Abstract. This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84 % to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%. 1

Abstract. We investigate the use of Monte-Carlo Tree Search (MCTS) within the field of computer Poker, more specifically No-Limit Texas Hold’em. The hidden information in Poker results in so called miximax game trees where opponent decision nodes have to be modeled as chance nodes. The probability distribution in these nodes is modeled by an opponent model that predicts the actions of the opponents. We propose a modification of the standard MCTS selection and backpropagation strategies that explicitly model and exploit the uncertainty of sampled expected values. The new strategies are evaluated as a part of a complete Poker bot that is, to the best of our knowledge, the first exploiting no-limit Texas Hold’em bot that can play at a reasonable level in games of more than two players. 1

Abstract –A reliable and precise classification of tumours is essential for successful treatment of cancer. Recent researches have confirmed the utility of ensemble machine learning algorithms for gene expression data analysis. In this paper, a new ensemble machine learning algorithm is proposed for classification and prediction on gene expression data. The algorithm is tested and compared with three popular adopted ensembles, i.e. bagging, boosting and arcing. The results show that the proposed algorithm greatly outperforms existing methods, achieving high accuracy over 12 gene expression datasets. Index Terms – ensemble machine learning, pattern recognition, microarray I.

Using sensors to extract gestural information for control parameters of digital audio effects is common practice. There has also been research using machine learning techniques to classify specific gestures based on audio feature analysis. In this paper, we will describe our experiments in training a computer to map the appropriate audio-based features to look like sensor data, in order to potentially eliminate the need for sensors. Specifically, we will show our experiments using the ESitar, a digitally enhanced sensor based controller modeled after the traditional North Indian sitar. We utilize multivariate linear regression to map continuous audio features to continuous gestural data. 1.

Abstract. This paper presents EDACluster, an Estimation of Distribution Algorithm (EDA) applied to the clustering task. EDA is an Evolutionary Algorithm used here to optimize the search for adequate clusters when very little is known about the target dataset. The proposed algorithm uses a mixed approach – density and grid-based – to identify sets of dense cells in the dataset. The output is a list of items and their associated clusters. Items in low-density areas are considered noise and are not assigned to any cluster. This work uses four public domain datasets to perform the tests that compare EDACluster with DBSCAN, a conventional density-based clustering algorithm. 1.

Industrial software is planned, managed, and created using a variety of approaches: sometimes a formal Software Development Life-cycle (SDLC) model is used, sometimes an approach that is less formal but has clearly identifiable stages is employed, and sometimes little if any discernible process is followed. In this paper, we extract and correlate the software development process with behaviour and data found within a project's source control repository. We do so by analyzing fine grained changes, revisions, and aggregations of revisions, so that we can correlate them with the stage of the software development process that the project was in at the time they were made. To label intervals of revisions we use machine learning and artificial intelligence techniques including N-Nearest Neighbours classifiers, Markov models, Hidden Markov Models, Markov Decision Processes and Partially Observable Markov Decision Processes. These techniques initially learn the stages from annotated data and then classify unknown data. We describe how to pose the problem using these tools and we evaluate their e#ectiveness on several case studies.

This paper reports on a series of studies focused on the geographical classification of Standard Arabic. The aim of these studies was to automatically classify a document based on the author's country of origin. The studies examined documents from newspapers in five

Poker playing computer bots can be divided into two categories. There are the game-theoretic bots, that play according to a strategy that gives rise to a Nash equilibrium. These bots are impossible to beat, but are also not able to exploit non-optimalities in their opponents. The other type of bot is the exploiting bot that employs game tree search and opponent modeling techniques to discover and exploit weaknesses of

Abstract: Problem statement: The importance of input representation has been recognized already in machine learning. Feature construction is one of the methods used to generate relevant features for learning data. This study addressed the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. In other words, this paper discusses the application of genetic algorithm to optimize the feature construction process to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Approach: The DARA algorithm was designed to summarize data stored in the non-target tables by clustering them into groups, where multiple records stored in non-target tables correspond to a single record stored in a target table. Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm. Since, the study addressed the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process, the involved task includes solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. Results: It is shown in the experimental results that the quality of summarized data is directly influenced by the methods used to create patterns that represent records in the (n×p) TF-IDF weighted frequency matrix. The results of the evaluation of the geneticbased feature construction algorithm showed that the data summarization results can be improved by constructing features by using the Cluster Entropy (CE) genetic-based feature construction algorithm. Conclusion: This study showed that the data summarization results can be improved by constructing features by using the cluster entropy genetic-based feature construction algorithm.

HIV is one of the most dreaded diseases of the century. Throughout the world efforts are underway to develop new vaccines and design new drugs so as to combat this viral menace. In an effort to probe deeper into the functioning of these viruses we present association based rules formulation so as to decipher the most frequently occurring amino acids in these viruses. This is a novel attempt of its kind since we are attempting to find put the most informative association rules using Apriori algorithm implemented through WEKA. The information generated can be of great use to molecular biologists and drug designers since the associated amino acids can be a very good drug targets. Our findings suggest that L-Selenocysteine and L-Pyrrolysine are most frequently associated amino acids in the 4 classes of virulent proteins analyzed for association rules and Cyteine and Arginine show the strongest association in one of the class analyzed i.e. Gp41. Hence these can be potential drug candidates.