Abstract

Abstract
Learning contents creation supported on computer tools has triggered the scientific community for a couple of decades. However, teachers have been facing more and different challenges, namely the emergence of other delivery learning approaches besides the traditional educational settings, the diversification of the student target population, and the recognition of different ways of learning. In education domain, diverse recommender systems have been developed so far for recommending learning activities and more specifically, learning objects. This research work is focused on teaching-learning techniques recommendation to assist teachers by providing them recommendation about which teaching-learning techniques should scaffold teaching-learning activities to be carried out by students. This paper presents a recommender model sustained in diverse elements, namely, a hybrid recommender system, an association rules mechanism to infer possible combinations of teaching-learning techniques, and collaborative work among several actors in education. An evaluation is carried out and the preliminary results are very encouraging, revealing that teachers seem very enthusiastic and motivated to rethink their teaching-learning techniques when designing teaching-learning activities.

Abstract
Poker has been gradually gaining the attention of the scientific community, mostly in researchers on Artificial Intelligence. The main reason is concerned with the fact that Poker provides great challenges to the research in the area. Unlike many other games, poker is a stochastic game of imperfect information, which creates a high amount of possibilities to every state of the game. In this work a different line of thought is followed by trying to create an agent capable of reproducing the way a professional Poker human player plays for all stages in a Texas Hold'em Poker game. For this purpose, a high level data model able to comprehend the maximum of information relevant to every state of the game was built, loaded with data from a database containing millions of plays made by a professional poker players, by using Talend Data Integration. To execute Data mining techniques Weka software package was used. The final results show that it is possible to create a virtual poker player that make very similar decisions of a professional poker player.

Abstract

Abstract

Abstract
Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.