In the digital age, the rapid spread of misinformation and rumors poses a critical challenge for social media platforms and users. Existing rumor containment methods often overlook the diverse range of topics associated with information and fail to consider user interests, resulting in incomplete understanding of rumor propagation. To address this issue, we introduce the Topic-aware Rumor-Truth Cascade (TRTC) model, which incorporates user interests and topic relevance to better capture the dynamics of information propagation. We define the Topic-aware Rumor Containment Maximization (TRCM) problem within TRTC model and prove its monotonicity and submodularity properties. To solve this problem, we propose Topic-aware Reverse Reachable Count (TopicRRC), an efficient index-based algorithm that leverages reverse sampling techniques to quickly identify effective truth seed sets for multiple online TRCM queries, thereby reducing both computational time and memory usage. The extensive experiments on real-world datasets demonstrate that TopicRRC outperforms existing approaches in terms of rumor containment effectiveness and computational efficiency.

Global Positioning System (GPS) has benefited many novel applications, e.g., navigation, ride-sharing, and location-based services, in our daily life. Although GPS works well in most places, its performance in urban canyons is well-known poor, due to the signal reflections of non-line-of-sight (NLOS) satellites. Tremendous efforts have been made to mitigate the impacts of NLOS signals, while previous works heavily rely on precise proprietary 3D city models or other third-party resources, which are not easily accessible. In this paper, we presentDeepGPS, a deep learning enhanced GPS positioning system that can correct GPS estimations by only considering some simple contextual information.DeepGPSfuses environmental factors, including building heights and road distribution around GPS's initial position, and satellite statuses to describe the positioning context, and exploits an encoder-decoder network model to implicitly learn the complex relationships between positioning contexts and GPS estimations from massive labeled GPS samples. As a result, the well-trained model can accurately predict the correct position for each erroneous GPS estimation given its positioning context. We further improve the model with a novel constraint mask to filter out invalid candidate locations, and enable continuous localization with a simple mobility model. A prototype system is implemented and experimentally evaluated using a large-scale bus trajectory dataset and real-field GPS measurements. Experimental results demonstrate thatDeepGPSsignificantly enhances GPS performance in urban canyons, e.g., on average effectively correcting 90.1% GPS estimations with accuracy improvement by 64.6%.