Learning From Complexity: Effects Of Prior Accidents And Incidents On Airlines' Learning [PDF]
, 2002 Using data on accidents and incidents experienced by U.S. commercial airlines from 1983 to 1997, we investigated variation in firm learning by examining whether firms learn more from errors with heterogeneous or homogeneous causes.
Haunschild, P. R., Sullivan, B. N.
core +1 more source
Ring Learning With Errors: A crossroads between postquantum cryptography, machine learning and number theory [PDF]
, 2020 The present survey reports on the state of the art of the different cryptographic functionalities built upon the ring learning with errors problem and its interplay with several classical problems in algebraic number theory.
Chacón, Iván Blanco
core +2 more sources
The dynamics of syntax acquisition: facilitation between syntactic structures [PDF]
, 2011 This paper sets out to show how facilitation between different clause structures operates over time in syntax acquisition. The phenomenon of facilitation within given structures has been widely documented, yet inter-structure facilitation has rarely been
Ben-Horin +15 more
core +1 more source
On Quantum Chosen-Ciphertext Attacks and Learning with Errors
Cryptography, 2020 Large-scale quantum computing poses a major threat to classical public-key cryptography. Recently, strong “quantum access” security models have shown that numerous symmetric-key cryptosystems are also vulnerable.
Gorjan Alagic +3 more
doaj +1 more source
Novel efficient lattice-based IBE schemes with CPK for fog computing
Mathematical Biosciences and Engineering, 2020 The data security of fog computing is a key problem for the Internet of things. Identity-based encryption (IBE) from lattices is extremely suitable for fog computing. It is able to not only simplify certificate management, but also resist quantum attacks.
Yanfeng Shi +3 more
doaj +1 more source
Linear Regression With Distributed Learning: A Generalization Error Perspective [PDF]
IEEE Transactions on Signal Processing, 2021 Distributed learning provides an attractive framework for scaling the learning task by sharing the computational load over multiple nodes in a network. Here, we investigate the performance of distributed learning for large-scale linear regression where the model parameters, i.e., the unknowns, are distributed over the network.
Martin Hellkvist +2 more
openaire +3 more sources
Delegation of Decryption Rights With Revocability From Learning With Errors
IEEE Access, 2018 The notion of decryption rights delegation was initially introduced by Blaze et al. in EUROCRYPT 1998. It, defined as proxy re-encryption, allows a semi-trusted proxy to convert a ciphertext intended for a party to another ciphertext of the same ...
Wei Yin +6 more
doaj +1 more source
Learning with Errors and Extrapolated Dihedral Cosets [PDF]
, 2018 The hardness of the learning with errors (LWE) problem is one of the most fruitful resources of modern cryptography. In particular, it is one of the most prominent candidates for secure post-quantum cryptography. Understanding its quantum complexity is therefore an important goal. We show that under quantum polynomial time reductions, LWE is equivalent
Brakerski, Zvika +3 more
openaire +3 more sources
Human Dorsal Striatal Activity during Choice Discriminates Reinforcement Learning Behavior from the Gambler’s Fallacy [PDF]
, 2011 Reinforcement learning theory has generated substantial interest in neurobiology, particularly because of the resemblance between phasic dopamine and reward prediction errors.
Jessup, Ryan K., O'Doherty, John P.
core +1 more source
Faster Homomorphic Trace-Type Function Evaluation
IEEE Access, 2021 Homomorphic encryption enables computations over encrypted data without decryption, and can be used for outsourcing computations to some untrusted source.
Yu Ishimaki, Hayato Yamana
doaj +1 more source