activity
20172026
most citedFuzzy Approach Topic Discovery in Health and Medical Corpora

77 citations · 93 across the 15 of their papers we have counts for

collaborators

19 papers

cs.CR2026

NoisePQC++: A Unified NIST-Compliant PQC and Hybrid-PQC Implementation of the Noise Protocol

Nadeem Ahmed, Aryya Gangopadhyay, Lei Zhang

The threat of quantum computers to classical public-key cryptography has created an urgent need to evolve secure communication protocols with post-quantum cryptographic (PQC) primi…

cs.RO2026

Hilbert-Augmented Reinforcement Learning for Scalable Multi-Robot Coverage and Exploration

Tamil Selvan Gurunathan, Aryya Gangopadhyay

We present a coverage framework that integrates Hilbert space-filling priors into decentralized multi-robot learning and execution. We augment DQN and PPO with Hilbert-based spatia…

cs.CR2025★ 1 cited

A Survey of Post-Quantum Cryptography Support in Cryptographic Libraries

Nadeem Ahmed, Lei Zhang, Aryya Gangopadhyay

The rapid advancement of quantum computing poses a significant threat to modern cryptographic systems, necessitating the transition to Post-Quantum Cryptography (PQC). This study e…

cs.CV2025

Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models

Md Azim Khan, Aryya Gangopadhyay, Jianwu Wang +1

Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights. Vision language models (VLMs) have become essenti…

cs.SE2024

A Lightweight Plug-in Module for Introducing Post-Quantum Cryptography in Higher Education: An Experience Report

Ainaz Jamshidi, Khushdeep Kaur, Karen Chen +2

Post-quantum cryptography (PQC) is increasingly important for computing education, yet integrating it into existing curricula is challenging because it draws on programming, mathem…

cs.CV2024

TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices

Hasib-Al Rashid, Argho Sarkar, Aryya Gangopadhyay +2

Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged a…