Research in affective computing is increasingly moving towards naturalistic data. Capturing and annotating such complex data is a massively challenging task. This paper describes a simple and efficient annotation scheme that promotes context-sensitive data labelling via an easy to use interface in an attempt to reduce reliance on expert or trained coders. Additionally, the same labelling interf...

This paper presents a novel approach for the generation of realistic speech synchronized 3D facial animation that copes with anticipatory and perseveratory coarticulation. The methodology is based on the measurement of 3D trajectories of fiduciary points marked on the face of a real speaker during the speech production of CVCV non-sense words. The trajectories are measured from standard video s...

Data envelopment analysis (DEA) identifies an empirical efficient frontier of a set of peer decision making units (DMUs) with multiple inputs and outputs. The efficient frontier is characterized by the DMUs with an unity efficiency score. The performance of inefficient DMUs is characterized with respect to the identified efficient frontier. If the performance of inefficient DMUs deteriorates or...

This paper proposes to use KLD between context-dependent HMMs as target cost in unit selection TTS systems. We train context-dependent HMMs to characterize the contextual attributes of units, and calculate Kullback-Leibler Divergence (KLD) between the corresponding models. We demonstrate that the KLD measure provides a statistically meaningful way to analyze the underlining relations among elem...

In this paper we introduce a new algorithm called Equality Set Projection (ESP) for computing the orthogonal projection of bounded, convex polytopes. Our solution addresses the case where the input polytope is represented as the intersection of a finite number of halfplanes and its projection is given in an irredundant halfspace form. Unlike many existing approaches, the key advantage offered b...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...

Modern Information Retrieval Systems match the terms contained in a user’s query with available documents through the use of an index. In this work, we propose a method for expanding the query with its associated terms, in order to increase the system recall. The proposed method is based on a novel fuzzy clustering of the

Interprocedural analyses can be classiied as either context-insensitive, which tend to sacri-ce precision to gain eeciency, or context-sensitive, which are more precise but also more expensive. This paper discusses a proole-guided approach to interprocedural analysis that is context-sensitive, and hence more precise, for the \important" call sites for a function, and context-insensitive, and he...

We present a practical technique for achieving a scalable and precise global static analysis by selectively applying context-sensitivity and the octagon relational domain. For precise analysis, context-sensitivity and relational analysis are key properties but it has been hard to practically combine both of them. Our approach turns on those precision improvement features only when the analysis ...

Due to the large size of typical Enterprise Architecture models, it is often difficult for humans to fully grasp their contents. Moreover, because of their inherent complexity, the task of generating additional value from these models is very challenging without suitable analysis methods. Impact analysis, which determines the effects of changes or failures on other architectural elements, can t...