No abstract

Point of interest (POI) in an urban space represents the perception of city dwellers and visitors of a certain place. LTE cell tower access trace data is one of the promising data sources which has the potential to show real-time POI exploitation analysis. However, there is not much discussion on how it is correlated to diachronic POIs and their exploitation pattern. In this paper, we first show that the access trace pattern from the LTE cell tower can be used to discover which types of POIs exist in a certain area. Then, we propose a daily POI exploitation discovery scheme which can extract patterns of how POIs are daily used. Our analysis can provide a good insight into future urban space-based services such as urban planning and tourism.

Human Activity Recognition (HAR) using social media provides a solid basis for a variety of context-aware applications. Existing HAR approaches have adopted supervised machine learning algorithms using texts and their meta-data such as time, venue, and keywords. However, their recognition accuracy may decrease when applied to imagesharing social media where users mostly describe their daily activities and thoughts using both texts and images. In this paper, we propose a semi-supervised multi-modal deep embedding clustering method to recognize human activities on Instagram. Our proposed method learns multi-modal feature representations by alternating a supervised learning phase and an unsupervised learning phase. By utilizing a large number of unlabeled data, it learns a more generalized feature distribution for each HAR class and avoids overfitting to limited labeled data. Evaluation results show that leveraging multi-modality and unlabeled data is effective for HAR and our method outperforms existing approaches.