Although fund activities whose target is to attract the members of rivals would seem to be very important for a proper evaluation of pension fund achievements, this topic has not been looked at by researchers. This pa...
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Complex software systems typically involve features like time, concurrency and probability, where probabilistic computations play an increasing role. It is challenging to formalize languages comprising all these featu...
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Complex software systems typically involve features like time, concurrency and probability, where probabilistic computations play an increasing role. It is challenging to formalize languages comprising all these features. In this paper, we integrate probability, time and concurrency in one single model, where the concurrency feature is modelled using shared-variable based communication. The probability feature is represented by a probabilistic nondeterministic choice, probabilistic guarded choice and a probabilistic version of parallel composition. We formalize an operational semantics for such an integration. Based on this model we define a bisimulation relation, from which an observational equivalence between probabilistic programs is investigated and a collection of algebraic laws are explored. We also implement a prototype of the operational semantics to animate the execution of probabilistic programs
Presently existing correlation analysis method for multiple data streams were all oriented single dimensions data streams only, which could not identify the real correlation between fields built by multiple variables....
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Presently existing correlation analysis method for multiple data streams were all oriented single dimensions data streams only, which could not identify the real correlation between fields built by multiple variables. To quickly detect correlations between two multiple dimension data streams under constrained resources, a novel correlation analysis algorithm based on canonical correlation analysis (CCA), called StreamCCA, is proposed. Focusing on the computational bottleneck of traditional CCA, StreamCCA introduces a low-rank approximation technique to reduce the dimensionality of product matrix resulted from sample correlation matrix and sample variance matrix, which improves computational performance efficiently on the premise of holding approximate precision. Theoretic analysis and experiments results on synthetic and real data sets indicate that StreamCCA can online detect correlations between multiple dimension data streams accurately. The algorithms proposed herein, are presented as generic forecasting and diagnosis tools, with a multitude of applications on data streams mining problems.
In the automotive field, software development methods and tools are used to cope with the high complexity of automotive software development. However, problems occur with the tracing of information, the assessment and...
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We describe an approach to on-the-fly real-time testing based on non-deterministic timed automata. The approach is based on standard computations on zone automata. We present algorithms for practical testing, as they ...
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We describe an approach to on-the-fly real-time testing based on non-deterministic timed automata. The approach is based on standard computations on zone automata. We present algorithms for practical testing, as they were implemented in the testing tool TorX.
Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot ...
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Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming and boosting technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a boosting technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach
We propose in this paper a new Bluetooth authentication scheme based on game-theoretic setting. Bluetooth is a short-ranged wireless communication protocol that can be used between different Bluetooth enabled devices ...
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We propose in this paper a new Bluetooth authentication scheme based on game-theoretic setting. Bluetooth is a short-ranged wireless communication protocol that can be used between different Bluetooth enabled devices (cell phones, laptops, PDAs,...). It has been chosen as a baseline of the IEEE (Institute of Electronic and Electrical Engineers) 802.15.1 standard for WPANs (Wireless Personal Area Neworks). Game theory is a branch of mathematics and logic which deals with the analysis of games. It is a formal study of interactive decision processes [13]. It enhances the understanding of conflict and cooperation by mathematical models and abstractions. An authentication between two Bluetooth devices is an unidirectional challenge-response procedure and consequently, has many vulnerabilities. We model a bidirectional Bluetooth authentication as a noncooperative non-zero-sum bimatrix game. Three strategies are defined for each player, and the best-responses strategies (also called Nash equilibria) for this game are computed. Using Simplex algorithm, we find only one Nash equilibrium corresponding to the case where both Bluetooth devices are trusted and trying to securily communicate together. In a Nash equilibrium, no player has an incentive to deviate from such situation. Our model is then implemented in the application level using the Windows Bluetooth socket stack.
A common difficulty for the existing global optimization methods is that they are not easy to escape from the local optimal solutions and therefore often not find the global optimal solution. In order to make it escap...
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A common difficulty for the existing global optimization methods is that they are not easy to escape from the local optimal solutions and therefore often not find the global optimal solution. In order to make it escapes from the local optimal solutions and find the global optimal solution easier, first, the authors construct a smoothing function. It can eliminate all such local optimal solutions worse than the best solution found so far. Moreover, it can keep the original function unchanged in the region in which the values of the original function are not worse than its value at the best solution found so far. Thus, if optimizing this smoothing function instead of the original objective function, the number of the local optimal solutions will be largely decreased with progress of the iterations. As a result, it becomes much easier for an algorithm to find a global optimal solution. Second, a new crossover operator is designed based on the properties of the smoothing function. It can adaptively generate high quality offspring for any situation. Third, by making use of the properties of the smoothing function, the line search technique is properly combined into the algorithm design, which will make the proposed algorithm converge much faster. Based on all these, a novel effective evolutionary algorithm for global optimization is proposed and its global convergence is proved. At last, the numerical simulations for several standard benchmark problems are made and the simulation results show that the proposed algorithm is very effective.
Markov chains are widely used to determine system performance and reliability characteristics. The vast majority of applications considers continuous-time Markov chains (CTMCs). This note motivates how concurrency the...
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Markov chains are widely used to determine system performance and reliability characteristics. The vast majority of applications considers continuous-time Markov chains (CTMCs). This note motivates how concurrency theory can be extended (as opposed to twisted ) to CTMCs. We provide the core motivation for the algebraic setup of Interactive Markov Chains . Therefore, this note should have better been baptized YIMC.
In this paper, we try to throw light on the information content of base rates announcements released by the National Bank of Poland (NBP). Focusing our attention on the rediscount rate changes over the period from 199...
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In this paper, we try to throw light on the information content of base rates announcements released by the National Bank of Poland (NBP). Focusing our attention on the rediscount rate changes over the period from 1995 to 2003, we examine whether the abnormal behavior of stock returns and trading volume of the most liquid firms listed on the Warsaw Stock Exchange (WSE) can be identified in the surroundings of the NBP announcements. The statistical test used here is based on the excess returns (volume) defined as the difference between actual rate of return (volume) and expected rate of return (volume). To generate the expected returns (volume), we employ the ARMA(1,1)-GARCH(1,1) specification with additional repressor, that is, return of the market portfolio (approximated by the market-capitalization weighted stock index called WIG) in the mean equation. The main finding is that the reversal of rediscount rate course has a significant impact on stock returns but not on trading volume.
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