Virtual Reality (VR) provides immersive and interactive experiences in healthcare, education, entertainment, and defense. However, cybersickness remains a major barrier to its widespread adoption, reducing user comfort and engagement. Early and accurate detection of cybersickness is critical to developing adaptive VR systems that ensure safety and improve usability. In this study, we propose a novel real-time cybersickness detection approach using Bidirectional Long Short-Term Memory (Bi-LSTM) networks trained on electroencephalography (EEG) signals. Power Spectral Density (PSD) and Signal Magnitude Area (SMA) features were extracted to capture frequency- and amplitude-related characteristics of cybersickness. EEG data were collected from six electrodes across frontal (F3–F4), prefrontal (FP1–FP2), and central parietal (P3–P4) regions during VR exposure. The proposed Bi-LSTM model achieved 95% classification accuracy, significantly outperforming baseline methods. Results indicate that cybersickness can be reliably detected with a compact EEG setup, supporting resource-efficient, real-time monitoring for adaptive VR environments.

This study investigates effective feedback mechanisms to maintain human engagement in interactive machine learning (IML) systems, focusing on social media platforms. We developed “Loop,” an IML system based on human-in-the-loop (HITL) principles that recommends content while encouraging users to report inaccuracies for model refinement. Loop implements three types of artificial intelligence (AI) feedback on user reports: (a) machine learning (ML)-centric, (b) personal-centric, and (c) community-centric feedback. In addition, we evaluated the relative effectiveness of these feedback types under two different task criticality scenarios: high and low. A user study with 30 participants was conducted to evaluate Loop through questionnaires and interviews. Results showed that participants preferred algorithmic improvements for personal benefit over altruistic contributions to the community, especially for low-criticality tasks. Furthermore, personal-centric feedback had a significant impact on user engagement and satisfaction. Our findings provide insights into the effectiveness of machine feedback in HITL-ML systems, contributing to the design of more engaging and effective IML interfaces. We discuss implications and strategies for encouraging proactive user engagement in HITL-ML-based systems, emphasizing the importance of tailored feedback mechanisms.

Music is intrinsically connected to human experience, yet the plethora of choices often renders the search for the ideal piece perplexing, especially when the search terms are ambiguous. This study questions the viability of employing visual data, specifically images, in innovative queries for music search, and it aims to better align search results with users’ moods and situational context. We designed and evaluated three prototype systems for music search—TTTune (text-based), VisTune (image-based), and VTTune (hybrid)—to comparatively assess user experience and system usability. In a comprehensive user study involving 236 participants, each participant interacted with one of the systems and subsequently completed post-experimental surveys. A subset of participants also participated in in-depth interviews to further elucidate the potential and the advantages of image-based music retrieval (IMR) systems. Our findings reveal a marked preference for the user experience and usability offered by the IMR approach, as compared with the traditional text-based method. This underscores the potential of the image in an effective search query. Based on these findings, we discuss interface design guidelines tailored for IMR systems and factors affecting system performance, contributing to the evolving landscape of music search methods.

We propose an intuitive image-to-music retrieval (IMR) framework to improve the user experience on these platforms. The proposed method extracts mood and theme tags by searching for images from a pre-built database that are similar to a query image and then retrieves music with matching tag information. We investigated the system’s effectiveness by comparing participants’ satisfaction, intention to use, and valence between those who interacted with the system and those who did not. We also examined whether using mood or theme attributes affected the user-perceived suitability of the retrieved music. Results showed that all three variables of the interaction group were significantly higher than that of the non-interaction group and that there was no difference in the perceived suitability of music between the mood and theme attributes. Our study concludes that image attributes are effective in successful music retrieval and that interaction is a crucial factor in designing IMR systems.

Artificial intelligence (AI) is beginning to be applied in the field of art, which had hitherto been an area exclusively reserved for human creativity. Using online AI tools, lay people can easily create artworks that imitate the style of famous artists. Consequently, human judgment on the authenticity of artworks has become critical. While many studies have focused on copyright or value of AI-created artworks, we examine whether human beings can distinguish between paintings drawn by artists and fake paintings created using AI tools. We selected the AI’s recommendations for each artwork and prior information about the artists as factors that can affect human judgment and investigated how the two factors affect people’s discriminative abilities. We found that people have difficulty distinguishing authentic from fake artwork and that additional information about artists and artworks can affect people’s criteria for judging paintings. Furthermore, AI recommendations can help discriminate fake paintings, suggesting that AI-assisted decision-making could play an assistive role in human identification of digitized fake paintings.

Unpredictable situations frequently occur in real driving environments, and it is often difficult to recognize road signs. In this case, autonomous vehicles (AVs) have a limited ability to predict areas that cannot be detected, making it difficult to judge objects accurately when some information is lost. Therefore, we propose a framework that helps AVs infer proper information under limited conditions. The entire process consists of three steps. First, the missing part of the road sign is restored using the image generative pre-trained transformer model. Next, the sample image with the highest classification accuracy and restored quality is selected among several sample images. Finally, the selected image is provided to users through the designed user interface. The proposed framework improved recognition accuracy compared with unrestored accuracy, indicating the possibility of application as a driving assistance system, and is meaningful in that it is a system that mimics human reasoning ability.