This paper studies the traveling location prediction problem for detecting whether mobile users will leave their living area and where they will go.We investigate the hidden connections between users’behaviors in dif...This paper studies the traveling location prediction problem for detecting whether mobile users will leave their living area and where they will go.We investigate the hidden connections between users’behaviors in different locations and online social interactions.We combine dynamic Bayesian networks with a majority voting model which is based on social interaction information to estimate the users’behaviors and predict the locations.By analyzing Instagram media records,spanning a period of 3 months,we explore rarely visited locations,which are often ignored as noise in previous research.In comparison,our model,using Instagram data with two existing location prediction models,shows that(1)our location prediction is more accurate and robust in both the general location and the location outside the living area;(2)social relations are instrumental in the location prediction as social interaction information can increase the accuracy of the prediction.展开更多
This paper gives a brief introduction to a novel voting system, the Network-based Voting System (NVS). The system design is based on the careful analysis and evaluation of a traditional voting system, the computer con...This paper gives a brief introduction to a novel voting system, the Network-based Voting System (NVS). The system design is based on the careful analysis and evaluation of a traditional voting system, the computer controlled and managed voting system. The new system integrates technologies such as image processing, networking and databases to enhance three aspects of system performance: data collection, data transfer, and data management. Experiments have proved that the performance of the network-based voting system is superior to the CCMVS.展开更多
基金The project is supported by the National Natural Science Foundation of China[grant number 71572109].
文摘This paper studies the traveling location prediction problem for detecting whether mobile users will leave their living area and where they will go.We investigate the hidden connections between users’behaviors in different locations and online social interactions.We combine dynamic Bayesian networks with a majority voting model which is based on social interaction information to estimate the users’behaviors and predict the locations.By analyzing Instagram media records,spanning a period of 3 months,we explore rarely visited locations,which are often ignored as noise in previous research.In comparison,our model,using Instagram data with two existing location prediction models,shows that(1)our location prediction is more accurate and robust in both the general location and the location outside the living area;(2)social relations are instrumental in the location prediction as social interaction information can increase the accuracy of the prediction.
文摘This paper gives a brief introduction to a novel voting system, the Network-based Voting System (NVS). The system design is based on the careful analysis and evaluation of a traditional voting system, the computer controlled and managed voting system. The new system integrates technologies such as image processing, networking and databases to enhance three aspects of system performance: data collection, data transfer, and data management. Experiments have proved that the performance of the network-based voting system is superior to the CCMVS.
