Opinion inference arises when opinions are expressed toward states and events which positive or negatively affect entities, i.e., benefactive and malefactive events. This paper addresses creating a lexicon of such eve...
Opinions may be expressed implicitly via inference over explicit sentiments and events that positively/negatively affect entities (goodFor/badFor events). We investigate how such inferences may be exploited to improve...
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While previous sentiment analysis research has concentrated on the interpretation of explicitly stated opinions and attitudes, this work addresses a type of opinion implicature (i.e., opinion-oriented default inferenc...
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Implicit opinions are commonly seen in opinion-oriented documents, such as political editorials. Previous work have utilized opinion inference rules to detect implicit opinions evoked by events that positively/negativ...
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This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent an...
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Heart rate monitoring is widely used in clinical care, fitness training, and stress management. However, tracking individuals' heart rate faces two major challenges, namely equipment availability and user motivati...
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ISBN:
(纸本)9781450324748
Heart rate monitoring is widely used in clinical care, fitness training, and stress management. However, tracking individuals' heart rate faces two major challenges, namely equipment availability and user motivation. In this paper, we present a novel technique, LivePulse Games (LPG), to measure users' heart rate in real time by having them play casual games on unmodified mobile phones. With LPG, heart rate is calculated by detecting changes in transparency of users' fingertips via a mobile device's built-in camera. More importantly, LPG integrate users' camera lens covering actions as an essential control mechanism for game play, and detect heart rate implicitly from intermittent lens covering actions. We explore the design space and trade-offs of LPG through three rounds of interactive design and report the preliminary results from a 12-subject user study.
We report the results of an empirical evaluation of structural simplification of Bayesian networks by removing weak arcs. We conduct a series of experiments on six networks built from real data sets selected from the ...
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Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abil...
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Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing.
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in th...
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Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly in-feasible for moderate to large scale datasets. A significant effort to find more efficient solutions to the problem has been made in the literature. One of the state-of-the-art methods that has been recently introduced is the Variational Dual-Tree (VDT) framework. Despite some of its unique features, VDT is currently restricted only to Euclidean spaces where the Euclidean distance quantifies the similarity. In this paper, we extend the VDT framework beyond the Euclidean distance to more general Bregman divergences that include the Euclidean distance as a special case. By exploiting the properties of the general Bregman divergence, we show how the new framework can maintain all the pivotal features of the VDT framework and yet significantly improve its performance in non-Euclidean domains. We apply the proposed framework to different text categorization problems and demonstrate its benefits over the original VDT.
This paper presents a new Rössler chaotic system using exponential nonlinearity and its application to two-channel synchronization. The proposed chaotic system exhibits a chaotic attractor that resembles the orig...
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ISBN:
(纸本)9781467362481
This paper presents a new Rössler chaotic system using exponential nonlinearity and its application to two-channel synchronization. The proposed chaotic system exhibits a chaotic attractor that resembles the original Rössler system with only six-term in three-dimensional ordinary equation systems using the exponential nonlinearity. Chaotic dynamics are described in terms of equilibria, Jacobian matrix, time domain waveforms, chaotic attractors, and bifurcation diagram. The circuit implementation is relatively compact and simple sine the exponential nonlinearity can be achieved by an inherent nonlinearity of single diode. An application to a two-channel secure communication are also demonstrated, showing a fast, low-error and robust synchronization processes.
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