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8th International Conference on Virtual Reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. Virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

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CUDA and Applications to Task-based Programming

CUDA and Applications to Task-based Programming

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43rd Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2022

作者： Kerbl, Bernhard Kenzel, Michael Winter, Martin Steinberger, Markus TU Wien Institute of Visual Computing and Human-Centered Technology Austria Saarland University Computer Graphics Lab Germany Intelligent Cloud Rendering Laboratory Huawei Technologies Austria Graz University of Technology Institute of Computer Graphics and Vision Austria

ISBN: (纸本)9783038681724

Since its inception, the CUDA programming model has been continuously evolving. Because the CUDA toolkit aims to consistently expose cutting-edge capabilities for general-purpose compute jobs to its users, the added features in each new version reflect the rapid changes that we observe in GPU architectures. Over the years, the changes in hardware, growing scope of built-in functions and libraries, as well as an advancing C++ standard compliance have expanded the design choices when coding for CUDA, and significantly altered the directives to achieve peak performance. In this tutorial, we give a thorough introduction to the CUDA toolkit, demonstrate how a contemporary application can benefit from recently introduced features and how they can be applied to task-based GPU scheduling in particular. For instance, we will provide detailed examples of use cases for independent thread scheduling, cooperative groups, and the CUDA standard library, libcu++, which are certain to become an integral part of clean coding for CUDA in the near future. © 2022 The Author(s)

关键词： Regulatory compliance

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Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation

arXiv

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arXiv 2025年

作者： Li, Xiangju Yang, Dong Zhu, Xiaogang Huang, Faliang Zhang, Peng Zhao, Zhongying College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266500 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China

Span-level emotion-cause-category triplet extraction represents a novel and complex challenge within emotion cause analysis. This task involves identifying emotion spans, cause spans, and their associated emotion categories within the text to form structured triplets. While prior research has predominantly concentrated on clause-level emotion-cause pair extraction and span-level emotion-cause detection, these methods often confront challenges originating from redundant information retrieval and difficulty in accurately determining emotion categories, particularly when emotions are expressed implicitly or ambiguously. To overcome these challenges, this study explores a fine-grained approach to span-level emotion-cause-category triplet extraction and introduces an innovative framework that leverages instruction tuning and data augmentation techniques based on large language models. The proposed method employs task-specific triplet extraction instructions and utilizes low-rank adaptation to fine-tune large language models, eliminating the necessity for intricate task-specific architectures. Furthermore, a prompt-based data augmentation strategy is developed to address data scarcity by guiding large language models in generating high-quality synthetic training data. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving at least a 12.8% improvement in span-level emotion-cause-category triplet extraction metrics. The results demonstrate the method’s effectiveness and robustness, offering a promising avenue for advancing research in emotion cause analysis. The source code is available at https://***/zxgnlp/InstruDa-LLM. Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

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It's the Same Conflict Every Time, on Repeat.

It's the Same Conflict Every Time, on Repeat.

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021

作者： Chin Derix, Eleanor Leong, Tuck Wah Prior, Julia Interaction Design + Human Practice Lab University of Technology Sydney Australia Uts Australia School of Computer Science University of Technology Sydney Australia

ISBN: (纸本)9781450380959

Family technology use can create or amplify conflict in parents' relationships - we found four key factors that contribute to this issue. We conducted a probe and interview study with eight sets of parents, to explore how and why technology use might cause conflict in their relationships. This paper presents data from two particular sets of parents to illustrate our findings. In doing so, it complements existing work that primarily focuses on parent-child relationships, and contributes to a more complete understanding of how family technology use can affect family dynamics. We also suggest directions for further work to address this issue of conflict between parents, associated with their family's use of technology. © 2021 ACM.

关键词： computer programming

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Please don't answer out of context: Personalized Dialogue Generation Fusing Persona and Context

Please don't answer out of context: Personalized Dialogue Ge...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fucheng Wang Yunfei Yin Faliang Huang Kaigui Wu College of Computer Science Chongqing University Chongqing China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China

In realistic conversations, “responses” are closely related to persona and context. However, current personalized dialogue generation methods only focus on the consistency of the persona of responses and yet ignore context coherence, as may produce low-quality responses. To address the issue, we propose a novel model, named PCF (Persona and Context Fusion), which builds two decoders for understanding personality consistency and context coherence respectively on a common encoder-decoder architecture. In this model, an inter-layer attention fusion mechanism is designed for the two decoders to effectively fuse persona and context, and a decoupled way of training is conducted on an additional large-scale non-dialogue inference dataset to enhance the consistent understanding ability of the two decoders. Furthermore, considering that generating responses may be tedious, the ScaleGrad loss function is applied to enhance the diversity of responses. Experimental results on two publicly available datasets show that the dialogues generated by our PCF are significantly higher in quality than strong baselines.

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Immersive Neural Graphics Primitives

arXiv

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arXiv 2022年

作者： Li, Ke Rolff, Tim Schmidt, Susanne Bacher, Reinhard Frintrop, Simone Leemans, Wim Steinicke, Frank Human-Computer Interaction Group Department of Informatics Universität Hamburg Germany Computer Vision Group Department of Informatics Universität Hamburg Germany Deutsches Elektronen-Synchrotron DESY Germany

Neural radiance field (NeRF), in particular its extension by instant neural graphics primitives, is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its potential, research on the combination of NeRF and virtual reality (VR) remains sparse. Currently, there is no integration into typical VR systems available, and the performance and suitability of NeRF implementations for VR have not been evaluated, for instance, for different scene complexities or screen resolutions. In this paper, we present and evaluate a NeRF-based framework that is capable of rendering scenes in immersive VR allowing users to freely move their heads to explore complex real-world scenes. We evaluate our framework by benchmarking three different NeRF scenes concerning their rendering performance at different scene complexities and resolutions. Utilizing super-resolution, our approach can yield a frame rate of 30 frames per second with a resolution of 1280×720 pixels per eye. We discuss potential applications of our framework and provide an open source implementation online.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Virtual reality

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Escape the Fake: Development and evaluation of an augmented reality escape room game for fighting fake news 21

Escape the Fake: Development and evaluation of an augmented ...

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8th ACM SIGCHI Annual Symposium on computer-human interaction in Play, CHI PLAY 2021

作者： Paraschivoiu, Irina Buchner, Josef Praxmarer, Robert Layer-Wagner, Thomas Center for Human-Computer Interaction University of Salzburg and Polycular Austria University of Duisburg-Essen Faculty of Educational Sciences Learning Lab Germany Polycular Austria

ISBN: (纸本)9781450383561

Today's digital natives easily master new devices, but there is a greater need to provide them with a strong foundation of attitudes, skills and values to interpret the world around them. Moreover, in the context of remote schooling, providing students with opportunities to learn outside of the classroom and in an interactive manner is equally important. In this article, we address these issues by providing an account of Escape Fake, an augmented reality (AR) escape room game for educating young people about fake news. We show how such a game can be designed for playing at home, thereby providing interactive learning opportunities remotely. We discuss how to teach information literacy, by introducing the design process and gameplay of Escape Fake. In our evaluation with teenagers and young adults (n=49), we investigated the flow experience, attitude towards learning effectiveness and interest in learning with an AR escape game. Escape Fake was very well accepted by youth, who are willing to play the game in their free time. These results provide considerations for educators, researchers and designers who develop educational content by means of AR escape rooms. © 2021 Owner/Author.

关键词： Augmented reality

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Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

arXiv

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arXiv 2025年

作者： Li, Qiang Lin, Xinze Lv, Shenghao Huang, Faliang Li, Xiangju Shandong Women’s University School of Data and Computer Science Jinan250002 China Shandong University of Science and Technology College of Computer Science and Engineering Qiangdao266500 China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning530100 China

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models∗ Copyright © 2025, The Authors. All rights reserved.

关键词： Recommender systems

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Questioning Wizard of Oz: Effects of Revealing the Wizard behind the Robot

Questioning Wizard of Oz: Effects of Revealing the Wizard be...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Jauwairia Nasir Pierre Oppliger Barbara Bruno Pierre Dillenbourg Computer-Human Interaction in Learning and Instruction (CHILI) Lab Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Wizard of Oz, a very commonly employed technique in human-robot interaction, faces the criticism of being deceptive as the humans interacting with the robot are told, if at all, only at the end of their interaction that there was in fact a human behind the robot. What if the robot reveals the wizard behind itself very early in the interaction? We built a deep wizard of Oz setup to allow for a robot to play together with a human against a computer AI in the context of Connect 4 game. This cooperative game interaction against a common opponent is then followed by a conversation between the human and the robot. We conducted an exploratory user study with 29 adults with three conditions where the robot reveals the wizard, lies about the wizard, and does not say anything, respectively. We also split the data based on how the participants perceive the robot in terms of autonomy. Using different metrics, we evaluate how the users interact with and perceive the robot in both the experimental and perceived conditions. We find that while there is indeed a significant difference in the participants willingness to follow robots suggestions between the experimental conditions as well as in the effort they put to prove themselves as humans (reverse Turing test), there isn’t any significant difference in their robot perception. Additionally, how humans perceive whether the robot is tele-operated or autonomous seems to be indifferent to the robot revealing its identity, i.e., the pre-conceived notions may be uninfluenced even if the robot explicitly states otherwise. Lastly, interestingly in the perception based conditions, absence of statistical significance may suggest that, in certain contexts, wizard of oz may not require hiding the wizard after all.

关键词： Atmospheric measurements Sociology Social robots Games Oral communication Particle measurements Time measurement

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From Lenses to Living Rooms: A Policy Brief on Eye Tracking in XR Before the Impending Boom

From Lenses to Living Rooms: A Policy Brief on Eye Tracking ...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Sean Anthony Byrne Nora Castner Efe Bozkir Diederick C. Niehorster Enkelejda Kasneci MoMiLab IMT School for Advanced Studies Lucca Lucca Italy Carl Zeiss Vision International GmbH Tübingen Germany Human-Centered Technologies for Learning Technical University of Munich Munich Germany Human-Computer Interaction University of Tübingen Tübingen Germany Dept. of Psychology Lund University Humanities Lab & Lund University Lund Sweden

As tech giants such as Apple and Meta invest heavily in Virtual and Augmented Reality (VR/AR) technologies, often collectively termed Extended Reality (XR) devices, a significant societal concern emerges: The use of eye-tracking technology within these devices. Gaze data holds immense value, revealing insights into user attention, health, and cognitive states. This raises substantial concerns over privacy and fairness, with potential risks of targeted ads, unauthorized surveillance, and data re-purposing. As the impact of eye tracking is due to transition from the lab to the broader public, this paper underscores these pivotal issues in a digestible manner to the general audience. To this end, we first outline the eye-tracking data collection process and its potential for user insights. Second, we introduce perspectives from the domain of privacy, emphasizing its significance as a pivotal measure to guard against the improper use of eye-tracking data. Third, we provide a set of guidelines created by researchers actively working within this space. These recommendations are designed to guide policymakers and the general public toward establishing informed, equitable, and privacy-centric standards surrounding these devices.

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