The Experts below are selected from a list of 153 Experts worldwide ranked by ideXlab platform
Piotr Sikora - One of the best experts on this subject based on the ideXlab platform.
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online service for polish Dependency parsing and results visualisation
Intelligent Information Systems, 2013Co-Authors: Alina Wroblewska, Piotr SikoraAbstract:The paper presents a new online service for the Dependency parsing of Polish. Given raw text as input, the service processes it and visualises Output Dependency trees. The service applies the parsing system – MaltParser – with a parsing model for Polish trained on the Polish Dependency Bank, and some additional publicly available tools.
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IIS - Online Service for Polish Dependency Parsing and Results Visualisation
Language Processing and Intelligent Information Systems, 2013Co-Authors: Alina Wroblewska, Piotr SikoraAbstract:The paper presents a new online service for the Dependency parsing of Polish. Given raw text as input, the service processes it and visualises Output Dependency trees. The service applies the parsing system – MaltParser – with a parsing model for Polish trained on the Polish Dependency Bank, and some additional publicly available tools.
Alina Wroblewska - One of the best experts on this subject based on the ideXlab platform.
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online service for polish Dependency parsing and results visualisation
Intelligent Information Systems, 2013Co-Authors: Alina Wroblewska, Piotr SikoraAbstract:The paper presents a new online service for the Dependency parsing of Polish. Given raw text as input, the service processes it and visualises Output Dependency trees. The service applies the parsing system – MaltParser – with a parsing model for Polish trained on the Polish Dependency Bank, and some additional publicly available tools.
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IIS - Online Service for Polish Dependency Parsing and Results Visualisation
Language Processing and Intelligent Information Systems, 2013Co-Authors: Alina Wroblewska, Piotr SikoraAbstract:The paper presents a new online service for the Dependency parsing of Polish. Given raw text as input, the service processes it and visualises Output Dependency trees. The service applies the parsing system – MaltParser – with a parsing model for Polish trained on the Polish Dependency Bank, and some additional publicly available tools.
Seyed Mohammad Mahdi Mortazavian - One of the best experts on this subject based on the ideXlab platform.
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an artificial intelligence approach for modeling volume and fresh weight of callus a case study of cumin cuminum cyminum l
Journal of Theoretical Biology, 2016Co-Authors: Ali Mansouri, Ali Fadavi, Seyed Mohammad Mahdi MortazavianAbstract:Cumin (Cuminum cyminum Linn.) is valued for its aroma and its medicinal and therapeutic properties. A supervised feedforward artificial neural network (ANN) trained with back propagation algorithms, was applied to predict fresh weight and volume of Cuminum cyminum L. calli. Pearson correlation coefficient was used to evaluate input/Output Dependency of the eleven input parameters. Area, feret diameter, minor axis length, perimeter and weighted density parameters were chosen as input variables. Different training algorithms, transfer functions, number of hidden nodes and training iteration were studied to find out the optimum ANN structure. The network with conjugate gradient fletcher-reeves (CGF) algorithm, tangent sigmoid transfer function, 17 hidden nodes and 2000 training epochs was selected as the final ANN model. The final model was able to predict the fresh weight and volume of calli more precisely relative to multiple linear models. The results were confirmed by R(2)≥0.89, R(i)≥0.94 and T value ≥0.86. The results for both volume and fresh weight values showed that almost 90% of data had an acceptable absolute error of ±5%.
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An artificial intelligence approach for modeling volume and fresh weight of callus – A case study of cumin (Cuminum cyminum L.)
Journal of theoretical biology, 2016Co-Authors: Ali Mansouri, Ali Fadavi, Seyed Mohammad Mahdi MortazavianAbstract:Cumin (Cuminum cyminum Linn.) is valued for its aroma and its medicinal and therapeutic properties. A supervised feedforward artificial neural network (ANN) trained with back propagation algorithms, was applied to predict fresh weight and volume of Cuminum cyminum L. calli. Pearson correlation coefficient was used to evaluate input/Output Dependency of the eleven input parameters. Area, feret diameter, minor axis length, perimeter and weighted density parameters were chosen as input variables. Different training algorithms, transfer functions, number of hidden nodes and training iteration were studied to find out the optimum ANN structure. The network with conjugate gradient fletcher-reeves (CGF) algorithm, tangent sigmoid transfer function, 17 hidden nodes and 2000 training epochs was selected as the final ANN model. The final model was able to predict the fresh weight and volume of calli more precisely relative to multiple linear models. The results were confirmed by R(2)≥0.89, R(i)≥0.94 and T value ≥0.86. The results for both volume and fresh weight values showed that almost 90% of data had an acceptable absolute error of ±5%.
Alon Rosen - One of the best experts on this subject based on the ideXlab platform.
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A Dichotomy for Local Small-Bias Generators
Journal of Cryptology, 2016Co-Authors: Benny Applebaum, Andrej Bogdanov, Alon RosenAbstract:We consider pseudorandom generators in which each Output bit depends on a constant number of input bits. Such generators have appealingly simple structure: They can be described by a sparse input–Output Dependency graph $$G$$ G and a small predicate $$P$$ P that is applied at each Output. Following the works of Cryan and Miltersen (MFCS’01) and by Mossel et al (STOC’03), we ask: which graphs and predicates yield “small-bias” generators (that fool linear distinguishers)? We identify an explicit class of degenerate predicates and prove the following. For most graphs, all non-degenerate predicates yield small-bias generators, $$f:\{0,1\}^n \rightarrow \{0,1\}^m$$ f : { 0 , 1 } n → { 0 , 1 } m , with Output length $$m = n^{1 + \epsilon }$$ m = n 1 + ϵ for some constant $$\epsilon > 0$$ ϵ > 0 . Conversely, we show that for most graphs, degenerate predicates are not secure against linear distinguishers, even when the Output length is linear $$m=n+\Omega (n)$$ m = n + Ω ( n ) . Taken together, these results expose a dichotomy: Every predicate is either very hard or very easy, in the sense that it either yields a small-bias generator for almost all graphs or fails to do so for almost all graphs. As a secondary contribution, we attempt to support the view that small-bias is a good measure of pseudorandomness for local functions with large stretch. We do so by demonstrating that resilience to linear distinguishers implies resilience to a larger class of attacks.
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A Dichotomy for Local Small-Bias Generators.
IACR Cryptology ePrint Archive, 2011Co-Authors: Benny Applebaum, Andrej Bogdanov, Alon RosenAbstract:We consider pseudorandom generators in which each Output bit depends on a constant number of input bits. Such generators have appealingly simple structure: they can be described by a sparse input-Output Dependency graph and a small predicate that is applied at each Output. Following the works of Cryan and Miltersen (MFCS’01) and by Mossel et al (STOC’03), we focus on the study of “small-bias” generators (that fool linear distinguishers). We prove that for most graphs, all but a handful of “degenerate” predicates yield smallbias generators, f : {0, 1} → {0, 1}, with Output length m = n for some constant e > 0. Conversely, we show that for most graphs, “degenerate” predicates are not secure against linear distinguishers. Taken together, these results expose a dichotomy: every predicate is either very hard or very easy, in the sense that it either yields a small-bias generator for almost all graphs or fails to do so for almost all graphs. As a secondary contribution, we attempt to support the view that small-bias is a good measure of pseudorandomness for local functions with large stretch. We do so by demonstrating that resilience to linear distinguishers implies resilience to a larger class of attacks.
Susumu Yamashiro - One of the best experts on this subject based on the ideXlab platform.
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Unit commitment scheduling by Lagrange relaxation method taking into account transmission losses
Electrical Engineering in Japan, 2005Co-Authors: Daisuke Murata, Susumu YamashiroAbstract:Since the application of the Lagrange relaxation method to the unit commitment scheduling by Muckstadt in 1979, many papers using this method have been published. The greatest advantage of applying the Lagrange relaxation method for the unit commitment problem is that it can relax (ignore) each generator's Output Dependency caused by the demand–supply balance constraint so that a unit commitment of each generator is determined independently by dynamic programming. However, when we introduce the transmission loss into the demand–supply balance constraint, we cannot decompose the problem into the partial problems in which each generator's unit commitment is determined independently and have to take some measures to obtain an optimal schedule by the Lagrange relaxation method directly. In this paper, we present an algorithm for the unit commitment schedule using the Lagrange relaxation method for the case of taking into account transmission losses. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(4): 27–33, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20119
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Unit Commitment Scheduling by Lagrange Relaxation Method Taking into Account Transmission Losses
IEEJ Transactions on Power and Energy, 2004Co-Authors: Daisuke Murata, Susumu YamashiroAbstract:After the application of the Lagrange relaxation method to the unit commitment scheduling by Muckstadt in 1979, many papers using this method have been reported so far. The greatest advantage of applying the Lagrange relaxation method for the unit commitment problem is that it can relax (ignore) each generator’s Output Dependency caused by demand-supply balance constraint so that an unit commitment of each generator is determined independently by Dynamic Programming. However, when we introduce the transmission loss into the demand-supply balance constraint, we cannot decompose the problem into the partial problems in which each generator’s unit commitment is determined independently and have to take some measures to get an optimal schedule by Lagrange relaxation method directly. In this paper, we present an algorithm for the unit commitment schedule using the Lagrange relaxation method for the case of taking into account transmission losses.
