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34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021

作者： Cheng, Yifei Yan, Yukang Yi, Xin Shi, Yuanchun Lindlbauer, David Department of Computer Science and Technology Tsinghua University China Swarthmore College United States Institute for Network Sciences and Cyberspace Tsinghua University China Human-Computer Interaction Institute Carnegie Mellon University United States

ISBN: (纸本)9781450386357

We present an optimization-based approach that automatically adapts Mixed Reality (MR) interfaces to different physical environments. Current MR layouts, including the position and scale of virtual interface elements, need to be manually adapted by users whenever they move between environments, and whenever they switch tasks. This process is tedious and time consuming, and arguably needs to be automated for MR systems to be beneficial for end users. We contribute an approach that formulates this challenge as a combinatorial optimization problem and automatically decides the placement of virtual interface elements in new environments. To achieve this, we exploit the semantic association between the virtual interface elements and physical objects in an environment. Our optimization furthermore considers the utility of elements for users' current task, layout factors, and spatio-temporal consistency to previous layouts. All those factors are combined in a single linear program, which is used to adapt the layout of MR interfaces in real time. We demonstrate a set of application scenarios, showcasing the versatility and applicability of our approach. Finally, we show that compared to a naive adaptive baseline approach that does not take semantic associations into account, our approach decreased the number of manual interface adaptations by 33%. © 2021 ACM.

关键词： Mixed reality

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Virtual Reality Pōwhiri—Practicing an indigenous welcoming ceremony 23

Virtual Reality Pōwhiri—Practicing an indigenous welcoming...

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Proceedings of the 35th Australian computer-human interaction Conference

作者： Laurence Lloyd-Jones Claudia Ott Holger Regenbrecht Hēmi Whaanga Human Computer Interaction (HCI) Lab University of Otago New Zealand Information Science University of Otago New Zealand Department of Information Science University of Otago New Zealand Te P?tahi-a-Toi Massey University New Zealand

ISBN: (纸本)9798400717079

Pōwhiri is a traditional welcoming ceremony in Māori culture in Aotearoa New Zealand. Certain protocols (tikanga) have to be followed and must be learned. There is a general lack of understanding regarding this due to the scarcity of pōwhiri. The immersive and experiential nature of Virtual Reality (VR) can be used as a tool to increase understanding and confidence. We have implemented a VR learning tool in the reconstructed context of Te Rau Aroha marae in Bluff which allows for safe practice of pōwhiri before applying it in a real welcoming ceremony. A cultural evaluation study was conducted first followed by a user study to determine understanding and confidence gains, highlighting the complexity of the topic. The user study clearly showed that the system was useful to increase participants’ understanding in regards to pōwhiri as well as their confidence surrounding pōwhiri. We are confident that our experiences and key findings from the studies can be used to drive further development of VR tools in the context of visualising cultural ceremonies.

关键词： 3D Reconstruction Indigenous User Interfaces VR Learning Volumetric Video

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism

Multivariate Time Series Anomaly Detection based on Pre-trai...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Yongqian Sun Yang Guo Minghan Liang Xidao Wen Junhua Kuang Shenglin Zhang Hongbo Li Kaixu Xia Dan Pei Nankai University Tianjin Key Laboratory of Software Experience and Human Computer Interaction BizSeer Haihe Laboratory of Information Technology Application Innovation Tencent Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%.

关键词： Training Adaptation models Time series analysis Production Reliability engineering Data models Software reliability Servers Anomaly detection Monitoring

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Minion: A Technology Probe for Resolving Value Conflicts through Expert-Driven and User-Driven Strategies in AI Companion Applications

arXiv

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arXiv 2024年

作者： Fan, Xianzhe Xiao, Qing Zhou, Xuhui Su, Yuran Lu, Zhicong Sap, Maarten Shen, Hong Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science City University of Hong Kong Hong Kong SAR China Language Technologies Institute Carnegie Mellon University PittsburghPA United States

AI companions based on large language models can role-play and converse very naturally. When value conflicts arise between the AI companion and the user, it may offend or upset the user. Yet, little research has examined such conflicts. We first conducted a formative study that analyzed 151 user complaints about conflicts with AI companions, providing design implications for our study. Based on these, we created Minion, a technology probe to help users resolve human-AI value conflicts. Minion applies a user-empowerment intervention method that provides suggestions by combining expert-driven and user-driven conflict resolution strategies. We conducted a technology probe study, creating 40 value conflict scenarios on *** and Talkie. 22 participants completed 274 tasks and successfully resolved conflicts 94.16% of the time. We summarize user responses, preferences, and needs in resolving value conflicts, and propose design implications to reduce conflicts and empower users to resolve them more effectively. Copyright © 2024, The Authors. All rights reserved.

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing

Auto-PIP: Real-time Identification of Critical Performance I...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Shenglin Zhang Xiao Xiong Mengyao Li Yongqian Sun Yongxin Zhao Xia Chen Bowen Deng Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area" (SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research.

关键词： Accuracy Manuals Software systems Market research Real-time systems Software reliability Stress Anomaly detection Optimization Testing

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

arXiv

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arXiv 2022年

作者： Haghpanah, Mohammad Amin Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science and Engineering University of Nevada Reno United States

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Unwritten Magic: Participatory Design of AI Dialogue to Empower Marginalized Voices 22

Unwritten Magic: Participatory Design of AI Dialogue to Empo...

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2nd ACM Conference on Information Technology for Social Good, GoodIT 2022

作者： Buddemeyer, Amanda Nwogu, Jennifer Solyst, Jaemarie Walker, Erin Nkrumah, Tara Ogan, Amy Hatley, Leshell Stewart, Angela School of Computing and Information Learning Research and Development Center University of Pittsburgh United States Human Computer Interaction Institute Carnegie Mellon University United States Center for Gender Equity in Science and Technology Arizona State University United States Uplift Inc.

ISBN: (纸本)9781450392846

Language is an act of identity, but AI has no identity other than that which its creators assign it. Technology creators who do not fully consider how identity information is encoded in AI dialogue risk creating representational harms that negatively impact users' interactions with the technology, particularly young users from groups marginalized in STEM education. One solution is to engage young users in Participatory Design of dialogue, pushing power away from the researcher and toward the young users. This positions users as technology creators who understand AI language as a technosocial phenomenon and who have their own perspective about how identity is best portrayed in AI language. In this work in progress paper, we provide initial impressions from a Youth Advisory Group (YAG) with 12 middle-school-aged learners of color who are all female or non-binary. © 2022 Owner/Author.

关键词： Engineering education

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Sociotechnical AI Governance: Challenges and Opportunities for HCI

Sociotechnical AI Governance: Challenges and Opportunities f...

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

作者： Kevin Feng, K.J. Pang, Rock Yuren Kuo, Tzu-Sheng Winecoff, Amy Tseng, Emily Widder, David Gray Suresh, Harini Reinecke, Katharina Zhang, Amy X. University of Washington SeattleWA United States Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Center for Democracy & Technology WashingtonDC United States Microsoft Research New YorkNY United States Cornell Tech New YorkNY United States Brown University ProvidenceRI United States

ISBN: (纸本)9798400713958

Rapid advancements in and adoption of frontier AI systems have amplified the need for AI governance measures across the public sector, academia, and industry. Prior work in technical AI governance has proposed agendas for governing technical components in AI development, such as data, models, and compute. However, recent calls for more sociotechnical approaches recognize the critical role of social infrastructures surrounding technical ones in shaping governance decisions and efforts. While scholars and practitioners have advocated for sociotechnical AI governance, concrete research directions in this area are only beginning to emerge. This workshop aims to gather the expertise of researchers in HCI and adjacent disciplines to chart promising paths forward for sociotechnical AI governance. To make problems in this area more tangible, we outline four core governance challenges for contributions: anticipating high-priority risks to address with governance, identifying where to focus governance efforts and who should lead those efforts, designing appropriate interventions and tools to implement governance actions in practice, and evaluating the effectiveness of these interventions and tools in context. Through papers, panel discussions, keynotes, and collaborative drafting of a research agenda, this workshop will build community and empower actionable efforts to tackle AI governance through a sociotechnical lens. © 2025 Copyright held by the owner/author(s).

关键词： AI governance human-AI interaction responsible AI sociotechnical methods

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Collaborative Framework for circRNA-Disease Associations Prediction using Dual Variational Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Changchun Wang, Lei Zhao, Bowei Wei, Mengmeng Li, Yang Lu, Mianshuo Wei, Yu Liang, Sizhe The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Key Lab of Human-machine Interaction and Intelligent Decision Guangxi Acad emy of Science Nanning530007 China The School of Computer Science and Information Engineer ing Hefei University of Technology Hefei230601 China The College of Computer and Information Science School of Software University of Chinese Academy of Sciences Urumqi830011 China

Many experiments have shown that circular RNA (circRNA) can act as biomarkers for complex diseases and play significant regulatory roles in multiple pathological processes. However, most circRNA-disease associations remain unknown, and discovering these associations through biological experimental approach is expensive and time-consuming. Taking into account the shortcomings of current methods, we introduce a new collaborative framework that utilizes multi-heterogeneous graphs, along with variational graph auto-encoders (VGAE) to predict associations between circRNA and diseases. First, we build multi-similarity networks using various biological attributes of circRNA and diseases, and integrate these similarity networks. Two subnetworks are constructed from association matrix and combined similarity network, which included a circRNA-based network and a disease-based network. We then employ random walk with restart and Singular Value Decomposition, for feature extraction from the similarity matrix. Finally, we use collaborative framework to predict the circRNA-disease association scores based on the two subnetworks. We integrate the two score matrices to obtain a final prediction scoring matrix. Using 5-fold cross-validation on the CircR2Disease dataset, our model achieved an AUC score of 0.9828 and an AUPR score of 0.9820. Additionally, among the top 30 highest-scoring circRNA-disease association pairs, 26 associations have already been validated. Our model shows strong performance and can accurately predict associations between circRNA and diseases, according to experimental results. © 2015 IEEE. All rights reserved.

关键词： Variational techniques

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