Constructing flow-based tools with generative and compositional techniques

被引:0
|
作者
Yang, JT
Wang, FJ
Chu, WC
Hu, CH
机构
[1] Natl Chiao Tung Univ, Inst Comp Sci & Informat Engn, Hsinchu, Taiwan
[2] Tung Hai Univ, Dept Comp & Informat Sci, Taichung, Taiwan
关键词
flow-based tool; attribute grammar; generative reuse; compositional reuse; object-oriented techniques;
D O I
10.1142/S0218194000000122
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a model called object-oriented attribute grammar (OOAG), which combines both compositional and generative techniques, is presented to effectively construct flow-based tools that deal with fine-grained language semantics as well as a mass of graphics-drawing activities. OOAG, which consists of two interrelated parts: a model-view-shape (MVS) class framework and an AG++, an object-oriented extension to traditional AGs, is intended to preserve both advantages introduced by respective OO and AG models, such as rapid prototyping, reusability, extensibility, incrementality, and applicability. So far, a flow-based editor associated with two flow-analyzer prototypes, DU/UD tools and a program slicer, have been implemented using OOAG on the Windows environment. Our flow-based editor can be used to construct programs by specifying the associated flow information in a visual way, while (incremental) flow analyzers incorporated into the editor can help analyze incomplete program fragments to locate and inform the user of possible errors or anomalies during programming.
引用
收藏
页码:203 / 226
页数:24
相关论文
共 50 条
  • [1] Flow-based Analytical Techniques
    Suzuki, Yasutada
    [J]. ANALYTICAL SCIENCES, 2018, 34 (08) : 865 - 865
  • [2] Flow-based Analytical Techniques
    Yasutada SuziKl
    [J]. Analytical Sciences, 2018, 34 : 865 - 865
  • [3] Adversarial Robustness of Flow-Based Generative Models
    Pope, Phillip
    Balaji, Yogesh
    Feizi, Soheil
    [J]. INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108, 2020, 108 : 3795 - 3804
  • [4] WAVEGLOW: A FLOW-BASED GENERATIVE NETWORK FOR SPEECH SYNTHESIS
    Prenger, Ryan
    Valle, Rafael
    Catanzaro, Bryan
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, : 3617 - 3621
  • [5] Expression Transfer Using Flow-based Generative Models
    Valenzuela, Andrea
    Segura, Carlos
    Diego, Ferran
    Gomez, Vicenc
    [J]. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021, 2021, : 1023 - 1031
  • [6] Securing the Flow: Security and Privacy Tools for Flow-based Programming
    Ioannidis, Thodoris
    Bolgouras, Vaios
    Politis, Ilias
    Xenakis, Christos
    [J]. 18TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY & SECURITY, ARES 2023, 2023,
  • [7] Information Flow-Based Security Construction for Compositional Interface Automata
    Xu, Mingdi
    Jin, Zhaoyang
    Zhang, Fan
    Cui, Feng
    [J]. TRUSTED COMPUTING AND INFORMATION SECURITY, CTCIS 2019, 2020, 1149 : 31 - 43
  • [8] Neural Encoding and Decoding With a Flow-Based Invertible Generative Model
    Zhou, Qiongyi
    Du, Changde
    Li, Dan
    Wang, Haibao
    Liu, Jian K. K.
    He, Huiguang
    [J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2023, 15 (02) : 724 - 736
  • [9] Human trajectory forecasting using a flow-based generative model
    Zhang, Bo
    Wang, Tao
    Zhou, Changdong
    Conci, Nicola
    Liu, Hongbo
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 115
  • [10] Flow-based Generative Models for Learning Manifold to Manifold Mappings
    Zhen, Xingjian
    Chakraborty, Rudrasis
    Yang, Liu
    Singh, Vikas
    [J]. THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 11042 - 11052