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高校专区

University of Cambridge(剑桥大学)

2026-04-17 至 2026-04-17 共收录 3
2604.13466 2026-04-17 cs.HC cs.AI cs.CL cs.LG

Functional Emotions or Situational Contexts? A Discriminating Test from the Mythos Preview System Card

功能情感还是情境背景?来自Mythos预览系统的区分测试

Hiranya V. Peiris

机构 * Institute of Astronomy & Kavli Institute for Cosmology, University of Cambridge(天文学研究所及卡弗里宇宙研究所,剑桥大学) Cavendish Laboratory, Department of Physics, University of Cambridge(卡文迪许实验室,物理系,剑桥大学)

AI总结 本文通过Mythos预览系统探讨模型内部情感向量与行为关系,区分功能情感与情境结构假说,以判断情感监控的有效性。

Comments 7 pages. v2: supplementary analysis added, references updated

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2311.01956 2026-04-17 cs.CR cs.AI

Towards Adaptive, Learning-Based Security in Decentralized Applications

迈向去中心化应用中的自适应、基于学习的安全性

Stefan Kambiz Behfar, Jon Crowcroft

机构 * Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom(计算机科学与技术系,剑桥大学,剑桥,英国)

AI总结 本文探讨了Web3系统中传统安全机制的局限性,提出基于学习的安全原语,通过AI驱动的智能证书实现持续推理与适应,以应对动态演变的攻击策略。

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2604.14643 2026-04-17 cs.CV cs.LG

Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification

基于物理的大气对抗扰动:增强遥感图像分类中的可迁移性和鲁棒性

Weiwei Zhuang, Wangze Xie, Qi Zhang, Xia Du, Zihan Lin, Zheng Lin, Hanlin Cai, Jizhe Zhou, Zihan Fang, Chi-man Pun, Wei Ni, Jun Luo

机构 * School of Computer and Information Engineering, Xiamen University of Technology(厦门理工学院计算机与信息工程学院) Faculty of Data Science, City University of Macau(澳门城市大学数据科学学院) Dundee International Institute, Central South University(中南大学 Dundee 国际学院) Department of Electrical and Computer Engineering, University of Hong Kong(香港大学电气与计算机工程系) Department of Engineering, University of Cambridge(剑桥大学工程系) School of Computer Science, Engineering Research Center of Machine Learning and Industry Intelligence, Sichuan University(四川大学计算机科学学院、机器学习与工业智能工程研究中心) Hong Kong JC STEM Lab of Smart City and Department of Computer Science, City University of Hong Kong(香港城市大学智慧城市JC STEM实验室与计算机科学系) Department of Computer and Information Science, Faculty of Science and Technology, University of Macau(澳门大学科学与技术学院计算机与信息科学系)

AI总结 本文提出FogFool框架,通过物理合理的扰动生成对抗样本,提升遥感图像分类的可迁移性和鲁棒性,实验表明其在白盒和黑盒场景中均表现优异。

Comments 14 pages, 11 figures

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