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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Liang, Xiaozhan Yong, Hu Shen, Xukun Yang, Tianyu Wang, Yu Zheng, Chenyue Piao, Dongxu School of New Media Art and Design Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing Beijing China

ISBN: (纸本)9798400713958

MR merges virtual and physical worlds, with MLLM enhancing contextual understanding and multisensory perception for smarter interactions. This study explores user behaviors and task needs through contextual inquiry and retrospective interviews (N=12), identifying key daily tasks and information requirements. A participatory design workshop (N=18) further refines interface displays and interactions based on the physical and semantic attributes of objects, resulting in a dynamic design space. By leveraging MLLM, this design space adapts to user needs, paving the way for improving intelligent perception and redefining the role of everyday objects in MR. © 2025 Copyright held by the owner/author(s).

关键词： Graphical user interfaces

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Multi-Target Distributed Maximum Correntropy Kalman Filter 24

Multi-Target Distributed Maximum Correntropy Kalman Filter

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12th International Conference on Communications and Broadband Networking, ICCBN 2024

作者： Deng, Xingyu Han, Hongyu Zhang, Sheng Xu, Yin College of Computer Science and Visual Computing Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610066 China The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798400717109

Multi-target tracking in sensor networks is a challenging problem, especially in scenarios where sensor observations are limited. Conventional centralized Kalman filters and distributed Kalman filters (DKFs) require each sensor to observe all targets, which is often difficult to achieve in practical distributed applications. With this in mind, and inspired by the promising robustness of the maximum correntropy criterion in non-Gaussian noise environments, this paper proposes a novel multi-target distributed maximum correntropy Kalman filter, named MT-DMCKF. The proposed algorithm can operate effectively in distributed multi-target scenarios and provide accurate and consistent tracking performance even when the number of sensors mounted on targets is limited. Within a single period, the algorithm requires only one exchange of information among sensors, thus reducing communication overhead compared to existing distributed Kalman filters. Simulation experiments validate the effectiveness and robustness of the proposed MT-DMCKF. © 2024 ACM.

关键词： Wiener filtering

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Tracking dynamic flow: Decoding flow fluctuations through performance in a fine motor control task

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IEEE Transactions on Affective Computing 2024年第2期16卷 891-902页

作者： Tian, Bohao Zhang, Shijun Chen, Sirui Zhang, Yuru Peng, Kaiping Zhang, Hongxing Wang, Dangxiao Tsinghua University Department of Psychology Beijing100084 China Beijing Institute of Life Omics Beijing102206 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Beihang University School of Mechanical Engineering and Automation Beijing100191 China Peng Cheng Laboratory Shenzhen518000 China

Flow, an optimal mental state merging action and awareness, significantly impacts our emotion, performance, and well-being. However, capturing its swift transitions on a fine timescale is challenging due to the sparsity of the existing flow detecting tools. Here we present a fine fingertip force control (F3C) task to induce flow, wherein the task challenge is set at a compatible level with personal skill, and to quantitatively track the flow state variations from synchronous motor control performance. We select eight performance metrics from fingertip force sequence and reveal their significant differences under distinct self-reported flow states. Further, we built a machine learning-based decoder that aims to predict the continuous flow intensity during the user experiment through the performance metrics, taking the self-reported flow as the label. Cross-validation shows that the predicted flow intensity reaches significant correlation with the self-reported flow intensity (r = 0.81). Based on the decoding results, we can capture the flow fluctuations during the intervals between sparse self-reporting probes. This study showcases the feasibility of tracking intrinsic flow variations with high temporal resolution using task performance measures and may serve as foundation for future work aiming to take advantage of flow's dynamics to enhance performance and positive emotions. © 2010-2012 IEEE.

关键词： Force Measurement Force Control Fluctuations Decoding Pressing Dynamics Affective Computing Transducers Real Time systems Flow Experience Intrinsic Fluctuations Fine Fingertip Force Control Task Performance Cross Validation Dynamics Task Performance Control Task Flow Dynamics Fine Control Flow Fluctuations Fine Motor Control Tasks Performance Metrics Volume Flow Flow Variables Personal Skills Force Control Finer Time Scales Synchronization Performance Paired T Test Prediction Error Cohens D Flow Meter Task Difficulty Target Range Random Permutations Power Spectral Density Analysis Flow Experience Experience Sampling Method Flow Probe Main Session Force Output Subjective Metrics Decoding Performance Target Force Decoding Accuracy

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Exploring Image Generation for UAV Change Detection

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1061-1072页

作者： Xuan Li Haibin Duan Yonglin Tian Fei-Yue Wang Peng Cheng Laboratory Shenzhen 518000China IEEE State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083 Department of Automation University of Science and Technology of ChinaHefei 230027 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense TechnologyChangsha 410073China

Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and ***,even the most advanced models require large amounts of data for model training and ***,sufficient labeled images with different imaging conditions are *** by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated *** simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection ***,we propose an image translation framework that translates simulated images to synthetic *** framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training *** experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.

关键词： Change detection computer graphics image translation simulated images synthetic images unmanned aerial vehicles(UAVs)

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Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning

arXiv

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arXiv 2025年

作者： Zhong, Hanwen Chen, Jiaxin Zhang, Yutong Huang, Di Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University China School of Computer Science and Engineering Beihang University Beijing China

Multi-Task Learning (MTL) for Vision Transformer aims at enhancing the model capability by tackling multiple tasks simultaneously. Most recent works have predominantly focused on designing Mixture-of-Experts (MoE) structures and integrating Low-Rank Adaptation (LoRA) to efficiently perform multi-task learning. However, their rigid combination hampers both the optimization of MoE and the effectiveness of reparameterization of LoRA, leading to sub-optimal performance and low inference speed. In this work, we propose a novel approach dubbed Efficient Multi-Task Learning (EMTAL) by transforming a pre-trained Vision Transformer into an efficient multi-task learner during training, and reparameterizing the learned structure for efficient inference. Specifically, we firstly develop the MoEfied LoRA structure, which decomposes the pre-trained Transformer into a low-rank MoE structure and employ LoRA to fine-tune the parameters. Subsequently, we take into account the intrinsic asynchronous nature of multi-task learning and devise a learning Quality Retaining (QR) optimization mechanism, by leveraging the historical high-quality class logits to prevent a well-trained task from performance degradation. Finally, we design a router fading strategy to integrate the learned parameters into the original Transformer, archiving efficient inference. Extensive experiments on public benchmarks demonstrate the superiority of our method, compared to the state-of-the-art multi-task learning approaches. The project page is available at https://***/Yewen1486/EMTAL. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi-task learning

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Hierarchical reinforcement learning guidance with threat avoidance

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Journal of systems Engineering and Electronics 2022年第5期33卷 1173-1185页

作者： LI Bohao WU Yunjie LI Guofei State Key Laboratory of Virtual Reality Technology and System Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Aircraft Control Laboratory Beijing 100191China School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China

The guidance strategy is an extremely critical factor in determining the striking effect of the missile operation.A novel guidance law is presented by exploiting the deep reinforcement learning(DRL)with the hierarchical deep deterministic policy gradient(DDPG)*** reward functions are constructed to minimize the line-of-sight(LOS)angle rate and avoid the threat caused by the opposed *** attenuate the chattering of the acceleration,a hierarchical reinforcement learning structure and an improved reward function with action penalty are put *** simulation results validate that the missile under the proposed method can hit the target successfully and keep away from the threatened areas effectively.

关键词： guidance law deep reinforcement learning(DRL) threat avoidance hierarchical reinforcement learning

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CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction 29

CETA: A Consensus Enhanced Training Approach for Denoising i...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Liu, Ruri Mo, Shasha Niu, Jianwei Fan, Shengda School of Cyber Science and Technology Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

Distantly supervised relation extraction aims to extract relational facts from texts but suffers from noisy instances. Existing methods usually select reliable sentences that rely on potential noisy labels, resulting in wrongly selecting many noisy training instances or underutilizing a large amount of valuable training data. This paper proposes a sentence-level DSRE method beyond typical instance selection approaches by preventing samples from falling into the wrong classification space on the feature space. Specifically, a theorem for denoising and the corresponding implementation, named Consensus Enhanced Training Approach (CETA), are proposed in this paper. By training the model with CETA, samples of different classes are separated, and samples of the same class are closely clustered in the feature space. Thus the model can easily establish the robust classification boundary to prevent noisy labels from biasing wrongly labeled samples into the wrong classification space. This process is achieved by enhancing the classification consensus between two discrepant classifiers and does not depend on any potential noisy labels, thus avoiding the above two limitations. Extensive experiments on widely-used benchmarks have demonstrated that CETA significantly outperforms the previous methods and achieves new state-of-the-art results. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Classification (of information)

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key Mention Pairs Guided Document-Level Relation Extraction 29

Key Mention Pairs Guided Document-Level Relation Extraction

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29th International Conference on Computational Linguistics, COLING 2022

作者： Jiang, Feng Niu, Jianwei Mo, Shasha Fan, Shengda State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China School of Cyber Science and Technology Beihang University Beijing100191 China

Document-level Relation Extraction (DocRE) aims at extracting relations between entities in a given document. Since different mention pairs may express different relations or even no relation, it is crucial to identify key mention pairs responsible for the entity-level relation labels. However, most recent studies treat different mentions equally while predicting the relations between entities, leading to sub-optimal performance. To this end, we propose a novel DocRE model called key Mention pairs Guided Relation Extractor (KMGRE) to directly model mention-level relations, containing two modules: a mention-level relation extractor and a key instance classifier. These two modules could be iteratively optimized with an EM-based algorithm to enhance each other. We also propose a new method to solve the multi-label problem in optimizing the mention-level relation extractor. Experimental results on two public DocRE datasets demonstrate that the proposed model is effective and outperforms previous state-of-the-art models. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Iterative methods

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Crossing Narrative: Exploring the Possibilities of Crossing the virtuality and reality in Interactive Narrative Experiences 23

Crossing Narrative: Exploring the Possibilities of Crossing ...

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2023 SIGGRAPH Asia Posters, SA 2023

作者： Liu, Zixiao Yan, Shuo Shen, Xukun School of New Media Art and Design Beihang University China School of New Media Art and Design State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

ISBN: (纸本)9798400703133

With the development of mixed reality technology, the convergence of virtual and real experiences has emerged as a trend in the interactive narratives. In this paper, we introduce "Crossing Narrative", an interactive narrative experience that seamlessly blends virtuality and reality by utilizing real-world views and bystanders. We discuss specific methods for designing cross-reality narrative experience, focusing on three key aspects of cross-reality interactions: diegetic objects, transition effects, and bystander’s avatar. Our design methods aim to enhance audience social interactions and understanding of the immersive narrative. © 2023 Copyright held by the owner/author(s).

关键词： Mixed reality

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Exploring the Overall Stiffness Distribution Law of Weakly Rigid Composite Fairings 7th

Exploring the Overall Stiffness Distribution Law of Weakly R...

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7th China Aeronautical science and technology Conference, CASTC 2024

作者： Yin, Rongying Kong, Chuifeng Dong, Yushuang Ma, Jichuan Gao, Hanjun Harbin Hafei Aviation Industry Co Ltd Harbin150060 China School of Mechanical Engineering and Automation State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China School of Technology Forestry University Beijing100083 China

ISBN: (纸本)9789819797677

To elucidate the stresses and deformations occurring during the assembly of the powerhouse fairing, finite element analysis (ABAQUS) was employed to emulate the assembly process. A numerical simulation model was constructed for the assembly process of the weakly rigid composite fairing by digitizing its geometry, material properties, and the forces exerted during assembly. Initially, the geometric data and material parameters of the fairing were meticulously collected and analyzed, considering its weakly rigid composite material properties. Subsequently, the finite element analysis method was utilized to partition the fairing’s geometry into minute regions, thereby establishing a numerical simulation model of the fairing assembly process. During the simulation, various forces acting on the fairing during assembly, such as the binding force of the assembly fixture and mechanical loads, were taken into account. The stress and deformation distributions during the fairing assembly process were derived through numerical simulation of the model. The findings indicate that the overall stiffness distribution law of the weakly rigid composite fairing can be summarized as follows: larger fixed boundaries, smaller applied forces, more rigid materials, and more intricate geometries enhance the overall stiffness of the fairing. © Chinese Society of Aeronautics and Astronautics 2025.

关键词： ABAQUS

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