

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589850

Xiaogang Zhao, Siwei Dong, Yiwei Dang, Hai Shen, Jun Hou, Ge Li

引用次数: 0

PM2.5 Quality Concentration Prediction Based on Local Average Decomposition and Support Vector Regression 基于局部平均分解和支持向量回归的PM2.5质量浓度预测

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589857

Yuan-Hang Ye, Wen-Bo Wang

{"title":"PM2.5 Quality Concentration Prediction Based on Local Average Decomposition and Support Vector Regression","authors":"Yuan-Hang Ye, Wen-Bo Wang","doi":"10.1145/3589845.3589857","DOIUrl":"https://doi.org/10.1145/3589845.3589857","url":null,"abstract":"In view of the nonlinear and nonstationary characteristics of atmospheric PM2.5 mass concentration, in order to improve the prediction accuracy of PM2.5 mass concentration. Herein, we use the \"decomposition and integration\" prediction method, established a mixed prediction model of local average decomposition (LOCAL Mean Decomposition, LMD) and minimum daily support vector machines (LSSVM). Firstly, LMD was used to decompose the original time series of PM2.5 mass concentration, and several relatively stationary components with different time scales are obtained, then the SVR algorithm is used to predict each component separately, at last, obtaining the sum of the predictive values of each component as the prediction result of the original PM2.5 quality concentration. We select the PM2.5 daily average mass concentration from March 1, 2014 to April 30, 2015 from the Wanliu Monitoring Station in Haidian District, Beijing. The PM2.5 daily the average mass concentration is used as an experimental sample set. The results of the research were compared with EEMD-LSSVM, EMD-LSSVM and a single LSSVM model, indicating that the LMD-LSSVM model effectively improves the predictive accuracy of PM2.5 quality concentration.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128291512","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

Security Analysis of Industrial Control S7 Protocol based on Peach 基于Peach的工业控制S7协议安全性分析

Proceedings of the 2023 9th International Conference on Computing and Data Engineering Pub Date : 2023-01-06 DOI: 10.1145/3589845.3589851

Quanjiang Shen, Liangliang Wang, Lei Zhang, Binbin Wang, Changjiang Liu, Ju-Wei Sha

引用次数: 0