ICN‐driven group psychology visualization analysis mechanism using reinforcement learning. Issue 5 (17th March 2021) - Qin, Yewen
Abstract : Reinforcement Learning (RL) has been widely considered as a robust method to complete the large‐scale data analysis with high computation efficiency, learning ability, and stability, and it has been applied into many fields, such as transaction detection, trajectory identification, behavior recognition and so on. With the rapid progress of human society, people's psychological pressure gradually becomes heavier and heavier. In order to avoid the social tragedy, it is certainly worth doing the group psychology analysis. Therefore, this paper uses RL to achieve the group psychology visualization analysis instead of the abstract data presentation. In other words, this paper also handles the analyzed data results on the group psychology behaviors by the visualization method. In addition, in order to accelerate the process of data visualization under the large‐scale environment, this paper also introduces the concept of Information‐Centric Networking (ICN) paradigm to support the name‐based community detection by separating IP addresses. The experiments include two parts. On one hand, RL‐based group psychology analysis method is evaluated. On the other hand, ICN‐based visualization method is evaluated. The results prove that the integrated group psychology visualization analysis mechanism is acceptable. Abstract : The group psychology behaviors include two attributes, that is, distance and direction. In terms of the distance, in order to show the assembly feature of group psychology while decrease the frequency of crash occurrence, an expected distance is given. In terms of the direction, in order to show the cocurrent feature of group psychology, all individuals' directions should keep consistent as many as possible.