arXiv subjects

Xian Wang

Publications and source records attributed to Xian Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

PersoNo: Personalised Notification Urgency Classifier in Mixed Reality

Mixed Reality (MR) is increasingly integrated into daily life, providing enhanced capabilities across various domains. However, users face growing notification streams that disrupt their immersive experience. We present PersoNo, a personalised notification urgency classifier for MR that intelligently classifies notifications based on individual user preferences. Through a user study (N=18), we created the first MR notification dataset containing both self-labelled and interaction-based data across activities with varying cognitive demands. Our thematic analysis revealed that, unlike in mobiles, the activity context is equally important as the content and the sender in determining notification urgency in MR. Leveraging these insights, we developed PersoNo using large language models that analyse users replying behaviour patterns. Our multi-agent approach achieved 81.5% accuracy and significantly reduced false negative rates (0.381) compared to baseline models. PersoNo has the potential not only to reduce unnecessary interruptions but also to offer users understanding and control of the system, adhering to Human-Centered Artificial Intelligence design principles.

cs.HC↗

arXiv2025

A Comprehensive Survey of Electrical Stimulation Haptic Feedback in Human-Computer Interaction

Haptic perception and feedback play a pivotal role in interactive experiences, forming an essential component of human-computer interaction (HCI). In recent years, the field of haptic interaction has witnessed significant advancements, particularly in the area of electrical haptic feedback, driving innovation across various domains. To gain a comprehensive understanding of the current state of research and the latest developments in electrical haptic interaction, this study systematically reviews the literature in this area. Our investigation covers key aspects including haptic devices, haptic perception mechanisms, the comparison and integration of electrical haptic feedback with other feedback modalities, and their diverse applications. Specifically, we conduct a systematic analysis of 110 research papers to explore the forefront of electrical haptic feedback, providing insights into its latest trends, challenges, and future directions.

cs.HC↗

arXiv2025

Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning

Recent innovations in autonomous drones have facilitated time-optimal flight in single-drone configurations, and enhanced maneuverability in multi-drone systems by applying optimal control and learning-based methods. However, few studies have achieved time-optimal motion planning for multi-drone systems, particularly during highly agile maneuvers or in dynamic scenarios. This paper presents a decentralized policy network using multi-agent reinforcement learning for time-optimal multi-drone flight. To strike a balance between flight efficiency and collision avoidance, we introduce a soft collision-free mechanism inspired by optimization-based methods. By customizing PPO in a centralized training, decentralized execution (CTDE) fashion, we unlock higher efficiency and stability in training while ensuring lightweight implementation. Extensive simulations show that, despite slight performance trade-offs compared to single-drone systems, our multi-drone approach maintains near-time-optimal performance with a low collision rate. Real-world experiments validate our method, with two quadrotors using the same network as in simulation achieving a maximum speed of 13.65 m/s and a maximum body rate of 13.4 rad/s in a 5.5 m * 5.5 m * 2.0 m space across various tracks, relying entirely on onboard computation.

cs.RO↗

arXiv2025

TeamPortal: Exploring Virtual Reality Collaboration Through Shared and Manipulating Parallel Views

Virtual Reality (VR) offers a unique collaborative experience, with parallel views playing a pivotal role in Collaborative Virtual Environments by supporting the transfer and delivery of items. Sharing and manipulating partners' views provides users with a broader perspective that helps them identify the targets and partner actions. We proposed TeamPortal accordingly and conducted two user studies with 72 participants (36 pairs) to investigate the potential benefits of interactive, shared perspectives in VR collaboration. Our first study compared ShaView and TeamPortal against a baseline in a collaborative task that encompassed a series of searching and manipulation tasks. The results show that TeamPortal significantly reduced movement and increased collaborative efficiency and social presence in complex tasks. Following the results, the second study evaluated three variants: TeamPortal+, SnapTeamPortal+, and DropTeamPortal+. The results show that both SnapTeamPortal+ and DropTeamPortal+ improved task efficiency and willingness to further adopt these technologies, though SnapTeamPortal+ reduced co-presence. Based on the findings, we proposed three design implications to inform the development of future VR collaboration systems.

cs.HC↗

arXiv2025

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Recently, vision-language models have made remarkable progress, demonstrating outstanding capabilities in various tasks such as image captioning and video understanding. We introduce Valley2, a novel multimodal large language model designed to enhance performance across all domains and extend the boundaries of practical applications in e-commerce and short video scenarios. Notably, Valley2 achieves state-of-the-art (SOTA) performance on e-commerce benchmarks, surpassing open-source models of similar size by a large margin (79.66 vs. 72.76). Additionally, Valley2 ranks second on the OpenCompass leaderboard among models with fewer than 10B parameters, with an impressive average score of 67.4. The code and model weights are open-sourced at https://github.com/bytedance/Valley.

cs.CV↗

arXiv2025

Assessing the Acceptance of a Mid-Air Gesture Syntax for Smart Space Interaction: An Empirical Study

This article explores the use of a location-aware mid-air gesture-based command triplet syntax to interact with a smart space. The syntax, inspired by human language, is built as a vocative case with an imperative structure. In a sentence like 'Light, please switch on', the object being activated is invoked via making a gesture that mimics its initial letter/acronym (vocative, coincident with the sentence's elliptical subject). A geometrical or directional gesture then identifies the action (imperative verb) and may include an object feature or a second object with which to network (complement), which also represented by the initial or acronym letter. Technically, an interpreter relying on a trainable multidevice gesture recognition layer makes the pair/triplet syntax decoding possible. The recognition layer works on acceleration and position input signals from graspable (smartphone) and free-hand devices (smartwatch and external depth cameras), as well as a specific compiler. On a specific deployment at a Living Lab facility, the syntax has been instantiated via the use of a lexicon derived from English (with respect to the initial letters and acronyms). A within-subject analysis with twelve users has enabled the analysis of the syntax acceptance (in terms of usability, gesture agreement for actions over objects, and social acceptance) and technology preference of the gesture syntax within its three device implementations (graspable, wearable, and device-free ones). Participants express consensus regarding the simplicity of learning the syntax and its potential effectiveness in managing smart resources. Socially, participants favoured the Watch for outdoor activities and the Phone for home and work settings, underscoring the importance of social context in technology design. The Phone emerged as the preferred option for gesture recognition due to its efficiency and familiarity.

cs.HC↗

arXiv2024

LMLPA: Language Model Linguistic Personality Assessment

Large Language Models (LLMs) are increasingly used in everyday life and research. One of the most common use cases is conversational interactions, enabled by the language generation capabilities of LLMs. Just as between two humans, a conversation between an LLM-powered entity and a human depends on the personality of the conversants. However, measuring the personality of a given LLM is currently a challenge. This paper introduces the Language Model Linguistic Personality Assessment (LMLPA), a system designed to evaluate the linguistic personalities of LLMs. Our system helps to understand LLMs' language generation capabilities by quantitatively assessing the distinct personality traits reflected in their linguistic outputs. Unlike traditional human-centric psychometrics, the LMLPA adapts a personality assessment questionnaire, specifically the Big Five Inventory, to align with the operational capabilities of LLMs, and also incorporates the findings from previous language-based personality measurement literature. To mitigate sensitivity to the order of options, our questionnaire is designed to be open-ended, resulting in textual answers. Thus, the AI rater is needed to transform ambiguous personality information from text responses into clear numerical indicators of personality traits. Utilising Principal Component Analysis and reliability validations, our findings demonstrate that LLMs possess distinct personality traits that can be effectively quantified by the LMLPA. This research contributes to Human-Computer Interaction and Human-Centered AI, providing a robust framework for future studies to refine AI personality assessments and expand their applications in multiple areas, including education and manufacturing.

cs.CL↗

arXiv2024

Lightweight equivariant model for efficient machine learning interatomic potentials

In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Furthermore, we conduct molecular dynamics (MD) simulations using the LEIGNN force field across solid, liquid, and gas systems. It is found that LEIGNN can achieve the accuracy of \textit{ab initio} MD across all examined systems.

cs.CE↗

arXiv2024

Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://github.com/Shen-Group/DeepRelax.

cond-mat.mtrl-sci↗

arXiv2024

Towards Massive Interaction with Generalist Robotics: A Systematic Review of XR-enabled Remote Human-Robot Interaction Systems

The rising interest of generalist robots seek to create robots with versatility to handle multiple tasks in a variety of environments, and human will interact with such robots through immersive interfaces. In the context of human-robot interaction (HRI), this survey provides an exhaustive review of the applications of extended reality (XR) technologies in the field of remote HRI. We developed a systematic search strategy based on the PRISMA methodology. From the initial 2,561 articles selected, 100 research papers that met our inclusion criteria were included. We categorized and summarized the domain in detail, delving into XR technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), and their applications in facilitating intuitive and effective remote control and interaction with robotic systems. The survey highlights existing articles on the application of XR technologies, user experience enhancement, and various interaction designs for XR in remote HRI, providing insights into current trends and future directions. We also identified potential gaps and opportunities for future research to improve remote HRI systems through XR technology to guide and inform future XR and robotics research.

cs.HC↗

arXiv2024

Exploring the Opportunity of Augmented Reality (AR) in Supporting Older Adults Explore and Learn Smartphone Applications

The global aging trend compels older adults to navigate the evolving digital landscape, presenting a substantial challenge in mastering smartphone applications. While Augmented Reality (AR) holds promise for enhancing learning and user experience, its role in aiding older adults' smartphone app exploration remains insufficiently explored. Therefore, we conducted a two-phase study: (1) a workshop with 18 older adults to identify app exploration challenges and potential AR interventions, and (2) tech-probe participatory design sessions with 15 participants to co-create AR support tools. Our research highlights AR's effectiveness in reducing physical and cognitive strain among older adults during app exploration, especially during multi-app usage and the trial-and-error learning process. We also examined their interactional experiences with AR, yielding design considerations on tailoring AR tools for smartphone app exploration. Ultimately, our study unveils the prospective landscape of AR in supporting the older demographic, both presently and in future scenarios.

cs.HC↗

arXiv2023

VR PreM+ : An Immersive Pre-learning Branching Visualization System for Museum Tours

We present VR PreM+, an innovative VR system designed to enhance web exploration beyond traditional computer screens. Unlike static 2D displays, VR PreM+ leverages 3D environments to create an immersive pre-learning experience. Using keyword-based information retrieval allows users to manage and connect various content sources in a dynamic 3D space, improving communication and data comparison. We conducted preliminary and user studies that demonstrated efficient information retrieval, increased user engagement, and a greater sense of presence. These findings yielded three design guidelines for future VR information systems: display, interaction, and user-centric design. VR PreM+ bridges the gap between traditional web browsing and immersive VR, offering an interactive and comprehensive approach to information acquisition. It holds promise for research, education, and beyond.

cs.HC↗

arXiv2023

Accelerating Quantum Optimal Control of Multi-Qubit Systems with Symmetry-Based Hamiltonian Transformations

We present a novel, computationally efficient approach to accelerate quantum optimal control calculations of large multi-qubit systems used in a variety of quantum computing applications. By leveraging the intrinsic symmetry of finite groups, the Hilbert space can be decomposed and the Hamiltonians block-diagonalized to enable extremely fast quantum optimal control calculations. Our approach reduces the Hamiltonian size of an $n$-qubit system from 2^n by 2^n to O(n by n) or O((2^n / n) by (2^n / n)) under Sn or Dn symmetry, respectively. Most importantly, this approach reduces the computational runtime of qubit optimal control calculations by orders of magnitude while maintaining the same accuracy as the conventional method. As prospective applications, we show that (1) symmetry-protected subspaces can be potential platforms for quantum error suppression and simulation of other quantum Hamiltonians, and (2) Lie-Trotter-Suzuki decomposition approaches can generalize our method to a general variety of multi-qubit systems.

quant-ph↗

arXiv2023

Designing Loving-Kindness Meditation in Virtual Reality for Long-Distance Romantic Relationships

Loving-kindness meditation (LKM) is used in clinical psychology for couples' relationship therapy, but physical isolation can make the relationship more strained and inaccessible to LKM. Virtual reality (VR) can provide immersive LKM activities for long-distance couples. However, no suitable commercial VR applications for couples exist to engage in LKM activities of long-distance. This paper organized a series of workshops with couples to build a prototype of a couple-preferred LKM app. Through analysis of participants' design works and semi-structured interviews, we derived design considerations for such VR apps and created a prototype for couples to experience. We conducted a study with couples to understand their experiences of performing LKM using the VR prototype and a traditional video conferencing tool. Results show that LKM session utilizing both tools has a positive effect on the intimate relationship and the VR prototype is a more preferable tool for long-term use. We believe our experience can inform future researchers.

cs.HC↗

arXiv2023

The Dark Side of Augmented Reality: Exploring Manipulative Designs in AR

Augmented Reality (AR) applications are becoming more mainstream, with successful examples in the mobile environment like Pokemon GO. Current malicious techniques can exploit these environments' immersive and mixed nature (physical-virtual) to trick users into providing more personal information, i.e., dark patterns. Dark patterns are deceiving techniques (e.g., interface tricks) designed to influence individuals' behavioural decisions. However, there are few studies regarding dark patterns' potential issues in AR environments. In this work, using scenario construction to build our prototypes, we investigate the potential future approaches that dark patterns can have. We use VR mockups in our user study to analyze the effects of dark patterns in AR. Our study indicates that dark patterns are effective in immersive scenarios, and the use of novel techniques such as `haptic grabbing' to drag participants' attention can influence their movements. Finally, we discuss the impact of such malicious techniques and what techniques can mitigate them.

cs.HC↗

arXiv2023

Bridging the Generational Gap: Exploring How Virtual Reality Supports Remote Communication Between Grandparents and Grandchildren

When living apart, grandparents and grandchildren often use audio-visual communication approaches to stay connected. However, these approaches seldom provide sufficient companionship and intimacy due to a lack of co-presence and spatial interaction, which can be fulfilled by immersive virtual reality (VR). To understand how grandparents and grandchildren might leverage VR to facilitate their remote communication and better inform future design, we conducted a user-centered participatory design study with twelve pairs of grandparents and grandchildren. Results show that VR affords casual and equal communication by reducing the generational gap, and promotes conversation by offering shared activities as bridges for connection. Participants preferred resemblant appearances on avatars for conveying well-being but created ideal selves for gaining playfulness. Based on the results, we contribute eight design implications that inform future VR-based grandparent-grandchild communications.

cs.HC↗

arXiv2022

Memory-Efficient Differentiable Programming for Quantum Optimal Control of Discrete Lattices

Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits and linear growth in memory requirements with increasing number of time steps. Employing QOC for discrete lattices reveals that these memory requirements are a barrier for simulating large models or long time spans. We employ a nonstandard differentiable programming approach that significantly reduces the memory requirements at the cost of a reasonable amount of recomputation. The approach exploits invertibility properties of the unitary matrices to reverse the computation during back-propagation. We utilize QOC software written in the differentiable programming framework JAX that implements this approach, and demonstrate its effectiveness for lattice gauge theory.

quant-ph↗

arXiv2022

Reducing Stress and Anxiety in the Metaverse: A Systematic Review of Meditation, Mindfulness and Virtual Reality

Meditation, or mindfulness, is widely used to improve mental health. With the emergence of Virtual Reality technology, many studies have provided evidence that meditation with VR can bring health benefits. However, to our knowledge, there are no guidelines and comprehensive reviews in the literature on how to conduct such research in virtual reality. In order to understand the role of VR technology in meditation and future research opportunities, we conducted a systematic literature review in the IEEE and ACM databases. Our process yielded 19 eligible papers and we conducted a structured analysis. We understand the state-of-art of meditation type, design consideration and VR and technology through these papers and conclude research opportunities and challenges for the future.

cs.HC↗