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Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing:
- solutions by mathematical methods of problems emerging in computer science
- solutions of mathematical problems inspired by computer science.
Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, & algebraic and categorical methods.
Authors: Redziejowski, Roman R.
Article Type: Research Article
Abstract: In an earlier paper, the author used derivatives to construct a deterministic automaton recognizing the language defined by an ω-regular expression. The construction was related to a determinization method invented by Safra. This paper describes a new construction, inspired by Piterman's improvement to Safra's method. It produces an automaton with fewer states. In addition, the presentation and proofs are simplified by going via a nondeterministic automaton having derivatives as states.
DOI: 10.3233/FI-2012-744
Citation: Fundamenta Informaticae, vol. 119, no. 3-4, pp. 393-406, 2012
Authors: Rzeszótko, Jarosław | Nguyen, Sinh Hoa
Article Type: Research Article
Abstract: Recently, GPS car navigation became popular enough for a significant number of drivers in any large city to be able to potentially send their current GPS positions and velocities to a central traffic-managing server in real time. This data could be used to reconstruct a map of current traffic in the whole city and make predictions for the future, which would be valuable for traffic control, congestion analysis and prevention. The difficulty lying in the traffic reconstruction problem is that the number of cars equipped with a GPS transmitter is a small in comparison with the total number of cars …in the road network. Information obtained from GPS transmitters is therefore rare and incomplete, raising the problem of approximating travel velocities from sparse information. In this context, the paper proposes approaches to solving two problems: (1) of reconstruction of routes of the individual cars in a street graph from a temporal stream of its coordinates and (2) of approximating the average velocity on a given street from irregular time series of instantaneous velocities of cars passing through that street. Determining the car's position in the road network is quite expensive computationally when done in a naive way, so we propose a method to speed up the car's localization based on the R-tree data structure [10]. Additionally, a resilient propagation neural network [13] is applied to approximate the average velocity on any edge of a street graph. Our methods were applied to one of the problems announced in the competition organized in June 2010 by Tom-Tom - a company producing automotive navigation systems [15]. The results of experiments show the capacity of our approach to make useful predictions. Show more
Keywords: machine learning, data mining, spatio-temporal data mining, GPS, R-Tree, GIS, neural networks
DOI: 10.3233/FI-2012-745
Citation: Fundamenta Informaticae, vol. 119, no. 3-4, pp. 407-420, 2012
Authors: Skowron, Andrzej | Stepaniuk, Jarosław | Jankowski, Andrzej | Bazan, Jan G. | Swiniarski, Roman
Article Type: Research Article
Abstract: We consider several issues related to reasoning about changes in systems interacting with the environment by sensors. In particular, we discuss challenging problems of reasoning about changes in hierarchical modeling and approximation of transition functions or trajectories. This paper can also be treated as a step toward developing rough calculus.
Keywords: rough sets, reasoning about changes, hierarchical modeling, granular computing, approximation space, relation (function) approximation, rough calculus, intelligent systems, computational finance, forex, algorithmic trading
DOI: 10.3233/FI-2012-746
Citation: Fundamenta Informaticae, vol. 119, no. 3-4, pp. 421-437, 2012
Article Type: Other
Citation: Fundamenta Informaticae, vol. 119, no. 3-4, pp. 439-440, 2012
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