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形式信任与递进归谬:人工智能生成内容可信度评估
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作者 杨艳妮 武雷龙 +1 位作者 向安玲 张家铖 《新媒体与社会》 2025年第2期124-143,464,465,共22页
构建科学有效的AIGC可信度评估体系是当前人机信任研究的重要议题。本研究融合智能传播视角下对AIGC媒介属性的考察,从技术系统、媒介传播、内容规制层面,自底向上搭建综合、系统的AIGC可信度评估体系。基于层次分析法,在专家充分论证... 构建科学有效的AIGC可信度评估体系是当前人机信任研究的重要议题。本研究融合智能传播视角下对AIGC媒介属性的考察,从技术系统、媒介传播、内容规制层面,自底向上搭建综合、系统的AIGC可信度评估体系。基于层次分析法,在专家充分论证的基础上,明晰AIGC可信度的主要影响要素。研究表明,内容可信是影响用户感知AIGC可信的第一要素,输出结果和训练数据对系统可信、立场中立性和社会影响力对媒介可信、实质可信和形式可信对内容可信等会产生显著影响,提出从系统技术、媒介平台、内容线索以及用户反向验证等方面增强AIGC可信度的优化策略,为AIGC产品优化设计、用户信任构建、信息生态治理提供借鉴。 展开更多
关键词 人工智能生成内容 AIGC 可信度 形式信任 递进归谬
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DG polynomial algebras and their homological properties
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作者 Xuefeng Mao Xudong Gao +1 位作者 yanni yang Jiahong Chen 《Science China Mathematics》 SCIE CSCD 2019年第4期629-648,共20页
In this paper, we introduce and study differential graded(DG for short) polynomial algebras. In brief, a DG polynomial algebra A is a connected cochain DG algebra such that its underlying graded algebra A~# is a polyn... In this paper, we introduce and study differential graded(DG for short) polynomial algebras. In brief, a DG polynomial algebra A is a connected cochain DG algebra such that its underlying graded algebra A~# is a polynomial algebra K[x_1, x_2,..., x_n] with |xi| = 1 for any i ∈ {1, 2,..., n}. We describe all possible differential structures on DG polynomial algebras, compute their DG automorphism groups, study their isomorphism problems, and show that they are all homologically smooth and Gorenstein DG algebras. Furthermore, it is proved that the DG polynomial algebra A is a Calabi-Yau DG algebra when its differential ?_A≠ 0 and the trivial DG polynomial algebra(A, 0) is Calabi-Yau if and only if n is an odd integer. 展开更多
关键词 DG polynomial ALGEBRA COHOMOLOGY GRADED ALGEBRA homologically SMOOTH GORENSTEIN CalabiYau
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Privacy-preserving human activity sensing:A survey
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作者 yanni yang Pengfei Hu +3 位作者 Jiaxing Shen Haiming Cheng Zhenlin An Xiulong Liu 《High-Confidence Computing》 EI 2024年第1期108-117,共10页
With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being sensed.While using such information provides critical and convenient services,we are gradually ... With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being sensed.While using such information provides critical and convenient services,we are gradually exposing every piece of our behavior and activities.Researchers are aware of the privacy risks and have been working on preserving privacy while sensing human activities.This survey reviews existing studies on privacy-preserving human activity sensing.We first introduce the sensors and captured private information related to human activities.We then propose a taxonomy to structure the methods for preserving private information from two aspects:individual and collaborative activity sensing.For each of the two aspects,the methods are classified into three levels:signal,algorithm,and system.Finally,we discuss the open challenges and provide future directions. 展开更多
关键词 Human activity sensing Privacy-preserving sensing Activity sensing algorithms Human sensors Privacy protection
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