Latest Articles for Human-Computer Interaction

Study explores how language models work with humans and each other in task completion.

TraveLER enhances video understanding through interactive questioning for better answers.

Examines the growth of communication between humans and robots using natural speech.

ChatGLM-RLHF improves AI interactions through human feedback and advanced training methods.

Discover how interactive visualization improves human-centered AI tools for better decision-making.

Highlighting the importance of formative situations in design evaluations.

MotionChain creates realistic human movements through conversation, combining text, images, and motion data.

Exploring the role of generative AI in enhancing software safety practices.

EMO-SUPERB project enhances speech emotion recognition through improved techniques and community collaboration.

A new framework enhances language models by recognizing and responding to different speech styles.

A new method improves goal recognition by focusing on varied agent behaviors.

DaTAPlan improves how robots assist us in daily tasks through task anticipation.

Research shows beta bursts improve brain-computer interface performance for imagined movements.

New techniques enable safer decision-making in reinforcement learning through human expert guidance.

Research explores how multisensory feedback influences user interaction in VR.

Exploring the viability of smaller language models for effective robotic task planning.

Examining the role of language models in supporting critical thinking among philosophers.

New methods improve accessibility and accuracy in audio captioning.

A new AI approach aims to improve image and video generation speed and efficiency.

New methods enhance accuracy of hand position detection during interactions.

Research aims to make language models safer and more useful for users.

Study examines how human-like descriptions affect trust in AI systems.

Clear communication builds trust in autonomous vehicles for all road users.

A new method to shape LLM personalities using the Big Five traits.

A new approach integrates visual and textual data for better emotion recognition.

A new method improves machine learning efficiency with human feedback.

Exploring new methods to improve speech emotion recognition using natural data.

A new method enhances emotion prediction accuracy using data augmentation techniques.

This research enhances action recognition using 2D hand movements from smart glasses.

EyeFormer predicts individual user focus to improve GUI design.

New models improve understanding of how hands interact with objects.

A new training method enhances language model performance and user experience.

This study enhances emotion recognition through multi-modal self-supervised methods.

TROT improves accuracy in recognizing human activities by leveraging temporal relationships.

A system generates lifelike hand interactions with various objects.

This study highlights how context enhances object description in machine learning models.

This study explores how problem-solving styles improve user engagement with XAI systems.

User feedback, especially follow-up comments, improves evaluations of dialogue systems.

Learn how PCAs enhance user interaction and experience in technology.

An overview of how memory enhances the performance of LLM-based agents.