Cultural heritage is essential for preserving the identity and history of civilizations, but its protection faces challenges from natural deterioration, environmental factors, and human activity. This study presents an intelligent monitoring system using an autonomous mobile robot, applied to Gongsanseong Fortress, a historic site in Korea. The system monitors structural damage at key points of interest, such as fortress walls and stone monuments, and also tracks long-term deterioration. It consists of an edge server for on-site data processing and localization, as well as a cloud server that performs deep learning-based damage detection. To assess potential collapse risks, a classification model is employed that considers factors including moisture and soil composition. Performance validation using data collected from Gongsanseong Fortress demonstrates the system’s effectiveness in damage detection, environmental impact assessment, and informed decision-making for heritage conservation.

Large Language Models (LLMs) acquire extensive factual knowledge from large-scale datasets and demonstrate remarkable performance across various tasks. However, since real-world knowledge is constantly changing, it is necessary to modify or expand the model's knowledge. To achieve this, knowledge editing techniques are employed to correct inaccurate or outdated information and inject new knowledge, thereby ensuring that the model remains current. However, in existing subject-centered editing approaches, repeatedly editing the same subject can lead to knowledge degradation, where previously edited knowledge is forgotten. In this paper, we analyze the causes of this knowledge degradation phenomenon and propose a two-step editing method that independently edits subjects and relations to mitigate this issue. Our method effectively alleviates knowledge degradation compared to existing knowledge editing techniques, achieving average performance improvements of 22.9% in multi-edit scenarios and 7.2% in sequential editing.

People with visual impairments often struggle to create content that relies heavily on visual elements, particularly when conveying spatial and structural information. Existing accessible drawing tools, which construct images line by line, are suitable for simple tasks like math but not for more expressive artwork. On the other hand, emerging generative AI-based text-to-image tools can produce expressive illustrations from descriptions in natural language, but they lack precise control over image composition and properties. To address this gap, our work integrates generative AI with a constructive approach that provides users with enhanced control and editing capabilities. Our system, AltCanvas, features a tile-based interface enabling users to construct visual scenes incrementally, with each tile representing an object within the scene. Users can add, edit, move, and arrange objects while receiving speech and audio feedback. Once completed, the scene can be rendered as a color illustration or as a vector for tactile graphic generation. Involving 14 blind or low-vision users in design and evaluation, we found that participants effectively used the AltCanvas’s workflow to create illustrations.

This work investigates large language models (LLMs) as teachable agents for learning by teaching (LBT). LBT with teachable agents helps learners identify knowledge gaps and discover new knowledge. However, teachable agents require expensive programming of subject-specific knowledge. While LLMs as teachable agents can reduce the cost, LLMs’ expansive knowledge as tutees discourages learners from teaching. We propose a prompting pipeline that restrains LLMs’ knowledge and makes them initiate “why” and “how” questions for effective knowledge-building. We combined these techniques into TeachYou, an LBT environment for algorithm learning, and AlgoBo, an LLM-based tutee chatbot that can simulate misconceptions and unawareness prescribed in its knowledge state. Our technical evaluation confirmed that our prompting pipeline can effectively configure AlgoBo’s problem-solving performance. Through a between-subject study with 40 algorithm novices, we also observed that AlgoBo’s questions led to knowledge-dense conversations (effect size=0.71). Lastly, we discuss design implications, cost-efficiency, and personalization of LLM-based teachable agents.

While great strides have been taken in advancing the field of Human-Robot Interaction (HRI), challenges abound in understanding and improving how Autonomous Vehicles (AVs) will interact with and within society. Through this paper, the authors attempt to paint the picture of challenges unique to the study and advancement of interfaces between AVs and vulnerable road users (VRUs). In turn, these gaps in research highlight the opportunities for academia, industry, and public policy to collaborate and advance the state of the art of AV-VRU interaction, and the need for a dedicated forum for sharing insights across these various sectors.

IEUM, a small data cube, is a fundamental agent of transportation envisioned in a future shared mobility system. By communicating with the user, IEUM understands what its user needs at the moment and acts as a mediating agent among humans, cars, and other traffic infrastructures. While taking you to your personalized route of the day, IEUM will also enhance the traffic and energy efficiency of our transportation systems with other IEUMs out on the road. With your buddy IEUM, moving is full of fun.