When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing works of tracking vehicles on lane-level mostly depend on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphones to sense driving conditions for vehicle lane-level localization on highways. By analyzing driving traces collected from real driving environments, we find that each type of lane-change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a Lane-Level Localization (L3) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane-change behaviors. Then, a Finite State Machine is employed to track vehicles on lane-level leveraging the patterns. Extensive experiments demonstrate that L3is accurate and robust in real driving environments. The experimental results show that, on average, L3achieves the accuracy of 91.49 percent on lane change detection and 90.31 percent on lane-level localization.

Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms.

With the rapid development of information technology, mobile devices have exhibited increasing popularity in recent years. To support the anytime-anywhere service model of mobile devices, one important problem related to the screen display arises when using these devices (e.g., smartphone and tablet) under various lighting conditions. On one hand, it is hard for users to see the display clearly under strong lighting conditions (e.g., sunlight). On the other hand, the screen appears dazzling under weak lighting conditions. This problem related to the mobile device display can significantly degrade user experience and undermine the successful deployment of the anytime-anywhere mobile service model. Existing solutions mainly focus on the automatic adjustment of brightness level under different light conditions. We show that merely utilizing brightness level to solve the display problem is not enough to maintain the user experience under both strong and weak lighting scenarios through experimenting with over 200 volunteers. In this work, we take a different approach by investigating automatic color scheme adjustment to improve user experience. We find that Readability, Comfort level, and Similarityare major factors that contribute to user experience. In recognizing these problems, we propose a system, ColorVert, which utilizes the DKL color space to adaptively transform color schemes by sensing ambient light to improve user experience under various lighting scenarios. Our experimental evaluation with over 200 precipitants and various mobile devices demonstrates that ColorVert is more effective in both maintaining as well as improving user experience compared with the existing automatic brightness adjustment system.

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing work on tracking vehicles on lane-level mostly depends on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphone sensing of driving conditions for vehicle lane-level localization on highways. We analyze the driving traces collected from real driving environments, finding that each type of lane change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a Lane-Level Localization (L3) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane change behaviors. Then a Gaussian Distribution is employed to track vehicles on lane-level with tolerance of false detections. Extensive experiments demonstrate that L3 is accurate and robust in real driving environments. The experimental results show that, on average, L3 achieves accuracy of 91.49% on lane change detection and 86.94% on lane-level localization.

When road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone. In this paper, we propose a vehicle Lane-Level Localization (L^3) system, which utilizes smartphone sensors to real-time locate the vehicle on lane-level on highways. The system is based on the observation that each type of lane change has its unique pattern on the vehicle's lateral acceleration. Extensive experiments demonstrate that L^3 is both accurate and robust in real driving environments.