We discuss the development and initial experiences with an instant messaging (IM) system that has been enhanced with functionality that facilitates the expression of affective states. This functionality is based on: (1) the use of a graphical, twodimensional base model developed by psychology researchers; (2) refinements of the base model to allow for increased precision in the expression of affective states while exchanging messages; and (3) the definition of meaningful icons associated with the aforementioned model. Experiments have been conducted which show that users are able to take advantage of the proposed IM enhancements when trying to convey affect.

In computer graphics, various processing operations are applied on 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In such context, perceptual quality assessment of 3D triangle meshes becomes a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of distortion distribution. We generate a perceptual distortion map for vertices in reference mesh while taking into account the visual masking effect of human visual system. The proposed metric extracts statistical descriptors from distortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt Support Vector Regression model to predict the mesh quality score. We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores.

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We present an approach for tackling the Sentiment Analysis problem in SemEval 2015. The approach is based on the use of a cooccurrence graph to represent existing relationships among terms in a document with the aim of using centrality measures to extract the most representative words that express the sentiment. These words are then used in a supervised learning algorithm as features to obtain the polarity of unknown documents. The best results obtained for the different datasets are: 77.76% for positive, 100% for negative and 68.04% for neutral, showing that the proposed graph-based representation could be a way of extracting terms that are relevant to detect a sentiment.

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