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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Li, Yaqing Mo, Weiyi Ren, Jing Sun, Tianyi Zheng, Chengguaang Wang, Ling Yang, Jiajia Ming, Dong Medical School Tianjin University Tianjin China ZhejiangLab Zhejiang Hangzhou China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brian-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Sleep plays a crucial role in individual physical and mental health. Insufficient sleep leads to inattention, memory loss, cognitive dysfunction and emotional disorders. The mesolimbic pathway, ventral tegmental area (VTA) to nucleus accumbens (NAc) dopaminergic neural pathway has been shown to be associated with sleep-wake behavior and depression-like behavior. In this study, we investigated the relation between chronic sleep deprivation (CSD)-induced depression-like behaviors and the activity of VTA-NAc dopaminergic pathway. CSD caused weight loss and depression-like behaviors in the mice, including anhedonia and helplessness. CSD prominently reduced the change of mice' s DA signal when moving freely. Additionally, NAc DA release became less noticeable in rewarding event and retreat behavior after exposing to CSD. In conclusion, our findings indicated that VTA-NAc dopaminergic neural pathway is directly related to depressive-like behavior caused by chronic sleep deprivation. This study provides a theoretical basis for the in-depth understanding of the physiological function of sleep, and provides a potential target for clinical research on improving or treating sleep disorder-related depression. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sleep research

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition 8

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Siahkali, Farbod Alavi, Seyed Amirmahdi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Islamic Azad University Human and Robot Interaction Laboratory School of Computer Science Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476232

In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks.1https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition © 2022 IEEE.

关键词： Model automobiles

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A Neural Architecture Search Method with Multi-Dimensional Correlation Representation for Fault Diagnosis 9

A Neural Architecture Search Method with Multi-Dimensional C...

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9th IEEE Smart World Congress, SWC 2023

作者： Liu, Yumeng Wang, Tingqi Zheng, Xu Tian, Ling Wang, Hongan Institute of Software Chinese Academy of Sciences Beijing Key Laboratory of Human-Computer Interaction Beijing China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350319804

As deep learning has become more widely used for fault diagnosis, the shortcomings of model transferability and human model design costs are growing increasingly evident. The current work has tackled each of these two issues. Domain adaptation methods can provide transportability by mapping data to a common domain. For the consumption of manually constructed networks, Neural Architecture Search (NAS) is an intelligent solution that automatically select the ideal network structure. Unfortunately, there is little work to connect these two methodologies in fault diagnosis. Furthermore, these approaches are incapable in extracting internal correlations from instrumental signal data. In this paper, we propose a transferrable NAS fault diagnosis method, which maximizes savings in manual costs. We also integrate graph neural networks (GNN) in a stepwise manner into the NAS and domain adaptation to provide a multi-dimension correlation representation. Firstly, we structure the signal data into graph based on temporal relationships. Then, NAS designs automatically for selecting the optimal network structure and useful features. In NAS, graph encoder of GNN is used to establish internal relationships. Finally, domain adaptation provides transferability. At the same time, GNN's weights in domain adaptation help to further mine data internal associations. We tested our method on two publicly available datasets and performed comparisons. The experimental results show the proposed method has superior performance. © 2023 IEEE.

关键词： Domain Adaptation Fault Diagnosis Graph Neural Network Neural Architecture Search

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Breath Tools: Exploring the Effects on Adherence and User Experience of 4 Sounds Assisting Runners with Coupling Breath to Steps

Breath Tools: Exploring the Effects on Adherence and User Ex...

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

作者： Van Rheden, Vincent Harbour, Eric Finkenzeller, Thomas Meschtscherjakov, Alexander Human Computer Interaction Division Department of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Department of Sport and Exercise Science University of Salzburg Rif Hallein Austria

ISBN: (纸本)9781450394222

Running is a popular sport worldwide. Locomotor-Respiratory Coupling (LRC), a breathing technique in which the breath is coupled to steps, can positively impact running experience and economy. LRC may be difficult to learn and maintain during a run. Sound cues are a promising method to guide LRC while running. We designed four guiding sounds and evaluated these in a preliminary study (N=9). All sounds supported participants with LRC (on average 74.9±16.2% of time attached to the LRC rhythm). Based on our findings we advocate sound guidance for LRC and present directions to improve adherence and run experience. © 2023 Owner/Author.

关键词： Sports

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CrowdSolve: Managing Tensions in an Expert-Led Crowdsourced Investigation

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Proceedings of the ACM on human-computer interaction 2021年第CSCW1期5卷 1-30页

作者： Venkatagiri, Sukrit Gautam, Aakash Luther, Kurt Department Of Computer Science Center For Human-Computer Interaction Virginia Tech-1 ArlingtonVA United States

Investigators in fields such as journalism and law enforcement have long sought the public's help with investigations. New technologies have also allowed amateur sleuths to lead their own crowdsourced investigations-that have traditionally only been the purview of expert investigators-with mixed results. Through an ethnographic study of a four-day, co-located event with over 250 attendees, we examine the human infrastructure responsible for enabling the success of an expert-led crowdsourced investigation. We find that the experts enabled attendees to generate useful leads;the attendees formed a community around the event;and the victims' families felt supported. Additionally, the co-located setting, legal structures, and emergent social norms impacted collaborative work practice. We also surface three important tensions to consider in future investigations and provide design recommendations to manage these tensions. © 2021 ACM.

关键词： Crowdsourcing

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Planet Purifiers: A Collaborative Immersive Experience Proposing New Modifications to HOMER and Fishing Reel interaction Techniques

arXiv

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arXiv 2025年

作者： Giovannelli, Alexander Murphy, Fionn Davis, Trey Lee, Chaerin Abulikemu, Rehema Gallagher, Matthew Sharma, Sahil Lisle, Lee Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States

This paper presents our solution to the 2025 3DUI Contest challenge. We aimed to develop a collaborative, immersive experience that raises awareness about trash pollution in natural landscapes while enhancing traditional interaction techniques in virtual environments. To achieve these objectives, we created an engaging multiplayer game where one user collects harmful pollutants while the other user provides medication to impacted wildlife using enhancements to traditional interaction techniques: HOMER and Fishing Reel. We enhanced HOMER to use a cone volume to reduce the precise aiming required by a selection raycast to provide a more efficient means to collect pollutants at large distances, coined as FLOW-MATCH. To improve the animal feed distribution to wildlife far away from the user with Fishing Reel, we created RAWR-XD, an asymmetric bi-manual technique to more conveniently adjust the reeling speed using the non-selecting wrist rotation of the user. Copyright © 2025, The Authors. All rights reserved.

关键词： Reels

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Deep Learning Based Take-Over Performance Prediction and Its Application on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

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The Alchemist: A Gesture-Based 3D User Interface for Engaging Arithmetic Calculations

The Alchemist: A Gesture-Based 3D User Interface for Engagin...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Logan Lane Alexander Giovannelli Ibrahim Tahmid Francielly Rodrigues Cory Ilo Darren Hsu Christos Lougiakis Shakiba Davari Doug A. Bowman Department of Computer Science Virginia Tech Center for Human-Computer Interaction Blacksburg VA USA

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

This paper presents our solution to the IEEE VR 2024 3DUI contest. We present The Alchemist, a VR experience tailored to aid children in practicing and mastering the four fundamental mathematical operators. In The Alchemist, players embark on a fantastical journey where they must prepare three potions to break a malevolent curse imprisoning the Gobbler kingdom. Our contributions include the development of a novel number input interface, Pinwheel, an extension of PizzaText [7], as well as four novel gestures, each corresponding to a distinct mathematical operator, designed to assist children in retaining practice with these operations. Preliminary tests indicate that Pinwheel and the four associated gestures facilitate the quick and efficient execution of mathematical operations.

关键词： Visualization Three-dimensional displays Conferences Virtual reality User interfaces Arithmetic

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Spatial grids assisted multi-task fusing framework for incremental point cloud mapping of large scenes

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Luo, Nan Hu, Daoru Liang, Jie Wang, Yifeng Zhao, Hui Wang, Quan Xidian University Key Laboratory of Smart Human Computer Interaction and Wearable Technology of Shannxi Province School of Computer Science and Technology Xi'an710126 China

Recovering the point cloud maps of large-scale scenes from multi-sensor data is the fundamental building block of numerous 3D related applications. Available SLAM (Simultaneous Localization and Mapping) frameworks require rigorous constraints in data capturing and suffer from heavy workload when facing large-scale scenes. To push the research to further extent, this work proposes an incremental multi-task mapping framework for large-scale scenes under the guidance of spatial grids. (1) This framework first encodes the target scene into spatial grids according to the GNSS (The Global Navigation Satellite System) coordinates, then the mapping task is accomplished via multiple sub-scene SLAM tasks that can be executed independently, and finally the mapped point clouds are incrementally aligned with the assistance of encoded spatial grids to form the whole picture of the large-scale scene. (2) In each sub-scene SLAM task, we develop a new fast loop detection method based on spatial grids and perceptual hashing, which replaces the memory-expensive BoW (bag-of-words) model for image similarity determination, saves the unnecessary computational cost and lifts the localization accuracy. (3) In the aligning part, a new grid-wise merging method is designed for sub-scene point clouds, which improves the robustness and efficiency of point cloud registration and the applicability for large-scale scenes, supporting incremental multi-task mapping. We extensively evaluate the localization accuracy, loop detection efficiency on indoor EuRoC dataset, and multi-machine point cloud mapping on outdoor KITTI dataset. Experimental results reveal that the proposed framework provides a feasible multi-task fusing solution for incremental mapping of large scenes. © 1963-2012 IEEE.

关键词： Global positioning system

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Interdisciplinary perspectives on privacy awareness in lifelogging technology development

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Journal of Ambient Intelligence and humanized Computing 2023年第3期14卷 2291-2312页

作者： Wilkowska, Wiktoria Offermann, Julia Colonna, Liane Florez-Revuelta, Francisco Climent-Pérez, Pau Mihailidis, Alex Poli, Angelica Spinsante, Susanna Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Aachen Germany Swedish Law and Informatics Research Institute Stockholm University Stockholm Sweden Department of Computer Technology University of Alicante Alicante Spain Department of Occupational Science and Occupational Therapy University of Toronto Toronto Canada Department of Information Engineering Marche Polytechnic University Ancona Italy

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL (‘Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People’), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies. © 2022, The Author(s).

关键词： human computer interaction

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