Home / Journals / JBD / Vol.3, No.3, 2021
  • Open Access

    ARTICLE

    Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

    Wei Fang1,2,*, Yupeng Chen1, Qiongying Xue1
    Journal on Big Data, Vol.3, No.3, pp. 97-110, 2021, DOI:10.32604/jbd.2021.016993
    Abstract In the past few years, deep learning has developed rapidly, and many researchers try to combine their subjects with deep learning. The algorithm based on Recurrent Neural Network (RNN) has been successfully applied in the fields of weather forecasting, stock forecasting, action recognition, etc. because of its excellent performance in processing Spatio-temporal sequence data. Among them, algorithms based on LSTM and GRU have developed most rapidly because of their good design. This paper reviews the RNN-based Spatiotemporal sequence prediction algorithm, introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction, and includes precipitation nowcasting… More >

  • Open Access

    ARTICLE

    WMA: A Multi-Scale Self-Attention Feature Extraction Network Based on Weight Sharing for VQA

    Yue Li, Jin Liu*, Shengjie Shang
    Journal on Big Data, Vol.3, No.3, pp. 111-118, 2021, DOI:10.32604/jbd.2021.017169
    Abstract Visual Question Answering (VQA) has attracted extensive research focus and has become a hot topic in deep learning recently. The development of computer vision and natural language processing technology has contributed to the advancement of this research area. Key solutions to improve the performance of VQA system exist in feature extraction, multimodal fusion, and answer prediction modules. There exists an unsolved issue in the popular VQA image feature extraction module that extracts the fine-grained features from objects of different scale difficultly. In this paper, a novel feature extraction network that combines multi-scale convolution and self-attention branches to solve the above… More >

  • Open Access

    ARTICLE

    CTSF: An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature

    Hengyang Wang, Jin Liu*, Haoliang Ren
    Journal on Big Data, Vol.3, No.3, pp. 119-126, 2021, DOI:10.32604/jbd.2021.017184
    Abstract The past decade has seen the rapid development of text detection based on deep learning. However, current methods of Chinese character detection and recognition have proven to be poor. The accuracy of segmenting text boxes in natural scenes is not impressive. The reasons for this strait can be summarized into two points: the complexity of natural scenes and numerous types of Chinese characters. In response to these problems, we proposed a lightweight neural network architecture named CTSF. It consists of two modules, one is a text detection network that combines CTPN and the image feature extraction modules of PVANet, named… More >

  • Open Access

    ARTICLE

    A QR Data Hiding Method Based on Redundant Region and BCH

    Ying Zhou*, Weiwei Luo
    Journal on Big Data, Vol.3, No.3, pp. 127-133, 2021, DOI:10.32604/jbd.2021.019236
    Abstract In recent years, QR code has been widely used in the Internet and mobile devices. It is based on open standards and easy to generate a code, which lead to that anyone can generate their own QR code. Because the QR code does not have the ability of information hiding, any device can access the content in QR code. Thus, hiding the secret data in QR code becomes a hot topic. Previously, the information hiding methods based on QR code all use the way of information hiding based on image, mostly using digital watermarking technology, and not using the coding… More >

Share Link

WeChat scan