5,098 research outputs found

    Visualization and clustering by 3D cellular automata: Application to unstructured data

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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)

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    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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