Results 231 to 240 of about 243,099 (282)

Machine Learning Prediction of Laccase-Catalyzed Oxidation of Aromatic Compounds Using Curated Enzyme-Specific Datasets. [PDF]

J Comput Chem
Kulagina Y, Goldhahn C, Weishaupt R, Schubert M.   +3 more
europepmc   +1 more source

Environmental dynamics shape human learning: change points versus random walks

Foucault C, Weber LA, Hunt L.
europepmc   +1 more source

Extrinsic Trust as a Contractual Framework for Accountable AI in Health Care: Viewpoint.

J Med Internet Res
Kelly A.
europepmc   +1 more source

Assessment of Blood Glucose Measurement Using New Noninvasive Technology: Protocol and Methodology.

JMIR Res Protoc
Suradji EW, Dhaliwal SS, Li-Feng Z, Zhou E, Lim A, Wong HH, Du Y, Fredicia F, Dianky A, Pasaribu P, Masayoshi Takashi EG, Tan JYA, Santoso M, Ang SB.   +13 more
europepmc   +1 more source

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SLAP: Simpler, Improved Private Stream Aggregation from Ring Learning with Errors

Journal of Cryptology, 2023
Information or data aggregation helps in many real-life settings to acquire or improve knowledge of the problem(s) at hand, in particular nowadays in the world of overwhelming internet-based time-series data streams. In order to master the analysis of multiple-user time-series data streams while maintaining user/data holder privacy efficiently, private
Takeshita, Jonathan, Karl, Ryan, Gong, Ting, Jung, Taeho   +3 more
openaire   +1 more source

Compact Ring Signatures from Learning with Errors

2021
Ring signatures allow a user to sign a message on behalf of a “ring” of signers, while hiding the true identity of the signer. As the degree of anonymity guaranteed by a ring signature is directly proportional to the size of the ring, an important goal in cryptography is to study constructions that minimize the size of the signature as a function of ...
Chatterjee R., Garg S., Hajiabadi M., Khurana D., Liang X., Malavolta G., Pandey O., Shiehian S.   +7 more
openaire   +2 more sources

Ring Learning with Errors Cryptography

2020
In this chapter, we will discuss Ring-Learning with Errors cryptography (RLWE) as one of the most powerful and challenging approaches for developing professional and complex applications and systems.
Marius Iulian Mihailescu, Stefania Loredana Nita   +1 more
openaire   +1 more source

On Ideal Lattices and Learning with Errors over Rings

Journal of the ACM, 2010
The “learning with errors” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worst-case lattice problems, and in recent years it has served as the foundation for a plethora of cryptographic applications. Unfortunately, these
Lyubashevsky, Vadim, Peikert, Chris, Regev, Oded   +2 more
openaire   +1 more source