User adaptive services are important features in many applications. To provide such services, techniques with various kinds of data are being used. In this paper, we propose a method to analyze a user's past moving paths to predict the goal position and the path to the goal by observing the user's current moving path. We developed a spatiotemporal similarity measure between paths. We chose a past path that was most similar to the current path using the similarity measure. Based on the chosen path, the user's spatiotemporal position was estimated. We performed experiments to confirm this method as useful and effective

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Recently, personalized-adaptive services became the center of interest in the world. However the services about music are not widely diffused out. That is because the analyzing of music information is more difficult than analyzing of text information. In this paper, we propose a music recommendation system which provides personalized services. The system keeps a user's listening list and analyzes it to select pieces of music similar to the user's preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a piece of music is mapped into a point in the property space and the time is converted into the weight of the point. At this time, if we select and analyze the group which is selected by user frequently, we can understand user's taste. However, it is not easy to predict how many groups are formed. To solve this problem, we apply the -means clustering algorithm to the weighted points. We modified the K-means algorithm so that the number of clusters is dynamically changed. This manner limits a diameter so that we can apply this algorithm effectively when we know the range of data. By this algorithm we can find the center of each group and recommend the similar music with the group. We also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the user's preference. We perform experiments with one hundred pieces of music. The result shows that our proposed algorithm is effective.

In this paper, we propose a music recommendation system based on user preference analysis. The system builds music models using Hidden Markov Models with Mel Frequency Cepstral Coefficients, which are features of sound wave. Each song is modeled with an HMM and the similarity measure between songs are defined based on the models. With the similarity measure, the songs the user listened to in the past are grouped and analyzed. The system recommends pieces of music to the user based on the result of the analysis. We evaluate our system with virtual users who have various preferences, and observe which recommendation lists the system generates. In most cases, the system recommends the pieces of music which are close to user's preference.

Korean Game industry, especially MMORPG(Massively Multiplayer Online Game) has been rapidly expanding in these days. But As game industry is growing, lots of online game security incidents have also been increasing and getting prevailing. One of the most critical security incidents is 'Game Bots', which are programs to play MMORPG instead of human players. If player let the game bots play for them, they can get a lot of benefic game elements (experience points, items, etc.) without any effort, and it is considered unfair to other players. Plenty of game companies try to prevent bots, but it does not work well. In this paper, we propose a behavior pattern model for detecting bots. We analyzed behaviors of human players as well as bots and identified six game features to build the model to differentiate game bots from human players. Based on these features, we made a Naive Bayesian classifier to reasoning the game bot or not. To evaluated our method, we used 10 game bot data and 6 human Player data. As a result, we classify Game bot and human player with 88% accuracy.