Jane Yung-jen Hsu

Sentiment dictionaries are essential for research in the sentiment analysis field. A two-step method integrates iterative regression and random walk with in-link normalization to build a concept-level sentiment dictionary. The approach uses ConceptNet as a framework to propagate sentiment values, based on the assumption that semantically related concepts share a common sentiment.

In this paper, we explore the challenges in applying and investigate methodologies to improve direct-touch interaction on intangible displays. Direct-touch interaction simplifies object manipulation, because it combines the input and display into a single integrated interface. While traditional tangible display-based direct-touch technology is commonplace, similar direct-touch interaction within an intangible display paradigm presents many challenges. Given the lack of tactile feedback, direct-touch interaction on an intangible display may show poor performance even on the simplest of target acquisition tasks. In order to study this problem, we have created a prototype of an intangible display. In the initial study, we collected user discrepancy data corresponding to the interpretation of 3D location of targets shown on our intangible display. The result showed that participants performed poorly in determining the z-coordinate of the targets and were imprecise in their execution of screen touches within the system. Thirty percent of positioning operations showed errors larger than 30mm from the actual surface. This finding triggered our interest to design a second study, in which we quantified task time in the presence of visual and audio feedback. The pseudo-shadow visual feedback was shown to be helpful both in improving user performance and satisfaction.

The capability of interpreting the conceptual and affective information associated with natural language through different modalities is a key issue for the enhancement of human-agent interaction. The proposed methodology, termed sentic blending, enables the continuous interpretation of semantics and sentics (i.e., the conceptual and affective information associated with natural language) based on the integration of an affective common-sense knowledge base with any multimodal signal-processing module. In this work, in particular, sentic blending is interfaced with a facial emotional classifier and an opinion mining engine. One of the main distinguishing features of the proposed technique is that it does not simply perform cognitive and affective classification in terms of discrete labels, but it operates in a multidimensional space that enables the generation of a continuous stream characterising user's semantic and sentic progress over time, despite the outputs of the unimodal categorical modules have very different time-scales and output labels.

Recognition of appliances states is an import building block for making energy-efficiency schemes and providing energy-saving advice and performing automatic control. Several existing approachs use smart outlets or detectors to acquire the information of individual appliance and recognize the operating state. However, such approachs have to install numerous devices if they want to monitor the states of all appliances. This will increase the cost and complexity of installation and maintenance. Therefore, we develop an appliance recognition system which minimizing the scope of deployment. We install smart meters at single-point, distribution board, to measure the power consumption at circuit-level. In addition, to improve the recognition accuracy of our system and detect the state changes in real time, We use dynamic baysian network to take user behavior into account and Bayes filter to perform online inference. Finally, we design several experiments to compare our approach with some commonly used classifiers, such as Naive Bayes, k-Nearest Neighbor (kNN) and Support Vector Machine (SVM). Results shows that our model outperforms these classifiers and the accuracies of all appliances are greater than 92%. Furthermore, we also compare the results of Bayes filter with Viterbi algorithm, which is an offline inference method. The difference in accuracy of every appliance between Bayes filter and Viterbi algorithm is less than 1%.