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2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Tahmid, Ibrahim A. Rodrigues, Francielly Giovannelli, Alexander Lisle, Lee Thomas, Jerald Bowman, Doug A. Virginia Tech Center for Human-Computer Interaction Department of Computer Science BlacksburgVA United States

ISBN: (纸本)9798350328912

Collaboration plays a vital role in both academia and industry whenever we need to browse through a big amount of data to extract meaningful insights. These collaborations often involve people living far from each other, with different levels of access to technology. Effective cross-border collaborations require reliable telepresence systems that provide support for communication, cooperation, and understanding of contextual cues. In the context of collaborative academic writing, while immersive technologies offer novel ways to enhance collaboration and enable efficient information exchange in a shared workspace, traditional devices such as laptops still offer better readability for longer articles. We propose the design of a hybrid cross-reality cross-device networked system that allows the users to harness the advantages of both worlds. Our system allows users to import documents from their personal computers (PC) to an immersive headset, facilitating document sharing and simultaneous collaboration with both colocated colleagues and remote colleagues. Our system also enables a user to seamlessly transition between Virtual Reality, Augmented Reality, and the traditional PC environment, all within a shared workspace. We present the real-world scenario of a global academic team conducting a comprehensive literature review, demonstrating its potential for enhancing cross-reality hybrid collaboration and productivity. © 2023 IEEE.

关键词： Augmented reality

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NoTeeline: Supporting Real-Time, Personalized Notetaking with LLM-Enhanced Micronotes 25

NoTeeline: Supporting Real-Time, Personalized Notetaking wit...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Huq, Faria Samee, Abdus Lin, David Chuan-En Tang, Alice Xiaodi Bigham, Jeffrey P Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400713064

Taking notes quickly while effectively capturing key information can be challenging, especially when watching videos that present simultaneous visual and auditory streams. Manually taken notes often miss crucial details due to the fast-paced nature of the content, while automatically generated notes fail to incorporate user preferences and discourage active engagement with the content. To address this, we propose an interactive system, NoTeeline, for supporting real-time, personalized notetaking. Given 'micronotes', NoTeeline automatically expands them into full-fledged notes using a Large Language Model (LLM). The generated notes build on the content of micronotes by adding relevant details while maintaining consistency with the user's writing style. In a within-subjects study (n=12), we found that NoTeeline creates high-quality notes that capture the essence of participant micronotes with 93.2% factual correctness and accurately align with participant writing style (8.33% improvement). Using NoTeeline, participants could capture their desired notes with significantly reduced mental effort, writing 47.0% less text and completing their notes in 43.9% less time compared to a manual notetaking baseline. Our results suggest that NoTeeline enables users to integrate LLM assistance in a familiar notetaking workflow while ensuring consistency with their preferences - providing an example of how to address broader challenges in designing AI-assisted tools to augment human capabilities without compromising user autonomy and personalization. © 2025 Copyright held by the owner/author(s).

关键词： Large Language Model

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Digital Shadows: Exploring the Other and the I in Communication with Thanabots 6

Digital Shadows: Exploring the Other and the I in Communicat...

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6th Social Robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Dobrosovestnova, Anna Machado, Felipe Reinboth, Tim Human Computer Interaction Group TU Wien Austria Department of Philosophy Cognitive Science Hub University of Vienna Austria

ISBN: (纸本)9781643685670

The development of large language models (LLMs) has led to the proliferation of chatbot services like ChatGPT, Replika and Project December further contributing to technologically mediated grief. Called variously griefbots, or thanabots, the postulated aim behind these technologies is to help people deal with the loss of their loved ones. Despite hype around these technologies in the media, little is still known about the actual mechanisms of how thanabots contribute to grieving. This paper contributes to answering this question by offering an interpretation of communication with thanabots through the prism of Yuri Lotman's autocommunication model. We start by examining how a thanabot can be framed as a communicational 'other'. Then we draw on phenomenology of grief literature to delineate the boundaries of such "otherness". In line with the Lotman's autocommunication model, we proceed to argue that conversing with a thanabot can be viewed as an instance of 'I-I' communication. Within this model, the bot functions as a secondary code that allows for new meaning to arise in the process of communication and, consequently, for a bereaved person to renegotiate their new identity in the face of loss. © 2025 IOS Press. All rights reserved.

关键词： I-I I-Other Large Language Models phenomenology of grief thanabots

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Shift Aliasing Signal Data Augmentation Method for SSVEP-based BCIs 30

Shift Aliasing Signal Data Augmentation Method for SSVEP-bas...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Huang, Jiayang Wen, Hao Yang, Pengfei Yang, Keyi Xiong, Bang Xidian University School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an710126 China

ISBN: (纸本)9798350391916

The performance of training-based spatial filtering methods for steady-state visual evoked potential (SSVEP) decoding heavily relies on the availability of sufficient and high-quality calibration data. Limited calibration data can lead to suboptimal spatial filters and low signal-to-noise ratios in individual SSVEP templates, ultimately degrading the decoding accuracy and efficiency. To address this challenge, we propose a novel data augmentation method called Shift Aliasing Signal (SAS), which generates synthetic SSVEP epochs by estimating a mixing matrix from the available training data and applying time-shift operations while maintaining the essential features of SSVEP signals, such as their phase-locked nature and frequency-specific patterns. The SAS method introduces meaningful variations to enhance the training dataset. We integrate the SAS method with state-of-the-art training-based spatial filtering methods to create a powerful decoding framework for SSVEP-based brain-computer interface (BCI) systems. Extensive experiments on benchmark datasets demonstrate that the proposed SAS method significantly improves classification accuracy and information transfer rate, particularly in scenarios with limited calibration data, outperforming state-of-the-art training-based spatial filtering methods and other data augmentation techniques. © 2024 IEEE.

关键词： Signal to noise ratio

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Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality 32

Exploring Multiscale Navigation of Homogeneous and Dense Obj...

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32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Pavanatto, Leonardo Giovannelli, Alexander Giera, Brian Bremer, Timo Miao, Haichao Bowman, Doug A. Virginia Tech Center for Human-Computer Interaction Department of Computer Science United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798331536459

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding. © 2025 IEEE.

关键词： Virtual reality

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A Corpus and Method for Chinese Named Entity Recognition in Manufacturing 30

A Corpus and Method for Chinese Named Entity Recognition in ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Ruiting Wang, Peiyan Wang, Libang Yang, Danqingxin Cai, Dongfeng School of Computer Science Shenyang Aerospace University Shenyang China Liaoning Professional Technology Innovation Center on Knowledge Engineering and Human-Computer Interaction Shenyang China

ISBN: (纸本)9782493814104

Manufacturing specifications are documents entailing different techniques, processes, and components involved in manufacturing. There is a growing demand for named entity recognition (NER) resources and techniques for manufacturing-specific named entities, with the development of smart manufacturing. In this paper, we introduce a corpus of Chinese manufacturing specifications, named MS-NERC, including 4,424 sentences and 16,383 entities. We also propose an entity recognizer named Trainable State Transducer (TST), which is initialized with a finite state transducer describing the morphological patterns of entities. It can directly recognize entities based on prior morphological knowledge without training. Experimental results show that TST achieves an overall 82.05% F1 score for morphological-specific entities in zero-shot. TST can be improved through training, the result of which outperforms neural methods in few-shot and rich-resource. We believe that our corpus and model will be valuable resources for NER research not only in manufacturing but also in other low-resource domains. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Transducers

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

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Tsinghua science and Technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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Dense 3D Gaussian Splatting Initialization for Sparse Image Data

Dense 3D Gaussian Splatting Initialization for Sparse Image ...

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45th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2024

作者： Seibt, S. Chang, T. von Rymon Lipinski, B. Latoschik, M.E. Game Tech Lab. Faculty of Computer Science Nuremberg Institute of Technology Nuremberg Germany Human-Computer Interaction Group Institute of Computer Science University of Wuerzburg Wuerzburg Germany

ISBN: (纸本)9783038682394

This paper presents advancements in novel-view synthesis with 3D Gaussian Splatting (3DGS) using a dense and accurate SfM point cloud initialization approach. We address the challenge of achieving photorealistic renderings from sparse image data, where basic 3DGS training may result in suboptimal convergence, thus leading to visual artifacts. The proposed method enhances precision and density of initially reconstructed point clouds by refining 3D positions and extrapolating additional points, even for difficult image regions, e.g. with repeating patterns and suboptimal visual coverage. Our contributions focus on improving "Dense Feature Matching for Structure-from-Motion" (DFM4SfM) based on a homographic decomposition of the image space to support 3DGS training: First, a grid-based feature detection method is introduced for DFM4SfM to ensure a well-distributed 3D Gaussian initialization uniformly over all depth planes. Second, the SfM feature matching is complemented by a geometric plausibility check, priming the homography estimation and thereby improving the initial placement of 3D Gaussians. Experimental results on the NeRF-LLFF dataset demonstrate that this approach achieves superior qualitative and quantitative results, even for fewer views, and the potential for a significantly accelerated 3DGS training with faster convergence. © 2024 The Authors.

关键词： Gaussian distribution

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Shaping Online Dialogue: Examining Community Rules and Discussion Structures on Reddit

Shaping Online Dialogue: Examining Community Rules and Discu...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Fang, Anna Yang, Wenjie Zhu, Haiyi Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University Pittsburgh PA United States Hong Kong University of Science and Technology Hong Kong Human Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States

ISBN: (纸本)9798400713958

Community rules play a key part in enabling or constraining the behaviors of members in online communities. However, little is known regarding how and to what degree rule changes actually affect community dynamics. In this paper, we explore Reddit to understand how explicit rules may shape the structure of group discussions. Our work uses the top 2000 communities on Reddit over a 1.5 year period and a unique methodology pipeline including (1) establishing a rule taxonomy of behavior-governing rules across Reddit, (2) network analysis to measure discussion structures, and (3) conduct propensity score matching to observe how rule changes may affect network structure. Our study aims to contribute to proactive forms of moderation, and offers several implications for community designers, such as how greater clustering occurs in communities with greater structural regulation and that restriction on allowable content does not reduce the volume of user activity. © 2025 Copyright held by the owner/author(s).

关键词： moderation network analysis online communities Reddit social computing

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. ©2024 IEEE.

关键词： Optical flows

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