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9th ACM SIGCHI Annual Symposium on computer-human interaction in Play, CHI PLAY 2022

作者： Sadprasid, Book Tabor, Aaron Scheme, Erik Bateman, Scott Faculty Of Computer Science University Of New Brunswick Canada Human Computer Interaction Lab University Of New Brunswick Canada Institute Of Biomedical Engineering University Of New Brunswick Canada

ISBN: (纸本)9781450392112

Adherence and frequency of play are well-established challenges for serious games that target breathing exercises, because exercises are most effective when practiced in short and frequent sessions. Researchers have recognized that elements of idle games ideally align with many therapeutic use cases because idle games have a frequently repeating gameplay cycle that draws people in for short play sessions. However, there is little research about how idle games can be used to motivate consistent, frequent practice of therapies like breathing exercises that are often recommended for chronic conditions like ADHD. This paper describes the design and implementation of a therapeutic idle game, Focus Cat. This game is designed to help people with ADHD incorporate breathing exercises into their symptom management routine. Our work demonstrates how the unique qualities of idle game design - including short, frequent gameplay sessions, simple mechanics that make mundane tasks engaging and mechanics that pull and push players into and out of active gameplay - can be used for ADHD breathing exercises to improve adherence and frequency of practice. © 2022 Owner/Author.

关键词： Game design

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CoLT: Enhancing Collaborative Literature Review Tasks with Synchronous and Asynchronous Awareness Across the Reality-Virtuality Continuum

CoLT: Enhancing Collaborative Literature Review Tasks with S...

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Ibrahim A. Tahmid Francielly Rodrigues Alexander Giovannelli Lee Lisle Jerald Thomas Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

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.

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Situational Adaptive Autonomy in human-AI Cooperation

Situational Adaptive Autonomy in Human-AI Cooperation

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2023 Workshop on Intervening, Teaming, Delegating, AutomationXP 2023

作者： Salikutluk, Vildan Frodl, Eric Herbert, Franziska Balfanz, Dirk Koert, Dorothea Interactive AI Algorithms & Cognitive Models for Human-AI Interaction Computer Science Department Technical University Darmstadt Germany Models of Higher Cognition Human Sciences Department Technical University Darmstadt Germany Centre for Cognitive Science Technical University Darmstadt Germany

human-AI teams have the potential to produce improved outcomes in various tasks as opposed to each team member working alone. However, there are various factors that influence human-AI team performance which potentially differ from classical human-computer interaction settings. Specifically, there is existing work indicating that it is beneficial for AI systems to automatically adapt their autonomy within the team and task in order to work towards achieving a shared goal more effectively. Thus, in this paper, we describe a concept of situational adaptive autonomy for human-AI cooperation in a shared workspace setting. We discuss that task complexity and models for the AI system’s understanding of their human teammate, i.e. Theory of Mind models (ToMMs) might be helpful to implement situational adaptation of AI autonomy such that interaction and team performance can be improved. We present an experimental setup for a cooperative real-world robotic task and a corresponding approximation in a grid-world in which we plan to investigate situational adaptive autonomy within a shared workspace in an interactive human-AI team. © 2023 Copyright for this paper by its authors.

关键词： human computer interaction

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Semantics-Controlled Gaussian Splatting for Outdoor Scene Reconstruction and Rendering in Virtual Reality 32

Semantics-Controlled Gaussian Splatting for Outdoor Scene Re...

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

作者： Schieber, Hannah Young, Jacob Langlotz, Tobias Zollmann, Stefanie Roth, Daniel Technical University of Munich Human-Centered Computing and Extended Reality Lab Tum University Hospital Clinic for Orthopedics and Sports Orthopedics Munich Germany Erlangen Germany University of Otago Department of Computer Science Dunedin New Zealand Aarhus University Department of Computer Science Aarhus Denmark

ISBN: (纸本)9798331536459

Advancements in 3D rendering like Gaussian Splatting (GS) allow novel view synthesis and real-time rendering in virtual reality (VR). However, GS-created 3D environments are often difficult to edit. For scene enhancement or to incorporate 3D assets, segmenting Gaussians by class is essential. Existing segmentation approaches are typically limited to certain types of scenes, e.g., "circular"scenes, to determine clear object boundaries. However, this method is ineffective when removing large objects in non-"circling"scenes such as large outdoor *** propose Semantics-Controlled GS (SCGS), a segmentation-driven GS approach, enabling the separation of large scene parts in uncontrolled, natural environments. SCGS allows scene editing and the extraction of scene parts for VR. Additionally, we introduce a challenging outdoor dataset, overcoming the "circling"setup. We outperform the state-of-the-art in visual quality on our dataset and in segmentation quality on the 3D-OVS dataset. We conducted an exploratory user study, comparing a 360-video, plain GS, and SCGS in VR with a fixed viewpoint. In our subsequent main study, users were allowed to move freely, evaluating plain GS and SCGS. Our main study results show that participants clearly prefer SCGS over plain GS. We overall present an innovative approach that surpasses the state-of-the-art both technically and in user experience. © 2025 IEEE.

关键词： Virtual environments

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Post-Training Quantization in Brain-computer Interfaces Based on Event-Related Potential Detection

Post-Training Quantization in Brain-Computer Interfaces Base...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hubert Cecotti Dalvir Dhaliwal Hardip Singh Yohesh Kumar Meena Department of Computer Science California State University Fresno USA Human-AI Interaction (HAIx) Lab IIT Gandhinagar India

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-computer Interfaces (BCI) that are fully portable and usable in various situations, it is necessary to provide approaches that are lightweight for storage and computation. In this paper, we propose the evaluation of post-training quantization on state-of-the-art approaches in brain-computer interfaces and assess their impact on accuracy. We evaluate the performance of the single-trial detection of event-related potentials representing one major BCI paradigm. The area under the receiver operating characteristic curve drops from 0.861 to 0.825 with PTQ when applied on both spatial filters and the classifier, while reducing the size of the model by about x 15. The results support the conclusion that PTQ can substantially reduce the memory footprint of the models while keeping roughly the same level of accuracy.

关键词： Quantization (signal) Accuracy Memory management Deep architecture Receivers Machine learning Brain modeling Spatial filters Brain-computer interfaces Hardware

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Quantifying Spatial Domain Explanations in BCI using Earth Mover’s Distance

Quantifying Spatial Domain Explanations in BCI using Earth M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Param Rajpura Hubert Cecotti Yogesh Kumar Meena Human-AI Interaction (HAIx) Lab IIT Gandhinagar India Department of Computer Science California State University Fresno USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are not fully integrated into clinical and commercial settings. It’s crucial to assess and explain BCI performance, offering clear explanations for potential users to avoid frustration when it doesn’t work as expected. This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG). We then propose an optimal transport theory-based approach using earth mover’s distance (EMD) to quantify the comparison of the feature relevance map with the domain knowledge of neuroscience. For this, we utilized explainable AI (XAI) techniques for generating feature relevance in the spatial domain to identify important channels for model outcomes. Three state-of-the-art models are implemented - 1) Riemannian geometry-based classifier, 2) EEGNet, and 3) EEG Conformer, and the observed trend in the model’s accuracy across different architectures on the dataset correlates with the proposed feature relevance metrics. The models with diverse architectures perform significantly better when trained on channels relevant to motor imagery than data-driven channel selection. This work focuses attention on the necessity for interpretability and incorporating metrics beyond accuracy, underscores the value of combining domain knowledge and quantifying model interpretations with data-driven approaches in creating reliable and robust Brain-computer Interfaces (BCIs).

关键词： Measurement Earth Accuracy computer architecture Brain modeling Motors Feature extraction

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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Daily Pain Prediction in Workplace Using Gaussian Processes

Daily Pain Prediction in Workplace Using Gaussian Processes

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16th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Puri, Chetanya Keyaerts, Stijn Szymanski, Maxwell Godderis, Lode Verbert, Katrien Luca, Stijn Vanrumste, Bart KU Leuven Belgium Leuven Belgium Department ofPublic Health and Primary Care KU Leuven Leuven Belgium Department of Computer Science KU Leuven Belgium Human-computer Interaction research group Department of Computer Science KU Leuven Belgium Department of Data Analysis and Mathematical Modelling Ghent University Ghent Belgium

Work-related Musculoskeletal disorders (MSDs) account for 60% of sickness-related absences and even permanent inability to work in the Europe, Long term impacts of MSDs include "Pain chronification'' which is the transition of temporary pain into persistent pain. Preventive pain management can lower the risk of chronic pain. It is therefore important to appropriately assess pain in advance, which can assist a person in improving their fear of returning to work. In this study, we analysed pain data acquired over time by a smartphone application from a number of participants. We attempt to forecast a person's future pain levels based on his or her prior pain data. Due to the self-reported nature of the data, modelling daily pain is challenging due to the large number of missing values. For pain prediction modelling of a test subject, we employ a subset selection strategy that dynamically selects a closest subset of individuals from the training data. The similarity between the test subject and the training subjects is determined via dynamic time warping-based dissimilarity measure based on the time limited historical data until a given point in time. The pain trends of these selected subset subjects is more similar to that of the individual of interest. Then, we employ a Gaussian processes regression model for modelling the pain. We empirically test our model using a leave-one-subject-out cross validation to attain 20% improvement over state-of-the-art results in early prediction of pain. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Prediction models

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Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models

Enhancing Cancer Gene Prediction through Aligned Fusion of M...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Han, Zebei Yu, Gufeng 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

ISBN: (纸本)9798350337488

Despite the considerable progress that has been made in cancer research, identifying cancer genes remains a significant challenge due to the intricate nature of the disease. Given the importance of incorporating gene interaction relationships in identifying potential cancer genes, Graph Neural Networks (GNNs) have garnered increasing attention for their ability to model gene associations effectively. Particularly, recent studies have demonstrated the superiority of GNNs in deciphering the knowledge embedded in Protein-Protein interaction (PPI) networks for cancer gene prediction. However, these studies primarily focused on a single PPI network, overlooking the valuable insights encapsulated within other PPI networks. Additionally, previous endeavors often centered around pan-cancer datasets, neglecting the importance of predicting specific cancer genes. To address these limitations, we present a novel method called MPIT, which employs Graph Transformer Networks (GTNs) to identify specific cancer driver genes. MPIT effectively integrates data from diverse PPI and multi-omics data via the alignment and fusion of gene representations learned from different PPI networks. We collect three distinct cancer cell line datasets to assess the model performance. Our experimental findings demonstrate the superiority of MPIT over the existing methods, achieving the state-of-the-art performance across all three datasets. © 2023 IEEE.

关键词： Cancer genes feature alignment feature fusion Graph Transformer Network specific cancer genes

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Exploring Digital Misinformation as a Sociotechnical Phenomenon: Insights from a Small-scale Study 23

Exploring Digital Misinformation as a Sociotechnical Phenome...

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Proceedings of the XXII Brazilian Symposium on human Factors in Computing Systems

作者： Alisson Andery Puska Roberto Pereira Human-Computer Interaction Laboratory Universidade Federal do Paraná Brasil Department of Computer Science Universidade Federal do Paraná Brasil

ISBN: (纸本)9798400717154

This article addresses the challenge of comprehending digital misinformation as a sociotechnical phenomenon within the field of human-computer interaction, particularly in the context of COVID-19-related misinformation in Brazil. The study employed an exploratory approach to analyze misinformation cases using various artifacts and conducted a focus group with eight experts in the field. The findings revealed 137 sociotechnical aspects and 29 stakeholders, shedding light on the complex interplay of factors influencing digital misinformation’s creation, dissemination, and consumption. The study emphasizes the necessity of adopting a sociotechnical approach and highlights the challenges in comprehensively analyzing stakeholders, communication channels, and the stages of the misinformation lifecycle. Overall, the research contributes to a deeper understanding of digital misinformation in sociotechnical contexts and its implications for human-computer interaction.

关键词： misinformation misinformation life cycle organizational semiotics sociotechnical analysis

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