15 citations · 88 across the 61 of their papers we have counts for
21 papers · 1 filter
LogHD: Robust Compression of Hyperdimensional Classifiers via Logarithmic Class-Axis Reduction
Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa +3
Hyperdimensional computing (HDC) suits memory, energy, and reliability-constrained systems, yet the standard "one prototype per class" design requires memory (with clas…
Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
Huynh T. T. Tran, Jacob Sander, Achraf Cohen +2
Transfer learning is commonly utilized in various fields such as computer vision, natural language processing, and medical imaging due to its impressive capability to address subta…
An Automated, Scalable Machine Learning Model Inversion Assessment Pipeline
Tyler Shumaker, Jessica Carpenter, David Saranchak +1
Machine learning (ML) models have the potential to transform military battlefields, presenting a large external pressure to rapidly incorporate them into operational settings. Howe…
Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inference typically provides; yet, retraining…
ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Large Vision-Language Models (LVLMs) exhibit strong multimodal capabilities but remain vulnerable to hallucinations from intrinsic errors and adversarial attacks from external expl…
Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization
Fei Xu Yu, Gina Adam, Nathaniel D. Bastian +1
Mathematical optimization is a core tool in engineering, operations research, and data science, but modeling real problems remains difficult and time-consuming. In practice, an exp…
MissionHD: Hyperdimensional Refinement of Distribution-Deficient Reasoning Graphs for Video Anomaly Detection
Sanggeon Yun, Raheeb Hassan, Ryozo Masukawa +2
LLM-generated reasoning graphs, referred to as mission-specific graphs (MSGs), are increasingly used for video anomaly detection (VAD) and recognition (VAR). However, they are typi…
Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments
Yasir Ali Farrukh, Syed Wali, Irfan Khan +1
Unsupervised Domain Adaptation (UDA) is a critical challenge in real-world vision systems, especially in resource-constrained environments like drones, where memory and computation…
TOGA: Temporally Grounded Open-Ended Video QA with Weak Supervision
Ayush Gupta, Anirban Roy, Rama Chellappa +3
We address the problem of video question answering (video QA) with temporal grounding in a weakly supervised setup, without any temporal annotations. Given a video and a question,…
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security
Emilia Rivas, Sabrina Saika, Ahtesham Bakht +3
Evolving attacks are a critical challenge for the long-term success of Network Intrusion Detection Systems (NIDS). The rise of these changing patterns has exposed the limitations o…
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning
Huynh T. T. Tran, Jacob Sander, Achraf Cohen +2
Network Intrusion Detection Systems (NIDS) play a vital role in protecting digital infrastructures against increasingly sophisticated cyber threats. In this paper, we extend ODXU,…
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge
Alexandre Broggi, Nathaniel Bastian, Lance Fiondella +1
Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This…
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments
Mario Leiva, Noel Ngu, Joshua Shay Kricheli +6
The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence a…
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection
Aditya Taparia, Noel Ngu, Mario Leiva +6
Although fusing multiple sensor modalities can enhance object detection performance, existing fusion approaches often overlook subtle variations in environmental conditions and sen…
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
Sanggeon Yun, Ryozo Masukawa, Hyunwoo Oh +2
Deep neural networks (DNNs) are highly susceptible to adversarial examples---small, malicious perturbations that can cause incorrect predictions. We introduce a lightweight, plug-i…
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
Trilok Padhi, Ramneet Kaur, Adam D. Cobb +7
We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for multi-modal large language models (LLMs). Existing state-of-the-art UQ methods rely on co…
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models
Chung-En, Yu, Hsuan-Chih +3
To develop trustworthy Vision-Language Models (VLMs), it is essential to address adversarial robustness and hallucination mitigation, both of which impact factual accuracy in high-…
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong +5
Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We present PacketCLIP, a multi-modal framework combining packet data with nat…
Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
Noel Ngu, Aditya Taparia, Gerardo I. Simari +5
Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often l…
Probabilistic Foundations for Metacognition via Hybrid-AI
Paulo Shakarian, Gerardo I. Simari, Nathaniel D. Bastian
Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and,…
Deceptive Sequential Decision-Making via Regularized Policy Optimization
Yerin Kim, Alexander Benvenuti, Bo Chen +5
Autonomous systems are increasingly expected to operate in the presence of adversaries, though adversaries may infer sensitive information simply by observing a system. Therefore,…