Data visualization blends art and science to convey stories from data via graphical representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two comp...Data visualization blends art and science to convey stories from data via graphical representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full force.While the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input data.With a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and decision.It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers.To address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical representations.We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial orientation.Moreover,a text mining technique was applied to extract practical insights from common visualization pitfalls.Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial orientation.The findings showed that the pie chart is the most misused graphical representation,and size is the most critical issue.It was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.展开更多
The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles.The Internet of Vehicles has meaningful...The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles.The Internet of Vehicles has meaningful applications,enabling connected and autonomous vehicles to interact with infrastructure,sensors,computing nodes,humans,and fellow vehicles.Vehicular hoc networks play an essential role in enhancing driving efficiency and safety by reducing traffic congestion while adhering to cryptographic security standards.This paper introduces a privacy-preserving Vehicle-to-Infrastructure authentication,utilizing encryption and the Moore curve.The proposed approach enables a vehicle to deduce the planned itinerary of Roadside Units(RSUs)before embarking on a journey.Crucially,the Certification Authority remains unaware of the specific route design,ensuring privacy.The method involves transforming all Roadside Units(RSUs)in a region into a vector,allowing for instant authentication of a vehicle’s route using RSU information.Real-world performance evaluations affirm the effectiveness of the proposed model.展开更多
文摘Data visualization blends art and science to convey stories from data via graphical representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full force.While the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input data.With a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and decision.It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers.To address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical representations.We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial orientation.Moreover,a text mining technique was applied to extract practical insights from common visualization pitfalls.Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial orientation.The findings showed that the pie chart is the most misused graphical representation,and size is the most critical issue.It was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.
文摘The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles.The Internet of Vehicles has meaningful applications,enabling connected and autonomous vehicles to interact with infrastructure,sensors,computing nodes,humans,and fellow vehicles.Vehicular hoc networks play an essential role in enhancing driving efficiency and safety by reducing traffic congestion while adhering to cryptographic security standards.This paper introduces a privacy-preserving Vehicle-to-Infrastructure authentication,utilizing encryption and the Moore curve.The proposed approach enables a vehicle to deduce the planned itinerary of Roadside Units(RSUs)before embarking on a journey.Crucially,the Certification Authority remains unaware of the specific route design,ensuring privacy.The method involves transforming all Roadside Units(RSUs)in a region into a vector,allowing for instant authentication of a vehicle’s route using RSU information.Real-world performance evaluations affirm the effectiveness of the proposed model.
