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.

Inside-Out: Perception of Key Finance Professionals about Theory and Practice of Islamic Banking

Abstract: Islamic banking has shown tremendous growth in the first decade of 21st century. By the end of 2008, global volume of assets under Islamic banking reached US$951 billion. In Pakistan, Islamic banking has displayed a tremendous annual average growth of 76% during the last seven years and accounts for 6% of the market share (SBP-2010). However, keeping in view the religious ideologies of the significant majority of Pakistanis, Islamic banking industry has expanded at less than its expected potential. This study documents the perception of key players in finance industry about Islamic banking, to highlight the underlying issues directly responsible for slower-than-potential-expansion of this industry. Findings suggest, although theoretically, that the industry perceives Islamic banking correctly, however professionals do not feel content with its practice.

Ensemble Learning Using Bagging And Inception-V3 For Anomaly Detection In Surveillance Videos

Abstract: The prevalent use of surveillance cameras in public places and advancements in computer vision warrants most sought-after research in the domain of anomalous activity detection. Several approaches have been proposed for the detection of an anomaly in videos. Spatio-temporal features using 3D Convolutional Network (C3D) is a state-of-the-art approach for this problem where deep multiple instance ranking framework is being investigated. However, this approach requires the segmentation of videos before feature extraction that can produce unstable segmentation results and can have a large memory footprint. In this paper, we extract video features using the Inception-v3 deep learning network which eliminates segmentation. Moreover, to improve the robustness of the backbone classifier we propose to use a homogeneous bagging ensemble of the 3-Layer Fully Connected (FC) Network. Experiments are conducted on the UCF-Anomaly detection dataset and exhibit improved performance over existing approaches.

AspectOCL: Extending OCL for Crosscutting Constraints

Abstract: Constraints play an important role in Model Driven Software Engineering Industrial systems commonly exhibit crosscutting behaviors in design artifacts While modeling of crosscutting behaviors has been addressed in literature, the modeling of crosscutting constraints remains a problem Presence of crosscutting constraints makes it difficult to maintain constraints defined on the models of large-scale industrial systems Multiple elements in a model may share common crosscutting constraints with minor variation Aspect orientation is well-established approach to model crosscutting behavior Current OCL specification does not support writing crosscutting constraints separately as aspects In this paper, we propose an extension of OCL language that brings benefits of aspect orientation to OCL constraints In our language, crosscutting constraints are specified as aspects, which can be woven in OCL constraints We demonstrate our proposed language through application on a published open source case study Results show that specifying crosscutting constraints as aspects can reduce the number of constraints to be specified