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Foreword Petr SOSÍK
Abstract. Membrane computing is a relatively young but fast emerging bio-inspired computing paradigm, nowadays with many branches and applications. Its original computing model is the catalytic P system. Although it was proven already in 2005 that catalytic P systems with two catalysts are computationally universal [2], no simple example of such a P system generating a non-semilinear set was known. The present paper fills this gap and provides such an example with 54 rules. It is expected, however, that this number of rules can be reduced and the minimal number of rules to generate a non-semilinear set in a catalytic P system with two catalysts remains open. Read the pdf
Ozgur EGE, Ismet KARACA
Abstract. In this paper we propose a mathematical framework that can be used for defining cohomology of digital images. We state the Eilenberg-Steenrod axioms and the Universal Coefficient Theorem for this cohomology theory. We show that the Künneth formula doesn't hold. A cup product is defined and its main properties are proved. Read the pdf.
Horia-Nicolai TEODORESCU, Mariana RUSU
Abstract.Recently, the combined Gauss Mixture and Uniform Distributions Mixture Model, shortly Gauss-Uniform Mixture Model (G-U-MM) was proposed to better relate to the nature of a complex distribution and to simplify the characterization of processes that need too many Gauss functions in a standard Gauss Mixed Model (GMM). For a reasonably large class of images, the Gauss-Uniform distribution mixed models are easier to apply than the GMM models because the former ones produce significantly smaller numbers of elements in the mixture. The method has solid mathematical foundation and might be better related to the processes of image segmentation performed by humans. In addition, while computationally simple, it produces remarkable results. We discuss supplementary reasons for the use of the G-U-MM heterogeneous models in image segmentation and improve the previously presented algorithm of segmentation by removing the possible confusion between sections of Gaussian distributions and intervals of uniform distribution. Consequently, the approximation precision of the histogram and the segmentation are improved. Several examples illustrate the algorithm performance. Read the pdf
Ricardo SOTO, Broderick CRAWFORD, Eric MONFROY,
Abstract. The nurse rostering problem consists in generating a configuration of daily schedules for a set of nurses satisfying a set of constraints. The problem is known to be computationally challenging as it must consider different requirements such as minimal area or floor allocations, different skills, working regulations, as well as personnel wishes. The literature presents several successful work devoted to this problem, however there still is limited evidence about real cases of nurse rostering, in particular solved with constraint programming. The aim of this paper is to illustrate a real case study involving the design of a constraint programming solution for nurse rostering. The solution is devoted to a set of mid-size Chilean hospitals where nurse rostering is done manually using a very uncommon shift sequence called the “fourth shift” system. We present a classic model and a global constraint-based model that can be applied generically to any fourth shift health care center. Read the pdf
Majid YOUSEFIKHOSHBAKHT, Farzad DIDEHVAR, Farhad RAHMATI
Abstract. This article presents a new modified version of the ant colony optimization (ACO) mixed with insert, swap and 2-opt algorithm called NMACO for solving the multiple traveling salesman problem (MTSP) which utilizes an effective criterion for escaping from the local optimum points. The MTSP is one of the most important combinatorial optimization problems in which the objective is to minimize the distance traveled by several salesmen for servicing a set of nodes. Since this problem belongs to NP-hard Problems, some metaheuristic approaches have been used to solve it in recent years. In contrast to the classical ACO, the proposed algorithm uses only a global updating for the current best solution and the best found solution until now. Furthermore, a new state transition rule and an efficient candidate list are used in order to assess the efficiency of the proposed algorithm. The proposed algorithm is tested on some standard instances available from the literature and their results were compared with other well-known meta-heuristic algorithms. The results indicate that the NMACO has been able to improve the efficiency of the ACO in all instances and is quite competitive with other meta-heuristic algorithms. Read the pdf
Daniel I. MORARIU, Radu G. CREŢULESCU, Lucian N. VINŢAN
Abstract. The most used method for text documents representation is based on words frequency vectors named VSM (Vector Space Model). This representation, based only on words, loses any “word context” information which is found in the document and therefore reduces the learning efficiency in the process of classifying/clustering. In this article we intend to make a comparison between the classical method of document representation and a method called STDM (Suffix Tree Document Model) based on representing documents in the Suffix Tree format. For the second method we proposed a new approach for documents representation by performing suffix tree only for any two documents. This approach is faster and it has lower memory consumption in comparison with the common approaches that represent all documents from data set in the same suffix tree. Also for this method we have proposed a formula for computing the similarity between documents, which improves substantially the clustering quality. This representation method was validated using two clustering algorithms, HAC - Hierarchical Agglomerative Clustering and k-Medoids. In this context we experiment also the stemming influence in the document pre-processing step and highlight the difference between similarity or dissimilarity measures to find “closer” documents. Read the pdf
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