Existing programmable simulators enable researchers to customize different driving scenarios to conduct inlab automotive driver simulations. However, softwarebased simulators for cognitive research generate and maintain their scenes with the support of 3D engines, which may affect users' experiences to a certain degree since they are not sufficiently realistic. Now, a critical issue is the question of how to build scenes into realworld ones. In this paper, we introduce the first step in utilizing video-to-video synthesis, which is a deep learning approach, in OpenDS framework, which is an open-source driving simulator software, to present simulated scenes as realistically as possible. Off-line evaluations demonstrated promising results from our study, and our future work will focus on how to merge them appropriately to build a close-to-reality, real-time driving simulator.

The term serendipity has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT , a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-idl-ucc/CHESTNUT/.

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