Informatica: An International Journal of Computing and Informatics

The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous reordering of the rows and the columns of a square matrix such that the nonzero entries are collected within a band of small width close to the main diagonal. The MBMP is a NP-complete problem, with applications in many scientific domains, linear systems, artificial intelligence, and real-life situations in industry, logistics, information recovery. The complex problems are hard to solve, that is why any attempt to improve their solutions is beneficent. Genetic algorithms and ant-based systems are Soft Computing methods used in this paper in order to solve some MBMP instances. Our approach is based on a learning agent-based model involving a local search procedure. The algorithm is compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic algorithm, using several instances from Matrix Market collection. Computational experiments confirm a good performance of the proposed algorithms for the considered set of MBMP instances. On Soft Computing basis, we also propose a new theoretical Reinforcement Learning model for solving the MBMP.

A new pseudo-random number generator (PRNG) is proposed. The principle of the method consists in mixing chaotic maps produced from an input initial vector. The algorithm uses permutations whose positions are computed and indexed by a chaotic function based on linear congruences. The performance of this scheme is evaluated through statistical analysis. Such a cryptosystem lets appear significant cryptographic qualities for a good security level.

Inventory management is an important part of production planning process for enterprises. Decisions for strategies to determine when and how many to buy or make can be made by classifying the inventory items based on their sorts. In this evaluation, ABC inventory classification is one of the most commonly used approaches. In this study, a fuzzy analytic network process approach was proposed to determine the weights of the criteria and the scores of the inventory items were determined with simple additive weighting by using linguistic terms. Applying fuzzy ANP to a multi-criteria inventory classification problem is the novelty of this study in the related literature. In addition, the application area of the problem which is the management of the engineering vehicles' items in a construction firm is different from the other studies.

In this paper, we present a cryptanalysis of a public key cryptosystem based on the matrix combinatorial problem proposed by Wang and Hu (2010). Using lattice-based methods finding small integer solutions of modular linear equations, we recover the secret key of this cryptosystem for a certain range of parameters. In experiments, for the suggested parameters by Wang and Hu, the secret key can be recovered in seconds.

This paper presents a new approach for the business and information systems (IS) alignment consisting of a framework, metamodel, process, and tools for implementing it in practice. The purpose of the approach is to fill in the gap between the existing conceptual business and IS alignment frameworks and the empirical business and IS alignment methods. The suggested approach is based on the SOA, GRAAL, and enterprise modeling techniques such as TOGAF, DoDAF, and UPDM. The proposed approach is applied on four real world projects. Both the application results and the small example are provided to validate the suitability of the approach.

The paper deals with the application of the theory of locally homogeneous and isotropic Gaussian fields (LHIGF) to probabilistic modelling of multivariate data structures. An asymptotic model is also studied, when the correlation function parameter of the Gaussian field tends to infinity. The kriging procedure is developed which presents a simple extrapolator by means of a matrix of degrees of the distances between pairs of the points of measurement. The resulting model is rather simple and can be defined only by the mean and variance parameters, efficiently evaluated by maximal likelihood method. The results of application of the extrapolation method developed for two analytically computed surfaces and estimation of the position of the spacecraft re-entering the atmosphere are given.

Based on an example, we describe how outcomes of computational experiment can be employed for study of stability of numerical algorithm, provided that related theoretical propositions are not proven yet. More precisely, we propose a systematic and generalized methodology, how to investigate the influence of the weight functions α(x) and β(x), present in the integral boundary conditions, on the stability of difference schemes, for some class of parabolic equations. The ground of the methodology is the investigation of the spectrum of a matrix, defining the transition to the upper layer of the difference scheme. Spectral structure of this matrix is analysed by both analytic method and computational experiment.

This investigate proposed a innovative Improved Hybrid PSO-GA (IHPG) algorithm which it combined the advantages of the PSO algorithm and GA algorithm. The IHPG algorithm uses the velocity and position update rules of the PSO algorithm and the GA algorithm in selection, crossover and mutation thought. This study explores the quality monitoring experiment by three existing neural network approaches to data fusion in wireless sensor module measurements. There are ten sensors deployed in a sensing area, the digital conversion and weight adjustment of the collected data need to be done. This experiment result can improve the accuracy of the estimated data and reduce the randomness of computing by adjustment optimization of smoothing parameter. According to the experimental analysis, the IHPG is better than the single PSO and GA in comparison the various neural network learning model.

We consider a generalization of heterogeneous meta-programs by (1) introducing an extra level of abstraction within the meta-program structure, and (2) meta-program transformations. We define basic terms, formalize transformation tasks, consider properties of meta-program transformations and rules to manage complexity through the following transformation processes: (1) reverse transformation, when a correct one-stage meta-program M¹ is transformed into the equivalent two-stage meta-meta-program M²; (2) two-stage forward transformations, when M² is transformed into a set of meta-programs, and each meta-program is transformed into a set of target programs. The results are as follows: (a) formalization of the transformation processes within the heterogeneous meta-programming paradigm; (b) introduction and approval of equivalent transformations of meta-programs into meta-meta-programs and vice versa; (c) introduction of metrics to evaluate complexity of meta-specifications. The results are approved by examples, theoretical reasoning and experiments.