Chatbots enhanced by VR can deliver rich social cues through both verbal and non-verbal communication. While existing research emphasizes verbal factors and visual anthropomorphism, systematic exploration of body movements remains limited. This study proposes the Interactive-Cheerful-Empathic (ICE) Movements Framework, mapping body movements to three psychological needs: autonomy, competence, and relatedness. We developed a VR chatbot (Hilie) with four movement modes (interactive, cheerful, empathic, and no movement) and conducted a single-factor within-subjects experiment involving 56 university students. Quantitative and qualitative results revealed that chatbots with body movements—particularly cheerful movements—significantly enhanced users’ self-disclosure willingness, satisfaction, trust, and intention to use compared to static counterparts. The ICE framework effectively addresses multi-level psychological needs through coordinated movements. This work pioneers the operationalization of self-determination theory in chatbot design, providing theoretical models and practical guidelines for developing highly anthropomorphic chatbots, while advancing optimization strategies for online mental health services.

In social virtual reality (VR), maintaining appropriate interpersonal distance is essential for user comfort and privacy. However, most existing locomotion methods provide limited support for respecting personal space, leaving users vulnerable to unintentional or socially inappropriate intrusions. To address this issue, we propose potential field-guided teleportation, a proactive locomotion framework consisting of two method implementations that dynamically adjust teleportation targets based on real-time interpersonal proximity, preventing entry into others' personal spaces without explicit user intervention. We evaluate our technique through two user studies: a preliminary study exploring energy-based constraint parameters, followed by a comparative study against conventional and negotiated teleportation methods. Experiments were conducted in socially interactive VR scenarios populated with simulated users exhibiting human-like behaviors. Results demonstrate that our methods reduce perceived social anxiety while maintaining locomotion efficiency and usability. This work presents a socially-aware locomotion strategy that balances personal space protection with effective and socially appropriate movement in shared virtual environments.

Geographically dispersed users often rely on virtual avatars as intermediaries to facilitate interactive communication and collaboration. However, existing methods for augmented reality (AR) telepresence applications exhibit limitations, including restricted movement within confined sub-areas, lack of smooth transitions, and the necessity for manually establishing object mapping between dissimilar environments. We present a novel interactive AR framework for virtual avatar locomotion adaption while preserving semantic coherence across dissimilar indoor scenes. Initially, we conduct a preliminary user study to identify key attributes influencing preferred avatar movement. These attributes are quantified as features, and a dataset of user annotations on avatar movements is created. Based on the user interaction and scene configurations, we employ a deep reinforcement learning neural network to guide the avatar to the ideal position while maximizing semantic coherence. We validate our proposed framework through simulations and user studies by implementing an AR-based 3D telepresence prototype, demonstrating the efficacy of our framework in conveying user intentions across dissimilar environments, enabling natural and immersive 3D telepresence interactions.

The locomotion and interaction of multi-user groups are critical components of social virtual reality (VR), where users collaboratively navigate shared spaces defined by their relationships. As metaverse and social VR platforms evolve, safeguarding group spatial integrity becomes paramount. While teleportation-widely adopted for efficient navigation-enhances communication, it risks unintended intrusion into group spaces, compromising privacy and security. This paper presents two novel negotiated user-to-group teleportation techniques, paired with dynamic zone computation methods, to address these challenges. Our approach enables guest users to join groups through spatially aware teleportation, mediated by real-time negotiation. The negotiation interaction process is designed to facilitate users to negotiate teleportation locations efficiently and smoothly. To validate our techniques, we conducted a user study with 36 participants in a VR art museum environment, where they performed collaborative social-tour tasks. The findings demonstrate that our techniques significantly enhance group privacy protection, effectively support user-to-group negotiation of teleportation joining requirements, and alleviate anxiety associated with unwanted proximity within social VR groups.

The growing adoption of social virtual reality (VR) platforms underscores the importance of safeguarding personal VR space to maintain user privacy and security. Teleportation, a prevalent instantaneous locomotion method in VR, facilitates user engagement but can also inadvertently intrude upon personal VR space, thereby raising privacy concerns. This paper introduces three innovative negotiated teleportation techniques designed to secure user-to-user teleportation and protect personal space privacy, all under a unified small-group development framework. We have designed and evaluated three types of negotiated teleportation techniques: Sector technique for directional control, Distance technique for minimum social distance control, and Area technique for defining circular permissible teleportation areas. These techniques foster a collaborative approach to selecting teleportation points that respect personal space. To evaluate the efficacy of these techniques, we conducted a user study with 20 participants who performed social tasks within a virtual campus environment. The findings demonstrate that our techniques significantly enhance privacy protection and alleviate anxiety associated with unwanted proximity in social VR.

Modern graphs exert colossal time and space pressure on graph analytics applications. In 2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many compression algorithms have been developed to support direct processing on compressed graphs to address this challenge. However, previous graph compression algorithms do not focus on leveraging redundancy in repeated neighbor sequences, so they do not save the amount of computation for graph analytics. We develop CompressGraph, an efficient rule-based graph analytics engine that leverages data redundancy in graphs to achieve both performance boost and space reduction for common graph applications. CompressGraph has three advantages over previous works. First, the rule-based abstraction of CompressGraph supports the reuse of intermediate results during graph traversal, thus saving time. Second, CompressGraph has intense expressiveness to support a wide range of graph applications. Third, CompressGraph scales well under high parallelism because the context-free rules have few dependencies. Experiments show that CompressGraph provides significant performance and space benefits on both CPUs and GPUs. On evaluating six typical graph applications, CompressGraph can achieve 1.97× speedup on the CPU, while 3.95× speedup on the GPU, compared to the state-of-the-art CPU and GPU methods, respectively. Moreover, CompressGraph can save an average of 71.27% memory savings on CPU and 70.36 on GPU.