arXiv subjects

Muhammad Nadeem

Publications and source records attributed to Muhammad Nadeem.

At least 19 recordsLinked to original sources

arXiv2026

Software Change Request in AI-Orchestrated Software Evolution: Literature Review and Research Agenda

In the emerging paradigm of holistic AI-orchestrated software maintenance and evolution, large-scale app stores continuously generate exponentially vast Software Change Requests (SCRs) streams through user feedbacks. These streams enable intelligent software evolution. Automating SCR extraction and classification is crucial with rapid growth of Mobile Apps (MA). However, significant challenges exist such as scare large-scale datasets, inconsistent classification schemes, limited data sharing and insufficient research. This study presents a first and thorough systematic survey of SCR identification from MA Reviews (MAR). In particular, we investigated how Machine Learning (ML) and Deep Learning (DL) enhanced identification and classification of SCR. This study has found pertinent datasets varying sizes from 5,000 to more than 18 million reviews. The key findings highlight the dominant use of Random Forest and Support Vector Machines, limited adoption of deep learning techniques, inconsistent F1-score results ranging from 0.08 to 1 and partial automation in existing SCR tools such as SURF and ChangeAdvisor. Finally, we have performed a synthesis of the studies to presents a comparative taxonomy of SCR methodologies, identify research gaps and annotation frameworks. We contend that classification schemes, shared datasets and annotation frameworks are essential for advancing SCRs and thereby improving the maintenance of mobile applications.

cs.SE↗

arXiv2026

ROAM: Reinforcement Learning Optimized Adaptive Moving Target Defense for Smart Grid

Smart grids rely on distributed phasor measurement units (PMUs) and wide-area communication networks whose data-acquisition pipelines are vulnerable to stealthy false data injection (FDI) attacks that evade conventional bad-data detection (BDD). Moving target defense (MTD) counters this exposure by continuously remapping sensor identities, but existing schemes apply static or pre-scheduled randomization and are each constrained by a single failure mode: seed-based methods are fast yet fragile to key compromise, while machine learning (ML) based prediction methods are resilient yet incur prohibitive latency. Neither adapts its randomization to live threat evidence. This paper presents ROAM, a deep reinforcement learning (DRL) driven MTD framework in which a deep Q-network (DQN) agent continuously re-optimizes the sensor-to-ID mapping at the aggregation layer. At each decision step, the agent selects the deception coverage, flags the specific sensors to remap, and sets the split of the remapped sensors between seed-based and ML-prediction-based recovery, without modifying PMU hardware and while preserving IEEE C37.118 compatibility. On held-out BDD-compliant FDI scenarios across the IEEE 14-, 57-, and 118- bus systems, ROAM keeps the percentage of stealthy attacks below 3% while sustaining detection rates above 93%. Against an adaptive soft actor-critic (SAC) attacker that observes the defender's configuration, ROAM keeps stealthy attacks below 2.6% with detection above 92%. Hardware-in-the-loop testing on an OPAL-RT platform confirms real-time feasibility, with sub-18ms average shuffle execution and an average reshuffle blind window of 2.6% of the 3-s decision cycle.

eess.SY↗

arXiv2026

Attack-Resiliency Analytics for Wide-Area Control Systems in Smart Grids

Wide-area monitoring, protection, and control (WAMPAC) systems damp inter-area oscillations in large interconnected grids, but their reliance on synchronized PMU measurements carried over wide-area networks exposes them to false data injection (FDI) attacks. This paper presents an attack-resiliency analytics framework that formally models the coupled dynamics of the wide-area damping loop, automatic generation control, and the governor and excitation systems, and formulates the optimal stealthy FDI attack as a mixed-integer linear program (MILP). The anomaly detection model (ADM) enters as a replaceable constraint set: either a static bad-data detection (BDD) rule or a boundary learned from benign operation calibrated to a common false-positive rate. On the IEEE 39 and 118 bus systems, with reference dynamics validated on an OPAL-RT hardware-in-the-loop testbed, the optimal attack with wide-area access attains 3.3 and 8.1 times the benign objective and reaches a 0.5Hz frequency excursion more than twice as fast as an attack confined to automatic generation control. Learned boundaries reduce the attack objective by 15.5 55.0% and prevent over-frequency relay trips in all configurations, with feasible stealthy attacks remaining in every case, and residual risk tracks the width of the learned boundary rather than the detector family.

eess.SY↗

arXiv2026

Physics-Aware Machine Unlearning for Cyber-Physical Systems

This paper proposes a physics-guided gradient-ascent-based machine unlearning method that couples the forgetting signal with the physical residual of the target cyber-physical systems, ensuring that weight updates during unlearning are steered toward physically feasible regions of the weight space. The physics residual acts as a safety fence during gradient ascent: the model is steered away from the poisoned behavioral basin and simultaneously toward physics-compliant territory, rather than toward an arbitrary alternative that may still violate domain constraints. We evaluate the proposed method against four baselines: naive gradient ascent, exact unlearning, SISA, and full retraining on an IEEE 34-bus distribution system, driven by two physics-informed neural network-based distribution energy resource controllers and validated through high-fidelity OpenDSS power-flow co-simulation. From the evaluation, we found that our proposed physics-guided model simultaneously removes poison and restores physical compliance, which are essential for the safe deployment of safety-critical cyber-physical systems

cs.LG↗

arXiv2026

Quantum Landscape of Superconducting Diodes

This study maps the quantum landscape of superconducting diodes (SDs) \cite{nadeem23} onto the quantum technology architecture, which is currently constrained by fundamental challenges in control and scalability. In the existing non-integrated quantum technology hardware, control and scalability related issues emerge at two fronts: First, nonlinear and nonreciprocal circuit elements, which are essential building blocks for quantum processors, are often complex, bulky, and dissipative. Second, the temperature gradient between classical control electronics ($T_C\gtrsim$ K), which is also dissipative, and the quantum processor at cryogenic temperatures ($T_Q\sim$ mK) makes scalability even more challenging. The main focus is to reveal how the built-in nonlinearity, nonreciprocity, and quantum functionalities of SDs are significant for on-chip integrated circuit quantum electrodynamics (c-QED), enabling scalable integration of noise-resilient qubit and qubit-interfaces for efficient power delivery, coherent control and memory, high-fidelity readout, and quantum-limited amplification. To this end, this study will also shed light on how thermodynamic constraints and field effects can be harnessed within a quantum-enhanced SD platform, thereby enabling thermal compatibility between classical and quantum workflows, isothermal all-electrical control, and on-chip scalability. This perspective is expected to play a pivotal role in the advancement of superconducting circuit-based quantum hardware with temperature-matched classical, quantum, and hybrid workflows.

cond-mat.supr-con↗

arXiv2026

Toward Efficient Speech Emotion Recognition via Spectral Learning and Attention

Speech Emotion Recognition (SER) traditionally relies on auditory data analysis for emotion classification. Several studies have adopted different methods for SER. However, existing SER methods often struggle to capture subtle emotional variations and generalize across diverse datasets. In this article, we use Mel-Frequency Cepstral Coefficients (MFCCs) as spectral features to bridge the gap between computational emotion processing and human auditory perception. To further improve robustness and feature diversity, we propose a novel 1D-CNN-based SER framework that integrates data augmentation techniques. MFCC features extracted from the augmented data are processed using a 1D Convolutional Neural Network (CNN) architecture enhanced with channel and spatial attention mechanisms. These attention modules allow the model to highlight key emotional patterns, enhancing its ability to capture subtle variations in speech signals. The proposed method delivers cutting-edge performance, achieving the accuracy of 97.49% for SAVEE, 99.23% for RAVDESS, 89.31% for CREMA-D, 99.82% for TESS, 99.53% for EMO-DB, and 96.39% for EMOVO. Experimental results show new benchmarks in SER, demonstrating the effectiveness of our approach in recognizing emotional expressions with high precision. Our evaluation demonstrates that the integration of advanced Deep Learning (DL) methods substantially enhances generalization across diverse datasets, underscoring their potential to advance SER for real-world deployment in assistive technologies and human-computer interaction.

cs.SD↗

arXiv2025

Wide-Area Feedback Control for Renewables-Heavy Power Systems: A Comparative Study of Reinforcement Learning and Lyapunov-Based Design

As renewable energy sources become more prevalent, accurately modeling power grid dynamics is becoming increasingly more complex. Concurrently, data acquisition and realtime system state monitoring are becoming more available for control centers. This motivates shifting from \textit{model- and Lyapunov-based} feedback controller designs toward \textit{model-free} ones. Reinforcement learning (RL) has emerged as a key tool for designing model-free controllers. Various studies have been carried out to study voltage/frequency control strategies via RL. However, usually a simplified system model is used neglecting detailed dynamics of solar, wind, and composite loads -- and damping system-wide oscillations and modeling power flows are all usually ignored. To that end, we pose an optimal feedback control problem for a detailed renewables-heavy power system, defined by a set of nonlinear differential algebraic equations (NDAE). The control problem is solved using a completely model-free design via RL as well as using a model-based approach built upon the Lyapunov stability theory with guarantees. The paper in its essence seeks to explore whether data-driven feedback control should be used in power grids over its model-driven counterpart. Theoretical developments and thorough case studies are presented with an eye on this exploration. Finally, a detailed analysis is provided to delineate the strengths and weaknesses of both approaches for renewables-heavy grids.

eess.SY↗

arXiv2025

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs

The rapid expansion of Internet of Things (IoT) networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging the Mel-frequency cepstral coefficients (MFCC) and ResNet-18, a deep learning model known for its effectiveness in feature extraction and image-based tasks. Learnable MFCCs enable adaptive spectral feature representation, capturing the temporal patterns inherent in network traffic more effectively than traditional fixed MFCCs. We demonstrate that transforming raw signals into MFCCs maps the data into a higher-dimensional space, enhancing class separability and enabling more effective multiclass classification. Our approach combines the strengths of MFCCs with the robust feature extraction capabilities of ResNet-18, offering a powerful framework for anomaly detection. The proposed model is evaluated on three widely used IoT intrusion detection datasets: CICIoT2023, NSL-KDD, and IoTID20. The experimental results highlight the potential of integrating adaptive signal processing techniques with deep learning architectures to achieve robust and scalable anomaly detection in heterogeneous IoT network landscapes.

cs.CR↗

arXiv2025

Tailoring Robust Quantum Anomalous Hall Effect via Entropy-Engineering

The development of quantum materials and the tailoring of their functional properties is of fundamental interest in materials science. Here, a new design concept is proposed for the robust quantum anomalous Hall effect via entropy engineering in 2D magnets. As a prototypical example, the configurational entropy of monolayer transition metal trihalide VCl$_3$ is manipulated by incorporating four different transition-metal cations [Ti,Cr,Fe,Co] into the honeycomb structure made of vanadium, such that all in-plane mirror symmetries, inversion and/or roto-inversion are broken. Monolayer VCl$_3$ is a ferromagnetic Dirac half-metal in which spin-polarized Dirac dispersion at valley momenta is accompanied by bulk states at the $Γ$-point and thus the spin-orbit interaction-driven quantum anomalous Hall phase does not exhibit fully gapped bulk band dispersion. Entropy-driven bandstructure renormalization, especially band flattening in combination with red- and blue-shifts at different momenta of the Brillouin zone and crystal-field effects, transforms Dirac half-metal to a Dirac spin-gapless semiconductor and leads to a robust quantum anomalous Hall phase with fully gapped bulk band dispersion and, thus, a purely topological edge state transport without mixing with dissipative bulk channels. These findings provide a paradigm for designing entropy-engineered 2D materials for the realization of robust quantum anomalous Hall effect and quantum device applications.

cond-mat.mes-hall↗

arXiv2025

Improving Power Systems Controllability via Edge Centrality Measures

Improving the controllability of power networks is crucial as they are highly complex networks operating in synchrony; even minor perturbations can cause desynchronization and instability. To that end, one needs to assess the criticality of key network components (buses and lines) in terms of their impact on system performance. Traditional methods to identify the key nodes/edges in power networks often rely on static centrality measures based on the network's topological structure ignoring the network's dynamic behavior. In this paper, using multi-machine power network models and a new control-theoretic edge centrality matrix (ECM) approach, we: (i) quantify the influence of edges (i.e., the line susceptances) in terms of controllability performance metrics, (ii) identify the most influential lines, and (iii) compute near-optimal edge modifications that improve the power network controllability. Employing various IEEE power network benchmarks, we validate the effectiveness of the ECM-based algorithm and demonstrate improvements in system reachability, control, and damping performance.

eess.SY↗

arXiv2024

Leveraging Deep Learning with Multi-Head Attention for Accurate Extraction of Medicine from Handwritten Prescriptions

Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) with Multi-Head Attention and Positional Embeddings. A novel dataset, featuring diverse handwritten prescriptions from various regions of Pakistan, was utilized to fine-tune the model on different handwriting styles. The Mask R-CNN model segments the prescription images to focus on the medicinal sections, while the TrOCR model, enhanced by Multi-Head Attention and Positional Embeddings, transcribes the isolated text. The transcribed text is then matched against a pre-existing database for accurate identification. The proposed approach achieved a character error rate (CER) of 1.4% on standard benchmarks, highlighting its potential as a reliable and efficient tool for automating medicine name extraction.

cs.CV↗

arXiv2024

Structure-Preserving Model Order Reduction for Nonlinear DAE Models of Power Networks

This paper deals with the joint reduction of the number of dynamic and algebraic states of a nonlinear differential-algebraic equation (NDAE) model of a power network. The dynamic states depict the internal states of generators, loads, renewables, whereas the algebraic ones define network states such as voltages and phase angles. In the current literature of power system model order reduction (MOR), the algebraic constraints are usually neglected and the power network is commonly modeled via a set of ordinary differential equations (ODEs) instead of NDAEs. Thus, reduction is usually carried out for the dynamic states only and the algebraic variables are kept intact. This leaves a significant part of the system's size and complexity unreduced. This paper addresses this aforementioned limitation by jointly reducing both dynamic and algebraic variables. As compared to the literature the proposed MOR techniques are endowed with the following features: (i) no system linearization is required, (ii) require no transformation to an equivalent or approximate ODE representation, (iii) guarantee that the reduced order model to be NDAE-structured and thus preserves the differential-algebraic structure of original power system model, and (iv) can seamlessly reduce both dynamic and algebraic variables while maintaining high accuracy. Case studies performed on a 2000-bus power system reveal that the proposed MOR techniques are able to reduce system order while maintaining accuracy.

eess.SY↗

arXiv2024

2024 roadmap on 2D topological insulators

2D topological insulators promise novel approaches towards electronic, spintronic, and quantum device applications. This is owing to unique features of their electronic band structure, in which bulk-boundary correspondences enforces the existence of 1D spin-momentum locked metallic edge states - both helical and chiral - surrounding an electrically insulating bulk. Forty years since the first discoveries of topological phases in condensed matter, the abstract concept of band topology has sprung into realization with several materials now available in which sizable bulk energy gaps - up to a few hundred meV - promise to enable topology for applications even at room-temperature. Further, the possibility of combining 2D TIs in heterostructures with functional materials such as multiferroics, ferromagnets, and superconductors, vastly extends the range of applicability beyond their intrinsic properties. While 2D TIs remain a unique testbed for questions of fundamental condensed matter physics, proposals seek to control the topologically protected bulk or boundary states electrically, or even induce topological phase transitions to engender switching functionality. Induction of superconducting pairing in 2D TIs strives to realize non-Abelian quasiparticles, promising avenues towards fault-tolerant topological quantum computing. This roadmap aims to present a status update of the field, reviewing recent advances and remaining challenges in theoretical understanding, materials synthesis, physical characterization and, ultimately, device perspectives.

cond-mat.mes-hall↗

arXiv2024

Sorta Solving the OPF by Not Solving the OPF: DAE Control Theory and the Price of Realtime Regulation

This paper presents a new approach to approximate the AC optimal power flow (ACOPF). By eliminating the need to solve the ACOPF every few minutes, the paper showcases how a realtime feedback controller can be utilized in lieu of ACOPF and its variants. By (i) forming the grid dynamics as a system of differential-algebraic equations (DAE) that naturally encode the non-convex OPF power flow constraints, (ii) utilizing DAELyapunov theory, and (iii) designing a feedback controller that captures realtime uncertainty while being uncertainty-unaware, the presented approach demonstrates promises of obtaining solutions that are close to the OPF ones without needing to solve the OPF. The proposed controller responds in realtime to deviations in renewables generation and loads, guaranteeing improvements in system transient stability, while always yielding approximate solutions of the ACOPF with no constraint violations. As the studied approach herein yields slightly more expensive realtime generator controls, the corresponding price of realtime control and regulation is examined. Cost comparisons with the traditional ACOPF are also showcased -- all via case studies on standard power networks.

eess.SY↗

arXiv2024

Possible Excitonic Insulating Phase in Quantum-Confined Sb Nanoflakes

In the 1960s, it was proposed that in small indirect band-gap materials, excitons can spontaneously form because the density of carriers is too low to screen the attractive Coulomb interaction between electrons and holes. The result is a novel strongly interacting insulating phase known as an excitonic insulator. Here we employ scanning tunnelling microscopy (STM) and spectroscopy (STS) to show that the enhanced Coulomb interaction in quantum-confined elemental Sb nanoflakes drives the system to the excitonic insulator state. The unique feature of the excitonic insulator, a charge density wave (CDW) without periodic lattice distortion, is directly observed. Furthermore, STS shows a gap induced by the CDW near the Fermi surface. Our observations suggest that the Sb(110) nanoflake is an excitonic insulator.

cond-mat.mtrl-sci↗

arXiv2023

Learning for System Identification of NDAE-modeled Power Systems

System identification through learning approaches is emerging as a promising strategy for understanding and simulating dynamical systems, which nevertheless faces considerable difficulty when confronted with power systems modeled by differential-algebraic equations (DAEs). This paper introduces a neural network (NN) framework for effectively learning and simulating solution trajectories of DAEs. The proposed framework leverages the synergy between Implicit Runge-Kutta (IRK) time-stepping schemes tailored for DAEs and NNs (including a differential NN (DNN)). The framework enforces an NN to cooperate with the algebraic equation of DAEs as hard constraints and is suitable for the identification of the ordinary differential equation (ODE)-modeled dynamic equation of DAEs using an existing penalty-based algorithm. Finally, the paper demonstrates the efficacy and precision of the proposed NN through the identification and simulation of solution trajectories for the considered DAE-modeled power system.

eess.SY↗

arXiv2023

On Scaling Robust Feedback Control and State Estimation Problems in Power Networks

Many mainstream robust control/estimation algorithms for power networks are designed using the Lyapunov theory as it provides performance guarantees for linear/nonlinear models of uncertain power networks but comes at the expense of scalability and sensitivity. In particular, Lyapunov-based approaches rely on forming semi-definite programs (SDPs) that are (i) not scalable and (ii) extremely sensitive to the choice of the bounding scalar that ensures the strict feasibility of the linear matrix inequalities (LMIs). This paper addresses these two issues by employing a celebrated non-Lyapunov approach (NLA) from the control theory literature. In lieu of linearized models of power grids, we focus on (the more representative) nonlinear differential algebraic equation (DAE) models and showcase the simplicity, scalability, and parameter-resiliency of NLA. For some power systems, the approach is nearly fifty times faster than solving SDPs via standard solvers with almost no impact on the performance. The case studies also demonstrate that NLA can be applied to more realistic scenarios in which (i) only partial state data is available and (ii) sparsity structures are imposed on the feedback gain. The paper also showcases that virtually no degradation in state estimation quality is experienced when applying NLA.

eess.SY↗

arXiv2023

Spin-Orbital Coupling in All-Inorganic Metal-Halide Perovskites: the Hidden Force that Matters

Highlighted with improved long-term thermal and environmental stability, all-inorganic metal halide perovskites exhibit tunable physical properties, cost-effective synthesis, and satisfactory optoelectronic performance, attracting increasing research interests worldwide. However, a less explored feature of these materials is their strong spin-orbit coupling (SOC), which is the hidden force influencing not only band structure but also properties including magnetoresistance, spin lifetime and singlet-triplet splitting. This review provides an overview of the fundamental aspects and the latest progress of the SOC and debate regarding Rashba effects in all-inorganic metal halide perovskites, providing critical insights into the physical phenomena and potential applications. Meanwhile, crystal structures and photophysics of all-inorganic perovskite are discussed in the context of SOC, along with the related experimental and characterization techniques. Furthermore, a recent understanding of the band topology in the all-inorganic halide perovskites is introduced to push the boundary even further for the novel applications of all-inorganic halide perovskites. Finally, an outlook is given on the potential directions of breakthroughs via leveraging the SOC in halide perovskites.

cond-mat.mtrl-sci↗