Abstract Given a deterministic finite automaton (DFA) A, we present a simple algorithm for constructing deterministic finite automata that accept the shortest forbidden factors, the shortest forbidden prefixes, the shortest forbidden suffixes, the shortest forbidden words, the shortest allowed suffixes, and the shortest allowed words of the automaton A. We refer to these sets as the shortest characteristic factors of the automaton A. If the given automaton is local, and therefore the language it accepts is strictly locally testable, the sets of its shortest characteristic factors are finite, and these automata are acyclic. Otherwise, they accept infinite languages. This approach simplifies existing methods for the extraction of forbidden factors, allows the extraction of more types of characteristic factors, and also generalizes the extraction for all classes of DFAs. Furthermore, we demonstrate that this type of extraction can be used for a sublinear run of an automaton for certain inputs. We define a positive position run of a deterministic finite automaton, representing all positions in an input string where the automaton reaches a final state. Finally, we present an algorithm for computing the positive position run of the automaton, which utilizes pattern set matching of its shortest forbidden factors and its shortest forbidden or allowed suffixes, provided that the sets are finite. We showcase the computation of the positive position run of a local automaton using backward pattern set matching, which can achieve sublinear time.

Nowadays, a new type of algorithms inspired by the quantum theory has arisen and brought to light new views of solving standard computational problems like searching, optimizing, scheduling etc. Some of the principles of quantum mechanics can be used even if a quantum computer is not available. The focus of this article is on the utilization of a quantum approach in a selected algorithm called DSOMA. A quantum computing based algorithm is described and all suggested improvements are demonstrated on the flowshop optimization problem. The novelty of the proposed algorithm consists in the use of quantum data. Individuals in other algorithms are usually encoded into a quantum state, whereas the individuals in our extension are just enriched by quantum information, and this data is used to improve behavior of the algorithm. Based on the outcomes of experiments carried out within our research the improved algorithm has achieved better results.

Self-organizing migrating algorithm (SOMA) is a modern stochastic optimization algorithm. It is built upon the principles of evolutionary and swarm computation and has been successfully applied to a variety of theoretical and practical optimization problems. The candidate solutions in SOMA are real- valued and the use of the algorithm for continuous optimization is straightforward. Its application to combinatorial optimization, on the other hand, requires a translation of candidate solutions from continuous search space to discrete problem solution space. In this work, a version of SOMA suitable for permutation problems is proposed and evaluated on two well-known hard permutation problems.

Visibly pushdown automata are pushdown automata whose pushdown operations are determined by the input symbol, where the input alphabet is partitioned into three parts for push, pop and local pushdown operations.It is well known that nondeterministic visibly pushdown automata can be determinised.In this paper a new algorithm for the determinisation of nondeterministic visibly pushdown automata is presented.The algorithm improves the existing methods and can result in significantly smaller deterministic pushdown automata.This is achieved in a way that only necessary and accessible states and pushdown symbols are computed and constructed during the determinisation.

Random Forest (RF) classification algorithm is widely used in the area of information retrieval and became a basis for some extended branches of classification and/or regression algorithms. Cluster Forest (CF) represents a particular branch, and brings usually better results than individual clustering algorithms. This article describes a new ensemble clustering algorithm based on CF that internally uses a probabilistic model called Gaussian Mixture Model (GMM). Finally, Expectation-maximization algorithm is used for estimation of GMM parameters. The proposed ensemble clustering algorithm will be compared with several different approaches and tested on eight datasets.

Increasing demands on web user interface (UI) usability, adaptability, and dynamic behavior drives ever growing development and maintenance complexity.Conventional design approaches scale poorly with such rising complexity, resulting in rapidly increasing costs.Much of the complexity centers around data presentation and processing.Recent work greatly reduces such data complexity through the application of Aspect-Oriented UI (AOUI) design, which separates various UI concerns; however, rendering in conventional and even AOUI approaches fails to maintain this separation, often resulting in high repetitions of concern fragments due to tangling.Even worse, mixing of dynamic and immutable components greatly limits caching efficacy as each have differing lifetimes.We extend AOUI design to push down concern separation to rendering, which reduces description size, through repetition reduction, and enables separate caching of individual concerns.Our results show considerable size reduction of UI descriptions for data presentations, faster load times and extended caching capabilities.

Artificial bee colony is a meta-heuristic optimization algorithm based on the behavior of honey bee swarm. These bees work largely independently of other bees, making the algorithm suitable for parallel implementation. Within this paper, we introduce the algorithm itself and its subsequent parallelization utilizing the CUDA platform. The runtime speedup is demonstrated on several commonly used test functions for optimization. The algorithm is subsequently applied to the problem of clustering real data.