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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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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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Troi: Towards Understanding Users Perspectives to Mobile Automatic Emotion Recognition System in Their Natural Setting

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Proceedings of the ACM on human-computer interaction 2022年第MHCI期6卷 1–22页

作者： Dissanayake, Vipula Tang, Vanessa Elvitigala, Don Samitha Wen, Elliott Wu, Michelle Nanayakkara, Suranga The University of Auckland New Zealand Augmented Human Lab ABI The University of Auckland New Zealand HCI BoDi Lab School of Computer Science and Engineering University of New South Wales Australia Augmented Human Lab Department of Information Systems & Analytics School of Computing National University of Singapore Singapore

Emotional Self-Awareness (ESA) plays a vital role in physical and mental well-being. Recent advancements in artificial intelligence technologies have shown promising emotion recognition results, opening new opportunities to build systems to support ESA. However, little research has been done to understand users’ perspectives on artificial-intelligence-based emotion recognition systems. We introduce Troi, an automatic emotion recognition mobile app using wearable signals. With Troi, we ran a multi-day user study with 12 users to understand user preference parameters, such as perceived accuracy, confidence, preferred emotion representations, effect of self-awareness of emotions, and real-time use cases. Further, we extend our study to evaluate the machine learning model in-the-wild to understand behaviours in-the-wild. We found that users perceived accuracy of the emotion recognition model is higher than the actual model prediction accuracy;there was no strong preference for one specific emotion representation, and users’ self-awareness of emotions improved over time. © 2022 Association for Computing Machinery.

关键词： Emotion Recognition

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Critically Examining the Broader Implications of Methodological Design in Cross-Cultural, Multi-sited Case Studies of Engineering Ethics Education [Work in Progress]

Critically Examining the Broader Implications of Methodologi...

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Clancy, Rockwell Franklin Zhu, Qin Streiner, Scott Thorpe, Ryan The Department of Humanities Arts and Social Sciences The Colorado School of Mines The Department of Values Technology and Innovation Delft University of Technology Netherlands The Department of Engineering Education and Affiliate Faculty The Department of Science Technology & Society The Center for Human-Computer Interaction Virginia Tech United States School of Engineering the University of Pittsburgh United States

Ethics has long been recognized as crucial to responsible engineering, but the increasingly globalized environments present challenges to effective engineering ethics training. This paper is part of a larger research project that aims to examine the effects of culture and education on ethics training in undergraduate engineering students at universities in the United States, China, and the Netherlands. We are interested in how students’ curricular and extra-curricular (e.g., internships, service projects) experiences and training impact their ethical reasoning and moral dispositions, and how this differs cross-culturally. To understand this, we are conducting mixed methods research longitudinally over four years to engineering students at our participating universities to gauge their moral dispositions and ethical reasoning skills and to measure any change in these. This work-in-progress paper, however, is not about the direct outcomes of this research project. Rather, it critically examines our own practices and methods in doing this research. We begin the paper by briefly introducing the larger research project and motivating the use of comparative, multi-institutional case studies as necessary for contextualizing, complementing, and interpreting quantitative data on ethical reasoning and moral dispositions. Because the conditions related to engineering ethics education differ widely per participating institution for institutional (and also likely cultural) reasons, interpreting and analyzing quantitative survey data will require understanding contextual conditions of education at each institution. Comparative case studies can supply missing contextual information to provide a more complete picture of the engineering ethics educational contexts, strategies, and practices at each of the participating universities. However, in considering how to design and conduct these case studies, we realized we were operating under certain assumptions such as ethics in engineeri

关键词： Students

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Generating a General Culturing Microorganism Pattern Using a Delta Parallel Robot and Cam-in-Hand Calibration Method

Generating a General Culturing Microorganism Pattern Using a...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Navid Pasiar Arvin Mohammadi Sajjad Ahangar Navid Asadi Khomami Mehdi Tale Masouleh Department of Mechanical Eng. Human and Robot Interaction Lab. University of Tehran Tehran Iran Human and Robot Interaction Lab. University of Tehran Tehran Iran Human and Robot Interaction Lab. School of Electrical and Computer Eng. University of Tehran Tehran Iran

Robots can play a vital role in laboratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explores a calibration method for minimizing errors of a performing two certain laboratory tasks of swabbing and pipetting. This leads to enhanced accuracy and productivity. This research intends to culture microorganisms in a petri-dish in a specific pattern, measuring the coordinates of each object in the output image so the object location in the real world can be calculated. This research investigates the Cam-in-Hand method for a Delta Parallel Robot, meaning that the camera is mounted on the End-Effector. In this approach, a method of translating image coordinates into real-world coordinates is introduced. It is essential to provide an additional coordinate (in this case $z -$value) before the conversion, given that the camera outputs a 2D image. A vector-based image, in SVG format, as input to the algorithm generates a set of coordinates which determines the main points for swabbing or pipetting operation. Using this data, an array of coordinates is linearly interpolated in 3D space for the swabbing operation. Conversely, the trajectory generated for pipetting uses the 4-5-6-7 interpolating polynomial. The robot then adheres to following the interpolated array of coordinates as a function of time, using a PID controller. The pipetting device is built with a 3D printer using PLA materials. The calibration is done in different heights. The Camin-Hand method leads to ±1 cm precision as a result. Minimum, maximum, and mean errors are 0.092,1.61 and 0.76 respectively for the central point.

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How can we can create an equitable CHI

How can we can create an equitable CHI

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Grady, Siobahn Day Bauer, Christine Wehbe, Rina Spiel, Katta Muller, Michael Harrington, Christina School of Library and Information Sciences North Carolina Central University DurhamNC United States Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Computer Science Dalhousie University HalifaxNS Canada Faculty of Informatics Tu Wien Vienna Austria Human Centered Ai Ibm Research CambridgeMA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450394222

This panel aims to generate conversation toward creating a more equitable CHI. In recognizing our community's hard work thus far, this panel seeks to engage panelists and participants with thought-provoking questions to garner and promote actionable items for the community. We intend to have an open dialogue on allyship, diversity, equity, and inclusion to achieve a CHI for all. © 2023 Owner/Author.

关键词： allyship diversity equality equity inclusion

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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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GFG - Gender-Fair Generation: A CALAMITA Challenge 10

GFG - Gender-Fair Generation: A CALAMITA Challenge

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10th Italian Conference on Computational Linguistics, CLiC-it 2024

作者： Frenda, Simona Piergentili, Andrea Savoldi, Beatrice Madeddu, Marco Rosola, Martina Casola, Silvia Ferrando, Chiara Patti, Viviana Negri, Matteo Bentivogli, Luisa Interaction Lab Heriot-Watt University Edinburgh United Kingdom Aequa-Tech Turin Italy Fondazione Bruno Kessler Trento Italy University of Trento Trento Italy Computer Science Department University of Turin Turin Italy Universitat de Barcelona Barcelona Spain MaiNLP MCML LMU Munich Germany

Gender-fair language aims at promoting gender equality by using terms and expressions that include all identities and avoid reinforcing gender stereotypes. Implementing gender-fair strategies is particularly challenging in heavily gender-marked languages, such as Italian. To address this, the Gender-Fair Generation challenge intends to help shift toward gender-fair language in written communication. The challenge, designed to assess and monitor the recognition and generation of gender-fair language in both mono- and cross-lingual scenarios, includes three tasks: (1) the detection of gendered expressions in Italian sentences, (2) the reformulation of gendered expressions into gender-fair alternatives, and (3) the generation of gender-fair language in automatic translation from English to Italian. The challenge relies on three different annotated datasets: the GFL-it corpus, which contains Italian texts extracted from administrative documents provided by the University of Brescia;GeNTE, a bilingual test set for gender-neutral rewriting and translation built upon a subset of the Europarl dataset;and Neo-GATE, a bilingual test set designed to assess the use of non-binary neomorphemes in Italian for both fair formulation and translation tasks. Finally, each task is evaluated with specific metrics: average of F1-score obtained by means of BERTScore computed on each entry of the datasets for task 1, an accuracy measured with a gender-neutral classifier, and a coverage-weighted accuracy for tasks 2 and 3. © 2024 CEUR-WS. All rights reserved.

关键词： Gender-fair language Generation Inclusive language Machine translation Neomorphemes Unfairness detection

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I Can’t Share Code, but I need Translation - An Empirical Study on Code Translation through Federated LLM

arXiv

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arXiv 2025年

作者： Kumar, Jahnavi Janapati, Venkata Lakshmana Sasaank Tanguturi, Mokshith Reddy Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Owing to the rapid evolution of technologies and project requirements, organizations need to upgrade the code base in their software projects to a new version of the programming language or even translating to an entirely new one. However, code translation is resource-intensive and requires expertise in both the source and target languages. While researchers have made progress in automating translations between legacy and modern languages, recent work has increasingly turned to pre-trained Large Language Models (LLMs) to translate efficiently. Given the proprietary nature of code, organizations prefer finetuning LLMs locally rather than relying on external APIs. This is one of the first empirical studies that proposes a Federated LLM-based approach for code translation. The proposed approach enables clients to jointly train a code translator without sharing sensitive data. This study demonstrates that participants can collaboratively develop a FedLLM for efficient code translation (particularly C# to Java and vice-versa) with superior results (more than 40% improvement in CodeLLaMA’s CodeBLEU score) compared to individual client models. Our findings indicate that FedLLM offers a collaborative approach to code translation and could serve as a promising direction for future research in this field. © 2025, CC BY.

关键词： Program translators

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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds

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IEEE Access 2025年 13卷 85507-85525页

作者： Kočić, Veljka Lukač, Niko Rožajac, Dž Schweng, Stefan Gollob, Christoph Nothdurft, Arne Stampfer, Karl Ser, Javier Del Holzinger, Andreas University of Natural Resources and Life Sciences Vienna Human-Centered AI Lab Institute of Forest Engineering Department of Ecosystem Management Climate and Biodiversity Vienna1190 Austria TU Graz Institute of Human-Centered Computing Graz8010 Austria University of Maribor Faculty of Electrical Engineering and Computer Science Maribor2000 Slovenia Institute of Forest Growth Department of Ecosystem Management Climate and Biodiversity University of Natural Resources and Life Sciences Vienna Vienna1190 Austria TECNALIA Derio48160 Spain Department of Mathematics Leioa48940 Spain

Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to real-world applications where explainability and robustness are paramount. © 2013 IEEE.

关键词： k-nearest neighbors

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