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IEEE Transactions on Technology and Society 2020年第2期1卷 73-82页

作者： Shneiderman, Ben Department of Computer Science University of Maryland at College Park College ParkMD20742 United States Human–Computer Interaction Laboratory University of Maryland at College Park College ParkMD20742 United States

Researchers’ goals shape the questions they raise, collaborators they choose, methods they use, and outcomes of their work. This article offers a fresh vision of artificial intelligence (AI) research by suggesting a simplification to two goals: 1) emulation to understand human abilities to build systems that perform tasks as well as or better than humans and 2) application of AI methods to build widely used products and services. Researchers and developers for each goal can fruitfully work along their desired paths, but this article is intended to limit the problems that arise when assumptions from one goal are used to drive work on the other goal. For example, autonomous humanoid robots are prominent with emulation researchers, but application developers avoid them, in favor of tool-like appliances or teleoperated devices for widely used commercial products and services. This article covers four such mismatches in goals that affect AI-guided application development: 1) intelligent agent or powerful tool;2) simulated teammate or teleoperated device;3) autonomous system or supervisory control;and 4) humanoid robot or mechanoid appliance. This article clarifies these mismatches to facilitate the discovery of workable compromise designs that will accelerate human-centered AI applications research. A greater emphasis on human-centered AI could reduce AI’s existential threats and increase benefits for users and society, such as in business, education, healthcare, environmental preservation, and community safety. © 2020 IEEE. All right reserved.

关键词： Anthropomorphic robots

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Dynamic Localisation of Spatial-Temporal Graph Neural Network

arXiv

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arXiv 2025年

作者： Duan, Wenying Guo, Shujun Zhou, Zimu Huang, Wei Rao, Hong He, Xiaoxi Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau China

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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Evaluation of Extended Reality Authoring Tool for General Aviation Weather Training

Evaluation of Extended Reality Authoring Tool for General Av...

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Conference on Digital Avionics Systems (DASC)

作者： Jiwon Kim Kexin Wang Michael Dorneich Eliot Winer Lori Brown Geoffrey Whitehurst Industrial and Manufacturing Systems Engineering Iowa State University Ames IA USA Human Computer Interaction Iowa State University Ames IA USA Mechanical Engineering Iowa State University Ames IA USA Aviation Flight Science Western Michigan University Kalamazoo MI USA

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

This paper presents the preliminary evaluation results of an extended reality (XR) authoring tool designed for aviation weather training. XR has the potential to improve educational outcomes in flight training programs. However, the complexity and time demands of existing XR authoring tools have slowed wider adoption. This work strives to address these gaps by developing and continuously refining an instructor-centered, user-friendly XR authoring tool. Seventeen flight instructors participated in developing two XR lessons and evaluated the tool's effectiveness. Results indicated that the introduction of the XR authoring tool significantly boosted the instructors' confidence in creating XR content and alleviated concerns related to time consumption. Additionally, the findings showed that the XR authoring tool is highly usable and demands a low workload, with the XR lessons produced being highly satisfactory. By employing an instructor-centered approach, the XR authoring tool enabled instructors who are not XR experts to improve students' learning experiences, focusing on aviation weather content without the burden of technical complexities.

关键词： Training Authoring systems Extended reality Refining Focusing Aerospace electronics Complexity theory Usability Faces Meteorology

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Surface defect identification using Bayesian filtering on a 3D mesh

arXiv

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arXiv 2025年

作者： Vedove, Matteo Dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025, CC BY-SA.

关键词： Mesh generation

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Surface defect identification using Bayesian filtering on a 3D mesh

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Measurement: Sensors 2025年 38卷

作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

关键词： Mesh generation

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Immersive Virtual Reality Improves Psychophysiological Responses to Agent-Guided Breathing Training

SSRN

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SSRN 2025年

作者： Corrò, Christian Chittaro, Luca Human-Computer Interaction Lab Department of Mathematics Computer Science and Physics University of Udine Via delle Scienze 206 Udine33100 Italy

Slow deep breathing (SDB) techniques offer physical and mental health benefits that help mitigate stress, anxiety, and pain. However, economic and logistical barriers limit access to traditional breathing training programs. While self-help resources like videos and apps are available, they lack the personalized guidance and feedback provided by human trainers and tend to have high dropout rates. The increasing availability of consumer-grade virtual reality (VR) headsets presents new opportunities for immersive breathing training experiences, potentially enhancing engagement and adherence. However, it remains unclear whether breathing training can benefit from immersive VR (iVR). This paper addresses this question by comparing agent-guided breathing training in iVR (headset-based) vs. non-immersive VR (based on a computer monitor). We introduce and evaluate a virtual agent that provides users with real-time, personalized feedback on their respiratory frequency and depth. We evaluate both self-reported measures (including engagement, attention, presence, and perception of the virtual agent) and objective physiological signals, comprising respiration, electrodermal activity, cardiac activity, and brain prefrontal cortex activity (acquired through functional near-infrared spectroscopy, fNIRS). The results of the user study on 36 participants highlighted several benefits of iVR over non-immersive VR. Results showed that iVR increased users’ attention, engagement, sense of presence, social presence, and perceived anthropomorphism of the virtual agent. Physiologically, iVR improved depth of breathing at the correct SDB frequency, reduced cognitive load, decreased sympathetic arousal and increased parasympathetic activity. The paper describes and discusses these findings, highlighting the important role that iVR can play in enhancing the effectiveness of breathing training. © 2025, The Authors. All rights reserved.

关键词： Audition

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans 10

Implementation of a Hip Fracture Classification Network Base...

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10th International Conference on computer and Communications, ICCC 2024

作者： Zhao, Kui Yang, Meng Zhang, Ti Hu, Die Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China University of Chinese Academy of Science Shenyang China Peking University Third Hospital Department of Orthopedics Beijing China

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition. © 2024 IEEE.

关键词： Classification ConvNeXt Hip Fracture Segmentation V-Net

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Machine learning-based prediction of low-value care for hospitalized patients

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Intelligence-Based Medicine 2023年 8卷 100115-100115页

作者： King, Andrew J. Tang, Lu Davis, Billie S. Preum, Sarah M. Bukowski, Leigh A. Zimmerman, John Kahn, Jeremy M. Department of Critical Care Medicine University of Pittsburgh School of Medicine Pittsburgh PA United States Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA United States Department of Computer Science Dartmouth College Hanover NH United States Human-Computer Interaction Institute Carnegie Mellon University School of Computer Science Pittsburgh PA United States

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period;and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system. © 2023 The Authors

关键词： Clinical decision support Evidence-based medicine Low-value care Machine learning Resuscitation

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Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm

Towards Robotised Palpation for Cancer Detection through Onl...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Luca Beber Edoardo Lamon Giacomo Moretti Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Information Engineering and Computer Science Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy Department of Industrial Engineering Università di Trento Trento Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots’ potential to be an effective tool for early-stage cancer diagnostics.

关键词： Dimensionality reduction Biological system modeling Heuristic algorithms Biological tissues Estimation Collaboration Robot sensing systems Information filters End effectors Kalman filters

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Proposing an Interactive Audit Pipeline for Visual Privacy Research

Proposing an Interactive Audit Pipeline for Visual Privacy R...

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IEEE International Conference on Big Data

作者： Jasmine DeHart Chenguang Xu Christan Grant Lisa Egede School of Computer Science University of Oklahoma Norman Oklahoma USA Human Computer Interaction Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781665445993

In an ideal world, deployed machine learning models will enhance our society. We hope that those models will provide unbiased and ethical decisions that will benefit everyone. However, this is not always the case; issues arise during the data preparation process throughout the steps leading to the models’ deployment. The continued use of biased datasets and biased processes will adversely damage communities and increase the cost to fix the problem later. In this work, we walk through the decision making process that a researcher should consider before, during, and after a system deployment to understand the broader impacts of their research in the community. Throughout this paper, we discuss fairness, privacy, and ownership issues in the machine learning pipeline, assert the need for a responsible human-over-the-loop methodology to bring accountability into machine learning pipeline, and finally, reflect on the need to explore research agendas that have harmful societal impacts. We examine visual privacy research and draw lessons that can apply broadly to artificial intelligence. Our goal is to provide a systematic analysis of the machine learning pipeline for visual privacy and bias issues. With this pipeline, we hope to raise stakeholder (e.g., researchers, modelers, corporations) awareness as these issues propagate in the various machine learning phases.

关键词： Training Visualization Privacy Data privacy Pipelines Decision making Machine learning

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