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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: van den Bergh, Frans | Engelbrecht, Andries Petrus
Article Type: Research Article
Abstract: The Particle Swarm Optimiser (PSO) is a population based stochastic optimisation algorithm, empirically shown to be efficient and robust. This paper provides a proof to show that the original PSO does not have guaranteed convergence to a local optimum. A flaw in the original PSO is identified which causes stagnation of the swarm. Correction of this flaw results in a PSO algorithm with guaranteed convergence to a local minimum. Further extensions with provable global convergence are also described. Experimental results are provided to elucidate the behavior of the modified PSO as well as PSO variations with global convergence.
Keywords: Particle swarm optimisation, convergence proofs, stochastic optimisation, local convergence, global convergence
DOI: 10.3233/FI-2010-370
Citation: Fundamenta Informaticae, vol. 105, no. 4, pp. 341-374, 2010
Authors: Ghassemi, Fatemeh | Fokkink, Wan | Movaghar, Ali
Article Type: Research Article
Abstract: We provide an equational theory for Restricted Broadcast Process Theory to reason about ad hoc networks. We exploit an extended algebra called Computed Network Theory to axiomatize restricted broadcast. It allows one to define the behavior of an ad hoc network with respect to the underlying topologies. We give a sound and ground-complete axiomatization for CNT terms with finite-state behavior, modulo what we call rooted branching computed network bisimilarity.
DOI: 10.3233/FI-2010-371
Citation: Fundamenta Informaticae, vol. 105, no. 4, pp. 375-415, 2010
Authors: Grigoryan, Hayk | Shoukourian, Arsen
Article Type: Research Article
Abstract: In this paper, the functional equivalence problem for processes, which is an important constituent of process optimization, is considered in object-oriented environments. Two basic cases are investigated: when the turn of operations execution is not essential for some objects while it is essential for the rest of them. In the first case, those objects are named commutative. Environments with both commutative and non-commutative objects are named partially commutative. It is shown, that the equivalence problem of processes in partially commutative environments that contain two or more commutative objects with more than one operation is unsolvable. The largest class of partially …commutative environments is outlined where the equivalence problem is solvable (decidable). It is shown, that if there is only one object with two or more operations among commutative objects then the equivalence problem is solvable. Show more
Keywords: object-oriented environment, environment model, model equation, process, equivalence of processes, multitape multidimensional automaton, partially commutative, regular expression
DOI: 10.3233/FI-2010-372
Citation: Fundamenta Informaticae, vol. 105, no. 4, pp. 417-434, 2010
Authors: Kenaza, Tayeb | Tabia, Karim | Benferhat, Salem
Article Type: Research Article
Abstract: Bayesian networks are very powerful tools for knowledge representation and reasoning under uncertainty. This paper shows the applicability of naive Bayesian classifiers to two major problems in intrusion detection: the detection of elementary attacks and the detection of coordinated ones. We propose two models starting with stating the problems and defining the variables necessary for model building using naive Bayesian networks. In addition to the fact that the construction of such models is simple and efficient, the performance of naive Bayesian networks on a representative data is competing with the most efficient state of the art classification tools. We show …how the decision rules used in naive Bayesian classifiers can be improved to detect new attacks and new anomalous activities. We experimentally show the effectiveness of these improvements on a recent Web-based traffic. Finally, we propose a naive Bayesian network-based approach especially designed to detect coordinated attacks and provide experimental results showing the effectiveness of this approach. Show more
Keywords: Bayesian networks, classification, naive Bayes classifiers, intrusion detection, coordinated attacks
DOI: 10.3233/FI-2010-373
Citation: Fundamenta Informaticae, vol. 105, no. 4, pp. 435-466, 2010
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