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92
Two Proof Methods For The Grafcet Language
"... : In this paper, we present two different methods to make proofs on the GRAFCET language. The first one is based on Transition Systems and the second one uses Polynomial Dynamical Systems. Theoretical and pratical aspects are presented. Key Words: Proof methods, GRAFCET, Transition Systems, Polyn ..."
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: In this paper, we present two different methods to make proofs on the GRAFCET language. The first one is based on Transition Systems and the second one uses Polynomial Dynamical Systems. Theoretical and pratical aspects are presented. Key Words: Proof methods, GRAFCET, Transition Systems
Neuro Language Generator
"... Machine ’ is based on neural network and finite state machine. The fundamental properties of neural network along with the power of Turing machine prove how it can be implemented for formal language processing. This paper elaborates the conventional dynamical language generators, limitations of the ..."
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Machine ’ is based on neural network and finite state machine. The fundamental properties of neural network along with the power of Turing machine prove how it can be implemented for formal language processing. This paper elaborates the conventional dynamical language generators, limitations
Proceedings of INTERSPEECH. Wordbased Probabilistic Phonetic Retrieval for Lowresource Spoken Term Detection
"... Two problems make Spoken Term Detection (STD) particularly challenging under lowresource conditions: the low quality of speech recognition hypotheses, and a high number of outofvocabulary (OOV) words. In this paper, we propose an intuitive way to handle OOV terms for STD on wordbased Confusion N ..."
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Networks using phonetic similarities, and generalize it into a probabilistic and vocabularyindependent retrieval framework. We then reflect on how several heuristics and Machine Learning based methods can be incorporated into this framework to improve retrieval performance. We present experimental
An objectoriented implementation of concurrent and hierarchical state machines
"... a b s t r a c t Context: State machine diagrams are a powerful means to describe the behavior of reactive systems. Unfortunately, the implementation of state machines is difficult, because state machine concepts, like states, events and transitions, are not directly supported in commonly used progr ..."
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a b s t r a c t Context: State machine diagrams are a powerful means to describe the behavior of reactive systems. Unfortunately, the implementation of state machines is difficult, because state machine concepts, like states, events and transitions, are not directly supported in commonly used
Symmetric alternation captures BPP
, 1995
"... We introduce the natural class 2 containing those languages whichmay be expressed in terms of two symmetric quantifiers. This class lies between # and # and naturally generates a "symmetric" hierarchy corresponding to the polynomialtime hierarchy.We demonstrate, using the p ..."
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Cited by 46 (1 self)
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the probabilistic method, new containment theorems for BPP.We show that MA #and hence BPP# lies within 2 , improving the constructions of #10, 8# #which show that BPP # # 2 #. Symmetric alternation is shown to enjoytwo strong structural properties which are used to prove the desired containment
Turing Completeness in the Language of Genetic Programming with Indexed Memory
"... Abstract: Genetic Programming is a method for evolving functions that find approximate or exact solutions to problems. There are many problems that traditional Genetic Programming (GP) cannot solve, due to the theoretical limitations of its paradigm. A Turing machine (TM) is a theoretical abstractio ..."
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Abstract: Genetic Programming is a method for evolving functions that find approximate or exact solutions to problems. There are many problems that traditional Genetic Programming (GP) cannot solve, due to the theoretical limitations of its paradigm. A Turing machine (TM) is a theoretical
J Intell Inf Syst manuscript No. (will be inserted by the editor) Link Classification with Probabilistic Graphs
"... Abstract The need to deal with the inherent uncertainty in realworld relational or networked data leads to the proposal of new probabilistic models, such as probabilistic graphs. Every edge in a probabilistic graph is associated with a probability whose value represents the likelihood of its exist ..."
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existence, or the strength of the relation between the entities it connects. The aim of this paper is to propose two machine learning techniques for the link classification problem in relational data exploiting the probabilistic graph representation. Both the proposed methods will exploit a language
Extending Bayesian Logic Programs for Plan Recognition and Machine Reading
"... Statistical relational learning (SRL) is the area of machine learning that integrates both firstorder logic and probabilistic graphical models. The advantage of these formalisms is that they can handle both uncertainty and structured/relational data. As a result, they are widely used in domains lik ..."
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Statistical relational learning (SRL) is the area of machine learning that integrates both firstorder logic and probabilistic graphical models. The advantage of these formalisms is that they can handle both uncertainty and structured/relational data. As a result, they are widely used in domains
Semantics of a purely quantum programming language
, 2008
"... Quantum algorithm is the key to dig the potential power of quantum computing and make quantum computation more efficient than classical analogue. However, present methods of designing quantum algorithms are too tricky and lack of systematic development. The aim of quantum programming languages is to ..."
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Cited by 1 (0 self)
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Quantum algorithm is the key to dig the potential power of quantum computing and make quantum computation more efficient than classical analogue. However, present methods of designing quantum algorithms are too tricky and lack of systematic development. The aim of quantum programming languages
Using Xmachines to model and test discrete event simulation programs
"... Abstract: Simulation is a powerful technique for the study of reallife complex problems. Simulation requires the development of a program, which mimics the problem under consideration. The simulation program development follows closely the software engineering process. Although, many techniques fo ..."
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in an intuitive manner but also because the method is used to prove that the implementation is correct with respect to the specification. A simple example is thoroughly investigated in terms of modelling and testing by employing the Xmachine formal methodology. A complete set of testcases is generated
Results 11  20
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92