Cdga: A GAN-based Controllable Domain Generation Algorithm

You Zhai; Jian Yang; Zixiang Wang; Longtao He; Liqun Yang; Zhoujun Li

doi:10.1109/TrustCom56396.2022.00056

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

Cdga: A GAN-based Controllable Domain Generation Algorithm

Year: 2022, Pages: 352-360

DOI Bookmark: 10.1109/TrustCom56396.2022.00056

Authors

You Zhai, Beihang University,State Key Lab of Software Development Environment
Jian Yang, Beihang University,State Key Lab of Software Development Environment
Zixiang Wang, Beihang University,State Key Lab of Software Development Environment
Longtao He, Coordination Center of China,National Computer Network Emergency Response Technical Team
Liqun Yang, Beihang University,School of Cyber Science and Technology
Zhoujun Li, Beihang University,State Key Lab of Software Development Environment

Abstract

In this paper we present a general framework to describe a trace-based learner modelling process. This framework includes three knowledge models: the first model is an explicit representation of observations about learner’s interactions with a TEL-system, the second model describes the structure and elements describing the Learner Model and the last model describes the main knowledge elements types that could be required to calculate and infer learner profile elements (leaner individual features).

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Cdga: A GAN-based Controllable Domain Generation Algorithm

Authors

Abstract

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