2 citations · 3 across the 8 of their papers we have counts for
9 papers
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…
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…
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…
Prompt-Induced Over-Generation as Denial-of-Service: A Black-Box Attack-Side Benchmark
Manu, Yi Guo, Kanchana Thilakarathna +5
Large Language Models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This degrades answer quality, inflate…
RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition
Nirhoshan Sivaroopan, Hansi Karunarathna, Chamara Madarasingha +2
Human Activity Recognition (HAR) underpins applications in healthcare, rehabilitation, fitness tracking, and smart environments, yet existing deep learning approaches demand datase…
A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
Nirhoshan Sivaroopan, Kaushitha Silva, Chamara Madarasingha +4
Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It enables the creation of synthetic data…
Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology
Nirhoshan Sivaroopan, Chamuditha Jayanga Galappaththige, Chalani Ekanayake +4
Machine-learning-assisted cancer subtyping is a promising avenue in digital pathology. Cancer subtyping models, however, require careful training using expert annotations so that t…
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging
Nirhoshan Sivaroopan, Dumindu Bandara, Chamara Madarasingha +3
Network data analytics are now at the core of almost every networking solution. Nonetheless, limited access to networking data has been an enduring challenge due to many reasons in…
Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
Nirhoshan Sivaroopan, Chamuditha Jayanga, Chalani Ekanayake +6
Deep neural network models can learn clinically relevant features from millions of histopathology images. However generating high-quality annotations to train such models for each…