This paper presents a novel approach to the estimation of user's affective states in Human-Computer Interaction. Most of the present approaches divide emotions strictly between positive or negative. However, recent discoveries in the field of Emotional Intelligence show that emotions should be rather perceived as context-sensitive engagements with the world. This leads to a need to specify whether the emotions conveyed in a conversation are appropriate for a situation they are expressed in. In the proposed method we use a system for affect analysis on textual input to recognize users’ emotions and a Web mining technique to verify the contextual appropriateness of those emotions. On this basis a conversational agent can choose to either sympathize with the user or help them manage their emotions. Finally, the results of evaluation of the proposed method with two different conversational agents are discussed, and perspectives for further development of the method are proposed.

The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of expression patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with Figure 1: Samples of images and associated annotation terms a new machine learning framework, Multi-Instance of the gene Actn in the stage ranges 11-12 and 13-16 in the Multi-Label learning (MIML). We propose a new BDGP database. The darkly stained region highlights the MIML support vector machine to solve the problems place where the gene is expressed. The darker the region, that beset the annotation task.

We propose a new topic model for tracking time-varying consumer purchase behavior, in which consumer interests and item trends change over time. The proposed model can adaptively track changes in interests and trends based on current purchase logs and previously estimated interests and trends. The online nature of the proposed method means we do not need to store past data for current inferences and so we can considerably reduce the computational cost and the memory requirement. We use real purchase logs to demonstrate the effectiveness of the proposed method in terms of the prediction accuracy of purchase behavior and the computational cost of the inference.

City-wide urban infrastructures are increasingly reliant on network technology to improve and ex-pand their services. As a side effect of this digitali-zation, large amounts of data can be sensed and analyzed to uncover patterns of human behavior. In this paper, we focus on the digital footprints from one type of emerging urban infrastructure: shared bicycling systems. We provide a spatiotemporal analysis of 13 weeks of bicycle station usage from Barcelona's shared bicycling system, called Bicing. We apply clustering techniques to identify shared behaviors across stations and show how these behaviors relate to location, neighborhood, and time of day. We then compare experimental results from four predictive models of near-term station usage. Finally, we analyze the impact of factors such as time of day and station activity in the prediction capabilities of the algorithms.

Mechanical design tools would be considerably more useful if we could interact with them in the way that human designers communicate design ideas to one another, i.e., using crude sketches and informal speech. Those crude sketches frequently contain pen strokes of two different sorts, one type portraying device structure, the other denoting gestures, such as arrows used to indicate motion. We report here on techniques we developed that use information from both sketch and speech to distinguish gesture strokes from non-gestures -- a critical first step in understanding a sketch of a device. We collected and analyzed unconstrained device descriptions, which revealed six common types of gestures. Guided by this knowledge, we developed a classifier that uses both sketch and speech features to distinguish gesture strokes from non-gestures. Experiments with our techniques indicate that the sketch and speech modalities alone produce equivalent classification accuracy, but combining them produces higher accuracy.

Location  plays a very important role in the retail business due to its huge and long-term investment. In this paper, we propose a novel semi-supervised regression model for evaluating convenience store location based on spatial data analysis. First, the input features for each convenience store can be extracted by analyzing the elements around it based on a geographic information system, and the turnover is used to evaluate its performance. Second, considering the practical application scenario, a manifold regularization model with one semi-supervised performance information constraint is provided. The promising experimental results in the real-world dataset demonstrate the effectiveness of the proposed approach  in performance prediction of  certain candidate locations for new convenience store opening.

Many applications are facing the problem of learning from an objective dataset, whereas information from other auxiliary sources may be beneficial but cannot be integrated into the objective dataset for learning. In this paper, we propose an omni-view learning approach to enable learning from multiple data collections. The theme is to organize heterogeneous data sources into a unified table with global data view. To achieve the omni-view learning goal, we consider that the objective dataset and the auxiliary datasets share some instance-level dependency structures. We then propose a relational k-means to cluster instances in each auxiliary dataset, such that clusters can help build new features to capture correlations between the objective and auxiliary datasets. Experimental results demonstrate that omni-view learning can help build models which outperform the ones learned from the objective dataset only. Comparisons with the co-training algorithm further assert that omni-view learning provides an alternative, yet effective, way for semi-supervised learning.

Multi-class problems have a richer structure than binary classification problems. Thus, they can potentially improve their performance by exploiting the relationship among class labels. While for the purposes of providing an automated classification result this class structure does not need to be explicitly unveiled, for human level analysis or interpretation this is valuable. We develop a multi-class large margin classifier that extracts and takes advantage of class relationships. We provide a bi-convex formulation that explicitly learns a matrix that captures these class relationships and is de-coupled from the feature weights. Our representation can take advantage of the class structure to compress the model by reducing the number of classifiers employed, maintaining high accuracy even with large compression. In addition, we present an efficient formulation in terms of speed and memory.

In many applications non-metric distances are better than metric distances in reflecting the perceptual distances of human beings. Previous studies on non-metric distances mainly focused on supervised setting and did not consider the usefulness of unlabeled data. In this paper, we present probably the first study of label propagation on graphs induced from non-metric distances. The challenge here lies in the fact that the triangular inequality does not hold for non-metric distances and therefore, a direct application of existing label propagation methods will lead to inconsistency and conflict. We show that by applying spectrum transformation, non-metric distances can be converted into metric ones, and thus label propagation can be executed. Such methods, however, suffer from the change of original semantic relations. As a main result of this paper, we prove that any non-metric distance matrix can be decomposed into two metric distance matrices containing different information of the data. Based on this recognition, our proposed NMLP method derives two graphs from the original non-metric distance and performs a joint label propagation on the joint graph. Experiments validate the effectiveness of the proposed NMLP method.

In this paper, we propose a general formulation for kernel nonnegative matrix factorization with flexible kernels. Specifically, we propose the Gaussian nonnegative matrix factorization (GNMF) algorithm by using the Gaussian kernel in the framework. Different from a recently developed polynomial NMF (PNMF), GNMF finds basis vectors in the kernel-induced feature space and the computational cost is independent of input dimensions. Furthermore, we prove the convergence and nonnegativity of decomposition of our method. Extensive experiments compared with PNMF and other NMF algorithms on several face databases, validate the effectiveness of the proposed method.