A novel data partitioning method adapted to a distributed parallel streams processing system for power industry is proposed. This method uses change-aware sampling algorithm that can guarantee low error to describe th...
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A novel data partitioning method adapted to a distributed parallel streams processing system for power industry is proposed. This method uses change-aware sampling algorithm that can guarantee low error to describe the distribution characteristics of the data-values first. And then it uses an improved heuristic constructing equal depth histograms algorithm to generate approximate partition vector efficiently. Experiments results on actual data prove that the proposed method is efficient, practical and suitable for time-varying data streams processing
This paper summarizes progress to date on the WSU model for engineering mathematics education, an NSF funded curriculum reform initiative at Wright State University. The WSU model seeks to increase student retention, ...
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Probabilistic pushdown automata (pPDA) have been identified as a natural model for probabilistic programs with recursive procedure calls. Previous works considered the decidability and complexity of the model-checking...
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Probabilistic pushdown automata (pPDA) have been identified as a natural model for probabilistic programs with recursive procedure calls. Previous works considered the decidability and complexity of the model-checking problem for pPDA and various probabilistic temporal logics. In this paper we concentrate on computing the expected values and variances of various random variables defined over runs of a given probabilistic pushdown automaton. In particular, we show how to compute the expected accumulated reward and the expected gain for certain classes of reward functions. Using these results, we show how to analyze various quantitative properties of pPDA that are not expressible in conventional probabilistic temporal logics.
We introduce a family of long-run average properties of Markov chains that are useful for purposes of performance and reliability analysis, and show that these properties can effectively be checked for a subclass of i...
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We introduce a family of long-run average properties of Markov chains that are useful for purposes of performance and reliability analysis, and show that these properties can effectively be checked for a subclass of infinite-state Markov chains generated by probabilistic programs with recursive procedures. We also show how to predict these properties by analyzing finite prefixes of runs, and present an efficient prediction algorithm for the mentioned subclass of Markov chains.
Latent Semantic Indexing (LSI) has been successfully applied to information retrieval and text classification. However, when LSI is used in classification, some important features for small classes may be ignored beca...
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ISBN:
(纸本)1595931406
Latent Semantic Indexing (LSI) has been successfully applied to information retrieval and text classification. However, when LSI is used in classification, some important features for small classes may be ignored because of their small feature values. To solve this problem, we propose the latent semantic classification (LSC) model which extends the LSI model in the following way: the classification information of the training documents is introduced into the latent semantic structure via a second set of latent variables, so that both indexing and classification information can be taken into account during the classification process. Our experiments on Reuters show that our new model performs better than the existing classification methods such as kNN and SVM.
Penalty functions are often used to handle constraints for constrained optimization problems in evolutionary algorithms. However it is difficult to control penalty parameters. To overcome this shortcoming, a new penal...
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Penalty functions are often used to handle constraints for constrained optimization problems in evolutionary algorithms. However it is difficult to control penalty parameters. To overcome this shortcoming, a new penalty function with easily-controlled penalty parameters is designed in this paper. The fitness function defined by this penalty function can distinguish feasible and infeasible solutions effectively. Meanwhile, the orthogonal design is used to generate initial population and design crossover operator. Based on these, a new genetic algorithm for constrained optimization problems is proposed. The simulations are made on five widely used benchmark problems, and the results indicate the proposed algorithm is effective.
Multi-agent system (MAS) is an important research field of distributed artificial intelligence. MAS negotiation is one of the key contents in MAS research. Combining with traditional contract net protocol, acquaintanc...
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Multi-agent system (MAS) is an important research field of distributed artificial intelligence. MAS negotiation is one of the key contents in MAS research. Combining with traditional contract net protocol, acquaintance model, acquaintance coalition inner task allocation distributed optimization algorithm, bulletin board model of agent information exchange with different knowledge and agent mental state parameters, this paper presents a new multi-agent system negotiation model based on expanded contract net protocol and introduces the design and implementation of it in detail.
The present work shows the results about the treatment of acquisition data on the surface of dental enamel using the reflectometry method, to which a noise reduction algorithm was applied since the data is acquired by...
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The present work shows the results about the treatment of acquisition data on the surface of dental enamel using the reflectometry method, to which a noise reduction algorithm was applied since the data is acquired by the optoelectronic device, therefore it can generate outlier data, besides an interpolation algorithm was applied so the curve is smoother and the form of the sample is observed. It also presents the development of a software to visualize a three dimensions reconstruction of dental pieces starting from the data acquisition that analyzes the dental enamel ruggedness and a possible diagnosis of the defects on the surface. The APDSED system (Acquisition and Processing of Defects of the Surface of Dental Enamel) is an experimental prototype for the detection of physical abnormalities of the dental enamel.
A novel genetic algorithm for the degree-constrained minimum spanning tree (d-MST) is proposed in this paper. In the proposed algorithm, a candidate solution is directly represented by set of the edges contained in th...
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ISBN:
(纸本)0780390059
A novel genetic algorithm for the degree-constrained minimum spanning tree (d-MST) is proposed in this paper. In the proposed algorithm, a candidate solution is directly represented by set of the edges contained in the corresponding spanning tree, and a specific initialization scheme, a heuristic crossover and a mutation operator are designed, respectively. Moreover, both the crossover operator and the mutation operator are very easy to execute, and the offspring generated by them always represent feasible spanning trees. Furthermore, the global convergence of the proposed algorithm to globally optimal solution with probability one is proved. At last, the simulated results show the effectiveness of the proposed algorithm.
This paper explores the use of online self assessment as a tool for guiding the assignment marking process. The study was motivated by a desire to assist staff in more efficiently completing assessment and to involve ...
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