455 research outputs found
Grey-box model identification via evolutionary computing
This paper presents an evolutionary grey-box model identification methodology that makes the best use of a priori knowledge on
a clear-box model with a global structural representation of the physical system under study, whilst incorporating accurate blackbox
models for immeasurable and local nonlinearities of a practical system. The evolutionary technique is applied to building
dominant structural identification with local parametric tuning without the need of a differentiable performance index in the
presence of noisy data. It is shown that the evolutionary technique provides an excellent fitting performance and is capable of
accommodating multiple objectives such as to examine the relationships between model complexity and fitting accuracy during the
model building process. Validation results show that the proposed method offers robust, uncluttered and accurate models for two
practical systems. It is expected that this type of grey-box models will accommodate many practical engineering systems for a better
modelling accuracy
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees
Systems based on artificial intelligence and machine learning models should
be transparent, in the sense of being capable of explaining their decisions to
gain humans' approval and trust. While there are a number of explainability
techniques that can be used to this end, many of them are only capable of
outputting a single one-size-fits-all explanation that simply cannot address
all of the explainees' diverse needs. In this work we introduce a
model-agnostic and post-hoc local explainability technique for black-box
predictions called LIMEtree, which employs surrogate multi-output regression
trees. We validate our algorithm on a deep neural network trained for object
detection in images and compare it against Local Interpretable Model-agnostic
Explanations (LIME). Our method comes with local fidelity guarantees and can
produce a range of diverse explanation types, including contrastive and
counterfactual explanations praised in the literature. Some of these
explanations can be interactively personalised to create bespoke, meaningful
and actionable insights into the model's behaviour. While other methods may
give an illusion of customisability by wrapping, otherwise static, explanations
in an interactive interface, our explanations are truly interactive, in the
sense of allowing the user to "interrogate" a black-box model. LIMEtree can
therefore produce consistent explanations on which an interactive exploratory
process can be built
A characterisation of S-box fitness landscapes in cryptography
Substitution Boxes (S-boxes) are nonlinear objects often used in the design
of cryptographic algorithms. The design of high quality S-boxes is an
interesting problem that attracts a lot of attention. Many attempts have been
made in recent years to use heuristics to design S-boxes, but the results were
often far from the previously known best obtained ones. Unfortunately, most of
the effort went into exploring different algorithms and fitness functions while
little attention has been given to the understanding why this problem is so
difficult for heuristics. In this paper, we conduct a fitness landscape
analysis to better understand why this problem can be difficult. Among other,
we find that almost each initial starting point has its own local optimum, even
though the networks are highly interconnected
Gaussianisation for fast and accurate inference from cosmological data
We present a method to transform multivariate unimodal non-Gaussian posterior
probability densities into approximately Gaussian ones via non-linear mappings,
such as Box--Cox transformations and generalisations thereof. This permits an
analytical reconstruction of the posterior from a point sample, like a Markov
chain, and simplifies the subsequent joint analysis with other experiments.
This way, a multivariate posterior density can be reported efficiently, by
compressing the information contained in MCMC samples. Further, the model
evidence integral (i.e. the marginal likelihood) can be computed analytically.
This method is analogous to the search for normal parameters in the cosmic
microwave background, but is more general. The search for the optimally
Gaussianising transformation is performed computationally through a
maximum-likelihood formalism; its quality can be judged by how well the
credible regions of the posterior are reproduced. We demonstrate that our
method outperforms kernel density estimates in this objective. Further, we
select marginal posterior samples from Planck data with several distinct
strongly non-Gaussian features, and verify the reproduction of the marginal
contours. To demonstrate evidence computation, we Gaussianise the joint
distribution of data from weak lensing and baryon acoustic oscillations (BAO),
for different cosmological models, and find a preference for flat CDM.
Comparing to values computed with the Savage-Dickey density ratio, and
Population Monte Carlo, we find good agreement of our method within the spread
of the other two.Comment: 14 pages, 9 figure
Designing cryptographically strong S-boxes with the use of cellular automata
Block ciphers are widely used in modern cryptography. Substitution boxes (S-boxes) are main elements of these types of ciphers. In this paper we propose a new method to create S-boxes, which is based on application of Cellular Automata (CA). We present the results of testing CA-based S-boxes. These results confirm that CA are able to realize efficiently the Boolean function corresponding to classical S-boxes the proposed CA-based S-boxes offer cryptographic properties comparable or better than classical S-box tables
A hybrid Grey Wolf optimizer with multi-population differential evolution for global optimization problems
The optimization field is the process of solving an optimization problem using an optimization algorithm. Therefore, studying this research field requires to study both of optimization problems and algorithms. In this paper, a hybrid optimization algorithm based on differential evolution (DE) and grey wolf optimizer (GWO) is proposed. The proposed algorithm which is called “MDE-GWONM” is better than the original versions in terms of the balancing between exploration and exploitation. The results of implementing MDE-GWONM over nine benchmark test functions showed the performance is superior as compared to other stat of arts optimization algorithm
Artificial Intelligence for the design of symmetric cryptographic primitives
Algorithms and the Foundations of Software technolog
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