National University of Computer and Emerging Sciences

About: National University of Computer and Emerging Sciences is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Computer science & The Internet. The organization has 1506 authors who have published 2438 publications receiving 26786 citations.

A comparison of two suffix tree-based document clustering algorithms

Abstract: Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, focuses in this domain shifted from traditional vector based document similarity for clustering to suffix tree based document similarity, as it offers more semantic representation of the text present in the document. In this paper, we compare and contrast two recently introduced approaches to document clustering based on suffix tree data model. The first is an Efficient Phrase based document clustering, which extracts phrases from documents to form compact document representation and uses a similarity measure based on common suffix tree to cluster the documents. The second approach is a frequent word/word meaning sequence based document clustering, it similarly extracts the common word sequence from the document and uses the common sequence/ common word meaning sequence to perform the compact representation, and finally, it uses document clustering approach to cluster the compact documents. These algorithms are using agglomerative hierarchical document clustering to perform the actual clustering step, the difference in these approaches are mainly based on extraction of phrases, model representation as a compact document, and the similarity measures used for clustering. This paper investigates the computational aspect of the two algorithms, and the quality of results they produced.

Classification of Cosmic Scale Factor via Noether Gauge Symmetries

Abstract: In this paper, a complete classification of Friedmann-Robertson-Walker (FRW) spacetime by using approximate Noether approach is presented. Considered spacetime is discussed for three different types of universe i.e. flat, open and closed. Different forms of cosmic scale factor a with respect to the nature of the universe, which posses the nontrivial Noether gauge symmetries (NGS) are reported. The perturbed Lagrangian corresponding to FRW metric in the Noether equation is used to get Noether operators. For different types of universe minimal and maximal set of Noether operators are reported. A list of Noether operators is also computed which is not only independent from the choice of the cosmic scale factor but also from the type of universe. Further, corresponding energy type first integral of motions are also calculated.

A location-sensitive and network-aware broker for recommending Web services

Abstract: Collaborative filtering (CF) is one of the renowned recommendation techniques that can be used for predicting unavailable Quality-of-Service (QoS) values of Web services. Although several CF-based approaches have been proposed in recent years, the accuracy of the QoS values, that these approaches provide, raises some concerns and hence, could undermine the real “quality” of Web services. To address these concerns, context information such as communication-network configuration and user location could be integrated into the process of developing recommendations. Building upon such context information, this paper proposes a CF-based Web services recommendation approach, which incorporates the effect of locations of users, communication-network configurations of users, and Web services run-time environments on the recommendations. To evaluate the accuracy of the recommended Web services based on the defined QoS values, a set of comprehensive experiments are conducted using a real dataset of Web services. The experiments are in line with the importance of integrating context into recommendations.

AspectOCL: using aspects to ease maintenance of evolving constraint specification

Abstract: Constraints play an important role in Model-Driven Software Engineering. Industrial systems commonly exhibit cross-cutting behaviors in design artifacts. Aspect-orientation is a well-established approach to deal with cross-cutting behaviors and has been successfully used for programming and design languages. In model-driven software engineering, the presence of cross-cutting constraints makes it difficult to maintain constraints defined on the models of large-scale industrial systems. In this work, we improve our previous work on AspectOCL, which is an extension of OCL that allows modeling of cross-cutting constraints. We provide the abstract and concrete syntax of the language. We add support for new constructs such as composite aspects and invariant specification on a package. We also provide tool support for writing cross-cutting constraints using AspectOCL. To evaluate AspectOCL, we apply it on benchmark case studies from the OCL repository. The results show that by separating the cross-cutting constraints, the number of constructs in the constraint specifications can be reduced to a large amount. AspectOCL reduces the maintenance effort by up to 55% in one case study. To explore the impact on maintenance time and accuracy, we also perform a controlled experiment with 90 student subjects. The results show that AspectOCL has a small magnitude of improvement in terms of maintenance time when compared to OCL, whereas modifications to OCL specification are more accurate. The post-experiment survey indicates that the majority of subjects favored AspectOCL, but faced challenges in applying aspect-orientation to constraint specification due to a lack of prior exposure.

Energy Efficient Clustering Based Routing Technique in WSN using Brain Storm Optimization

Abstract: In Wireless Sensor Networks (WSNs), clustering has continued to be one of the most informed concerns when it comes to the lifetime of the network. However, we cannot study routing without considering effective clustering measures to optimize such problem in WSNs. The form of energy model used is identified to be the main supplier of consumed energy in WSN. The Brain Storm Optimization (BSO) is a swarm intellectual formulation that can achieve results to a problem more suitably. In this paper, the proposed approach is called Energy Efficient Clustering-Brain Storm Optimization (EEC-BSO). Furthermore, improved BSO technique is applied to obtain the best cluster centers (heads) that can amplify energy efficiency, coverage percentage and packet data rate. In addition, we explored an energy consumption model based on cluster transmission with an exact WSN. Simulation results demonstrate that the proposed protocol overcomes the others successfully.