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User Compliance With the Health Emergency and Disaster Management System: Systematic Literature Review

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Journal of Medical Internet Research 2023年第1期25卷 e41168页

作者： Fitriani, Widia Resti Sutanto, Juliana Handayani, Putu Wuri Hidayanto, Achmad Nizar Faculty of Computer Science Universitas Indonesia Depok Indonesia Department Human Centred Computing Faculty of Information Technology Monash University Melbourne Australia

Background: Health-related hazards have a detrimental impact on society. The health emergency and disaster management system (Health EDMS), such as a contact-tracing application, is used to respond to and cope with health-related hazards. User compliance with Health EDMS warnings is key to its success. However, it was reported that user compliance with such a system remains low. Objective: Through a systematic literature review, this study aims to identify the theories and corresponding factors that explain user compliance with the warning message provided by Health EDMS. Methods: The systematic literature review was conducted using Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. The search was performed using the online databases Scopus, scienceDirect, ProQuest, IEEE, and PubMed, for English journal papers published between January 2000 and February 2022. Results: A total of 14 papers were selected for the review based on our inclusion and exclusion criteria. Previous research adopted 6 theories when examining user compliance, and central to the research was Health EDMS. To better understand Health EDMS, based on the literature reviewed, we mapped the activities and features of Health EDMS with the key stakeholders involved. We identified features that require involvement from individual users, which are surveillance and monitoring features and medical care and logistic assistance features. We then proposed a framework showing the individual, technological, and social influencing factors of the use of these features, which in turn affects compliance with the warning message from Health EDMS. Conclusions: Research on the Health EDMS topic increased rapidly in 2021 due to the COVID-19 pandemic. An in-depth understanding of Health EDMS and user compliance before designing the system is essential for governments and developers to increase the effectiveness of Health EDMS. Through a systematic literature review, this study proposed a

关键词： contact-tracing application health emergency and disaster management system influential factors systematic literature review theoretical lens user compliance

Enhancing DDoS Attack Detection in SDN with a Stacked Model Framework Utilizing Deep Neural Networks

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Enhancing DDoS Attack Detection in SDN with a Stacked Model ...

IEEE International Conference on Consumer Electronics (ICCE)

作者： Aishita Sharma Sunil K. Singh Sudhakar Kumar Shin Hung Pan Brij B. Gupta Kwok Tai Chui Department of CSE Chandigarh College of Engineering and Technology Chandigarh India Department of Information Management Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taiwan University of Economics and Human Sciences in Warsaw Poland Hong Kong Metropolitan University Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Software Defined Networking enhances network management and security through centralized control, vital for the growth of consumer electronics. With the rise of smart devices and IoT systems, the demand for scalable network management has intensified. While SDN streamlines network operations and supports essential technologies, it remains vulnerable to DDoS attacks that can disrupt these services. However, SDN environments are vulnerable to Distributed Denial of Service (DDoS) attacks, posing significant risks to operational integrity. This paper analyzes the performance of various machine learning models at the individual level, providing insights into their effectiveness in detecting DDoS attacks. Based on these findings, a hybrid model is introduced, utilizing stacking techniques with a Deep Neural Network (DNN) as the meta-learner. The hybrid approach achieves a remarkable accuracy of 99.09%. The results underscore the crucial role of machine learning, particularly deep learning methodologies, in safeguarding SDN networks against cyber threats. As reliance on complex networked systems in consumer electronics increases, enhancing the security of SDN environments becomes essential for maintaining seamless operations.

关键词： Deep learning Stacking Artificial neural networks Denial-of-service attack Security computer crime Software defined networking Smart devices Consumer electronics Centralized control

The effects of virtual reality on muscle synergies during walking and balancing in healthy adults

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Gait & Posture 2024年 113卷 55-56页

作者： Johannes Eichwalder Paul Kaufmann Thomas Mandl Brian Horsak Hans Kainz University of Vienna Centre for Sport Science and University Sports- Department of Sport and Human Movement Science Vienna Austria University of Applied Sciences for Technology and Digitalization Faculty Computer Science- Department Applied Mathematics & Physics Vienna Austria University of Applied Sciences St. Pölten Department of Health Sciences- Center for Digital Health and Social Innovation St. Pölten Austria

Semantic Visual Simultaneous Localization and Mapping: A Survey

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arXiv 2022年

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Institute of Computer Vision College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Human-Robot Interfaces and Interaction Lab Istituto Italiano di Tecnologia Genoa Italy National University of Defense Technology China Università di Genova Genoa Italy School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Institute of Computer Vision School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM cannot achieve good localization in dynamic and complex environments. Numerous publications have reported that, by combining with the semantic information with vSLAM, the semantic vSLAM systems have the capability of solving the above problems in recent years. Nevertheless, there is no comprehensive survey about semantic vSLAM. To fill the gap, this paper first reviews the development of semantic vSLAM, explicitly focusing on its strengths and differences. Secondly, we explore three main issues of semantic vSLAM: the extraction and association of semantic information, the application of semantic information, and the advantages of semantic vSLAM. Then, we collect and analyze the current state-of-the-art SLAM datasets which have been widely used in semantic vSLAM systems. Finally, we discuss future directions that will provide a blueprint for the future development of semantic vSLAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting

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arXiv 2022年

作者： Igamberdiev, Timour Arnold, Thomas Habernal, Ivan Trustworthy Human Language Technologies Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent. © 2022, CC BY-SA.

关键词： Transparency

Traffic-Domain Video Question Answering with Automatic Captioning

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arXiv 2023年

作者： Qasemi, Ehsan Francis, Jonathan M. Oltramari, Alessandro Department of Computer Science University of Southern California Los AngelesCA United States Human-Machine Collaboration Bosch Center for Artificial Intelligence PittsburghPA United States

Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and Intelligent Transportation Systems. Nevertheless, the integration of urban traffic scene knowledge into VidQA systems has received limited attention in previous research endeavors. In this work, we present a novel approach termed Traffic-domain Video Question Answering with Automatic Captioning (TRIVIA), which serves as a weak-supervision technique for infusing traffic-domain knowledge into large video-language models. Empirical findings obtained from the SUTD-TrafficQA task highlight the substantial enhancements achieved by TRIVIA, elevating the accuracy of representative video-language models by a remarkable 6.5 points (19.88%) compared to baseline settings. This pioneering methodology holds great promise for driving advancements in the field, inspiring researchers and practitioners alike to unlock the full potential of emerging video-language models in traffic-related applications. © 2023, CC BY.

关键词： Domain Knowledge

Toward Anxiety-Reducing Pocket Robots for Children

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arXiv 2025年

作者： Frederiksen, Morten Roed Stoy, Kasper Matarić, Maja Computer science department of The IT-University of Copenhagen Rued Langgaards vej 7 Copenhagen2300 Denmark Interaction Lab in the Computer Science Department The University of Southern California United States

A common denominator for most therapy treatments for children who suffer from an anxiety disorder is daily practice routines to learn techniques needed to overcome anxiety. However, applying those techniques while experiencing anxiety can be highly challenging. This paper presents the design, implementation, and pilot study of a tactile hand-held pocket robot "AffectaPocket", designed to work alongside therapy as a focus object to facilitate coping during an anxiety attack. The robot does not require daily practice to be used, has a small form factor, and has been designed for children 7 to 12 years old. The pocket robot works by sensing when it is being held and attempts to shift the child’s focus by presenting them with a simple three-note rhythm-matching game. We conducted a pilot study of the pocket robot involving four children aged 7 to 10 years, and then a main study with 18 children aged 6 to 8 years;neither study involved children with anxiety. Both studies aimed to assess the reliability of the robot’s sensor configuration, its design, and the effectiveness of the user tutorial. The results indicate that the morphology and sensor setup performed adequately and the tutorial process enabled the children to use the robot with little practice. This work demonstrates that the presented pocket robot could represent a step toward developing low-cost accessible technologies to help children suffering from anxiety disorders. © 2025, CC BY-NC-ND.

关键词： Machine design

Trust in Automated Shuttle Buses Predicts the Intention to Use Them and Expectations of Their Behavior

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SSRN

SSRN 2024年

作者： Marsja, Erik Thellman, Sam Anund, Anna Disability Research Division Department of Behavioural Sciences and Learning Linköping University Sweden Division for Human-Centered Systems Department of Computer and Information Science Linköping University Sweden The Swedish National Road Transport Research Institute Sweden

Automated shuttle buses (ASBs) are a promising technology for enhancing public transportation, but their acceptance and adoption depend on various factors, such as trust, expectations, and age. This study examined how trust in the capabilities of ASBs influences the behavioral intention to use ASBs as well as expectations on the braking behavior of ASBs, especially from the perspective of vulnerable road users (VRUs), such as pedestrians and cyclists. The study also investigated how age moderates these relationships. Five hundred participants completed an online questionnaire measuring their trust and behavioral intention to use ASBs. They then watched video scenarios of ASBs interacting with VRUs and reported their expectations regarding the ASBs’ ability to brake to avoid collisions. The data were analyzed using structural equation modeling (SEM). The results showed that trust was positively related to both behavioral intentions to use and expectations of ASBs’ braking behavior, and that age was negatively related to trust. However, age was not related to behavioral intention to use or expectations of ASBs’ braking behavior. The findings suggest that trust is a key factor influencing individuals’ acceptance and expectations of ASBs, and that trust can be enhanced by providing reliable, transparent, and user-friendly information and services. Furthermore, age is not a barrier to the adoption of ASBs, as long as individuals trust them. It offers practical implications for ASB developers, providers, and policymakers. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images 25th

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MCCNet: Multi-Color Cascade Network with Weight Transfer for...

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting