1,135,660 research outputs found
Mutual learning in a tree parity machine and its application to cryptography
Mutual learning of a pair of tree parity machines with continuous and
discrete weight vectors is studied analytically. The analysis is based on a
mapping procedure that maps the mutual learning in tree parity machines onto
mutual learning in noisy perceptrons. The stationary solution of the mutual
learning in the case of continuous tree parity machines depends on the learning
rate where a phase transition from partial to full synchronization is observed.
In the discrete case the learning process is based on a finite increment and a
full synchronized state is achieved in a finite number of steps. The
synchronization of discrete parity machines is introduced in order to construct
an ephemeral key-exchange protocol. The dynamic learning of a third tree parity
machine (an attacker) that tries to imitate one of the two machines while the
two still update their weight vectors is also analyzed. In particular, the
synchronization times of the naive attacker and the flipping attacker recently
introduced in [1] are analyzed. All analytical results are found to be in good
agreement with simulation results
Cryptography based on neural networks - analytical results
Mutual learning process between two parity feed-forward networks with
discrete and continuous weights is studied analytically, and we find that the
number of steps required to achieve full synchronization between the two
networks in the case of discrete weights is finite. The synchronization process
is shown to be non-self-averaging and the analytical solution is based on
random auxiliary variables. The learning time of an attacker that is trying to
imitate one of the networks is examined analytically and is found to be much
longer than the synchronization time. Analytical results are found to be in
agreement with simulations
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination
In this paper, we consider multi-sensor classification when there is a large
number of unlabeled samples. The problem is formulated under the multi-view
learning framework and a Consensus-based Multi-View Maximum Entropy
Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the
stochastic agreement between multiple classifiers on the unlabeled dataset, the
algorithm simultaneously learns multiple high accuracy classifiers. We
demonstrate that our proposed method can yield improved performance over
previous multi-view learning approaches by comparing performance on three real
multi-sensor data sets.Comment: 5 pages, 4 figures, Accepted in 40th IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 15
Cloud services, interoperability and analytics within a ROLE-enabled personal learning environment
The ROLE project (Responsive Open Learning Environments, EU 7th Framework Programme, grant agreement no.: 231396, 2009-2013) was focused on the next generation of Personal Learning Environments (PLEs). A ROLE PLE is a bundle of interoperating widgets - often realised as cloud services - used for teaching and learning. In this paper, we first describe the creation of new ROLE widgets and widget bundles at Galileo University, Guatemala, within a cloud-based infrastructure. We introduce an initial architecture for cloud interoperability services including the means for collecting interaction data as needed for learning analytics. Furthermore, we describe the newly implemented widgets, namely a social networking tool, a mind-mapping tool and an online document editor, as well as the modification of existing widgets. The newly created and modified widgets have been combined in two different bundles that have been evaluated in two web-based courses at Galileo University, with participants from three different Latin-American countries. We measured emotional aspects, motivation, usability and attitudes towards the environment. The results demonstrated the readiness of cloud-based education solutions, and how ROLE can bring together such an environment from a PLE perspective
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