This paper aims to support the cultivation of proper cognitive skills for academic English listening. First of all, this paper identified several listening strategies proved to be effective for cultivating listening skills through past research and builds up the respective strategy models, based on which we designed and developed various functional units as strategy objects, and the mashup environment where these function units can be assembled to serve as a personal learning environment. We also attached listening strategies and tactics to each object, in order to make learners aware of the related strategies and tactics applied during learning. Both short-term and mid-term case studies were carried out, and the data collected showed several positive results and some interesting indications.

In most existing warships combat simulation system, the tactics of a warship is manipulated by human operators. For this reason, the simulation results are restricted due to the capabilities of human operators. To deal with this, we have employed the genetic algorithm for supporting the evolutionary simulation environment. In which, the tactical decision by human operators is replaced by the human model with a rule-based chromosome for representing tactics so that the population of simulations are created and hundreds of simulation runs are continued on the basis of the genetic algorithm without any human intervention until finding emergent tactics which shows the best performance throughout the simulation. Several simulation tests demonstrate the techniques.

In this paper, a new method for clustering of players in order to analyze games in soccer videos is proposed. The proposed method classifies players who are closely related in terms of soccer tactics into one group. Considering soccer tactics, the players in one group are located near each other. For this reason, the Euclidean distance between the players is an effective measurement for the clustering of players. However, the distance is not sufficient to extract tactics-based groups. Therefore, we utilize a modified version of the community extraction method, which finds community structure by dividing a non-directed graph. The use of this method in addition to the distance enables accurate clustering of players.