Many web services like eBay, Tripadvisor, Epinions, etc, provide historical product ratings so that users can evaluate the quality of products. Product ratings are important since they affect how well a product will be adopted by the market. The challenge is that we only have " partial information " on these ratings: Each user provides ratings to only a " small subset of products ". Under this ...

We propose a novel active contour model by incorporating particle based electrostatic interactions into the geometric active contour framework. The proposed active contour, embedded in level sets, propagates under the joint influence of a boundary attraction force and a boundary competition force. Unlike other contour models, the proposed vector field dynamically adapts by updating itself when ...

This paper uses the metaphor of "being there" to differentiate two basic modes of mediated presence. "'Being' there" refers to presence in a remote environment through sensory extension, and "being 'there" refers to presence in a virtual environment through sensory simulation. In "'being' there," users believe that they are in contact with a real, albeit remote, environment, and their sense of ...

Responsive Regulation translated an ongoing academic debate about behavior orientation and regulatory enforcement into a synthetic framework. Yet ethnographic studies reveal that ambiguity pervades regulator–regulatee interactions and suggest that the reality of regulatory encounters may be too ambivalent to fit the picture of the regulatory “game” at the heart of Ayres and Braithwaite’s theory...

Social progress and demographic changes favor increased life expectancy and the number of people in situations of dependency. As a consequence, the demand for support systems for personal autonomy is increasing. This article outlines the vision @ home project, whose goal is the development of vision-based services for monitoring and recognition of the activity carried out by individuals in the ...

In this paper, we propose a learning approach for the Mixture of Hidden Markov Models (MHMM) based on the Method of Moments (MoM). Computational advantages of MoM make MHMM learning amenable for large data sets. It is not possible to directly learn an MHMM with existing learning approaches, mainly due to a permutation ambiguity in the estimation process. We show that it is possible to resolve t...

We propose a new supervised topic model that uses a nonparametric density estimator to model the distribution of real-valued metadata given a topic. The model is similar to Topics Over Time, but replaces the beta distributions used in that model with a Dirichlet process mixture of normals. The use of a nonparametric density estimator allows for the fitting of a greater class of metadata densiti...

Statistical anomaly detection typically focuses on finding individual point anomalies. Often the most interesting or unusual things in a data set are not odd individual points, but rather larger scale phenomena that only become apparent when groups of points are considered. In this paper, we propose generative models for detecting such group anomalies. We evaluate our methods on synthetic data ...

Sample and statistically based singing synthesizers typically require a large amount of data for automatically generating expressive synthetic performances. In this paper we present a singing synthesizer that using two rather small databases is able to generate expressive synthesis from an input consisting of notes and lyrics. The system is based on unit selection and uses the Wide-Band Harmoni...

Rare category detection is an open challenge in machine learning. It plays the central role in applications such as detecting new financial fraud patterns, detecting new network malware, and scientific discovery. In such cases rare categories are hidden among huge volumes of normal data and observations. In this paper, we propose a new method for rare category detection named SEDER, which requi...