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.

Exact solution of an electroosmotic flow for generalized Burgers fluid in cylindrical domain

Abstract: The present paper reports a theoretical study of the dynamics of an electroosmotic flow (EOF) in cylindrical domain. The Cauchy momentum equation is first simplified by incorporating the electrostatic body force in the electric double layer and the generalized Burgers fluid constitutive model. The electric potential distribution is given by the linearized Poisson–Boltzmann equation. After solving the linearized Poisson–Boltzmann equation, the Cauchy momentum equation with electrostatic body force is solved analytically by using the temporal Fourier and finite Hankel transforms. The effects of important involved parameters are examined and presented graphically. The results obtained reveal that the magnitude of velocity increases with increase of the Debye–Huckel and electrokinetic parameters. Further, it is shown that the results presented for generalized Burgers fluid are quite general so that results for the Burgers, Oldroyd-B, Maxwell and Newtonian fluids can be obtained as limiting cases.

Numerical investigation of different flow regimes for multiple staggered rows

Abstract: In this numerical exploration, the combined effect of Reynolds number (30 ≤ Re ≤ 150) and gap spacing (1 ≤ g ≤ 7) is studied for two dimensional cross flow across multiple staggered rows of square cylinders. Flow is simulated by using lattice Boltzmann method. Outcomes show that for the outset of vortex shedding phenomenon, the critical Re increases as the normalized g increases. At large Re and at g = 7, 6 and 5, the primary vortex shedding frequency controls the flow whereas the secondary frequency almost vanishes. The jets in the gap region have strong influence upon the wake interaction. The nature of the wakes is changed by changing the g and Re which is visualized by the change of wake size behind the cylinders. These g depending on the Re are used to split the flow regimes into chaotic, quasiperiodic-I and quasiperiodic-II flow regimes. Some physical parameters of practical importance are also analysed.

Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI)

Abstract: Clustering validity index (CVI) plays an important role in data partitioning and image segmentation. In this paper, a new CVI is proposed to perform the color image segmentation. The proposed CVI combines compactness, separation and overlap to assess the clustering quality effectively. The aggregation operators (t-norms and t-conorms) are used to build a new reliable and robust overlap measure. Moreover, a genetic algorithm is employed to dynamically optimize the clusters centroids and get the best possible data partition. The clustering of super-pixels is performed to reduce the computational cost and convergence time. The genetic algorithm with new clustering validity index is able to find the best data partitioning. The performance of the proposed algorithm is evaluated on the Berkeley image segmentation database. The extensive experimentation shows that the proposed algorithm performs better compared to other state-of-the-art methods.

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Abstract: With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.