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  • Open Access


    Withdrawal notice to: A PageRank-Based WeChat User Impact Assessment Algorithm

    Qiong Wang1, Yuewen Luo1, Hongliang Guo1, Peng Guo2, Jinghao Wei1, Tie Lin1,*

    Journal of New Media, Vol.3, No.4, pp. 153-153, 2021, DOI:10.32604/jnm.2021.019519

    Abstract This article has no abstract. More >

  • Open Access


    Vehicle Matching Based on Similarity Metric Learning

    Yujiang Li1,2, Chun Ding1,2, Zhili Zhou1,2,*

    Journal of New Media, Vol.4, No.1, pp. 51-58, 2022, DOI:10.32604/jnm.2022.028775

    Abstract With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems. Recent methods explored the vehicle matching based on the feature extraction. Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching. But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times. To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity metric from both domains (i.e.,… More >

  • Open Access


    Design of Middle School Chemistry Experiment Simulation System Based on Apriori Algorithm

    Guwei Li1, Zhou Li1,*, Cong Zheng1, Zhengyuan Li2

    Journal of New Media, Vol.4, No.1, pp. 41-50, 2022, DOI:10.32604/jnm.2022.027883

    Abstract Aiming at the safety problems of toxic, flammable and explosive chemicals used in middle school chemical experiments, such as human poisoning, skin corrosion, fire or explosion caused by improper experimental operation, a virtual simulation method of chemical experiments based on unity is proposed. Due to the need to analyze and compare the data in chemical experiments, summarize the experimental characteristics and data relevance. Therefore, based on the Apriori algorithm, this method deeply excavates the data obtained in the chemical experiment, uses Maya to model the experimental environment, uses unity to design the interactive functions in the experimental process, and uses… More >

  • Open Access


    Image Super-Resolution Reconstruction Based on Dual Residual Network

    Zhe Wang1, Liguo Zhang1,2,*, Tong Shuai3, Shuo Liang3, Sizhao Li1,4

    Journal of New Media, Vol.4, No.1, pp. 27-39, 2022, DOI:10.32604/jnm.2022.027826

    Abstract Research shows that deep learning algorithms can effectively improve a single image's super-resolution quality. However, if the algorithm is solely focused on increasing network depth and the desired result is not achieved, difficulties in the training process are more likely to arise. Simultaneously, the function space that can be transferred from a low-resolution image to a high-resolution image is enormous, making finding a satisfactory solution difficult. In this paper, we propose a deep learning method for single image super-resolution. The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in low-resolution images. Finally, these features… More >

  • Open Access


    Cross-Modal Relation-Aware Networks for Fake News Detection

    Hui Yu, Jinguang Wang*

    Journal of New Media, Vol.4, No.1, pp. 13-26, 2022, DOI:10.32604/jnm.2022.027312

    Abstract With the speedy development of communication Internet and the widespread use of social multimedia, so many creators have published posts on social multimedia platforms that fake news detection has already been a challenging task. Although some works use deep learning methods to capture visual and textual information of posts, most existing methods cannot explicitly model the binary relations among image regions or text tokens to mine the global relation information in a modality deeply such as image or text. Moreover, they cannot fully exploit the supplementary cross-modal information, including image and text relations, to supplement and enrich each modality. In… More >

  • Open Access


    Key Frame Extraction Algorithm of Surveillance Video Based on Quaternion Fourier Significance Detection

    Zhang Yunzuo1,*, Zhang Jiayu1, Cai Zhaoquan2

    Journal of New Media, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jnm.2022.027054

    Abstract With the improvement of people's security awareness, numerous monitoring equipment has been put into use, resulting in the explosive growth of surveillance video data. Key frame extraction technology is a paramount technology for improving video storage efficiency and enhancing the accuracy of video retrieval. It can extract key frame sets that can express video content from massive videos. However, the existing key frame extraction algorithms of surveillance video still have deficiencies, such as the destruction of image information integrity and the inability to extract key frames accurately. To this end, this paper proposes a key frame extraction algorithm of surveillance… More >

  • Open Access


    Ground Nephogram Enhancement Algorithm Based on Improved Adaptive Fractional Differentiation

    Xiaoying Chen1,*, Jie Kang1, Cong Hu2

    Journal of New Media, Vol.3, No.4, pp. 151-180, 2021, DOI:10.32604/jnm.2021.024665

    Abstract The texture of ground-based nephogram is abundant and multiplicity. Many cloud textures are not as clear as artificial textures. A nephogram enhancement algorithm based on Adaptive Fractional Differential is established to extract the natural texture of visible ground-based cloud image. GrunwaldLentikov (G-L) and Grunwald-Lentikov (R-L) fractional differential operators are applied to the enhancement algorithm of ground-based nephogram. An operator mask based on adaptive differential order is designed. The corresponding mask template is used to process each pixel. The results show that this method can extract image texture and edge details and simplify the process of differential order selection. More >

  • Open Access


    Blockchain-Based Decentralized Reputation Management System for Internet of Everything in 6G-Enabled Cybertwin Architecture

    Meimin Wang, Zhili Zhou*, Chun Ding

    Journal of New Media, Vol.3, No.4, pp. 137-150, 2021, DOI:10.32604/jnm.2021.024543

    Abstract Internet of Everything (IoE) has emerged as a promising paradigm for the purpose of connecting and exchanging data among physical objects and humans over the Internet, and it can be widely applied in the fields of industry, transportation, commerce, and education. Recently, the emergence of 6G-enabled cybertwin network architecture provides the technical and theoretical foundation for the realization of IoE paradigm. However, the IoE has three open issues in the 6G-enabled cybertwin architecture, i.e., data authenticity, data storage and node reliability. To address these issues, we propose a blockchain-based decentralized reputation management system (BC-DRMS) for IoE in 6G-enabled Cybertwin architecture.… More >

  • Open Access


    Review of Unsupervised Person Re-Identification

    Yang Dai*, Zhiyuan Luo

    Journal of New Media, Vol.3, No.4, pp. 129-136, 2021, DOI:10.32604/jnm.2021.023981

    Abstract Person re-identification (re-ID) aims to match images of the same pedestrian across different cameras. It plays an important role in the field of security and surveillance. Although it has been studied for many years, it is still considered as an unsolved problem. Since the rise of deep learning, the accuracy of supervised person re-ID on public datasets has reached the highest level. However, these methods are difficult to apply to real-life scenarios because a large number of labeled training data is required in this situation. Pedestrian identity labeling, especially cross-camera pedestrian identity labeling, is heavy and expensive. Why we cannot… More >

  • Open Access


    Design of Hybrid Recommendation Algorithm in Online Shopping System

    Yingchao Wang1, Yuanhao Zhu1, Zongtian Zhang1, Huihuang Liu1,* , Peng Guo2

    Journal of New Media, Vol.3, No.4, pp. 119-128, 2021, DOI:10.32604/jnm.2021.016655

    Abstract In order to improve user satisfaction and loyalty on e-commerce websites, recommendation algorithms are used to recommend products that may be of interest to users. Therefore, the accuracy of the recommendation algorithm is a primary issue. So far, there are three mainstream recommendation algorithms, content-based recommendation algorithms, collaborative filtering algorithms and hybrid recommendation algorithms. Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings. The contentbased recommendation algorithm has the problem of the diversity of recommended items, while the collaborative filtering algorithm has the problem of data sparsity and scalability. On the basis of these two algorithms, the hybrid… More >

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