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To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans. However, most existing works focus on rendering specified emotions in responses or empathetically respond to the emotion of users, yet the individual difference in emotion expression is overlooked. This may lead to inconsistent emotional expressions and disinterest users. To tackle this issue, we propose to equip the dialog system with personality and enable it to automatically select emotions in responses by simulating the emotion transition of humans in conversation. In detail, the emotion of the dialog system is transitioned from its preceding emotion in context. The transition is triggered by the preceding dialog context and affected by the specified personality trait. To achieve this, we first model the emotion transition in the dialog system as the variation between the preceding emotion and the response emotion in the Valence-Arousal-Dominance (VAD) emotion space. Then, we design neural networks to encode the preceding dialog context and the specified personality traits to compose the variation. Finally, the emotion for response is selected from the sum of the preceding emotion and the variation. We construct a dialog dataset with emotion and personality labels and conduct emotion prediction tasks for evaluation. Experimental results validate the effectiveness of the personality-affected emotion transition. 1 .

In recent years, mobile sensing data are widely used for analyzing human's activities, usage patterns, emotions, health conditions and social relationships. In order to understand and analyze human's behaviors, several frameworks have been proposed to collect mobile sensing data. In this paper we extend previous works and design StarLog, which is a distributed and energy-configurable framework for both mobile data collecting and analyzing. It collects fine-grained sensing data of five categories, reflecting user's locations, activities, interactions with smart phone, social contacts and device setting habits. Data analyses are developed on both client side and server side to understand individual as well as crowd behaviors. Besides, StarLog proposes optional modes for collecting sensory data from GPS, accelerometer, gyroscope and magnetometer to make it configurable for battery concern.

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