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.

Analysis and Development of Urdu POS Tagged Corpus

Abstract: In this paper, two corpora of Urdu (with 110K and 120K words) tagged with different POS tagsets are used to train TnT and Tree taggers. Error analysis of both taggers is done to identify frequent confusions in tagging. Based on the analysis of tagging, and syntactic structure of Urdu, a more refined tagset is derived. The existing tagged corpora are tagged with the new tagset to develop a single corpus of 230K words and the TnT tagger is retrained. The results show improvement in tagging accuracy for individual corpora to 94.2% and also for the merged corpus to 91%. Implications of these results are discussed.

Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images

Abstract: In this paper, we have proposed a method for segmentation of lungs from Computed Tomography (CT)-scanned images using spatial Fuzzy C-Mean and morphological techniques known as Fuzzy Entropy and Morphology based Segmentation. To determine dynamic and adaptive optimal threshold, we have incorporated Fuzzy Entropy. We have proposed a novel histogram-based background removal operator. The proposed system is capable to perform fully automatic segmentation of CT Scan Lung images, based solely on information contained by the image itself. We have used different cluster validity functions to find out optimal number of clusters. The proposed system can be used as a basic building block for Computer-Aided Diagnosis. The technique was tested against the 25 datasets of different patients received from Aga Khan Medical University, Pakistan. The results confirm the validity of technique as well as enhanced performance.

Efficacy of Live DDoS Detection with Hadoop

Abstract: Distributed Denial of Service (DDoS) flooding attacks are one of the biggest challenges to the availability of online services today. These DDoS attacks overwhelm the victim with huge volume of traffic and render it incapable of performing normal communication or crashes it completely. If there are delays in detecting the flooding attacks, nothing much can be done except to manually disconnect the victim and fix the problem. In this paper, we propose HADEC, a Hadoop based Live DDoS Detection framework to tackle efficient analysis of flooding attacks by harnessing MapReduce and HDFS. We implemented a counter-based DDoS detection algorithm for four major flooding attacks (TCP-SYN, HTTP GET, UDP and ICMP) in MapReduce. Subsequently we deployed a testbed to evaluate the performance of HADEC framework for live DDoS detection. Based on the experiment we showed that HADEC is capable of processing and detecting DDoS attacks in affordable time.

Determinants of Dividend Policy: A Sectoral Analysis from Pakistan

Abstract: The present study is an endeavour to evaluate the determinants of dividend policy. The study has been conducted on non-financial sector of Pakistan. There are five important determinants identified in this study i.e. profitability, size, tax, investment opportunities and life cycle stage of firm. A sample of 174 non-financial firms listed on Karachi Stock Exchange for the period of 2005 to 2010 has been chosen for this study. The researcher has analyzed the relationships of variables firstly for overall non-financial sector and further for sub sectors of non-financial sector. Over all sector analysis has been performed through panel data analysis. Further to check sectoral differences, sector wise regression analysis has been performed. The results have identified profitability, tax, size and investment opportunities as most influential determinants of dividend policy. However, there is a lack of stable dividend policy in the market.