arXiv subjects

Bilal Khan

Publications and source records attributed to Bilal Khan.

16 recordsLinked to original sources

arXiv2026

A Hybrid Usability Approach for Rating Evaluation of M-Commerce Applications

The success of any mobile application relies on its usefulness and rating is considered as an important measure in this regard. This research work focuses on identifying usability factors, which contribute significantly towards the rating of M-commerce apps. This work intends to explore existing usability models consisting of different factors along with a set of criteria and evaluate in terms of rating estimation by considering 5 well-known mobile applications, namely (i) daraz, (ii) shophive, (iii) home shopping, (iv) Symbios. (v) yayvo. Then, this work provides a hybrid usability model for rating prediction of M-commerce applications. The initial hybrid usability model comprises of (i) learnability, (ii) consistency, (iii) human factors,(iv)communicativeness,(v)effectiveness, (vi) Operability, (vii) efficiency, (viii) satisfaction. Each factor consists of some criteria. Keeping in view the factors of hybrid usability model, the data was collected from 40 users for each application. Furthermore, Forward Stepwise Multiple Linear Regression based rating prediction model is suggested by analyzing each criterion of all factors of hybrid usability model. Finally, the model is assessed and validated by using PRED(x) and K-fold techniques.

cs.SE↗

arXiv2026

A Maxwell Quadratic-Form Representation of the Parallel-Plate Casimir Trace from Codimension-Three Riesz Reduction

We formulate a Maxwell version of the codimension-three Riesz/Gaussian quadratic-form representation for perfectly conducting parallel plates. This paper is the Maxwell follow-up to the scalar codimension-three Riesz/Gaussian representation theorem presented earlier in arXiv:2605.06693(2026): the same transverse Riesz reduction and prescribed-covariance quadratic-form mechanism are carried over here to the physical parallel-plate Maxwell operator. The construction is carried out in finite lateral volume $\Omega_{L,a}=T_L^2\times[0,a]$, using the physical electric-field Hilbert space of divergence-free fields satisfying the perfect-conductor tangential condition $n\times E=0$, with the static normal zero mode removed. The Maxwell curl-curl operator is defined by its closed quadratic form, and an explicit Fourier-domain analysis proves the finite-volume spectral gap, compact resolvent, and heat-trace admissibility needed for the stochastic construction. For this reduced Maxwell operator $\mathcal L_{\mathrm{Mx}}$, the codimension-three Riesz integral gives the transversely reduced Riesz mediator $g\mathcal L_{\mathrm{Mx}}^{-1}$. A prescribed heat-regularized Gaussian source with covariance $(\hbar c/g)\mathcal L_{\mathrm{Mx}}^{3/2}e^{-\tau\mathcal L_{\mathrm{Mx}}}$ then has expected quadratic Green energy equal to the heat-regularized physical Maxwell trace. The finite-volume trace is shown to be spectrally equivalent to a scalar Dirichlet channel plus a scalar Neumann channel with its constant zero mode removed. Under the standard parallel-plate interaction finite-part prescription, the large-area energy density is $$ -\frac{\pi^2\hbar c}{720a^3}. $$ The result is a representation theorem for the Maxwell parallel-plate trace under a prescribed covariance in the flat-plate geometry considered here.

math.GM↗

arXiv2026

A Quadratic-Form Representation of the Scalar Casimir Trace from Codimension-Three Riesz Reduction

Under a prescribed heat-regularized Gaussian source covariance, we give a quadratic-form representation of the scalar Casimir trace associated with a codimension-three Riesz reduction. For a product operator $L_M=L_B-\Delta_\perp$, with $L_B$ positive self-adjoint and bounded below, transverse reduction of the ambient Riesz operator $L_M^{-s}$ produces the brane multiplier $L_B^{m/2-s}$, up to an explicit Gamma-function constant. The exponent $s=1+m/2$ is therefore the critical Riesz exponent for obtaining the ordinary brane Green operator $L_B^{-1}$; in codimension three this gives $s=5/2$. Using this induced Green kernel, we prescribe a Gaussian generalized scalar source with covariance proportional to $L_B^{3/2}e^{-\tau L_B}$. The expectation of its quadratic Green-kernel energy is then exactly the heat-regularized scalar Casimir trace \[ \frac{\hbar c}{2} \operatorname{Tr}\!\left(L_B^{1/2}e^{-\tau L_B}\right). \] With the same finite-part prescription, the identity specializes in the Dirichlet parallel-plate geometry to the standard scalar finite part. We also record a deterministic flat Green-energy calibration at the plate scale. Within the plate-compatible rectangular aspect-ratio family, the cubical cell is selected by spectral, heat-trace, and Green-energy extremal criteria, and the associated comparison coefficient is the corresponding extremal calibration value. The construction is a scalar spectral representation theorem; no electromagnetic, gravitational, brane-dynamical, or fundamental-constant identification is asserted.

math.GM↗

arXiv2025

Cross-layer Integrated Sensing and Communication: A Joint Industrial and Academic Perspective

Integrated sensing and communication (ISAC) enables radio systems to simultaneously sense and communicate with their environment. This paper, developed within the Hexa-X-II project funded by the European Union, presents a comprehensive cross-layer vision for ISAC in 6G networks, integrating insights from physical-layer design, hardware architectures, AI-driven intelligence, and protocol-level innovations. We begin by revisiting the foundational principles of ISAC, highlighting synergies and trade-offs between sensing and communication across different integration levels. Enabling technologies (such as multiband operation, massive and distributed MIMO, non-terrestrial networks, reconfigurable intelligent surfaces, and machine learning) are analyzed in conjunction with hardware considerations including waveform design, synchronization, and full-duplex operation. To bridge implementation and system-level evaluation, we introduce a quantitative cross-layer framework linking design parameters to key performance and value indicators. By synthesizing perspectives from both academia and industry, this paper outlines how deeply integrated ISAC can transform 6G into a programmable and context-aware platform supporting applications from reliable wireless access to autonomous mobility and digital twinning.

eess.SP↗

arXiv2025

Leveraging Language Models for Analyzing Longitudinal Experiential Data in Education

We propose a novel approach to leveraging pre-trained language models (LMs) for early forecasting of academic trajectories in STEM students using high-dimensional longitudinal experiential data. This data, which captures students' study-related activities, behaviors, and psychological states, offers valuable insights for forecasting-based interventions. Key challenges in handling such data include high rates of missing values, limited dataset size due to costly data collection, and complex temporal variability across modalities. Our approach addresses these issues through a comprehensive data enrichment process, integrating strategies for managing missing values, augmenting data, and embedding task-specific instructions and contextual cues to enhance the models' capacity for learning temporal patterns. Through extensive experiments on a curated student learning dataset, we evaluate both encoder-decoder and decoder-only LMs. While our findings show that LMs effectively integrate data across modalities and exhibit resilience to missing data, they primarily rely on high-level statistical patterns rather than demonstrating a deeper understanding of temporal dynamics. Furthermore, their ability to interpret explicit temporal information remains limited. This work advances educational data science by highlighting both the potential and limitations of LMs in modeling student trajectories for early intervention based on longitudinal experiential data.

cs.LG↗

arXiv2024

A Novel GAN Approach to Augment Limited Tabular Data for Short-Term Substance Use Prediction

Substance use is a global issue that negatively impacts millions of persons who use drugs (PWUDs). In practice, identifying vulnerable PWUDs for efficient allocation of appropriate resources is challenging due to their complex use patterns (e.g., their tendency to change usage within months) and the high acquisition costs for collecting PWUD-focused substance use data. Thus, there has been a paucity of machine learning models for accurately predicting short-term substance use behaviors of PWUDs. In this paper, using longitudinal survey data of 258 PWUDs in the U.S. Great Plains collected by our team, we design a novel GAN that deals with high-dimensional low-sample-size tabular data and survey skip logic to augment existing data to improve classification models' prediction on (A) whether the PWUDs would increase usage and (B) at which ordinal frequency they would use a particular drug within the next 12 months. Our evaluation results show that, when trained on augmented data from our proposed GAN, the classification models improve their predictive performance (AUROC) by up to 13.4% in Problem (A) and 15.8% in Problem (B) for usage of marijuana, meth, amphetamines, and cocaine, which outperform state-of-the-art generative models.

cs.LG↗

arXiv2023

Benchmarking Neural Network Training Algorithms

Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workloads (e.g., better update rules, tuning protocols, learning rate schedules, or data selection schemes) could save time, save computational resources, and lead to better, more accurate, models. Unfortunately, as a community, we are currently unable to reliably identify training algorithm improvements, or even determine the state-of-the-art training algorithm. In this work, using concrete experiments, we argue that real progress in speeding up training requires new benchmarks that resolve three basic challenges faced by empirical comparisons of training algorithms: (1) how to decide when training is complete and precisely measure training time, (2) how to handle the sensitivity of measurements to exact workload details, and (3) how to fairly compare algorithms that require hyperparameter tuning. In order to address these challenges, we introduce a new, competitive, time-to-result benchmark using multiple workloads running on fixed hardware, the AlgoPerf: Training Algorithms benchmark. Our benchmark includes a set of workload variants that make it possible to detect benchmark submissions that are more robust to workload changes than current widely-used methods. Finally, we evaluate baseline submissions constructed using various optimizers that represent current practice, as well as other optimizers that have recently received attention in the literature. These baseline results collectively demonstrate the feasibility of our benchmark, show that non-trivial gaps between methods exist, and set a provisional state-of-the-art for future benchmark submissions to try and surpass.

cs.LG↗

arXiv2021

Ethics of AI: A Systematic Literature Review of Principles and Challenges

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assess the ethical capabilities of AI systems and provide best practices for further improvements.

cs.CY↗

arXiv2018

A method for assessing the success and failure of community-level interventions in the presence of network diffusion, social reinforcement, and related social effects

Prevention and intervention work done within community settings often face unique analytic challenges for rigorous evaluations. Since community prevention work (often geographically isolated) cannot be controlled in the same way other prevention programs and these communities have an increased level of interpersonal interactions, rigorous evaluations are needed. Even when the `gold standard' randomized control trials are implemented within community intervention work, the threats to internal validity can be called into question given informal social spread of information in closed network settings. A new prevention evaluation method is presented here to disentangle the social influences assumed to influence prevention effects within communities. We formally introduce the method and it's utility for a suicide prevention program implemented in several Alaska Native villages. The results show promise to explore eight sociological measures of intervention effects in the face of social diffusion, social reinforcement, and direct treatment. Policy and research implication are discussed.

cs.SI↗

arXiv2017

One-step Estimation of Networked Population Size: Respondent-Driven Capture-Recapture with Anonymity

Population size estimates for hidden and hard-to-reach populations are particularly important when members are known to suffer from disproportion health issues or to pose health risks to the larger ambient population in which they are embedded. Efforts to derive size estimates are often frustrated by a range of factors that preclude conventional survey strategies, including social stigma associated with group membership or members' involvement in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, to be used in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. Provably sufficient conditions for the consistency of these estimators (in large configuration networks) are given. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which are seen to perform well on a real-world location-based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable tradeoffs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population estimates can be derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. Limitations and future work are discussed in the concluding section.

cs.SI↗

arXiv2017

Combination interventions for Hepatitis C and Cirrhosis reduction among people who inject drugs: An agent-based, networked population simulation experiment

Hepatitis C virus (HCV) infection is endemic in people who inject drugs (PWID), with prevalence estimates above 60 percent for PWID in the United States. Previous modeling studies suggest that direct acting antiviral (DAA) treatment can lower overall prevalence in this population, but treatment is often delayed until the onset of advanced liver disease (fibrosis stage 3 or later) due to cost. Lower cost interventions featuring syringe access (SA) and medically assisted treatment (MAT) for addiction are known to be less costly, but have shown mixed results in lowering HCV rates below current levels. Little is known about the potential synergistic effects of combining DAA and MAT treatment, and large-scale tests of combined interventions are rare. While simulation experiments can reveal likely long-term effects, most prior simulations have been performed on closed populations of model agents--a scenario quite different from the open, mobile populations known to most health agencies. This paper uses data from the Centers for Disease Control's National HIV Behavioral Surveillance project, IDU round 3, collected in New York City in 2012 by the New York City Department of Health and Mental Hygiene to parameterize simulations of open populations. Our results show that, in an open population, SA/MAT by itself has only small effects on HCV prevalence, while DAA treatment by itself can significantly lower both HCV and HCV-related advanced liver disease prevalence. More importantly, the simulation experiments suggest that cost effective synergistic combinations of the two strategies can dramatically reduce HCV incidence. We conclude that adopting SA/MAT implementations alongside DAA interventions can play a critical role in reducing the long-term consequences of ongoing infection.

q-bio.PE↗

arXiv2017

Topology Control Schema for Better QoS in Hybrid RF/FSO Mesh Networks

The practical limitations and challenges of radio frequency (RF) based communication networks have become increasingly apparent over the past decade, leading researchers to seek new hybrid communication approaches. One promising strategy that has been the subject of considerable interest is the augmentation of RF technology by Free Space Optics (FSO), using the strength of each communication technology to overcome the limitations of the other. In this article, we introduce a new scheme for controlling the topology in hybrid Radio-Frequency/Free Space Optics (RF/FSO) wireless mesh networks. Our scheme is based on adaptive adjustments to both transmission power (of RF and FSO transmitters) and the optical beam-width (of FSO transmitters) at individual nodes, with the objective of meeting specified Quality of Service (QoS) requirements, specifically end-to-end delay and throughput. We show how one can effectively encode the instantaneous objectives and constraints of the system as an instance of Integer Linear Programming (ILP). We demonstrate that the technique of Lagrangian Relaxation (LR), augmented with iterative repair heuristics, can be used to determine good (albeit sub-optimal) solutions for the ILP problem, making the approach feasible for mid-sized networks. We make the proposed scheme viable for large-scale networks in terms of number of nodes, number of transceivers, and number of source-destination pairs by solving the ILP problem using a Particle Swarm Optimization (PSO) implementation.

cs.NI↗

arXiv2017

One-step Estimation of Networked Population Size with Anonymity Using Respondent-Driven Capture-Recapture and Hashing

Estimates of population size for hidden and hard-to-reach individuals are of particular interest to health officials when health problems are concentrated in such populations. Efforts to derive these estimates are often frustrated by a range of factors including social stigma or an association with illegal activities that ordinarily preclude conventional survey strategies. This paper builds on and extends prior work that proposed a method to meet these challenges. Here we describe a rigorous formalization of a one-step, network-based population estimation procedure that can be employed under conditions of anonymity. The estimation procedure is designed to be implemented alongside currently accepted strategies for research with hidden populations. Simulation experiments are described that test the efficacy of the method across a range of implementation conditions and hidden population sizes. The results of these experiments show that reliable population estimates can be derived for hidden, networked population as large as 12,500 and perhaps larger for one family of random graphs. As such, the method shows potential for cost-effective implementation health and disease surveillance officials concerned with hidden populations. Limitations and future work are discussed in the concluding section.

cs.SI↗

arXiv2004

A graphic generalization of arithmetic

In this paper, we extend the classical arithmetic defined over the set of natural numbers N, to the set of all finite directed connected multigraphs having a pair of distinct distinguished vertices. Specifically, we introduce a model F on the set of such graphs, and provide an interpretation of the language of arithmetic L={0,1,<=,+,x} inside F. The resulting model exhibits the property that the standard model on N embeds in F as a submodel, with the directed path of length n playing the role of the standard integer n. We will compare the theory of the larger structure F with classical arithmetic statements that hold in N. For example, we explore the extent to which F enjoys properties like the associativity and commutativity of + and x, distributivity, cancellation and order laws, and decomposition into irreducibles.

math.CO↗

arXiv2003

On the Hanna Neumann Conjecture

The Hanna Neumann conjecture states that if F is a free group, then for all nontrivial finitely generated subgroups H,K <= F, rank(H intersect K) - 1 <= [rank(H)-1] [rank(K)-1]. Where most papers to date have considered a direct graph theoretic interpretation of the conjecture, here we consider the use of monomorphisms. We illustrate the effectiveness of this approach with two results. First, we show that for any finitely generated groups H,K <= F either the pair H,K or the pair H^{-}, K satisfy the Hanna Neumann conjecture--Here {-} denotes the automorphism which sends each generator of F to its inverse. Next, using particular monomorphisms from F to F_2, we obtain that if the Hanna Neumann conjecture is false then there is a counterexample H,K < F_2 having the additional property that all the branch vertices in the foldings of H and K are of degree 3, and all degree 3 vertices have the same local structure or ``type''.

math.GR↗

arXiv2000

The Hanna Neumann Conjecture is true when one subgroup has a positive generating set

The Hanna Neumann conjecture states that if F is a free group, then for all finitely generated subgroups H,K <= F, rank(H intersect K) - 1 <= [ rank(H)-1 ] [ rank(K)-1 ] In this paper, we show that if one of the subgroups, say H, has a generating set consisting of only positive words, then H is not part of any counterexample to the conjecture. We further show that if H <= F({a,b}) is part of a counterexample to the conjecture, then its folding Gamma_H must contain source and/or sink vertices.

math.GR↗