208 citations · 384 across the 39 of their papers we have counts for
41 papers
How People Use ChatGPT in Australia: A WildChat Analysis
Ying Ma, Katy Gero, Clément Canonne +2
Generative AI chatbots are increasingly embedded in everyday life, yet most large-scale studies describe global patterns. This paper presents an Australia-focused analysis of WildC…
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…
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…
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…
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…
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…
FedVideoMAE: Efficient Federated Video Moderation with Differential Privacy and Secure Aggregation
Ziyuan Tao, Chuanzhi Xu, Sandaru Jayawardana +4
Short-form video moderation is increasingly pushed toward edge and privacy-sensitive settings, where users may intend videos for a limited audience, such as friends or private grou…
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 Multi-Drone Multi-View Dataset and Deep Learning Framework for Pedestrian Detection and Tracking
Kosta Dakic, Kanchana Thilakarathna, Rodrigo N. Calheiros +1
Multi-drone surveillance systems offer enhanced coverage and robustness for pedestrian tracking, yet existing approaches struggle with dynamic camera positions and complex occlusio…
The Hidden Cost of Correlation: Rethinking Privacy Leakage in Local Differential Privacy
Sandaru Jayawardana, Sennur Ulukus, Ming Ding +1
Local differential privacy (LDP) has emerged as a promising paradigm for privacy-preserving data collection in distributed systems, where users contribute multi-dimensional records…
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…
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
Seunghyun Lee, Omid Tavallaie, Shuaijun Chen +4
Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. H…
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders
Kosta Dakic, Kanchana Thilakarathna, Rodrigo N. Calheiros +1
Multiview systems have become a key technology in modern computer vision, offering advanced capabilities in scene understanding and analysis. However, these systems face critical c…
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
Omid Tavallaie, Kanchana Thilakarathna, Suranga Seneviratne +2
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Ind…
ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks
Niousha Nazemi, Omid Tavallaie, Shuaijun Chen +5
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data…
TripletViNet: Mitigating Misinformation Video Spread Across Platforms
Petar Smolovic, Thilini Dahanayaka, Kanchana Thilakarathna
There has been rampant propagation of fake news and misinformation videos on many platforms lately, and moderation of such content faces many challenges that must be overcome. Rece…
CAFe: Cost and Age aware Federated Learning
Sahan Liyanaarachchi, Kanchana Thilakarathna, Sennur Ulukus
In many federated learning (FL) models, a common strategy employed to ensure the progress in the training process, is to wait for at least clients out of the total clients…
The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Youyang Qu, Ming Ding, Nan Sun +3
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing…
DiffPMAE: Diffusion Masked Autoencoders for Point Cloud Reconstruction
Yanlong Li, Chamara Madarasingha, Kanchana Thilakarathna
Point cloud streaming is increasingly getting popular, evolving into the norm for interactive service delivery and the future Metaverse. However, the substantial volume of data ass…
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…
The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting
Teng-Hui Huang, Thilini Dahanayaka, Kanchana Thilakarathna +2
Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characterist…
3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulation
Amaya Dharmasiri, Dinithi Dissanayake, Mohamed Afham +3
3D generative models have been recently successful in generating realistic 3D objects in the form of point clouds. However, most models do not offer controllability to manipulate t…