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LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models.

IEEE transactions on visualization and computer graphics
The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3...

Disentangled Active Learning on Graphs.

Neural networks : the official journal of the International Neural Network Society
Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling...

Dual-view graph-of-graph representation learning with graph Transformer for graph-level anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Graph-Level Anomaly Detection (GLAD) endeavors to pinpoint a small subset of anomalous graphs that deviate from the normal data distribution within a given set of graph data. Existing GLAD methods typically rely on Graph Neural Networks (GNNs) to ext...

Large Language Model-Driven 3D Hyper-Realistic Interactive Intelligent Digital Human System.

Sensors (Basel, Switzerland)
Digital technologies are undergoing comprehensive integration across diverse domains and processes of the human economy, politics, culture, society, and ecological civilization. This integration brings forth novel concepts, formats, and models. In th...

A graph neural network explainability strategy driven by key subgraph connectivity.

Journal of biomedical informatics
Current explainability strategies for Graph Neural Networks (GNNs) often focus on individual nodes or edges, neglecting the significance of key subgraphs in decision-making processes. This limitation can result in dispersed and less reliable explanat...

Contrastive graph auto-encoder for graph embedding.

Neural networks : the official journal of the International Neural Network Society
Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, ...

SensARy Substitution: Augmented Reality Techniques to Enhance Force Perception in Touchless Robot Control.

IEEE transactions on visualization and computer graphics
The lack of haptic feedback in touchless human-robot interaction is critical in applications such as robotic ultrasound, where force perception is crucial to ensure image quality. Augmented reality (AR) is a promising tool to address this limitation ...

FovealNet: Advancing AI-Driven Gaze Tracking Solutions for Efficient Foveated Rendering in Virtual Reality.

IEEE transactions on visualization and computer graphics
Leveraging real-time eye tracking, foveated rendering optimizes hardware efficiency and enhances visual quality virtual reality (VR). This approach leverages eye-tracking techniques to determine where the user is looking, allowing the system to rende...

Hit Around: Substitutional Moving Robot for Immersive and Exertion Interaction with Encountered-Type Haptic.

IEEE transactions on visualization and computer graphics
Previous works have shown the potential of immersive technologies to make physical activities a more engaging experience. With encountered-type haptic feedback, users can perceive a more realistic sensation for exertion interaction in substitutions r...

Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations.

IEEE transactions on visualization and computer graphics
Robotic ultrasound systems have the potential to improve medical diagnostics, but patient acceptance remains a key challenge. To address this, we propose a novel system that combines an AI-based virtual agent, powered by a large language model (LLM),...