Abstract Within the context of ubiquitous computing environments, we are carrying out research into the discovery of distributed, self describing software entities in home area networks. Motivated by the identification of a scalability limit to the already impressive performance of our “Nexus” middleware framework, we herein provide experimental data to validate our solution and demonstrate an improvement in scalability over other... Within the context of ubiquitous computing environments, we are carrying out research into the discovery of distributed, self describing software entities in home area networks. Motivated by the identification of a scalability limit to the already impressive performance of our “Nexus” middleware framework, we herein provide experimental data to validate our solution and demonstrate an improvement in scalability over other tested solutions by at least three orders of magnitude. Read More Read Less

Abstract In a distributed environment, making a system dynamically adaptable to the fine-grained changes in the functionalities of the system is a big challenge in the software industries. Currently, the solution for adaptation is achieved using reflective middleware. Since reflective middleware is not an efficient solution for dynamic adaptation, in this paper a solution is proposed by combining aspect oriented approach... In a distributed environment, making a system dynamically adaptable to the fine-grained changes in the functionalities of the system is a big challenge in the software industries. Currently, the solution for adaptation is achieved using reflective middleware. Since reflective middleware is not an efficient solution for dynamic adaptation, in this paper a solution is proposed by combining aspect oriented approach with reflective middleware. In this proposed aspect oriented reflective middleware model, fine grained changes in the functionalities are captured as ‘dynamic aspects’ that are dynamically weaved with application components on the basis of point-cut specification. This model is implemented using Reflection pattern, Aspect oriented approach and Remote Method Invocation communication model. This model is analyzed using the factors such as programmatic complexity and execution time. The novel approach used in this model increases the possibilities of incorporating the changes without affecting the core functionalities of the system and reduces the performance overhead. Read More Read Less

Abstract XML information retrieval aims to retrieve relevant document fragments (XML elements) instead of retrieving whole documents. The retrieved XML elements must not only contain relevant information. They must be of good level of granularity. They must be the most specific and the most exhaustive at once. The coexistence of content and structural information in XML documents makes that multiple heterogynous... XML information retrieval aims to retrieve relevant document fragments (XML elements) instead of retrieving whole documents. The retrieved XML elements must not only contain relevant information. They must be of good level of granularity. They must be the most specific and the most exhaustive at once. The coexistence of content and structural information in XML documents makes that multiple heterogynous relevance factors (sources) can be used in retrieval process. As in traditional information retrieval, the most majority of XML information retrieval approaches try to model the notion of relevance and use score function to attribute a relevance score to each XML element. The number of relevance factors used simultaneously is then limited due to their heterogeneity. Our goal is to develop an XML information retrieval approach in which several relevance factors can be used in retrieval process. We propose an approach based on relational aggregation paradigm. The relevance process is not guided by the estimation of relevance degree, but by the comparisons of each pair of XML elements in order to determine a final order of all XML elements, from the most relevant to the less relevant. Read More Read Less

Abstract There is still an urgent need of finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimize such a relationship via developing a mathematical relationship between model variables is urgently needed. In this paper, we explore the use of Genetic Programming (GP) to develop a... There is still an urgent need of finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimize such a relationship via developing a mathematical relationship between model variables is urgently needed. In this paper, we explore the use of Genetic Programming (GP) to develop a software cost estimation model utilizing the effect of both the developed line of code and the used methodology during the development. An application of estimating the effort for some NASA software projects is introduced. The performance of the developed GP based model was tested and compared to known models in the literature. The developed GP model was able to provide valuable estimation capabilities compared to other models. Read More Read Less

Abstract As tobacco redrying process has characteristics of multi-interference, strong coupling, great hysteresis, nonlinear and uncertainty, it is very difficult to establish the physical model. This paper presented an innovative method with the variable-learning-rate-based back propagation neural network (BPNN) for establishing the quality prediction model of tobacco redrying process. First, characteristics of the process and correlation of the process variables are... As tobacco redrying process has characteristics of multi-interference, strong coupling, great hysteresis, nonlinear and uncertainty, it is very difficult to establish the physical model. This paper presented an innovative method with the variable-learning-rate-based back propagation neural network (BPNN) for establishing the quality prediction model of tobacco redrying process. First, characteristics of the process and correlation of the process variables are analyzed, and determine eight input parameters and two output quality indicators of model. Then, a quality prediction model of tobacco redrying process is established by using the BPNN structure. In the process of network training, BP algorithm is improved by using the method of variable learning rate, and satisfactory prediction results are obtained. Finally, in order to verify the effectiveness of this method, the improved BPNN model is applied for simulation experiment, and is compared with ordinary BPNN. The prediction results show that the improved model possesses strong self-learning function and higher prediction accuracy. Read More Read Less