arXiv subjects

Rajesh Kumar

Publications and source records attributed to Rajesh Kumar.

At least 37 records · Page 2Linked to original sources

arXiv2026

Investigating Gender Bias in Touch Biometrics

Behavioral biometrics offer a promising approach for continuous authentication, but their fairness across demographic groups remains largely unexplored. This paper investigates gender bias in swipe-based authentication using the BBMAS (117 users) and ANTAL (71 users) datasets and evaluates XGBoost and DenseNet classifiers through False Acceptance Rate (FAR) and False Rejection Rate (FRR). XGBoost achieved authentication accuracies of 92% and 94% on the BBMAS and ANTAL datasets, respectively, while statistical tests (Kolmogorov-Smirnov, Mann-Whitney, and Wasserstein permutation) found no significant gender differences in authentication error rates across almost all experimental settings. These findings suggest that swipe-based authentication can achieve high accuracy while maintaining comparable performance for male and female users, supporting its potential as a fair and reliable behavioral biometric modality.

cs.CY↗

arXiv2026

Deterministic vs. LLM-Controlled Orchestration for COBOL-to-Python Modernization

Modernizing legacy COBOL systems remains difficult due to scarce expertise, large and long-lived codebases, and strict correctness requirements. Recent large language model (LLM)-based modernization systems increasingly rely on agentic workflows in which the model controls multi-step tool execution. However, it remains unclear whether delegating execution control to the LLM improves correctness, robustness, or efficiency in structured software engineering workflows. We present a controlled empirical study of deterministic and LLM-controlled orchestration for COBOL-to-Python modernization. Using a unified experimental framework, we hold the language models, prompts, tools, configurations, and source programs constant while varying only the execution control strategy. This isolates orchestration as the sole experimental variable. We evaluate both approaches using functional correctness, robustness across repeated stochastic runs, and computational efficiency. Across multiple models, deterministic orchestration achieves comparable computational accuracy to LLM-controlled orchestration while improving worst-case robustness and reducing performance variability across runs. Deterministic execution also reduces token consumption by up to 3.5x, leading to substantially lower operational cost. These results suggest that, in structured modernization workflows with explicit validation stages, fixed execution policies provide more stable and cost-efficient behavior than fully agentic orchestration without reducing translation quality.

cs.SE↗

arXiv2026

Crystal Anisotropy Implications on the Magneto-Optical Properties of van der Waals FePS3

Antiferromagnetic FePS3 has recently gained significant interest in its potential applications in spin-related devices. Here, we show that in-plane structural anisotropy has a major impact in shaping the optical responses of FePS3 single-crystals from the bulk form down to the monolayer limit. X-ray diffraction on a bulk FePS3 crystal confirms a distorted FeS6 octahedron causing inequivalent Fe-Fe distances and consequently resulting in a higher a/b lattice parameter ratio. Micro-photoluminescence observations on bulk and monolayer FePS3 reveal four emissions: one intra-atomic d-d transition (band A, centered at ~1.24 eV) and three p-d charge transfer transitions (bands B, C, and D, centered around ~1.79 eV, ~2.3 eV, and ~2.56 eV, respectively). These bands exhibit different polarization behaviors, which persist down to the monolayer limit. Density functional theory calculations from bulk to monolayer FePS3 reveal the underlying electronic structure, assign the observed emissions, and indicate why these peaks have contrasting linear and circular polarization responses. These results establish a direct structure-optics relation in FePS3, highlighting the strong coupling between lattice anisotropy, electronic transitions, and symmetry-selective optical selection rules.

cond-mat.mtrl-sci↗

arXiv2026

Germanium-tin (GeSn) avalanche photodiode with up to 2.7 micro cutoff wavelength for extended SWIR detection

Separate absorption charge multiplication germanium tin on silicon avalanche photodiode offers a viable solution to achieve CMOS compatible, high sensitivity detection technology in SWIR or extended SWIR range, leveraging the excellent k-factor of Si as multiplication layer and SWIR or e-SWIR band absorption of GeSn. However, unlike well-established growth of GeSn on Si with thick Ge buffer in-between to reduce threading dislocation density due to lattice mismatch, GeSn on Si APD design requires relatively thin Ge buffer to limit electric field drop through the background p-doped buffer and efficiently transporting photocarrier from GeSn absorber to Si multiplication layer, therefore making growth of high Sn content APD for e-SWIR coverage very challenging. In this work, we experimentally demonstrate GeSn on Si APD up to 12.7 percent Sn, monolithically grown on Si substrate with 122-nm-thick Ge buffer in between, which is considerably thinner than widely used 700-900 nm thick Ge buffer. Stronger relaxation of GeSn absorber via thin Ge buffer favors Sn incorporation, leading to higher Sn content than the nominal target of 8 percent Sn. Device detection range is significantly improved compared to previous work - with cutoff wavelength increased up to 2.7 micro at 300 K, in parallel with high avalanche gain at 77 K up to 21 at 1.55 micro and up to 52 at 2 micro, and good responsivity in SWIR or e-SWIR range, up to 1.45 AW-1 at 1.55 micro and 0.66 AW-1 at 2 micro.

physics.app-ph↗

arXiv2026

Detecting LLM-Assisted Academic Dishonesty using Keystroke Dynamics

The rapid adoption of generative AI tools has heightened concerns regarding academic integrity, as students increasingly engage in dishonest practices by copying or paraphrasing AI-generated content. Existing plagiarism detection systems, which rely primarily on text-intrinsic features, are ineffective at identifying AI-assisted or paraphrased submissions. Our prior conference work introduced a behavioral detection approach that leverages how text is produced, captured through keystroke dynamics, in addition to what is written, enabling discrimination between genuine and assisted writing. That study, conducted on keystroke data from 40 participants, demonstrated promising performance. This paper substantially extends and systemizes the prior work by: (1) expanding the dataset with 90 additional participants and introducing an explicit paraphrasing condition to model realistic plagiarism strategies; (2) formalizing a threat model and evaluating detection under adversarial and deception-oriented scenarios; and (3) performing a comprehensive empirical comparison against state-of-the-art text-only detectors and human evaluators. Experimental results demonstrate that keystroke-based models significantly outperform text-based approaches in practical deployment settings, while revealing limitations under more challenging adversarial conditions.

cs.HC↗

arXiv2026

AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous Software Engineering

Large language models generate plausible code but cannot verify correctness. Existing multi-agent systems simulate execution or leave verification optional. We introduce execution-grounded verification as a first-class principle: every code change must survive sandboxed execution before propagation. We instantiate this principle in AGENTFORGE, a multi-agent framework where Planner, Coder, Tester, Debugger, and Critic agents coordinate through shared memory and a mandatory Docker sandbox. We formalize software engineering with LLMs as an iterative decision process over repository states, where execution feedback provides a stronger supervision signal than next-token likelihood. AGENTFORGE achieves 40.0\% resolution on SWE-BENCH Lite, outperforming single-agent baselines by 26--28 points. Ablations confirm that execution feedback and role decomposition each independently drive performance. The framework is open-source at https://github.com/raja21068/AutoCodeAI.

cs.SE↗

arXiv2026

VGGT-SLAM++

We introduce VGGT-SLAM++, a complete visual SLAM system that leverages the geometry-rich outputs of the Visual Geometry Grounded Transformer (VGGT). The system comprises a visual odometry (front-end) fusing the VGGT feed-forward transformer and a Sim(3) solution, a Digital Elevation Map (DEM)-based graph construction module, and a back-end that jointly enable accurate large-scale mapping with bounded memory. While prior transformer-based SLAM pipelines such as VGGT-SLAM rely primarily on sparse loop closures or global Sim(3) manifold constraints - allowing short-horizon pose drift - VGGT-SLAM++ restores high-cadence local bundle adjustment (LBA) through a spatially corrective back-end. For each VGGT submap, we construct a dense planar-canonical DEM, partition it into patches, and compute their DINOv2 embeddings to integrate the submap into a covisibility graph. Spatial neighbors are retrieved using a Visual Place Recognition (VPR) module within the covisibility window, triggering frequent local optimization that stabilizes trajectories. Across standard SLAM benchmarks, VGGT-SLAM++ achieves state-of-the-art accuracy, substantially reducing short-term drift, accelerating graph convergence, and maintaining global consistency with compact DEM tiles and sublinear retrieval.

cs.CV↗

arXiv2026

S-wave kaon condensation in neutron-star matter within a chiral model framework with dynamical meson masses

We investigate s-wave kaon condensation in dense matter and neutron stars within the updated Chiral Mean Field model with an improved meson description (mCMF), which incorporates dynamically generated in-medium meson masses arising from explicit chiral symmetry breaking and vector-meson self-interactions. In contrast to conventional relativistic mean-field descriptions with constant meson masses, the mCMF framework introduces a self-consistent feedback between the meson sector and the dense-matter equations of motion. The kaon dispersion relation is derived from the nonlinear SU(3) Lagrangian, including the Weinberg-Tomozawa interaction and additional baryon-pseudoscalar couplings, and the onset of condensation is determined under conditions of charge neutrality and $β$ equilibrium. Our calculations include the full baryon octet together with electrons and muons at zero temperature. We analyze the impact of hyperons, muons, and kaon condensation on the equation of state, on neutron-star mass--radius relations, and neutron-star thermal evolution, and examine the sensitivity of the onset density and stellar properties to variations in the nucleon--kaon scattering length and to different model vector parameters and vector self-interactions. We find that $K^{-}$ condensation sets in between $n \sim (2-8)\, n_0$ (in units of nuclear saturation density) and leads to a moderate to strong softening (in one case, a slight stiffening of the equation of state), depending on the interplay of kaons and hyperons, while remaining compatible with current $2\,M_\odot$ and small-radius neutron-star observational constraints and producing distinguishable behavior in the neutron-star cooling. This work provides an improved and thermodynamically consistent framework for studying exotic degrees of freedom in neutron-star matter.

nucl-th↗

arXiv2026

Systematic study of high performance GeSn photodiodes with thick absorber for SWIR and extended SWIR detection

Germanium-tin (GeSn) photodiodes potentiate a viable solution to integrate SWIR and extended SWIR detection technology into CMOS processing line. However, challenges in the growth of thick, high quality GeSn limit the device absorber thickness, making it impossible to ascertain the performance limit of GeSn photodiodes. An in-depth understanding of their device physics and a clear optimization pathway towards commercial-grade devices remain elusive. This work presents a systematic empirical study of GeSn photodiodes with thick absorber (2 to 8% Sn content, up to 2630 nm thick), showing high responsivity up to 0.59 A.W-1 at 1.55 μm and 0.43 A.W-1 at 2 μm wavelengths, low dark current density down to 2 x 10-2 A.cm-2, and high detection cutoff wavelengths up to 2.1 and 2.5 μm at 5% and 8% Sn, respectively. Using specific doping design (P-i-N and N-i-P), an in-depth analysis is presented on the impact of junction position, p-type background carrier concentration, bulk/ surface defects and photocarrier diffusion length - on photodetection performance. Different optimization strategies for GeSn photodiodes, in particular at high Sn content, are proposed.

physics.ins-det↗

arXiv2026

Chemical potential differentials in the QCD phase diagram from heavy-ion isobar collisions

Temperature and baryon, charge, and strangeness chemical potentials characterize QCD matter under extreme conditions. Differences between these chemical potentials and their ratios probe conserved-charge correlations and the system's response in the multidimensional QCD phase diagram. We extract these quantities from STAR Ru+Ru and Zr+Zr isobar collisions using a Bayesian thermal analysis of hadron yields, which substantially reduces systematic uncertainties, and compare them with Taylor-expanded lattice-QCD and Chiral Mean Field model predictions. Isobar collisions thus emerge as a precision probe of four-dimensional QCD thermodynamics.

nucl-th↗

arXiv2025

A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies

Delicate snap-fit assemblies, such as inserting a lens into an eye-wear frame or during electronics assembly, demand timely engagement detection and rapid force attenuation to prevent overshoot-induced component damage or assembly failure. We address these challenges with two key contributions. First, we introduce SnapNet, a lightweight neural network that detects snap-fit engagement from joint-velocity transients in real-time, showing that reliable detection can be achieved using proprioceptive signals without external sensors. Second, we present a dynamical-systems-based dual-arm coordination framework that integrates SnapNet driven detection with an event-triggered impedance modulation, enabling accurate alignment and compliant insertion during delicate snap-fit assemblies. Experiments across diverse geometries on a heterogeneous bimanual platform demonstrate high detection accuracy (over 96% recall) and up to a 30% reduction in peak impact forces compared to standard impedance control.

cs.RO↗

arXiv2025

RCMCL: A Unified Contrastive Learning Framework for Robust Multi-Modal (RGB-D, Skeleton, Point Cloud) Action Understanding

Human action recognition (HAR) with multi-modal inputs (RGB-D, skeleton, point cloud) can achieve high accuracy but typically relies on large labeled datasets and degrades sharply when sensors fail or are noisy. We present Robust Cross-Modal Contrastive Learning (RCMCL), a self-supervised framework that learns modality-invariant representations and remains reliable under modality dropout and corruption. RCMCL jointly optimizes (i) a cross-modal contrastive objective that aligns heterogeneous streams, (ii) an intra-modal self-distillation objective that improves view-invariance and reduces redundancy, and (iii) a degradation simulation objective that explicitly trains models to recover from masked or corrupted inputs. At inference, an Adaptive Modality Gating (AMG) network assigns data-driven reliability weights to each modality for robust fusion. On NTU RGB+D 120 (CS/CV) and UWA3D-II, RCMCL attains state-of-the-art accuracy in standard settings and exhibits markedly better robustness: under severe dual-modality dropout it shows only an 11.5% degradation, significantly outperforming strong supervised fusion baselines. These results indicate that self-supervised cross-modal alignment, coupled with explicit degradation modeling and adaptive fusion, is key to deployable multi-modal HAR.

eess.SP↗

arXiv2025

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts

Keystroke dynamics is a promising modality for active user authentication, but its effectiveness under varying LLM-assisted typing and cognitive conditions remains understudied. Using data from 50 users and cognitive labels from Bloom's Taxonomy, we evaluate keystroke-based authentication in Korean across three realistic typing scenarios: bona fide composition, LLM content paraphrasing, and transcription. Our pipeline incorporates continuity-aware segmentation, feature extraction, and classification via SVM, MLP, and XGB. Results show that the system maintains reliable performance across varying LLM usages and cognitive contexts, with Equal Error Rates ranging from 5.1% to 10.4%. These findings demonstrate the feasibility of behavioral authentication under modern writing conditions and offer insights into designing more context-resilient models.

cs.CR↗

arXiv2025

How Well Do LLMs Imitate Human Writing Style?

Large language models (LLMs) can generate fluent text, but their ability to replicate the distinctive style of a specific human author remains unclear. We present a fast, training-free framework for authorship verification and style imitation analysis. The method integrates TF-IDF character n-grams with transformer embeddings and classifies text pairs through empirical distance distributions, eliminating the need for supervised training or threshold tuning. It achieves 97.5\% accuracy on academic essays and 94.5\% in cross-domain evaluation, while reducing training time by 91.8\% and memory usage by 59\% relative to parameter-based baselines. Using this framework, we evaluate five LLMs from three separate families (Llama, Qwen, Mixtral) across four prompting strategies - zero-shot, one-shot, few-shot, and text completion. Results show that the prompting strategy has a more substantial influence on style fidelity than model size: few-shot prompting yields up to 23.5x higher style-matching accuracy than zero-shot, and completion prompting reaches 99.9\% agreement with the original author's style. Crucially, high-fidelity imitation does not imply human-like unpredictability - human essays average a perplexity of 29.5, whereas matched LLM outputs average only 15.2. These findings demonstrate that stylistic fidelity and statistical detectability are separable, establishing a reproducible basis for future work in authorship modeling, detection, and identity-conditioned generation.

cs.CL↗

arXiv2025

LLM-Assisted Cheating Detection in Korean Language via Keystrokes

This paper presents a keystroke-based framework for detecting LLM-assisted cheating in Korean, addressing key gaps in prior research regarding language coverage, cognitive context, and the granularity of LLM involvement. Our proposed dataset includes 69 participants who completed writing tasks under three conditions: Bona fide writing, paraphrasing ChatGPT responses, and transcribing ChatGPT responses. Each task spans six cognitive processes defined in Bloom's Taxonomy (remember, understand, apply, analyze, evaluate, and create). We extract interpretable temporal and rhythmic features and evaluate multiple classifiers under both Cognition-Aware and Cognition-Unaware settings. Temporal features perform well under Cognition-Aware evaluation scenarios, while rhythmic features generalize better under cross-cognition scenarios. Moreover, detecting bona fide and transcribed responses was easier than paraphrased ones for both the proposed models and human evaluators, with the models significantly outperforming the humans. Our findings affirm that keystroke dynamics facilitate reliable detection of LLM-assisted writing across varying cognitive demands and writing strategies, including paraphrasing and transcribing LLM-generated responses.

cs.LG↗

arXiv2025

Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs

Large Language Models (LLMs) have made it easier to create realistic fake profiles on platforms like LinkedIn. This poses a significant risk for text-based fake profile detectors. In this study, we evaluate the robustness of existing detectors against LLM-generated profiles. While highly effective in detecting manually created fake profiles (False Accept Rate: 6-7%), the existing detectors fail to identify GPT-generated profiles (False Accept Rate: 42-52%). We propose GPT-assisted adversarial training as a countermeasure, restoring the False Accept Rate to between 1-7% without impacting the False Reject Rates (0.5-2%). Ablation studies revealed that detectors trained on combined numerical and textual embeddings exhibit the highest robustness, followed by those using numerical-only embeddings, and lastly those using textual-only embeddings. Complementary analysis on the ability of prompt-based GPT-4Turbo and human evaluators affirms the need for robust automated detectors such as the one proposed in this study.

cs.SI↗

arXiv2025

Ge0.95Sn0.05 on Si avalanche photodiode with Spectral Response Cutoff at 2.14 micrometer

GeSn-based avalanche photodiode (APD) operating in shortwave infrared (SWIR) wavelength was demonstrated in this work. A separate absorption and charge multiplication (SACM) structure was employed to take advantage of long wavelength absorption in GeSn and low impact ionization ratio of Si. Due to lattice mismatch between Si and GeSn that would degrade GeSn material quality if with direct growth, a 240-nm-thick Ge buffer was utilized which simultaneously allows for the transporting photo generated electrons from GeSn absorber to Si multiplication layer. Spectral response showed the cut off wavelength beyond 2.1 μm at room temperature. Dart current and capacitance-voltage measurements indicated a punch-through voltage of -10 V. The measured responsivities were 0.55 A/W and 0.34 A/W under 1.55 μm and 1.9 μm excitation lasers, respectively. The maximum gain was obtained as 3.44 at 77 K under 1.9 μm laser. Even at 250 K, the calculated gain was greater than unity. Simulation of electric field distribution revealed that the GeSn is partially depleted at operating voltages, which can be improved by reducing the background doping levels in GeSn absorber and Ge buffer layer.

physics.app-ph↗

arXiv2025

LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions

Scheduling virtual machines (VMs) on hosts in cloud data centers dictates efficiency and is an NP-hard problem with incomplete information. Prior work improved VM scheduling with predicted VM lifetimes. Our work further improves lifetime-aware scheduling using repredictions with lifetime distributions versus one-shot prediction. Our approach repredicts and adjusts VM and host lifetimes when incorrect predictions emerge. We also present novel approaches for defragmentation and regular system maintenance, which are essential to our data center reliability and optimizations, and are not explored in prior work. We show repredictions deliver a fundamental advance in effectiveness over one-shot prediction. We call our novel combination of distribution-based lifetime predictions and scheduling algorithms Lifetime Aware VM Allocation (LAVA). LAVA reduces resource stranding and increases the number of empty hosts, which are critical for large VM scheduling, cloud system updates, and reducing dynamic energy consumption. Our approach runs in production within Google's hyperscale cloud data centers, where it improves efficiency by decreasing stranded compute and memory resources by ~3% and ~2% respectively. It increases empty hosts by 2.3-9.2 pp in production, reducing dynamic energy consumption, and increasing availability for large VMs and cloud system updates. We also show a reduction in VM migrations for host defragmentation and maintenance. In addition to our fleet-wide production deployment, we perform simulation studies to characterize the design space and show that our algorithm significantly outperforms the prior state of the art lifetime-based scheduling approach.

cs.DC↗