Narrow Results

This paper investigates the influence of the interconnection network topology of a parallel system on the delivery time of an ensemble of messages, called the communication scheme. More specifically, we focus on the impact on the performance of structure in network topology and communication scheme. We introduce causal structure learning algorithms for the modeling of the communication time. The experimental data, from which the models are learned automatically, is retrieved from simulations. The qualitative models provide insight about which and how variables influence the communication performance. Next, a generic property is defined which characterizes the performance of individual communication schemes and network topologies. The property allows the accurate quantitative prediction of the runtime of random communication on random topologies. However, when either communication scheme or network topology exhibit regularities the prediction can become very inaccurate. The causal models can also differ qualitatively and quantitatively. Each combination of communication scheme regularity type, e.g. a one-to-all broadcast, and network topology regularity type, e.g. torus, possibly results in a different model which is based on different characteristics.

Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. It has become a challenge for researchers to turn these documents into a more useful information utility. In this paper, we introduce a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to their similar structural and semantic representations. We develop a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two individual documents. The experimental analysis shows the method to be fast and accurate.

The paper describes a technique called ISE for image segmentation using entropy. The relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of gray levels.

In order to implement the model, we have introduced a well-known technique of Problem Solving. In our model, the most important roles are played by the Evaluation Function (EF) and the Control Strategy. The EF is related to the ratio between the entropy of one region or zone of the picture and the entropy of the entire picture, while the Control Strategy determines the optimal path in the search tree (quadtree) so that the nodes in the optimal path have minimal entropy.

The paper shows some comparisons between ISE and classical edge detection techniques.

Rule-based systems are typically tested using a set of inputs which will produce known outputs. However, one does not know how thoroughly the software has been exercised. Traditional test-coverage metrics do not account for the dynamic data-driven flow of control in rule-based systems. Our literature review found that there has been little prior work on coverage metrics for rule-based systems. This paper proposes test-coverage metrics for rule-based systems derived from metrics defined by prior work, and presents an industrial scale case study.

We conducted a case study to evaluate the practicality and usefulness of the proposed metrics. The case study applied the metrics to a system for computational fluid-dynamics models based on a rule-based application framework. These models were tested using a regression-test suite. The data-flow structure built by the application framework, along with the regression-test suite, provided case-study data. The test suite was evaluated against three kinds of coverage. The measurements indicated that complete coverage was not achieved, even at the lowest level definition. Lists of rules not covered provided insight into how to improve the test suite. The case study illustrated that structural coverage measures can be utilized to measure the completeness of rule-based system testing.

Tuomela's philosophical account of joint intentions is formalized in a special setting in which fully specified plans are available for the execution of the intended joint action. Using additional modal logical assumptions the definition is simplified and used to investigate how the presence of a joint intention can be efficiently checked.

Structural and parametric identification of nonlinear continuous dynamic systems with a closed cycle on a set of continuous block-oriented models with feedback is considered. The method of structural identification in the steady state based on the observation of the system's input and output variables at the input periodic influences is proposed. The solution of the parameter identification problems, which can be immediately connected with the structural identification problem, is carried out in the steady and transient states by the method of least squares. The structural and parametric identification algorithms are investigated by means of both theoretical analysis and computer modeling.

A knowledge graph is a visual method that can display the information contained in the knowledge points, core structure, and comprehensive knowledge structure technology. In recent years, with the innovation of science and technology, the business field became keen on knowledge graphs and the graphical display method. However, the application of knowledge graphs in the business field is mainly limited to search engines, question, and answer systems because of the technical difficulties of knowledge extraction and knowledge graph drawing of unstructured text, especially the knowledge extraction of amorphous culture. It can provide knowledgeable service to users by analyzing the knowledge entity contained in encyclopedia knowledge or knowledge base. This paper will focus on the critical link of knowledge extraction of the knowledge graph, adopt a depth learning algorithm to solve this urgent problem and combine with the application of knowledge graph in substation fault to analyze the construction process of substation fault knowledge map based on AI.