Many studies have been conducted on how to detect emotion classes or magnitudes from multimedia information such as text, audio, and images. However, the methods that can use predicted emotion classes and magnitudes to render emotion expressions in Embodied Conversational Agents (ECA) are still unclear. This paper proposes a computer graphics methodology that uses predicted non-linear regression values to render facial expressions using mesh morphing techniques. Results of the rendering technique are presented and discussed.

Robots are becoming increasingly common in home, industrial and medical environments. Their end users may know what they want the robots to do but lack the required technical skills to program them. We present a case-based reasoning approach for training a control module that controls a multi-purpose robotic platform. The control module learns by observing an expert performing a task and does not require any human intervention to program or modify the control module. To avoid requiring the control module to be modified when the robot it controls is repurposed, smart sensors and effectors register with the control module allowing it to dynamically modify the case structure it uses and how those cases are compared. This allows the hardware configuration to be modified, or completely changed, without having to change the control module. We present a case study demonstrating how a robot can be trained using learning by observation and later repurposed with new sensors and then retrained.

Search algorithms such as Rapidly-exploring Random Trees (RRTs) are common in robot programming. Including graphical representations of the output of these algorithms in a robotics framework can make the algorithms more accessible to students, and can also help programmers analyze and account for unexpected results. For this project, we used the Tekkotsu open source robot programming framework, available at Tekkotsu.org. We extended Tekkotsu’s graphical user interface for displaying vision data and maps to also display the output of an RRT search. We created several demos using two types of searches: one from a navigation path planner, and one from an arm path planner. In some cases the search had no solution, and the graphical output helped to illustrate why. This confirms the utility of the RRT visualization for explaining unexpected search results. We expect that this tool will also contribute to improved student understanding of the search algorithm.

In this paper, we present a novel approach NL2OCL to translate English specification of constraints to formal constraints such as OCL (Object Constraint language). In the used approach, input English constraints are syntactically and semantically analyzed to generate a SBVR (Semantics of Business Vocabulary and Rules) based logical representation that is finally mapped to OCL. During the syntactic and semantic analysis we have also addressed various syntactic and semantic ambiguities that make the presented approach robust. The presented approach is implemented in Java as a proof of concept. A case study has also been solved by using our tool to evaluate the accuracy of the presented approach. The results of evaluation are also compared to the pattern based approach to highlight the significance of the used approach.

The accuracy of an automated reading tutor in tracking the reader’s position is affected by phenomena at the frontier of the speech recognizer’s output as it evolves in real time. We define metrics of real-time tracking accuracy computed from the recognizer’s successive partial hypotheses, in contrast to previous metrics computed from the final hypothesis. We analyze the resulting considerable loss in real-time accuracy, and propose and evaluate a method to address it. Our method raises real-time accuracy from 58% to 70%, which should improve the quality of the tutor’s feedback.

In the area of student knowledge assessment, knowledge tracing is a model that has been used for over a decade to predict student knowledge and performance. Many modifications to this model have been proposed and evaluated, however, the modifications are often based on a combination of intuition and experience in the domain. This method of model improvement can be difficult for researchers without high level of domain experience and furthermore, the best improvements to the model could be unintuitive ones. Therefore, we propose a completely data driven approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvement. Our results suggest a variety of different improvements to knowledge tracing many of which have not been explored.

This paper compared the linguistic and psychological word uses in English and Chinese languages with LIWC (Linguistic Inquiry and Word Count) programs. A Principal Component Analysis uncovered six linguistic and psychological components, among which five components were significantly correlated. The correlated components were ranked as Negative Valence (r=.92), Embodiment (r=.88), Narrative (r=.68), Achievement (r=.65), and Social Relation (r=.64). However, the results showed the order of the representative features differs in two languages and certain word categories co-occurred with different components in English and Chinese. The differences were interpreted from the perspective of distinctive eastern and western cultures.

For many classification problems, genetic algorithms prove to be effective without extensive domain engineering. However, the chess King-Rook-King endgame problem appears to be an exception. We explore whether modifications to a baseline parallel genetic algorithm can improve the accuracy on this particular problem. After describing the problem domain and our implementation of a parallel genetic algorithm, we present an empirical evaluation of several approaches intended to improve overall performance. Our results confirm the challenging nature of this domain. We describe several directions that may yet deliver significant improvements.

Different studies provide evidence that the computational psycholinguistic algorithm called Latent Semantic Analysis (LSA) allows measuring local and global coherence in texts similarly to human evaluation (Foltz, Kintsch, Landauer 1998; McNamara, Cai & Louwerse 2007; McCarthy, Briner, Rus, & McNamara, 2007; McNamara, Louwerse & Jeuniaux 2009; Louwerse, McCarthy & Graesser 2010). The texts used in all these studies are written in English and correspond to scientific and literary texts. In Spanish, there are some studies using LSA that measure the semantic similarity between texts in automatic summary assessment (Pérez, Alfonseca, Rodríguez, Gliozzo, Strapparava & Magnini 2005; León, Olmos, Escudero, Cañas & Salmerón 2006; Venegas 2007, 2009, 2011); however, automatic measurement of coherence in Spanish has not yet been sufficiently investigated. The present study aimed at identifying a global and local coherence profile in a corpus of speeches in Spanish of three Latin American Heads-of-States (Perón, Castro and Pinochet), using Latent Semantic Analysis. Local coherence is calculated through the measurement of implicit semantic similarity between adjacent sentences and global coherence through the measurement of the similarity among the semantic content of the paragraphs. The corpus under analysis corresponds to a sample of 107 speeches. The semantic space was built using a multi-register corpus and it is available through the “Interface for the measurement of lexical-semantic similarity” in the El Grial interface (www.elgrial.cl). Results showed a systematic difference between the speeches of the Heads-of-State in terms of both local and global coherence. The Bonferroni analysis established an effect that distinguishes Perón’s speeches from Pinochet’s and Castro’s speeches. This results show that Perón’s speeches are more topically related than the other leaders’, probably due to a discourse strategy to persuade voters. The identification of a profile of coherence might be relevant to predict cues of government discourse styles.

Current approaches for word-sense disambiguation (WSD) try to relate the senses of the target words by optimizing a score for each sense in the context of all other words' senses. However, by scoring each sense separately, they often fail to optimize the relations between the resulting senses. We address this problem by proposing a HITS-inspired method that attempts to optimize the score for the entire sense combination rather than one-word-at-a-time. We also exploit word-sense disambiguation via topic-models, when retrieving senses from heterogeneous sense inventories. Although this entails the relaxation of several assumptions behind current WSD algorithms, we show that our proposed method E-WSD achieves better results than current state-of-the-art approaches, without the need for additional background knowledge.