Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders

This study presents a novel approach for intelligent user interaction interface generation and optimization, grounded in the variational autoencoder (VAE) model. With the rapid advancement of intelligent technologies, traditional interface design methods struggle to meet the evolving demands for diversity and personalization, often lacking flexibility in real-time adjustments to enhance the user experience. Human-Computer Interaction (HCI) plays a critical role in addressing these challenges by focusing on creating interfaces that are functional, intuitive, and responsive to user needs. This research leverages the RICO dataset to train the VAE model, enabling the simulation and creation of user interfaces that align with user aesthetics and interaction habits. By integrating real-time user behavior data, the system dynamically refines and optimizes the interface, improving usability and underscoring the importance of HCI in achieving a seamless user experience. Experimental findings indicate that the VAE-based approach significantly enhances the quality and precision of interface generation compared to other methods, including autoencoders (AE), generative adversarial networks (GAN), conditional GANs (cGAN), deep belief networks (DBN), and VAE-GAN. This work contributes valuable insights into HCI, providing robust technical solutions for automated interface generation and enhanced user experience optimization.

Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning

In an era where user interaction with technology is ubiquitous, the importance of user interface (UI) design cannot be overstated. A well-designed UI not only enhances usability but also fosters more natural, intuitive, and emotionally engaging experiences, making technology more accessible and impactful in everyday life. This research addresses this growing need by introducing an advanced emotion recognition system to significantly improve the emotional responsiveness of UI. By integrating facial expressions, speech, and textual data through a multi-branch Transformer model, the system interprets complex emotional cues in real-time, enabling UIs to interact more empathetically and effectively with users. Using the public MELD dataset for validation, our model demonstrates substantial improvements in emotion recognition accuracy and F1 scores, outperforming traditional methods. These findings underscore the critical role that sophisticated emotion recognition plays in the evolution of UIs, making technology more attuned to user needs and emotions. This study highlights how enhanced emotional intelligence in UIs is not only about technical innovation but also about fostering deeper, more meaningful connections between users and the digital world, ultimately shaping how people interact with technology in their daily lives.