2 citations · 4 across the 20 of their papers we have counts for
23 papers
CyberWorld: World Models for Sample-Efficient Autonomous Cyber Defense
Ryozo Masukawa, Sanggeon Yun, Raheeb Hassan +3
Deep reinforcement learning has become a prominent approach to autonomous cyber defense. Existing methods are predominantly model-free and consequently require extensive environmen…
World State Generator
Sungheon Jeong, Sanggeon Yun, Ryozo Masukawa +3
Language agents solve complex tasks through plans and actions. A single step the world refuses puts the goal out of reach, and what the agent does next decides the task. Prompted p…
AudioLens: Multi-Perspective Speech Clustering with Reasoning Audio-Language Models
Wenjun Huang, Qiaosong Chu, Tiger Shao +11
Audio clustering is a fundamental task for organizing rapidly growing speech collections, supporting applications such as conversational analysis and speech-driven discovery. Howev…
Vector Symbolic Policy Gradient
Ryozo Masukawa, Sanggeon Yun, SungHeon Jeong +6
We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to t…
Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)
Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi +6
Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost exclusively against static, h…
FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
Sanggeon Yun, Ryozo Masukawa, Minhyoung Na +5
Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, latency, and reliability budgets…
State-Centric Decision Process
Sungheon Jeong, Ryozo Masukawa, Sanggeon Yun +2
Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtime structure that MDP analysi…
TorR: Towards Brain-Inspired Task-Oriented Reasoning via Cache-Oriented Algorithm-Architecture Co-design
Hyunwoo Oh, SungHeon Jeong, Suyeon Jang +4
Task-oriented object detection (TOOD) atop CLIP offers open-vocabulary, prompt-driven semantics, yet dense per-window computation and heavy memory traffic hinder real-time, power-l…
MERIT: Multi-domain Efficient RAW Image Translation
Wenjun Huang, Shenghao Fu, Yian Jin +10
RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their d…
Fair Context Learning for Evidence-Balanced Test-Time Adaptation in Vision-Language Models
Sanggeon Yun, Ryozo Masukawa, SungHeon Jeong +3
Vision-Language Models (VLMs) such as CLIP enable strong zero-shot recognition but suffer substantial degradation under distribution shifts. Test-Time Adaptation (TTA) aims to impr…
HopFormer: Sparse Graph Transformers with Explicit Receptive Field Control
Sanggeon Yun, Raheeb Hassan, Ryozo Masukawa +2
Graph Transformers typically rely on explicit positional or structural encodings and dense global attention to incorporate graph topology. In this work, we show that neither is ess…
Internal Flow Signatures for Self-Checking and Refinement in LLMs
Sungheon Jeong, Sanggeon Yun, Ryozo Masukawa +3
Large language models can generate fluent answers that are unfaithful to the provided context, while many safeguards rely on external verification or a separate judge after generat…
Draft and Refine with Visual Experts
Sungheon Jeong, Ryozo Masukawa, Jihong Park +5
While recent Large Vision-Language Models (LVLMs) exhibit strong multimodal reasoning abilities, they often produce ungrounded or hallucinated responses because they rely too heavi…
Uncertainty-Weighted Fusion of Image and Synthetic Event for Video Anomaly Detection
Sungheon Jeong, Jihong Park, Mohsen Imani
Most existing video anomaly detectors rely on RGB frames alone, which limit their ability to capture abrupt or transient motion cues that are critical for identifying anomalous eve…
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…
Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge
Sanggeon Yun, Ryozo Masukawa, Hanning Chen +6
The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computation…
Neuro-Photonix: Enabling Near-Sensor Neuro-Symbolic AI Computing on Silicon Photonics Substrate
Deniz Najafi, Hamza Errahmouni Barkam, Mehrdad Morsali +6
Neuro-symbolic Artificial Intelligence (AI) models, blending neural networks with symbolic AI, have facilitated transparent reasoning and context understanding without the need for…
Expanding Event Modality Applications through a Robust CLIP-Based Encoder
Sungheon Jeong, Hanning Chen, Sanggeon Yun +4
This paper introduces a powerful encoder that transfers CLIP`s capabilities to event-based data, enhancing its utility and expanding its applicability across diverse domains. While…
Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare
SungHeon Jeong, Hamza Errahmouni Barkam, Sanggeon Yun +3
Hyperdimensional computing (HDC) enables efficient data encoding and processing in high-dimensional space, benefiting machine learning and data analysis. However, underutilization…
VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation
Hanning Chen, Yang Ni, Wenjun Huang +6
Vision Transformers (ViTs) have emerged as the backbone of many segmentation models, consistently achieving state-of-the-art (SOTA) performance. However, their success comes at a s…
Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach
Wenjun Huang, Yang Ni, Arghavan Rezvani +5
Human pose estimation (HPE) is crucial for various applications. However, deploying HPE algorithms in surveillance contexts raises significant privacy concerns due to the potential…
NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval
Sanggeon Yun, Ryozo Masukawa, SungHeon Jeong +1
Customizable image retrieval from large datasets remains a critical challenge, particularly when preserving spatial relationships within images. Traditional hashing methods, primar…
A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission
Wenjun Huang, Arghavan Rezvani, Hanning Chen +4
Applications in the Internet of Things (IoT) utilize machine learning to analyze sensor-generated data. However, a major challenge lies in the lack of targeted intelligence in curr…