1,090 research outputs found
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
On the Evolution of Boomerang Uniformity in Cryptographic S-boxes
S-boxes are an important primitive that help cryptographic algorithms to be
resilient against various attacks. The resilience against specific attacks can
be connected with a certain property of an S-box, and the better the property
value, the more secure the algorithm. One example of such a property is called
boomerang uniformity, which helps to be resilient against boomerang attacks.
How to construct S-boxes with good boomerang uniformity is not always clear.
There are algebraic techniques that can result in good boomerang uniformity,
but the results are still rare. In this work, we explore the evolution of
S-boxes with good values of boomerang uniformity. We consider three different
encodings and five S-box sizes. For sizes and , we
manage to obtain optimal solutions. For , we obtain optimal
boomerang uniformity for the non-APN function. For larger sizes, the results
indicate the problem to be very difficult (even more difficult than evolving
differential uniformity, which can be considered a well-researched problem).Comment: 15 pages, 3 figures, 4 table
Artificial Intelligence for the design of symmetric cryptographic primitives
Algorithms and the Foundations of Software technolog
A Multiobjective Approach Applied to the Protein Structure Prediction Problem
Interest in discovering a methodology for solving the Protein Structure Prediction problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-ray crystallographic studies or solution Nuclear Magnetic Resonance Spectroscopy, to mathematical modeling, such as minimum energy models are used to solve this problem. Recently, Evolutionary Algorithm studies at the Air Force Institute of Technology include the following: Simple Genetic Algorithm (GA), messy GA, fast messy GA, and Linkage Learning GA, as approaches for potential protein energy minimization. Prepackaged software like GENOCOP, GENESIS, and mGA are in use to facilitate experimentation of these techniques. In addition to this software, a parallelized version of the fmGA, the so-called parallel fast messy GA, is found to be good at finding semi-optimal answers in reasonable wall clock time. The aim of this work is to apply a Multiobjective approach to solving this problem using a modified fast messy GA. By dividing the CHARMm energy model into separate objectives, it should be possible to find structural configurations of a protein that yield lower energy values and ultimately more correct conformations
Anomaly Detection, Rule Adaptation and Rule Induction Methodologies in the Context of Automated Sports Video Annotation.
Automated video annotation is a topic of considerable interest in computer vision due to its applications in video search, object based video encoding and enhanced broadcast content. The domain of sport broadcasting is, in particular, the subject of current research attention due to its fixed, rule governed, content. This research work aims to develop, analyze and demonstrate novel methodologies that can be useful in the context of adaptive and automated video annotation systems. In this thesis, we present methodologies for addressing the problems of anomaly detection, rule adaptation and rule induction for court based sports such as tennis and badminton. We first introduce an HMM induction strategy for a court-model based method that uses the court structure in the form of a lattice for two related modalities of singles and doubles tennis to tackle the problems of anomaly detection and rectification. We also introduce another anomaly detection methodology that is based on the disparity between the low-level vision based classifiers and the high-level contextual classifier. Another approach to address the problem of rule adaptation is also proposed that employs Convex hulling of the anomalous states. We also investigate a number of novel hierarchical HMM generating methods for stochastic induction of game rules. These methodologies include, Cartesian product Label-based Hierarchical Bottom-up Clustering (CLHBC) that employs prior information within the label structures. A new constrained variant of the classical Chinese Restaurant Process (CRP) is also introduced that is relevant to sports games. We also propose two hybrid methodologies in this context and a comparative analysis is made against the flat Markov model. We also show that these methods are also generalizable to other rule based environments
Adaptive Search and Constraint Optimisation in Engineering Design
The dissertation presents the investigation and development of novel adaptive
computational techniques that provide a high level of performance when searching
complex high-dimensional design spaces characterised by heavy non-linear constraint
requirements. The objective is to develop a set of adaptive search engines that will allow
the successful negotiation of such spaces to provide the design engineer with feasible high
performance solutions.
Constraint optimisation currently presents a major problem to the engineering designer and
many attempts to utilise adaptive search techniques whilst overcoming these problems are
in evidence. The most widely used method (which is also the most general) is to
incorporate the constraints in the objective function and then use methods for
unconstrained search. The engineer must develop and adjust an appropriate penalty
function. There is no general solution to this problem neither in classical numerical
optimisation nor in evolutionary computation. Some recent theoretical evidence suggests
that the problem can only be solved by incorporating a priori knowledge into the search
engine.
Therefore, it becomes obvious that there is a need to classify constrained optimisation
problems according to the degree of available or utilised knowledge and to develop search
techniques applicable at each stage. The contribution of this thesis is to provide such a
view of constrained optimisation, starting from problems that handle the constraints on the
representation level, going through problems that have explicitly defined constraints (i.e.,
an easily computed closed form like a solvable equation), and ending with heavily
constrained problems with implicitly defined constraints (incorporated into a single
simulation model). At each stage we develop applicable adaptive search techniques that
optimally exploit the degree of available a priori knowledge thus providing excellent
quality of results and high performance. The proposed techniques are tested using both well
known test beds and real world engineering design problems provided by industry.British Aerospace,
Rolls Royce and Associate
Intelligent 3D Vision System for Robotic System integration
Este projeto tem como objetivo principal desenvolver um sistema de visão capaz de detetar objetos retangulares, tais como caixas de medicamentos. Um braço robótico recebe a informação proveniente do algoritmo desenvolvido de visão para mover essas caixas para os locais desejados.This project has as main objective the development of a vision system capable of detecting rectangular boxes, such as a medicine package. The information obtained from the developed vision algorithm serves as input for a robotic arm to move the boxes to the desired locations
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