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  1. A Quasi-Deflationary Solution to the Problems of Mixed Inferences and Mixed Compounds.Zhiyuan Zhang - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    Truth pluralism is the view that there is more than one truth property. The strong version of it (i.e. strong pluralism) further contends that no truth property is shared by all true propositions. In this paper, I help strong pluralism solve two pressing problems concerning mixed discourse: the problem of mixed inferences (PI) and the problem of mixed compounds (PC). According to PI, strong pluralism is incompatible with the truth- preservation notion of validity; according to PC, strong pluralists cannot find (...)
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  2.  20
    The Relationship Between Family Support and e-Learning Engagement in College Students: The Mediating Role of e-Learning Normative Consciousness and Behaviors and Self-Efficacy.Hong Gao, Yangli Ou, Zhiyuan Zhang, Menghui Ni, Xinlian Zhou & Li Liao - 2021 - Frontiers in Psychology 12.
    Due to the current COVID-19 pandemic, colleges and universities have implemented network teaching. E-learning engagement is the most important concern of educators and parents because this will directly affect student academic performance. Hence, this study focuses on students’ perceived family support and their e-learning engagement and analyses the effects of e-learning normative consciousness and behaviours and self-efficacy on the relationship between family support and e-learning engagement in college students. Prior to this study, the relationship between these variables was unknown. Four (...)
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  3.  13
    A Smart Privacy-Preserving Learning Method by Fake Gradients to Protect Users Items in Recommender Systems.Guixun Luo, Zhiyuan Zhang, Zhenjiang Zhang, Yun Liu & Lifu Wang - 2020 - Complexity 2020:1-10.
    In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories and experiments, we (...)
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