activity
20172026
most citedCharacterizing and Detecting Money Laundering Activities on the Bitcoin Network

46 citations · 102 across the 28 of their papers we have counts for

collaborators

31 papers

cs.CV2026

SUMI: Scalable Unified Model for 3D Point Cloud Inference

Yanlong Li, Kanchana Thilakarathna

Point cloud completion commonly follows a coarse-to-fine paradigm, where a low-density coarse shape is first predicted and then upsampled to the target resolution. Although recent…

cs.CR2026

Dependency Triad: A Metric to Quantify the Dependencies Between Attributes for Local Differential Privacy

Sandaru Jayawardana, Sennur Ulukus, Ming Ding +1

Collecting multidimensional user data is essential for extracting rich insights across various applications. Local Differential Privacy (LDP) has emerged as a de facto standard for…

cs.LG2026

RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment

Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha +2

Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Yet, existing deep learning ap…

cs.LG2026

STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition

Nirhoshan Sivaroopan, Albert Zomaya, Kanchana Thilakarathna

HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and…

cs.LG2026

Memory Retrieval in Transformers: Insights from The Encoding Specificity Principle

Viet Hung Dinh, Ming Ding, Youyang Qu +1

While explainable artificial intelligence (XAI) for large language models (LLMs) remains an evolving field with many unresolved questions, increasing regulatory pressures have spur…

cs.CR2026

SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks

Nirhoshan Sivaroopan, Kanchana Thilakarathna, Albert Zomaya +6

Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that fail on semantically meaningfu…