Zobrazeno 1 - 10
of 3 836
pro vyhledávání: '"Dmitrienko, A. A."'
Securing sensitive operations in today's interconnected software landscape is crucial yet challenging. Modern platforms rely on Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, to isolate security sensitive code from the ma
Externí odkaz:
http://arxiv.org/abs/2411.11567
Autor:
Bright, E. Lawrence, Ovchinnikova, E. N., Harding, L. M., Porter, D. G., Springell, R., Dmitrienko, V. E., Caciuffo, R., Lander, G. H.
Publikováno v:
Physical Review B 110, 125138 (2024)
We have conducted a series of scattering experiments at the uranium M4 absorption edge on low-symmetry uranium compounds (U2N3 and U3O8) produced as epitaxial films. At weak and forbidden reflections, we find a resonant signal, independent of tempera
Externí odkaz:
http://arxiv.org/abs/2409.03392
Product lifecycle tracing is increasingly in the focus of regulators and producers, as shown with the initiative of the Digital Product Pass. Likewise, new methods of counterfeit detection are developed that are, e.g., based on Physical Unclonable Fu
Externí odkaz:
http://arxiv.org/abs/2408.17049
Autor:
Finke, Moritz, Dmitrienko, Alexandra
Facial recognition systems have become an integral part of the modern world. These methods accomplish the task of human identification in an automatic, fast, and non-interfering way. Past research has uncovered high vulnerability to simple imitation
Externí odkaz:
http://arxiv.org/abs/2408.14829
The surge in popularity of machine learning (ML) has driven significant investments in training Deep Neural Networks (DNNs). However, these models that require resource-intensive training are vulnerable to theft and unauthorized use. This paper addre
Externí odkaz:
http://arxiv.org/abs/2403.06581
Ethereum smart contracts, which are autonomous decentralized applications on the blockchain that manage assets often exceeding millions of dollars, have become primary targets for cyberattacks. In 2023 alone, such vulnerabilities led to substantial f
Externí odkaz:
http://arxiv.org/abs/2312.16533
Autor:
Fereidooni, Hossein, Pegoraro, Alessandro, Rieger, Phillip, Dmitrienko, Alexandra, Sadeghi, Ahmad-Reza
Federated learning (FL) is a collaborative learning paradigm allowing multiple clients to jointly train a model without sharing their training data. However, FL is susceptible to poisoning attacks, in which the adversary injects manipulated model upd
Externí odkaz:
http://arxiv.org/abs/2312.04432
In this paper we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials
Externí odkaz:
http://arxiv.org/abs/2311.14889
Due to costly efforts during data acquisition and model training, Deep Neural Networks (DNNs) belong to the intellectual property of the model creator. Hence, unauthorized use, theft, or modification may lead to legal repercussions. Existing DNN wate
Externí odkaz:
http://arxiv.org/abs/2310.16453
Publikováno v:
J. Phys.: Condens. Matter 36 (2024) 165603 (12pp)
In cubic helimagnets MnSi and Cu2OSeO3 with their nearly isotropic magnetic properties, the magnetic structure undergoes helical deformation, which is almost completely determined by the helicoid wavenumber k = D / J, where magnetization field stiffn
Externí odkaz:
http://arxiv.org/abs/2309.14480