Rumors spread dramatically fast through online social media services, and people are exploring methods to detect rumors automatically. Existing methods typically learn semantic representations of all reposts to a rumor candidate for prediction. However, it is crucial to efficiently detect rumors as early as possible before they cause severe social disruption, which has not been well addressed by previous works. In this paper, we present a novel early rumor detection model, Credible Early Detection (CED). By regarding all reposts to a rumor candidate as a sequence, the proposed model will seek an early point-in-time for making a credible prediction. We conduct experiments on three real-world datasets, and the results demonstrate that our proposed model can remarkably reduce the time span for prediction by more than 85%, with better accuracy performance than all state-of-the-art baselines.

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This study explores the use of the graphics processing units (GPUs) for performing the two-dimensional discrete wavelet transform (DWT) of images. The study of fast wavelet transforms has been driven both by the enormous volumes of data produced by modern cameras and by the need for real-time processing of these data. With the emergence of general computing on GPUs, many time-consuming applications have started to reap the associated benefits. In the implementation of a GPU-based DWT, two approaches are used according to the published works, which are the rowcolumn (RC) approach and the block-based (BB) approach. Most state-of-the-art techniques are based on the RC approach, which utilises the parallelism between different rows and columns; few works are based on the BB approach, which explores the parallelism between different blocks of the image. Although easy to implement, resource usage of the RC approach is usually related to the image size. Another shortcoming of the RC approach lies in the fact, according to the author's analysis, that more global memory access is required. The authors thus select the BB approach in this study. Experiment results show that the proposed BB approach outperforms the RC approach, being 99× faster than a native CPU implementation for 4096 × 4096 images.

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