Muhammad Abdullah

Jucai Zhai, Muhammad Abdullah, Boyang Chen et al.

In scientific computing, the formulation of numerical discretisations of partial differential equations (PDEs) as untrained convolutional layers within Convolutional Neural Networks (CNNs), referred to by some as Neural Physics, has demonstrated good efficiency for executing physics-based solvers on GPUs. However, classical grid-based methods still face computational bottlenecks when solving problems involving billions of degrees of freedom. To address this challenge, this paper proposes a novel framework called 'Quantum Neural Physics' and develops a Hybrid Quantum-Classical CNN Multigrid Solver (HQC-CNNMG). This approach maps analytically-determined stencils of discretised differential operators into parameter-free or untrained quantum convolutional kernels. By leveraging amplitude encoding, the Linear Combination of Unitaries technique and the Quantum Fourier Transform, the resulting quantum convolutional operators can be implemented using quantum circuits with a circuit depth that scales as O(log K), where K denotes the size of the encoded input block. These quantum operators are embedded into a classical W-Cycle multigrid using a U-Net. This design enables seamless integration of quantum operators within a hierarchical solver whilst retaining the robustness and convergence properties of classical multigrid methods. The proposed Quantum Neural Physics solver is validated on a quantum simulator for the Poisson equation, diffusion equation, convection-diffusion equation and incompressible Navier-Stokes equations. The solutions of the HQC-CNNMG are in close agreement with those from traditional solution methods. This work establishes a mapping from discretised physical equations to logarithmic-scale quantum circuits, providing a new and exploratory path to exponential memory compression and computational acceleration for PDE solvers on future fault-tolerant quantum computers.

Muhammad Abdullah, Anne Querfurth, Deepak Bhatia et al.

This paper investigates the use of deep learning approaches to estimate the femur caput-collum-diaphyseal (CCD) angle from X-ray images. The CCD angle is an important measurement in the diagnosis of hip problems, and correct prediction can help in the planning of surgical procedures. Manual measurement of this angle, on the other hand, can be time-intensive and vulnerable to inter-observer variability. In this paper, we present a deep-learning algorithm that can reliably estimate the femur CCD angle from X-ray images. To train and test the performance of our model, we employed an X-ray image dataset with associated femur CCD angle measurements. Furthermore, we built a prototype to display the resulting predictions and to allow the user to interact with the predictions. As this is happening in a sterile setting during surgery, we expanded our interface to the possibility of being used only by voice commands. Our results show that our deep learning model predicts the femur CCD angle on X-ray images with great accuracy, with a mean absolute error of 4.3 degrees on the left femur and 4.9 degrees on the right femur on the test dataset. Our results suggest that deep learning has the potential to give a more efficient and accurate technique for predicting the femur CCD angle, which might have substantial therapeutic implications for the diagnosis and management of hip problems.

Usama Khalid, Muhammad Abdullah, Kashif Inayat

The development and analysis of mobile applications in term of security have become an active research area from many years as many apps are vulnerable to different attacks. Especially the concept of hybrid applications has emerged in the last three years where applications are developed in both native and web languages because the use of web languages raises certain security risks in hybrid mobile applications as it creates possible channels where malicious code can be injected inside the application. WebView is an important component in hybrid mobile applications which used to implements a sandbox mechanism to protect the local resources of smartphone devices from un-authorized access of JavaScript. However, the WebView application program interfaces (APIs) also have security issues. For example, an attacker can attack the hybrid application via JavaScript code by bypassing the sandbox security through accessing the public methods of the applications. Cross-site scripting (XSS) is one of the most popular malicious code injection technique for accessing the public methods of the application through JavaScript. This research proposes a framework for detection and prevention of XSS attacks in hybrid applications using state-of-the-art machine learning (ML) algorithms. The detection of the attacks have been perform by exploiting the registered Java object features. The dataset and the sample hybrid applications have been developed using the android studio. Then the widely used toolkit, RapidMiner, has been used for empirical analysis. The results reveal that the ensemble based Random Forest algorithm outperforms other algorithms and achieves both the accuracy and F-measures as high as of 99%.

Shahbano, Muhammad Abdullah, Kashif Inayat

In recent decades, the automatic video surveillance system has gained significant importance in computer vision community. The crucial objective of surveillance is monitoring and security in public places. In the traditional Local Binary Pattern, the feature description is somehow inaccurate, and the feature size is large enough. Therefore, to overcome these shortcomings, our research proposed a detection algorithm for a human with or without carrying baggage. The Local tri-directional pattern descriptor is exhibited to extract features of different human body parts including head, trunk, and limbs. Then with the help of support vector machine, extracted features are trained and evaluated. Experimental results on INRIA and MSMT17 V1 datasets show that LtriDP outperforms several state-of-the-art feature descriptors and validate its effectiveness.