Other Conferences最新文献_第4页

Research on key distribution methods supporting trusted data migration of cloud platform 支持云平台可信数据迁移的密钥分发方法研究

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3032077

Fuqiang Tian, Jun Mou, Angang Liu, Zhongkui Zhu, Maonan Lin, Hongyu Wu

{"title":"Research on key distribution methods supporting trusted data migration of cloud platform","authors":"Fuqiang Tian, Jun Mou, Angang Liu, Zhongkui Zhu, Maonan Lin, Hongyu Wu","doi":"10.1117/12.3032077","DOIUrl":"https://doi.org/10.1117/12.3032077","url":null,"abstract":"Cloud computing is a significant leap in the development process of the information industry. It integrates computing, storage, network, and other information resources organically, providing more convenient means for deep sharing and utilization, making the acquisition of information resources no longer limited by time and space. Cloud computing platform has become one of the important components of Information infrastructure, providing an important computing foundation for promoting the Digital transformation of the industry. As cloud computing platforms are increasingly accepted, more and more enterprises are migrating applications and Data migration to the cloud to effectively break through the limitations of local computing and storage resources, and make better use of the development trend of the cloud’s powerful analysis and processing capabilities. Therefore, to ensure the security and credibility of cloud platform Data migration, this paper proposes a key allocation method based on the hash function to support the trusted migration of cloud platform data. Experimental results have shown that the cloud platform key allocation scheme proposed in this article can effectively improve the security capabilities of the cloud platform compared to traditional key allocation schemes.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378363","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

EPTDMS: efficient and privacy-preserving top-k disease matching scheme for cloud-assisted e-healthcare system EPTDMS：用于云辅助电子医疗系统的高效且保护隐私的顶k疾病匹配方案

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3031898

ou ruan, xin jiang

引用次数: 0

A hierarchical software architecture for smart firefighting platform 智能消防平台的分层软件架构

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3032097

Zixiang Zhang, Nady Slam, Zhengqiang Di, Yu Zhu

{"title":"A hierarchical software architecture for smart firefighting platform","authors":"Zixiang Zhang, Nady Slam, Zhengqiang Di, Yu Zhu","doi":"10.1117/12.3032097","DOIUrl":"https://doi.org/10.1117/12.3032097","url":null,"abstract":"To enhance the fire service system, many countries have conducted extensive research on “Smart Firefighting” platform architecture and achieved significant results. However, there are still numerous challenges that persist in the field. To address the challenges related to storage, computing, security, and scalability of large-scale data in the \"Smart Firefighting\" domain, we have conducted an in-depth study and comparison of existing \"Smart Firefighting\" platform architectures. Based on the analysis, we propose a novel architectural for the \"Smart Firefighting\" platform, which combines the strengths of traditional hierarchical architecture with the Cloud-Edge-End architectural, this integration effectively preserves the key features of both architectural styles, and the proposed fusion model is able to satisfy the computation and storage of large amounts of firefighting data while guaranteeing security. This architectural model can serve as a reference for the construction of highly available, high-performance, and more secure and stable \"Smart Firefighting\" platforms and holds significant importance in accelerating the development and maintenance of \"Smart Firefighting\" to ensure long-term social stability and security.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379950","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

Just-in-time software defect prediction based on feature selection 基于特征选择的即时软件缺陷预测

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3031976

Shipeng cai, Hongmin Ren

{"title":"Just-in-time software defect prediction based on feature selection","authors":"Shipeng cai, Hongmin Ren","doi":"10.1117/12.3031976","DOIUrl":"https://doi.org/10.1117/12.3031976","url":null,"abstract":"At the present stage, just-time software defect prediction has garnered significant attention from researchers due to its granularity and immediacy. Primarily utilizing machine learning classifiers, these models are trained on information from code repositories to predict whether future changes may lead to defects. However, a current challenge with these classifiers lies in the vast number of features, leading to decreased prediction efficiency. These features not only impact model performance but can sometimes result in a decline in predictive accuracy. This paper explores a feature selection technique that combines random forests and self-attention to discard less important features without compromising performance. Through this approach, the number of features required for training is significantly reduced, often to less than 50% of the original features. In our study across six software projects, we observed that using feature selection in the KNN model led to a 9% improvement in the F1 metric and a 6% improvement in the AUC metric compared to logistic regression and Bayesian models. Finally, we applied SHAP for interpretability analysis of the model. This research contributes to enhancing the accuracy and efficiency of just-in-time software defect prediction, providing valuable insights for research and practice in related fields.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380831","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 adaptive beamforming algorithm based on FPGA 基于 FPGA 的自适应波束成形算法研究

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3032050

Youbang Su

引用次数: 0

FMAMPN: lightweight feature map attention multipath network for semantic segmentation of remote sensing image FMAMPN：用于遥感图像语义分割的轻量级特征图注意多径网络

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3031941

Songqi Hou, Ying Yuan

引用次数: 0

An improved method for reverse engineering ECU firmware 改进的 ECU 固件逆向工程方法

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3032054

Yuhao Qiu

引用次数: 0

Automatic mapping based on CVE and ATT&CK 基于 CVE 和 ATT&CK 的自动映射

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3032103

Lei Wang, Li Sun, Duo Shang, Huihong He, Guangtao Nie, Haikuo Dong

引用次数: 0

A method for microservice partitioning of monolithic systems based on multifeature fusion 基于多特征融合的单体系统微服务分区方法

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3031975

Xiaoyan Gao, Junfeng Zhao

{"title":"A method for microservice partitioning of monolithic systems based on multifeature fusion","authors":"Xiaoyan Gao, Junfeng Zhao","doi":"10.1117/12.3031975","DOIUrl":"https://doi.org/10.1117/12.3031975","url":null,"abstract":"With the popularity of microservices architecture, how to refactor a monolithic system into a microservices architecture has become a challenge. Traditional system refactoring strategies often focus on a single aspect, such as conducting source code analysis or system load analysis in isolation. This approach has a limited perspective and cannot comprehensively integrate the diverse characteristics of the system for refactoring. This paper proposes a microservices partitioning method for monolithic systems that integrates multiple features. Firstly, it integrates source code and runtime system data to comprehensively acquire system characteristics. By calculating the semantic similarity of class documents, the similarity of abstract syntax trees, and the frequency of interaction between classes, three types of weights are generated. Then, by assigning different weights, a comprehensive weight is calculated to construct an undirected weighted graph representing dependency relationships. Finally, the Chinese Whisper clustering algorithm is used to partition monolithic systems, obtaining suitable microservice modules. Experimental results show that this method can help developers better understand and partition the system, achieving a microservices architecture with high cohesion and low coupling.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375973","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

Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking 基于 QUBO 模型与量子启发算法，增强 CVQKD 系统，确保黑客攻击的安全性

Other Conferences Pub Date : 2024-06-06 DOI: 10.1117/12.3031949

Feiyue Zhu, Haifeng Qiu, Ziyu Wang

引用次数: 0