AI Medical Compendium Journal:
IEEE transactions on visualization and computer graphics

Showing 11 to 20 of 74 articles

How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?

IEEE transactions on visualization and computer graphics
In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer's perception of data, leading to misinterpretations and decisio...

HuBar: A Visual Analytics Tool to Explore Human Behavior Based on fNIRS in AR Guidance Systems.

IEEE transactions on visualization and computer graphics
The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reaso...

Towards Dataset-Scale and Feature-Oriented Evaluation of Text Summarization in Large Language Model Prompts.

IEEE transactions on visualization and computer graphics
Recent advancements in Large Language Models (LLMs) and Prompt Engineering have made chatbot customization more accessible, significantly reducing barriers to tasks that previously required programming skills. However, prompt evaluation, especially a...

Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features.

IEEE transactions on visualization and computer graphics
Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR-based classroom is a significant concern. Several factors, including mind wandering, external noise...

RingGesture: A Ring-Based Mid-Air Gesture Typing System Powered by a Deep-Learning Word Prediction Framework.

IEEE transactions on visualization and computer graphics
Text entry is a critical capability for any modern computing experience, with lightweight augmented reality (AR) glasses being no exception. Designed for all-day wearability, a limitation of lightweight AR glass is the restriction to the inclusion of...

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts.

IEEE transactions on visualization and computer graphics
Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVIS, a...

Eliciting Model Steering Interactions From Users via Data and Visual Design Probes.

IEEE transactions on visualization and computer graphics
Visual and interactive machine learning systems (IML) are becoming ubiquitous as they empower individuals with varied machine learning expertise to analyze data. However, it remains complex to align interactions with visual marks to a user's intent f...

Synthesize Personalized Training for Robot-Assisted Upper Limb Rehabilitation With Diversity Enhancement.

IEEE transactions on visualization and computer graphics
For upper limb rehabilitation, the robot-assisted technique in combination with serious games requires well-specified training plans. For the best quality of the rehabilitation process, customized game levels for each user are desired, while it is la...

Machine Learning Approaches for 3D Motion Synthesis and Musculoskeletal Dynamics Estimation: A Survey.

IEEE transactions on visualization and computer graphics
The inference of 3D motion and dynamics of the human musculoskeletal system has traditionally been solved using physics-based methods that exploit physical parameters to provide realistic simulations. Yet, such methods suffer from computational compl...

Personalized Language Model Selection Through Gamified Elicitation of Contrastive Concept Preferences.

IEEE transactions on visualization and computer graphics
Language models are widely used for different Natural Language Processing tasks while suffering from a lack of personalization. Personalization can be achieved by, e.g., fine-tuning the model on training data that is created by the user (e.g., social...