School of Engineering Department of Computer Science and Engineering 113 Pathfinding in Multi-dimensional Road Networks Supervisor: LIU Ziyi / CSE Student: WONG Cheuk Yuen / MATH-GM Course: UROP 1100, Summer The existing route planning problems expect the user query consider only AC or only NAC. While, it gives more flexibility to user in route planning if the algorithm is compatible with both AC and NAC. In this project, we define a new problem in route planning (Mixed-Constrained Shortest Path) to deal with the user query that potentially considers both AC and NAC. Moreover, we design a Mixed-CSP index structure with it’s construction algorithm and an extension of existing query algorithms to solve the problem efficiently. Assess User Experience to Design Effective Visual Representation and Interaction in Virtual Reality Supervisor: MA Xiaojuan / CSE Student: ZHENG Fanshi / CPEG Course: UROP 1100, Spring The field of data visualization has witnessed significant expansion into the realm of virtual reality (VR) which offers immersive and interactive experiences that enhance the understanding and exploration of complicated large datasets. Inspired by the multimodalities that VR can provide with users, this project is dedicated to design a multimodal VR tool for data visualization by applying voice recognition, head gaze, gesture recognition and so on. And this report provides an overview of the progress made in the initial stages, including project preparation, development, and exploration of relevant VR toolkits such as IATK and DxR. As for the interaction part, Meta Interaction SDK v62 is used as the toolkit and my initial interaction is build based on it. Moreover, the report highlights the development of a prototype, focusing on integrating voice input as a means of interaction in the VR environment. This prototype serves as a steppingstone towards the final goal. Designing Conversational Agents for Neurocognitive Disorders Screening Supervisor: MA Xiaojuan / CSE Student: CHEN Yi-long / COMP Course: UROP 1100, Spring Neurocognitive disorders (NCDs) negatively impact those affected and healthcare systems. With a growing aging population, early detection for timely management and intervention becomes extra important. Common in-person NCD screenings conducted currently face challenges in scalability and accessibility. This study explores the user feedback of conversational agents (CAs) for NCD screening with the elderly's perceptions, needs, and concerns. Through a speed dating interview format, preliminary pilot study results highlight themes such as user motivation, trust in technology, and the need for additional assistance. This ongoing study aims to enhance the acceptance and use of AI-driven solutions and spot design opportunities and risks to guide the development of effective and user-friendly CAs in the future.
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