5,098 research outputs found
Visualization and clustering by 3D cellular automata: Application to unstructured data
Given the limited performance of 2D cellular automata in terms of space when
the number of documents increases and in terms of visualization clusters, our
motivation was to experiment these cellular automata by increasing the size to
view the impact of size on quality of results. The representation of textual
data was carried out by a vector model whose components are derived from the
overall balancing of the used corpus, Term Frequency Inverse Document Frequency
(TF-IDF). The WorldNet thesaurus has been used to address the problem of the
lemmatization of the words because the representation used in this study is
that of the bags of words. Another independent method of the language was used
to represent textual records is that of the n-grams. Several measures of
similarity have been tested. To validate the classification we have used two
measures of assessment based on the recall and precision (f-measure and
entropy). The results are promising and confirm the idea to increase the
dimension to the problem of the spatiality of the classes. The results obtained
in terms of purity class (i.e. the minimum value of entropy) shows that the
number of documents over longer believes the results are better for 3D cellular
automata, which was not obvious to the 2D dimension. In terms of spatial
navigation, cellular automata provide very good 3D performance visualization
than 2D cellular automata.Comment: 10 pages, 8 figure
FELFCNCA: Fast & Efficient Log File Compression Using Non Linear Cellular Automata Classifier
Log Files are created for Traffic Analysis, Maintenance, Software debugging,
customer management at multiple places like System Services, User Monitoring
Applications, Network servers, database management systems which must be kept
for long periods of time. These Log files may grow to huge sizes in this
complex systems and environments. For storage and convenience log files must be
compressed. Most of the existing algorithms do not take temporal redundancy
specific Log Files into consideration. We propose a Non Linear based Classifier
which introduces a multidimensional log file compression scheme described in
eight variants, differing in complexity and attained compression ratios. The
FELFCNCA scheme introduces a transformation for log file whose compressible
output is far better than general purpose algorithms. This proposed method was
found lossless and fully automatic. It does not impose any constraint on the
size of log fileComment: International Journal on Communications (IJC) Volume 1 Issue 1,
December 2012 http://www.seipub.org/ij
Lempel-Ziv complexity analysis of one dimensional cellular automata
Cellular automata (CA) have long attracted attention as dynamical systems
with local updating rules and yet can exhibit, for certain rules, complex, long
space and time correlated patterns. This contrast with other rules which
results in trivial patterns being homogeneous or periodic. In this article we
approach CA from two related angles: we analyze the information transfer in the
time evolution of CA driven sequences and; we revisit the sensibility of the
initial configuration on sequence evolution. In order to do so, we borrow a
recently reported information distance based on Kolmogorov algorithmic
complexity. The normalized information distance has been used previously to
find a hierarchical clustering of CA rules. What is different in our approach,
is the temporal analysis of the sequence evolutions by correlating different
calculated distances with entropy density. Entropy rate, is a length invariant
measure of the amount of irreducible randomness in a dynamical process. In
order to perform our analysis, we incorporate to the practical calculation of
the entropy rate and the distance measure, the use of Lempel-Ziv complexity.
Lempel-Ziv complexity carries a number of practical advantages while avoiding
the uncomputable nature of Kolmogorov randomness. The reduction of entropy
density during time evolution can be related to energy dissipation through
Landauer principle. Related to the last fact, is the computational capabilities
of CA as information processing rules, were the performed analysis could be
used to select CA rules amiable for simulating different physical process. The
tools developed in this article for the analysis of the CA are easily
extendible to the study of other one dimensional dynamical systems.Comment: Accepted at Chao
Reservoir Computing using Cellular Automata
We introduce a novel framework of reservoir computing. Cellular automaton is
used as the reservoir of dynamical systems. Input is randomly projected onto
the initial conditions of automaton cells and nonlinear computation is
performed on the input via application of a rule in the automaton for a period
of time. The evolution of the automaton creates a space-time volume of the
automaton state space, and it is used as the reservoir. The proposed framework
is capable of long short-term memory and it requires orders of magnitude less
computation compared to Echo State Networks. Also, for additive cellular
automaton rules, reservoir features can be combined using Boolean operations,
which provides a direct way for concept building and symbolic processing, and
it is much more efficient compared to state-of-the-art approaches.Comment: 9 pages, 4 figure
An evolutionary computational based approach towards automatic image registration
Image registration is a key component of various image processing operations
which involve the analysis of different image data sets. Automatic image
registration domains have witnessed the application of many intelligent
methodologies over the past decade; however inability to properly model object
shape as well as contextual information had limited the attainable accuracy. In
this paper, we propose a framework for accurate feature shape modeling and
adaptive resampling using advanced techniques such as Vector Machines, Cellular
Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be
effective in improving feature matching as well as resampling stages of
registration and complexity of the approach has been considerably reduced using
corset optimization The salient features of this work are cellular neural
network approach based SIFT feature point optimisation, adaptive resampling and
intelligent object modelling. Developed methodology has been compared with
contemporary methods using different statistical measures. Investigations over
various satellite images revealed that considerable success was achieved with
the approach. System has dynamically used spectral and spatial information for
representing contextual knowledge using CNN-prolog approach. Methodology also
illustrated to be effective in providing intelligent interpretation and
adaptive resampling.Comment: arXiv admin note: substantial text overlap with arXiv:1303.671
A Biomimetic Approach Based on Immune Systems for Classification of Unstructured Data
In this paper we present the results of unstructured data clustering in this
case a textual data from Reuters 21578 corpus with a new biomimetic approach
using immune system. Before experimenting our immune system, we digitalized
textual data by the n-grams approach. The novelty lies on hybridization of
n-grams and immune systems for clustering. The experimental results show that
the recommended ideas are promising and prove that this method can solve the
text clustering problem.Comment: 10 pages, 4 figure
Theory and Applications of Two-dimensional, Null-boundary, Nine-Neighborhood, Cellular Automata Linear rules
This paper deals with the theory and application of 2-Dimensional,
nine-neighborhood, null- boundary, uniform as well as hybrid Cellular Automata
(2D CA) linear rules in image processing. These rules are classified into nine
groups depending upon the number of neighboring cells influences the cell under
consideration. All the Uniform rules have been found to be rendering multiple
copies of a given image depending on the groups to which they belong where as
Hybrid rules are also shown to be characterizing the phenomena of zooming in,
zooming out, thickening and thinning of a given image. Further, using hybrid CA
rules a new searching algorithm is developed called Sweepers algorithm which is
found to be applicable to simulate many inter disciplinary research areas like
migration of organisms towards a single point destination, Single Attractor and
Multiple Attractor Cellular Automata Theory, Pattern Classification and
Clustering Problem, Image compression, Encryption and Decryption problems,
Density Classification problem etc.Comment: 17 pages, 41 figures, a portion of this paper is accepted in the
journal as well as proceedings at WSEAS,200
Programmable Cellular Automata Based Efficient Parallel AES Encryption Algorithm
Cellular Automata(CA) is a discrete computing model which provides simple,
flexible and efficient platform for simulating complicated systems and
performing complex computation based on the neighborhoods information. CA
consists of two components 1) a set of cells and 2) a set of rules .
Programmable Cellular Automata(PCA) employs some control signals on a Cellular
Automata(CA) structure. Programmable Cellular Automata were successfully
applied for simulation of biological systems, physical systems and recently to
design parallel and distributed algorithms for solving task density and
synchronization problems. In this paper PCA is applied to develop cryptography
algorithms. This paper deals with the cryptography for a parallel AES
encryption algorithm based on programmable cellular automata. This proposed
algorithm based on symmetric key systems
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
We demonstrate that the algorithmic information content of a system is deeply
connected to its potential dynamics, thus affording an avenue for moving
systems in the information-theoretic space and controlling them in the phase
space. To this end we performed experiments and validated the results on (1) a
very large set of small graphs, (2) a number of larger networks with different
topologies, and (3) biological networks from a widely studied and validated
genetic network (e.coli) as well as on a significant number of differentiating
(Th17) and differentiated human cells from high quality databases (Harvard's
CellNet) with results conforming to experimentally validated biological data.
Based on these results we introduce a conceptual framework, a model-based
interventional calculus and a reprogrammability measure with which to steer,
manipulate, and reconstruct the dynamics of non- linear dynamical systems from
partial and disordered observations. The method consists in finding and
applying a series of controlled interventions to a dynamical system to estimate
how its algorithmic information content is affected when every one of its
elements are perturbed. The approach represents an alternative to numerical
simulation and statistical approaches for inferring causal
mechanistic/generative models and finding first principles. We demonstrate the
framework's capabilities by reconstructing the phase space of some discrete
dynamical systems (cellular automata) as case study and reconstructing their
generating rules. We thus advance tools for reprogramming artificial and living
systems without full knowledge or access to the system's actual kinetic
equations or probability distributions yielding a suite of universal and
parameter-free algorithms of wide applicability ranging from causation,
dimension reduction, feature selection and model generation.Comment: 50 pages with Supplementary Information and Extended Figures. The
Online Algorithmic Complexity Calculator implements the methods in this
paper: http://complexitycalculator.com/ Animated video available at:
https://youtu.be/ufzq2p5tVL
Investigating Cellular Automata Based Network Intrusion Detection System For Fixed Networks (NIDWCA)
Network Intrusion Detection Systems (NIDS) are computer systems which monitor
a network with the aim of discerning malicious from benign activity on that
network. With the recent growth of the Internet such security limitations are
becoming more and more pressing. Most of the current network intrusion
detection systems relay on labeled training data. An Unsupervised CA based
anomaly detection technique that was trained with unlabelled data is capable of
detecting previously unseen attacks. This new approach, based on the Cellular
Automata classifier (CAC) with Genetic Algorithms (GA), is used to classify
program behavior as normal or intrusive. Parameters and evolution process for
CAC with GA are discussed in detail. This implementation considers both
temporal and spatial information of network connections in encoding the network
connection information into rules in NIDS. Preliminary experiments with KDD Cup
data set show that the CAC classifier with Genetic Algorithms can effectively
detect intrusive attacks and achieve a low false positive rate. Training a
NIDWCA (Network Intrusion Detection with Cellular Automata) classifier takes
significantly shorter time than any other conventional techniques.Comment: 2008 International Conference on Advanced Computer Theory and
Engineerin
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