The distributed evaluation of functional programs and the communication between computational nodes require high-level process description and coordination mechanism. This paper presents the D-Clean high-level functio...
The distributed evaluation of functional programs and the communication between computational nodes require high-level process description and coordination mechanism. This paper presents the D-Clean high-level functional language, which supports the distributed computation of Clean functions over a cluster. The lazy functional programming language Clean is extended by new language elements in order to achieve parallel features. The distributed computations of functions are expressed in the form of process-networks. D-Clean introduces language primitives to control the dataflow in a distributed process-network. A process scheme defines a partial computation graph, where the nodes are functions to be evaluated and the edges are communication channels. The computational nodes are implemented as statically typed Clean programs. The schemes are parameterized by functions, types and data for defining process networks. D-Clean is compiled to an intermediate level language called D-Box. The D-Clean generic constructs are instantiated into D-Box expressions. D-Box is designed for the description of the computational nodes. D-Box expressions hide implementation details and enable direct control over the process-network. The asynchronous communication is based on language-independent middleware services. The present paper provides the syntax and the informal semantics of both coordination languages. To illustrate the definition of a distributed functional computational pattern using the D-Clean language a farm skeleton running example is presented.
In the present article, we have considered the issue of selection of potential locations of trade objects as a multi-factor decision-making in the conditions of uncertainty by applying the theory of fuzzy sets. Exampl...
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We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree q...
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In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding...
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Long-term predictions of time series is a task with multiple applications. Exploiting neural networks for such problem has not been studied enough for financial purposes. An empirical study of specific cases with real...
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The paper formulates an algorithm for searching the optimal temperature regime of the catalytic process in an ideal mixing reactor. The problem of optimal control of the catalytic process is formulated in a general fo...
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Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in Io...
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Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.
In this paper we present the LearnLib, a library for automata learning and experimentation. Its modular structure allows users to configure their tailored learning scenarios, which exploit specific properties of the e...
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ISBN:
(纸本)1595931481
In this paper we present the LearnLib, a library for automata learning and experimentation. Its modular structure allows users to configure their tailored learning scenarios, which exploit specific properties of the envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios. Copyright 2005 ACM.
The paper gives an overview of research devoted to developing a semi-automatic methodology of building a semantic model of medical diagnostic knowledge. The methodology is based on natural language processing methods ...
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