3 citations · 5 across the 19 of their papers we have counts for
19 papers
Memory as an Attack Surface in LLM Agents: A Study on Multiple-Choice Question Answering
Shahnewaz Karim Sakib, Anindya Bijoy Das
AI agents extend conventional large language model (LLM) applications by integrating language understanding with task execution, external tool use, and memory mechanisms. While mem…
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
Shahnewaz Karim Sakib, Anindya Bijoy Das
Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision. Ho…
Adversarial Reframing: A Framework for Targeted Generation in Language Models
Shahnewaz Karim Sakib, Swati Kar, Anindya Bijoy Das
Large Language Models (LLMs) are widely deployed in diverse real-world settings, yet remain vulnerable to jailbreaking, where prompt-based attacks bypass safety filters. We present…
Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework
Md Rafid Kaysar Shagor, Zannatul Ferdousy Mouri, Farhina Haque +1
The growing use of fast-switching power electronics has made partial discharge (PD) analysis under switching-voltage excitation increasingly important, yet more challenging than un…
Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities
Anindya Bijoy Das, Shahnewaz Karim Sakib, Shibbir Ahmed
Large Language Models (LLMs) are increasingly applied to medical imaging tasks, including image interpretation and synthetic image generation. However, these models often produce h…
Can Large Language Models Challenge CNNs in Medical Image Analysis?
Shibbir Ahmed, Shahnewaz Karim Sakib, Anindya Bijoy Das
This study presents a multimodal AI framework designed for precisely classifying medical diagnostic images. Utilizing publicly available datasets, the proposed system compares the…
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning
Satyavrat Wagle, Anindya Bijoy Das, David J. Love +1
Augmenting federated learning (FL) with device-to-device (D2D) communications can help improve convergence speed and reduce model bias through local information exchange. However,…
Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models
Anindya Bijoy Das, Shibbir Ahmed, Shahnewaz Karim Sakib
Clinical summarization is crucial in healthcare as it distills complex medical data into digestible information, enhancing patient understanding and care management. Large language…
Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis
David R. Nickel, Anindya Bijoy Das, David J. Love +1
Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approach…
Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models
Shahnewaz Karim Sakib, Anindya Bijoy Das, Shibbir Ahmed
Adversarial factuality refers to the deliberate insertion of misinformation into input prompts by an adversary, characterized by varying levels of expressed confidence. In this stu…
Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness
Anindya Bijoy Das, Shahnewaz Karim Sakib
Large Language Model (LLM)-based recommendation systems excel in delivering comprehensive suggestions by deeply analyzing content and user behavior. However, they often inherit bia…
Sparsity-Preserving Encodings for Straggler-Optimal Distributed Matrix Computations at the Edge
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Matrix computations are a fundamental building-block of edge computing systems, with a major recent uptick in demand due to their use in AI/ML training and inference procedures. Ex…
Constant Modulus Waveform Design with Space-Time Sidelobe Reduction for DFRC Systems
Byunghyun Lee, Anindya Bijoy Das, David Love +2
Dual-function radar-communication (DFRC) is a key enabler of location-based services for next-generation communication systems. In this paper, we investigate the problem of designi…
Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim +2
Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent p…
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs
David R. Nickel, Anindya Bijoy Das, David J. Love +1
Opportunistic spectrum access has the potential to increase the efficiency of spectrum utilization in cognitive radio networks (CRNs). In CRNs, both spectrum sensing and resource a…
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
Seohyun Lee, Anindya Bijoy Das, Satyavrat Wagle +1
One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D…
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim +2
A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust perf…
Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches
Junghoon Kim, Taejoon Kim, Anindya Bijoy Das +3
Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim t…
Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems
Byunghyun Lee, Anindya Bijoy Das, David J. Love +2
Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an…