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  • Open AccessOpen Access

    ARTICLE

    A Generation Method of Letter-Level Adversarial Samples

    Huixuan Xu1, Chunlai Du1, Yanhui Guo2,*, Zhijian Cui1, Haibo Bai1
    Journal on Artificial Intelligence, Vol.3, No.2, pp. 45-53, 2021, DOI:10.32604/jai.2021.016305
    Abstract In recent years, with the rapid development of natural language processing, the security issues related to it have attracted more and more attention. Character perturbation is a common security problem. It can try to completely modify the input classification judgment of the target program without people’s attention by adding, deleting, or replacing several characters, which can reduce the effectiveness of the classifier. Although the current research has provided various methods of perturbation attacks on characters, the success rate of some methods is still not ideal. This paper mainly studies the sample generation of optimal perturbation characters and proposes a characterlevel… More >

  • Open AccessOpen Access

    ARTICLE

    Evaluation Model of Farmer Training Effect Based on AHP–A Case Study of Hainan Province

    Shengjie Li, Chaosheng Tang*
    Journal on Artificial Intelligence, Vol.3, No.2, pp. 55-62, 2021, DOI:10.32604/jai.2021.017408
    Abstract On the basis of studying the influencing factors of training effect evaluation, this paper constructs an AHP-fuzzy comprehensive evaluation model for farmers’ vocational training activities in Hainan Province to evaluate farmers’ training effect, which overcomes the limitations of traditional methods. Firstly, the content and index system of farmer training effect evaluation are established by analytic hierarchy process, and the weight value of each index is determined. Then, the fuzzy comprehensive evaluation (FCE) of farmer training effect is carried out by using multi-level FCE. The joint use of AHP and FCE improves the reliability and effectiveness of the evaluation process and… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Efficient Convolution Operators for Visual Tracking

    Yu Wang*
    Journal on Artificial Intelligence, Vol.3, No.2, pp. 63-72, 2021, DOI:10.32604/jai.2021.010455
    Abstract Visual tracking is a classical computer vision problem with many applications. Efficient convolution operators (ECO) is one of the most outstanding visual tracking algorithms in recent years, it has shown great performance using discriminative correlation filter (DCF) together with HOG, color maps and VGGNet features. Inspired by new deep learning models, this paper propose a hybrid efficient convolution operators integrating fully convolution network (FCN) and residual network (ResNet) for visual tracking, where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects, respectively. Compared with the traditional VGGNet,… More >

  • Open AccessOpen Access

    ARTICLE

    Semantic Link Network Based Knowledge Graph Representation and Construction

    Weiyu Guo1,*, Ruixiang Jia1, Ying Zhang2
    Journal on Artificial Intelligence, Vol.3, No.2, pp. 73-79, 2021, DOI:10.32604/jai.2021.018648
    Abstract A knowledge graph consists of a set of interconnected typed entities and their attributes, which shows a better performance to organize, manage and understand knowledge. However, because knowledge graphs contain a lot of knowledge triples, it is difficult to directly display to researchers. Semantic Link Network is an attempt, and it can deal with the construction, representation and reasoning of semantics naturally. Based on the Semantic Link Network, this paper explores the representation and construction of knowledge graph, and develops an academic knowledge graph prototype system to realize the representation, construction and visualization of knowledge graph. More >

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