High-density street-level reliable landmarks are one of the important foundations for street-level geolocation.However,the existing methods cannot obtain enough street-level landmarks in a short period of time.In this...High-density street-level reliable landmarks are one of the important foundations for street-level geolocation.However,the existing methods cannot obtain enough street-level landmarks in a short period of time.In this paper,a street-level landmarks acquisition method based on SVM(Support Vector Machine)classifiers is proposed.Firstly,the port detection results of IPs with known services are vectorized,and the vectorization results are used as an input of the SVM training.Then,the kernel function and penalty factor are adjusted for SVM classifiers training,and the optimal SVM classifiers are obtained.After that,the classifier sequence is constructed,and the IPs with unknown service are classified using the sequence.Finally,according to the domain name corresponding to the IP,the relationship between the classified server IP and organization name is established.The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially,and the median geolocation error using evaluated landmarks is reduced by about 2 km.展开更多
Street-level health bureaucrats have actively contributed to implementing the COVID-19 prevention,control,and treatment policies of the Myanmar government.However,the need for bureaucrats on the frontlines of policy i...Street-level health bureaucrats have actively contributed to implementing the COVID-19 prevention,control,and treatment policies of the Myanmar government.However,the need for bureaucrats on the frontlines of policy implementation to maintain a safe distance from others to prevent the spread of COVID-19 has posed challenges for the sharing and exchange of information related to health risks.In this context,this study examined what health risk communication patterns have emerged and developed among streetlevel health bureaucrats during the COVID-19 pandemic,and how this risk communication has been affected by streetlevel health bureaucrats'perceptions of client meaningfulness and willingness to implement COVID-19 policies.The results reveal that street-level health bureaucrats in the health risk communication network are embedded in reciprocally or transitively connected discussion relationships that sustain their health risk communication over time.Moreover,when specific healthcare staffmembers perceive more benefits of COVID-19 policies for their patients and are more willing to care for patients,other healthcare staffavoid them to protect themselves from COVID-19 infection.Due to their higher level of understanding of the adopted measures,healthcare staffmembers who are highly willing to implement COVID-19 policies are frequently approached by other staffmembers to communicate about COVID-19 issues.This study empirically contributes to the literature on street-level bureaucrats in times of pandemic crisis by examining the formation of health risk communications in the context of street-level health bureaucrats'responses to and participation in public healthcare policy implementation processes.展开更多
基金The work presented in this paper is supported by the National Key R&D Program of China[Nos.2016YFB0801303,2016QY01W0105]the National Natural Science Foundation of China[Nos.U1636219,U1804263,61602508,61772549,U1736214,61572052]Plan for Scientific Innovation Talent of Henan Province[No.2018JR0018].
文摘High-density street-level reliable landmarks are one of the important foundations for street-level geolocation.However,the existing methods cannot obtain enough street-level landmarks in a short period of time.In this paper,a street-level landmarks acquisition method based on SVM(Support Vector Machine)classifiers is proposed.Firstly,the port detection results of IPs with known services are vectorized,and the vectorization results are used as an input of the SVM training.Then,the kernel function and penalty factor are adjusted for SVM classifiers training,and the optimal SVM classifiers are obtained.After that,the classifier sequence is constructed,and the IPs with unknown service are classified using the sequence.Finally,according to the domain name corresponding to the IP,the relationship between the classified server IP and organization name is established.The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially,and the median geolocation error using evaluated landmarks is reduced by about 2 km.
文摘Street-level health bureaucrats have actively contributed to implementing the COVID-19 prevention,control,and treatment policies of the Myanmar government.However,the need for bureaucrats on the frontlines of policy implementation to maintain a safe distance from others to prevent the spread of COVID-19 has posed challenges for the sharing and exchange of information related to health risks.In this context,this study examined what health risk communication patterns have emerged and developed among streetlevel health bureaucrats during the COVID-19 pandemic,and how this risk communication has been affected by streetlevel health bureaucrats'perceptions of client meaningfulness and willingness to implement COVID-19 policies.The results reveal that street-level health bureaucrats in the health risk communication network are embedded in reciprocally or transitively connected discussion relationships that sustain their health risk communication over time.Moreover,when specific healthcare staffmembers perceive more benefits of COVID-19 policies for their patients and are more willing to care for patients,other healthcare staffavoid them to protect themselves from COVID-19 infection.Due to their higher level of understanding of the adopted measures,healthcare staffmembers who are highly willing to implement COVID-19 policies are frequently approached by other staffmembers to communicate about COVID-19 issues.This study empirically contributes to the literature on street-level bureaucrats in times of pandemic crisis by examining the formation of health risk communications in the context of street-level health bureaucrats'responses to and participation in public healthcare policy implementation processes.
