Assessing individual's personality traits has important implications in psychology, sociology, and economics. Conventional personality measurement methods were questionnaire-based, which are time-consuming and manpower-expensive. With the pervasive deployment of mobile communication applications, smartphone usage data was found to relate to people's social behavioral and psychological aspects. In this paper, we propose a deep learning approach to infer people's Big Five personality traits based on smartphone data. Specifically, we collect smartphone usage snapshots with an Android App, and extract features from the collected data. We propose a multi-view multi-task learning approach with a deep neural network model to fuse the extracted features and learn the Big Five personality traits jointly. Extensive experiments based on the real-world smartphone data collected from university volunteers show that the proposed approach significantly outperforms the state-of-the-art algorithms in personality prediction.

Sensor-based human activity recognition is a fundamental research problem in ubiquitous computing, which uses the rich sensing data from multimodal embedded sensors such as accelerometer and gyroscope to infer human activities. The existing activity recognition approaches either rely on domain knowledge or fail to address the spatial-temporal dependencies of the sensing signals. In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition. AttnSense introduce the framework of combining attention mechanism with a convolutional neural network (CNN) and a Gated Recurrent Units (GRU) network to capture the dependencies of sensing signals in both spatial and temporal domains, which shows advantages in prioritized sensor selection and improves the comprehensibility. Extensive experiments based on three public datasets show that AttnSense achieves a competitive performance in activity recognition compared with several state-of-the-art methods.