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Author

Lujiao Shao

Bio: Lujiao Shao is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has co-authored 1 publications.

Papers
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Journal ArticleDOI
TL;DR: In this article , a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of nonpharmaceutical interventions.

4 citations

Book ChapterDOI
27 Aug 2021
TL;DR: Zhang et al. as discussed by the authors proposed a transformer-based decoupled attention network, which is able to decouple the attention and prediction processes in attention mechanism, which can not only increase prediction accuracy, but also increase the inference speed.
Abstract: Optical character recognition (OCR) of shopping receipts plays an important role in smart business and personal financial management. Many challenging issues remain in current OCR systems for text recognition of shopping receipts captured by mobile phones. This research constructs a multi-task model by integrating saliency object detection as a branch task, which enables us to filter out irrelevant text instances by detecting the outline of a shopping receipt. Moreover, the developed model utilized a deformable convolution so as to learning visual information more effectively. On the other hand, to deal with attention drift of text recognition, we propose a transformer-based decoupled attention network, which is able to decouple the attention and prediction processes in attention mechanism. This mechanism can not only increase prediction accuracy, but also increase the inference speed. Extensive experimental results on a large-scale real-life dataset exhibit the effectiveness of our proposed method.

3 citations

Journal ArticleDOI
TL;DR: In this paper , an epidemiological framework for mimicking the multi-directional mutation process of SARS-CoV-2 and the epidemic spread of COVID-19 under realistic scenarios considering multiple variants.
Journal ArticleDOI
TL;DR: This article proposed a transformer-based text recognition network model by developing an adaptive 2D spatial attention module to extract the 2D correlation information of image features, which can provide smart retail with precise data analysis for commodity management and supply chain optimization.
Abstract: Shopping receipts, which are regarded as a kind of consumption proof provided to consumers, contain important information for trade. The digitalization of shopping receipts by extracting text information from images can provide smart retail with precise data analysis for commodity management and supply chain optimization. Despite the fact that traditional optical character recognition (OCR) systems have performed well on document template-based recognition, accurate recognition of receipts taken by cell-phones remains difficult due to the uncertainty of the shooting environment. To address irregular text recognition for receipts, in this research we propose a transformer-based text recognition network model by developing an adaptive 2D spatial attention module to extract the 2D correlation information of image features. We examined the performance of our proposed model on both public benchmarks and a large-scale real-world shopping receipt text recognition dataset. Results demonstrate the efficacy of the proposed method in comparison to extant approaches.

Cited by
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Journal ArticleDOI
TL;DR: In this paper , a global and dynamic ratio is developed to summarize different investor profiles according to their attitude toward risk and to consider the dynamic and changing nature of the economy and financial markets.

3 citations

Journal ArticleDOI
TL;DR: In this article , a new method is proposed to address the aforementioned research gap while simultaneously considering irregular and low-resolution English letters, which comprises a rectification module, a convolutional neural network (CNN) extractor, a semantic context module (SCM), a global context module(GCM), and a lightweight transformer decoder that can exhibit improved training speed.
Abstract: Abstract Text recognition has been applied in many fields recently, such as robot vision, video retrieval, and scene understanding. However, minimal research has been conducted in the field of logistics wherein images of express sheets captured by cameras are mostly curved, distorted, and have low resolution. In this study, a new method is proposed to address the aforementioned research gap while simultaneously considering irregular and low-resolution English letters. The entire approach comprises a rectification module, a convolutional neural network (CNN) extractor, a semantic context module (SCM), a global context module (GCM), and a lightweight transformer decoder that can exhibit improved training speed. In particular, we propose the idea of context modeling in our proposed method. (1) The proposed SCM is introduced to capture full-image dependencies and generates rich semantic context information. (2) We propose the GCM, which not only enhances long-range dependencies from the output of SCM but also outputs abundant pixel information to the self-attention decoder. (3) To solve the low-resolution text recognition problem in a large number of express sheet scenes, we propose Chinese datasets for improving intelligent logistics. Experiments conducted on six public benchmarks demonstrate that the developed method achieves better robustness to low-resolution and irregular text images.

2 citations

Journal ArticleDOI
TL;DR: In this paper , an epidemiological framework for mimicking the multi-directional mutation process of SARS-CoV-2 and the epidemic spread of COVID-19 under realistic scenarios considering multiple variants.
Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a hierarchical reinforcement learning decision framework for multi-mode epidemic control with multiple interventions called HRL4EC, which transforms the multimode intervention decision problem into a multi-level control problem, and employs hierarchical RL to find the optimal strategies.