Using realistic factors to simulate catastrophic congestion events in a network [0.03%]
运用现实因素模拟网络重大拥堵事件
Christopher Dabrowski,Kevin Mills
Christopher Dabrowski
With the rapid growth of the Internet, there has been increased interest in the research literature in the use of computer models to study the dynamics of communication networks. An important example of this has been the study of dramatic, ...
A game theory-based COVID-19 close contact detecting method with edge computing collaboration [0.03%]
基于游戏理论的边缘计算协作的COVID-19密切接触检测方法
Yue Shen,Bowen Liu,Xiaoyu Xia et al.
Yue Shen et al.
People all throughout the world have suffered from the COVID-19 pandemic. People can be infected after brief contact, so how to assess the risk of infection for everyone effectively is a tricky challenge. In view of this challenge, the comb...
A new federated learning-based wireless communication and client scheduling solution for combating COVID-19 [0.03%]
一种新的基于联邦学习的无线通信和客户端调度解决方案以抗击新冠疫情
Shuhong Chen,Zhiyong Jie,Guojun Wang et al.
Shuhong Chen et al.
Federated learning is a machine learning method that can break the data island. Its inherent privacy-preserving property has an important role in training medical image models. However, federated learning requires frequent communication, wh...
Reliable and efficient emergency rescue networks: A blockchain and fireworks algorithm-based approach [0.03%]
基于区块链和烟花算法的可靠高效应急救援网络方法研究
Bin Chen,Weihua Zhang,Yijin Shi et al.
Bin Chen et al.
In recent years, coronavirus disease 2019 (COVID-19) has been a severe issue the world faces. Emergency rescue networks concerning the distribution of relief materials have gained extensive attention to tackle COVID-19 and related emergency...
A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd [0.03%]
基于半监督感知率学习的CMAB方案，通过人群中的可信数据采集来对抗COVID-19
Jianheng Tang,Kejia Fan,Wenxuan Xie et al.
Jianheng Tang et al.
The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies either assume that the qualities of workers are known in advance, or assume that the platform knows the qualities of workers on...
Online learning resource recommendation method based on multi-similarity metric optimization under the COVID-19 epidemic [0.03%]
基于多相似性度量优化的在线学习资源推荐方法研究——以新冠肺炎疫情为例
Jia Wang,Shuhao Jiang,Jincheng Ding
Jia Wang
With the continuous COVID-19 pneumonia epidemic, online learning has become a normal choice for many learners. However, the problems of information overload and knowledge maze have been aggravated in the process of online learning. A learni...
NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory [0.03%]
基于博弈论的用于COVID-19的非交互式可验证联合学习聚合方案NVAS
Haitao Deng,Jing Hu,Rohit Sharma et al.
Haitao Deng et al.
The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wirele...
Attribute-based multi-user collaborative searchable encryption in COVID-19 [0.03%]
基于属性的多用户协作可搜索加密在新冠肺炎疫情中的应用研究
Fan Zhao,Changgen Peng,Dequan Xu et al.
Fan Zhao et al.
With the outbreak of COVID-19, the government has been forced to collect a large amount of detailed information about patients in order to effectively curb the epidemic of the disease, including private data of patients. Searchable encrypti...
Federal learning edge network based sentiment analysis combating global COVID-19 [0.03%]
基于联邦学习边缘网络的疫情舆论分析方法研究——以抗击全球新冠疫情为例
Wei Liang,Xiaohong Chen,Suzhen Huang et al.
Wei Liang et al.
As one of the important research topics in the field of natural language processing, sentiment analysis aims to analyze web data related to COVID-19, e.g., supporting China government agencies combating COVID-19. There are popular sentiment...
A Survey of Machine Learning-Based Zero-Day Attack Detection: Challenges and Future Directions [0.03%]
基于机器学习的零日攻击检测研究：挑战与未来方向
Yang Guo
Yang Guo
Zero-day attacks exploit unknown vulnerabilities so as to avoid being detected by cybersecurity detection tools. The studies [1], [2], [3] show that zero-day attacks are wide spread and are one of the major threats to computer security. The...