Home / Journals / JBD / Vol.2, No.2, 2020
  • Open Access

    ARTICLE

    Research on Copyright Protection Method of Material Genome Engineering Data Based on Zero-Watermarking

    Lulu Cui2,3,*, Yabin Xu1,2,3
    Journal on Big Data, Vol.2, No.2, pp. 53-62, 2020, DOI:10.32604/jbd.2020.010590
    Abstract In order to effectively solve the problem of copyright protection of materials genome engineering data, this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology. First, the important attribute values are selected from the materials genome engineering database; then, use the method of remainder to group the selected attribute values and extract eigenvalues; then, the eigenvalues sequence is obtained by the majority election method; finally, XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center. When a copyright dispute requires copyright authentication… More >

  • Open Access

    ARTICLE

    Research on the Development of Project Cost Informatization in the Era of Big Data

    Huiyu Long1, Yan Ma1, Xiang Mao1, Xinyuerong Sun2,*
    Journal on Big Data, Vol.2, No.2, pp. 63-70, 2020, DOI:10.32604/jbd.2020.011214
    Abstract Under the background of big data, Informatization plays an important role in the development of the engineering cost industry. The rapid development of the industry and the increasing complexity of construction projects require higher standards of informatization. The current information processing methods and models have been difficultly to meet new requirements. Based on this, this study deeply analyzes the key factors that impede the informatization of engineering cost development, and tries to find corresponding solutions through theoretical analysis and empirical research to break these constraints. This will play a guiding role in the development of informatization in China's engineering cost… More >

  • Open Access

    ARTICLE

    A Survey on Adversarial Examples in Deep Learning

    Kai Chen1,*, Haoqi Zhu2, Leiming Yan1, Jinwei Wang1
    Journal on Big Data, Vol.2, No.2, pp. 71-84, 2020, DOI:10.32604/jbd.2020.012294
    Abstract Adversarial examples are hot topics in the field of security in deep learning. The feature, generation methods, attack and defense methods of the adversarial examples are focuses of the current research on adversarial examples. This article explains the key technologies and theories of adversarial examples from the concept of adversarial examples, the occurrences of the adversarial examples, the attacking methods of adversarial examples. This article lists the possible reasons for the adversarial examples. This article also analyzes several typical generation methods of adversarial examples in detail: Limited-memory BFGS (L-BFGS), Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Iterative Least-likely… More >

  • Open Access

    ARTICLE

    Research on the Best Shooting State Based on the “Three Forces” Model

    Xuguang Liu1, Ruqing Zhao2, Qifei Chen2, Ming Shi3, Ziling Xing2, Yanan Zhang4,*
    Journal on Big Data, Vol.2, No.2, pp. 85-93, 2020, DOI:10.32604/jbd.2020.013845
    Abstract The shooting state during shooting refers to the basketball’s shooting speed, shooting angle and the ball’s rotation speed. The basketball flight path is also related to these factors. In this paper, based on the three forces of Gravity, Air Resistance and Magnus Force, the “Three Forces” model is established, the Kinetic equations are derived, the basketball flight trajectory is solved by simulation, and the best shot state when shooting is obtained through the shooting percentage. Compared with the “Single Force” model that only considers Gravity, the shooting percentage of the “Three Forces” model is higher. The reason is that the… More >

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