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Proceedings of the CHI Conference on human Factors in Computing Systems

作者： Jordan Taylor Ellen Simpson Anh-Ton Tran Jed R. Brubaker Sarah E Fox Haiyi Zhu Human-Computer Interaction Institute Carnegie Mellon University United States Department of Information Science University of Colorado United States Georgia Institute of Technology United States Department of Information Science University of Colorado Boulder United States

ISBN: (纸本)9798400703300

LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work, we review all research mentioning LGBTQ+ people across the HCI venues of CHI, CSCW, DIS, and TOCHI. Since 2014, we find a linear growth in the number of papers substantially about LGBTQ+ people and an exponential increase in the number of mentions. Research about LGBTQ+ people tends to center experiences of being politicized, outside the norm, stigmatized, or highly vulnerable. LGBTQ+ people are typically mentioned as a marginalized group or an area of future research. We identify gaps and opportunities for (1) research about and (2) the discussion of LGBTQ+ in HCI and provide a dataset to facilitate future Queer HCI research.

关键词： LGBTQ+ people Literature Review Marginalized Communities Queer HCI Queer People

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Investigating the Role of Metaphors in human-AI Conversation and Engagement

Investigating the Role of Metaphors in Human-AI Conversation...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Pragati Maheshwary Qiawen Liu Rui Mao Human-Computer Interaction Institute Carnegie Mellon University United States Department of Psychology University of Wisconsin-Madison United States College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages.

关键词： Analytical models Conferences Oral communication Linguistics Chatbots Regression analysis Data mining Artificial intelligence

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Android Malware Detection: An Empirical Investigation into Machine Learning Classifiers

Android Malware Detection: An Empirical Investigation into M...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Aaditya Raval Mohd Anwar Computer Science Department Human-Centered AI Lab North Carolina Agricultural and Technical State University Greensboro NC USA

ISBN: (数字)9798350351187

ISBN: (纸本)9798350351194

Smartphones have become an all-in-one device due to their constant connectivity and ability to provide a wide range of functions. However, they can expose users to various forms of malware, posing significant threats to their privacy, security, and financial well-being. In this context, the detection and classification of Android malware have emerged as critical research areas in cybersecurity. Our study develops six distinct models/classifiers for detecting and classifying Android malware, leveraging the following machine-learning algorithms: MLP, logistic regression, random forest, SVM, XGBoost, and AdaBoost. Through a systematic evaluation process, we assess the efficacy of each model, highlighting their respective strengths and weaknesses. These findings not only contribute to the existing body of knowledge but also pave the way for future research and innovation in the field of Android security. Furthermore, we investigate the impact of data preprocessing and feature selection strategies on model performance and generalization capabilities. Our experimental results reveal that Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers outperformed others in classifying Android malware, showcasing performance of around $93 \%$ and $92 \%$, respectively across accuracy, precision, recall, and f1-score. With an AUC of 0.93 for RF and 0.92 for XGBoost, these models can clearly distinguish between malware and benign samples with minimum misclassifications. Our findings shed light on the effectiveness of machine learning algorithms in combating Android malware and offer valuable insights into the most suitable models. Ultimately, this research study advances the understanding of Android malware detection and classification, providing a foundation for developing robust security solutions in the mobile computing landscape.

关键词： Radio frequency Support vector machines Technological innovation Privacy Systematics Feature extraction Malware Rough surfaces Random forests Smart phones

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Designing a Sustainable Material for 3D Printing with Spent Coffee Grounds 23

Designing a Sustainable Material for 3D Printing with Spent ...

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Proceedings of the 2023 ACM Designing Interactive Systems Conference

作者： Michael L. Rivera S. Sandra Bae Scott E. Hudson ATLAS Institute and Department of Computer Science University of Colorado Boulder United States ATLAS Institute University of Colorado Boulder United States Human-Computer Interaction Institute Carnegie Mellon University United States

ISBN: (纸本)9781450398930

The widespread adoption of 3D printers exacerbates existing environmental challenges as these machines increase energy consumption, waste output, and the use of plastics. Material choice for 3D printing is tightly connected to these challenges, and as such researchers and designers are exploring sustainable alternatives. Building on these efforts, this work explores using spent coffee grounds as a sustainable material for prototyping with 3D printing. This material, in addition to being compostable and recyclable, can be easily made and printed at home. We describe the material in detail, including the process of making it from readily available ingredients, its material characteristics and its printing parameters. We then explore how it can support sustainable prototyping practices as well as HCI applications. In reflecting on our design process, we discuss challenges and opportunities for the HCI community to support sustainable prototyping and personal fabrication. We conclude with a set of design considerations for others to weigh when exploring sustainable materials for 3D printing and prototyping.

关键词： environmental sustainability zero-waste prototyping personal fabrication 3D printing bio-based materials

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Toward a Secure Educational Metaverse: A Tale of Blockchain Design for Educational Environments

Toward a Secure Educational Metaverse: A Tale of Blockchain ...

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Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Dario Di Dario Umberto Bilott Maurizio Sibilio Carmine Gravino Fabio Palomba Department of Computer Science SeSa Lab University of Salerno Salerno Italy Department of Human Science Lab H University of Salerno Salerno Italy

In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persistent, virtual, three-dimensional environment that is supposed to address most of the limitations of 2D information technologies. Nonetheless, there are still software engineering challenges to face to enable such a metaverse, especially when turning to software security and privacy. In this paper, we aim at performing the first steps toward an improved understanding of the security perspective of educational metaverse, by analyzing how blockchain can be employed within educational environments and how applications may be designed. Our ultimate goal is to provide insights into how blockchain can be further tailored in the context of educational metaverse. We conduct a systematic literature review, which targets 20 primary studies. The key findings of the study showcase the use of blockchain in 3 educational tasks, other than describing the blockchain design approaches, which protocol they commonly use and the associated limitations. We conclude by developing a conceptualization of a blockchain-based educational metaverse.

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Working in Extended Reality in the Wild: Worker and Bystander Experiences of XR Virtual Displays in Real-World Settings

arXiv

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arXiv 2024年

作者： Pavanatto, Leonardo Biener, Verena Chandran, Jennifer Kalamkar, Snehanjali Lu, Feiyu Dudley, John J. Hu, Jinghui Nikki Ramirez-Saffy, G. Kristensson, Per Ola Giovannelli, Alexander Schlueter, Luke Müller, Jörg Grubert, Jens Bowman, Doug A. The Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States The Visualization Research Center University of Stuttgart Germany Coburg University of Applied Sciences and Arts Germany The University of Bayreuth Germany The Department of Engineering University of Cambridge United Kingdom

Although access to sufficient screen space is crucial to knowledge work, workers often find themselves with limited access to display infrastructure in remote or public settings. While virtual displays can be used to extend the available screen space through extended reality (XR) head-worn displays (HWD), we must better understand the implications of working with them in public settings from both users’ and bystanders’ viewpoints. To this end, we conducted two user studies. We first explored the usage of a hybrid AR display across real-world settings and tasks. We focused on how users take advantage of virtual displays and what social and environmental factors impact their usage of the system. A second study investigated the differences between working with a laptop, an AR system, or a VR system in public. We focused on a single location and participants performed a predefined task to enable direct comparisons between the conditions while also gathering data from bystanders. The combined results suggest a positive acceptance of XR technology in public settings and show that virtual displays can be used to accompany existing devices. We highlighted some environmental and social factors. We saw that previous XR experience and personality can influence how people perceive the use of XR in public. In addition, we confirmed that using XR in public still makes users stand out and that bystanders are curious about the devices, yet have no clear understanding of how they can be used. Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual environments

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Friction in Processual Ethics: Reconfiguring Ethical Relations in Interdisciplinary Research 25

Friction in Processual Ethics: Reconfiguring Ethical Relatio...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Rachael Garrett Patrick Brundell Simon Castle-Green Kat Hawkins Paul Tennent Feng Zhou Airi Lampinen Kristina Höök Steve Benford Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Mixed Reality Lab School of Computer Science University of Nottingham Nottingham United Kingdom Computer Science University of Nottingham Nottingham United Kingdom Centre for Dance Research Coventry University Coventry United Kingdom Computer Science / Mixed Reality Lab University of Nottingham Nottingham United Kingdom Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (纸本)9798400713941

Friction - disagreement and breakdown - is an omnipresent aspect of conducting interdisciplinary research yet is rarely presented in formal research reporting. We analyse a performance-led research process where professional dancers with different disabilities explored how to improvise with an industrial robot, with the support of an interdisciplinary team of human-computer and human-robot interaction researchers. We focus on one site of friction in our research process;how to dance - safely - with robots? By presenting our research process, we exemplify the different ways in which we encountered this friction and how we reconfigured the research process around it. We contribute five ways in which we arrived at a generative ethical outcome, which may be helpful in productively engaging with friction in interdisciplinary collaboration. © 2025 Copyright held by the owner/author(s).

关键词： ethics

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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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Failure Explanation in Privacy-Sensitive Contexts: An Integrated Systems Approach

Failure Explanation in Privacy-Sensitive Contexts: An Integr...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Sihui Li Sriram Siva Terran Mott Tom Williams Hao Zhang Neil Dantam Department of Computer Science Colorado School of Mines Human-Centered Robotics Lab University of Massachusetts Amherst

In this paper, we explore how robots can properly explain failures during navigation tasks with privacy concerns. We present an integrated robotics approach to generate visual failure explanations, by combining a language-capable cognitive architecture (for recognizing intent behind commands), an object- and location-based context recognition system (for identifying the locations of people and classifying the context in which those people are situated) and an infeasibility proof-based motion planner (for explaining planning failures on the basis of contextually mediated privacy concerns). The behavior of this integrated system is validated using a series of experiments in a simulated medical environment.

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