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Retraction notice to "An intrusion identification and prevention for cloud computing: From the perspective of deep learning" [Optik 270 (2022) 170044] (Optik (2022) 270, (S003040262201302X), (10.1016/***.2022.170044))

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Optik 2025年 335卷

作者： Kumar, Anil Umurzoqovich, Rajabov Sherzod Duong, Nguyen Duc Kanani, Pratik Kuppusamy, Arulmani Praneesh, M. Hieu, Minh Ngyen Data Science Research Group School of Computing DIT University Dehradun India The Head of the Department of Quality Control of Education Tashkent Institute of Finance Tashkent Uzbekistan East Asia University of Technology Hanoi Viet Nam Department of Computer Engineering D. J. Sanghvi College of Engineering Mumbai400056 India SASTRA Deemed to be University Thanjavur India PG & Research Department of Computer Science Sri Ramakrishna College of Arts & Science Tamil Nadu Coimbatore India Pusa Research Lab Viet Nam

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. Post-publication, an investigation conducted on behalf of the journal by Elsevier's Research Integrity & Publishing Ethics team discovered suspicious changes in authorship between the original submission and the revised version of this paper. During revision, the authors Anil Kumar, Rajabov Sherzod Umurzoqovich, Nguyen Duc Duong, Pratik Kanani, Arulmani Kuppusamy, and Praneesh M were added to the revised paper;and three authors were removed;without explanation and without exceptional approval by the journal editor, which is contrary to the journal policy on changes to authorship. None of the Authors responded to three emails that were sent related to this investigation and requesting further information. As such and due to the nature of the irregularities uncovered, the Editor has lost confidence in the validity/integrity and findings of the article and decided to retract the article. The scientific community takes a very strong view on this matter and apologies are offered to readers. © 2025 Elsevier GmbH

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DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information

Patterns

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Patterns 2025年

作者： Yongfan Lai Jiabo Chen Qinghao Zhao Deyun Zhang Yue Wang Shijia Geng Hongyan Li Shenda Hong State Key Laboratory of General Artificial Intelligence Beijing 100871 China School of Intelligence Science and Technology Peking University Beijing 100871 China National Institute of Health Data Science Peking University Beijing 100191 China PKU-WUHAN Institute for Artificial Intelligence Wuhan 430073 China Tianjin Key Laboratory of Visual Computing and Intelligent Perception College of Computer Science Nankai University Tianjin 300350 China Department of Cardiology Peking University People’s Hospital Beijing 100044 China HeartVoice Medical Technology Hefei 230088 China

The scarcity of high-quality electrocardiogram (ECG) data, driven by privacy concerns and limited medical resources, creates a pressing need for effective ECG signal generation. Existing approaches for generating ECG signals typically rely on small training datasets, lack comprehensive evaluation frameworks, and overlook potential applications beyond data augmentation. To address these challenges, we propose DiffuSETS, a framework capable of generating ECG signals with high semantic alignment and fidelity. DiffuSETS accepts various modalities of clinical text reports and patient-specific information as inputs, enabling the creation of clinically meaningful ECG signals. Additionally, we introduce a comprehensive benchmarking methodology to assess the effectiveness of ECG generative models. Our model achieves excellent results in tests, proving its superiority in the task of ECG generation. Furthermore, we showcase its potential to mitigate data scarcity while exploring applications in cardiology education and medical knowledge discovery.

关键词： cardiology electrocardiogram signal processing ECG generation diffusion model

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Deep Domain Adaptation for Pavement Crack Detection

arXiv

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arXiv 2021年

作者： Liu, Huijun Yang, Chunhua Li, Ao Huang, Sheng Feng, Xin Ruan, Zhimin Ge, Yongxin College of Computer Science Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Chongqing City Management College Chongqing401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China College of Computer Science And Engineering Chongqing University of Technology Chongqing400054 China Co. Ltd Chongqing400060 China

Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain. Copyright © 2021, The Authors. All rights reserved.

关键词： Crack detection

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Experiences of young people and their caregivers of using technology to manage type 1 diabetes mellitus: Systematic literature review and narrative synthesis

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JMIR Diabetes 2021年第1期6卷 e20973页

作者： Brew-Sam, Nicola Chhabra, Madhur Parkinson, Anne Hannan, Kristal Brown, Ellen Pedley, Lachlan Brown, Karen Wright, Kristine Pedley, Elizabeth Nolan, Christopher J. Phillips, Christine Suominen, Hanna Tricoli, Antonio Desborough, Jane Department of Health Services Research and Policy Research School of Population Health College of Health and Medicine Australian National University Building 62 Mills Rd Acton Canberra 2601 ACT Australia Canberra Health Services Canberra Australia ANU Medical School College of Health and Medicine Australian National University Canberra Australia John Curtin School of Medical Research College of Health and Medicine Australian National University Canberra Australia School of Computing College of Engineering and Computer Science Australian National University Canberra Australia Department of Computing University of Turku Turku Finland Data61 Commonwealth Scientific and Industrial Research Organisation Canberra Australia Nanotechnology Research Lab Research School of Chemistry College of Science Australian National University Canberra Australia

Background: In the last decade, diabetes management has begun to transition to technology-based care, with young people being the focus of many technological advances. Yet, detailed insights into the experiences of young people and their caregivers of using technology to manage type 1 diabetes mellitus are lacking. Objective: The objective of our study was to describe the breadth of experiences and perspectives on diabetes technology use among children and adolescents with type 1 diabetes mellitus and their caregivers. Methods: This systematic literature review used integrated thematic analysis to guide a narrative synthesis of the included studies. We analyzed the perspectives and experiences of young people with type 1 diabetes mellitus and their caregivers reported in qualitative studies, quantitative descriptive studies, and studies with a mixed methods design. Results: Seventeen articles met the inclusion criteria, and they included studies on insulin pump, glucose sensors, and remote monitoring systems. The following eight themes were derived from the analysis: (1) expectations of the technology prior to use, (2) perceived impact on sleep and overnight experiences, (3) experiences with alarms, (4) impact on independence and relationships, (5) perceived usage impact on blood glucose control, (6) device design and features, (7) financial cost, and (8) user satisfaction. While many advantages of using diabetes technology were reported, several challenges for its use were also reported, such as cost, the size and visibility of devices, and the intrusiveness of alarms, which drew attention to the fact that the user had type 1 diabetes mellitus. Continued use of diabetes technology was underpinned by its benefits outweighing its challenges, especially among younger people. Conclusions: Diabetes technologies have improved the quality of life of many young people with type 1 diabetes mellitus and their caregivers. Future design needs to consider the impact of these te

关键词： Adolescents Children Diabetes Experiences Perspectives Self-management Systematic review Technology Type 1 diabetes mellitus

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First Detection of the Monkeypox Virus Using Wastewater-Based Surveillance in Miami-Dade County

SSRN

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SSRN 2022年

作者： Sharkey, Mark E. Babler, Kristina M. Abelson, Samantha M. Alsuliman, Bader Amirali, Ayaaz Comerford, Samuel Grills, George S. Kumar, Naresh Laine, Jennifer Lamar, Walter E. Mason, Christopher E. Penso, Johnathon Reding, Brian D. Schürer, Stephan C. Shukla, Bhavarth S. Stevenson, Mario Vidovic, Dusica Solo-Gabriele, Helena M. Department of Medicine University of Miami Miller School of Medicine MiamiFL United States Department of Chemical Environmental and Materials Engineering University of Miami Coral GablesFL United States Department of Public Health Sciences University of Miami Miller School of Medicine MiamiFL United States Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine MiamiFL United States Environmental Health and Safety University of Miami MiamiFL United States Facilities Safety & Compliance University of Miami Miller School of Medicine MiamiFL United States Department of Physiology and Biophysics Weill Cornell Medical College New York CityNY United States Department of Molecular & Cellular Pharmacology University of Miami Miller School of Medicines MiamiFL United States Institute for Data Science & Computing University of Miami Coral GablesFL United States

Measurements of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA from wastewater have been utilized for evaluating the prevalence of COVID-19 illness within communities and other sites, such as institutions of higher learning. A wastewater-based surveillance program was established at the University of Miami to monitor SARS-CoV-2 RNA levels and subsequently exploited to detect both RNA and DNA of additional biological targets present in concentrated wastewater samples. Here we report on the use of wastewater to detect nucleic acids of Monkeypox virus (MPXV) which has recently emerged to become a concern worldwide. Results show that MPXV can be detected in wastewater based on PCR amplification of both MPXV DNA and RNA. Levels of MPXV nucleic acids in wastewater generally reflect the trends of clinical cases in the community and since its initial detection, MPXV levels have remained above PCR detection limits throughout the sampling period. We recommend the expansion of current wastewater-based programs’ methods to detect a broader range of pathogens of concern in wastewater and propose complementary approaches that target both DNA and RNA to increase the sensitivity of detecting disease causing pathogens within communities. © 2022, The Authors. All rights reserved.

关键词： Coronavirus

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computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement.

arXiv

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arXiv 2020年

作者： Hematialam, Hossein Garbayo, Luciana Gopalakrishnan, Seethalakshmi Zadrozny, Wlodek College of Computing UNC Charlotte CharlotteNC United States School of Data Science UNC Charlotte CharlotteNC United States Departments of Philosophy & Medical Education U. of Central Florida OrlandoFL United States

Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem – breast cancer screening. We show that these differences arise from knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors. The computational models we build and analyze agree with the near-peer epistemic model of expert disagreement proposed by Garbayo. Even though the article is a case study focused on one set of guidelines, the proposed methodology is broadly applicable. In addition to proposing a novel graph-based similarity model for comparing collections of documents, we perform an extensive analysis of the model performance. In a series of a few dozen experiments, in three broad categories, we show, at a very high statistical significance level of 3-4 standard deviations for our best models, that the high similarity between expert annotated model and our concept based, automatically created, computational models is not accidental. Our best model achieves roughly 70% similarity. We also describe possible extensions of this work. Copyright © 2020, The Authors. All rights reserved.

关键词： Computational methods

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Improving fast segmentation with teacher-student learning 29

Improving fast segmentation with teacher-student learning

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29th British Machine Vision Conference, BMVC 2018

作者： Xie, Jiafeng Shuai, Bing Hu, Jian-Fang Lin, Jingyang Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Nanyang Technological University Singapore Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing MOE Hong Kong

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks. However, these models are very heavy and generally suffer from low inference speed, which limits their application scenarios in practice. Meanwhile, existing fast segmentation models usually fail to obtain satisfactory segmentation accuracies on public benchmarks. In this paper, we propose a teacher-student learning framework that transfers the knowledge gained by a heavy and better performed segmentation network (i.e. teacher) to guide the learning of fast segmentation networks (i.e. student). Specifically, both zero-order and first-order knowledge depicted in the fine annotated images and unlabeled auxiliary data are transferred to regularize our student learning. The proposed method can improve existing fast segmentation models without incurring extra computational overhead, so it can still process images with the same fast speed. Extensive experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate that the proposed teacher-student learning framework is able to significantly boost the performance of student network. © 2018. The copyright of this document resides with its authors.

关键词： Students

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Distributed State Estimation for Nonlinear Networked System with Correlated Noises

Distributed State Estimation for Nonlinear Networked System ...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Yuan Liang Sibo Hu Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Beihang University Beijing P. R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P. R. China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

In this paper, the problem of distributed state estimation for nonlinear networked system with correlated noises is investigated. First, a distributed weighted consensus-based cubature information filtering algorithm is designed, of which the goal is to achieve accurate estimated states with the presence of correlated noises in a fully distributed fashion. Then based on the statistical linear approximation method, it is further proved that, the estimated states of the proposed filtering algorithm are consistent. Finally, the improved performance of the proposed filtering algorithm are confirmed by the numerical simulation.

关键词： Sensors Sensor fusion Noise measurement Sensor systems Wireless sensor networks State estimation Kalman filters

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CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

arXiv

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arXiv 2022年

作者： Dorent, Reuben Kujawa, Aaron Ivory, Marina Bakas, Spyridon Rieke, Nicola Joutard, Samuel Glocker, Ben Cardoso, Jorge Modat, Marc Batmanghelich, Kayhan Belkov, Arseniy Calisto, Maria Baldeon Choi, Jae Won Dawant, Benoit M. Dong, Hexin Escalera, Sergio Fan, Yubo Hansen, Lasse Heinrich, Mattias P. Joshi, Smriti Kashtanova, Victoriya Kim, Hyeon Gyu Kondo, Satoshi Kruse, Christian N. Lai-Yuen, Susana K. Li, Hao Liu, Han Ly, Buntheng Oguz, Ipek Shin, Hyungseob Shirokikh, Boris Su, Zixian Wang, Guotai Wu, Jianghao Xu, Yanwu Yao, Kai Zhang, Li Ourselin, Sébastien Shapey, Jonathan Vercauteren, Tom School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom University of Pennsylvania Philadelphia United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States NVIDIA United States Department of Computing Imperial College London Department of Computing London United Kingdom School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Center for Data Science Peking University Beijing China Department of Radiology Armed Forces Yangju Hospital Yangju Korea Republic of Vanderbilt University Nashville United States University of Liverpool Liverpool United Kingdom School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China Inria Université Côte d'Azur Sophia Antipolis France Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Universitat de Barcelona Barcelona Spain Institute of Medical Informatics Universität zu Lübeck Germany Universidad San Francisco de Quito Quito Ecuador University of South Florida Tampa United States Muroran Institute of Technology Muroran Japan Moscow Institute of Physics and Technology Moscow Russia Skolkovo Institute of Science and Technology Moscow Russia Moscow Russia Department of Neurosurgery King's College Hospital London United Kingdom

Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT1 scans (N=105) and unpaired non-annotated hrT2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice score-VS: 88.4%;Cochleas: 85.7%) and close to full supervision (median Dice score-VS: 92.5%;Cochleas: 87.7%). All top-pe

关键词： Medical imaging

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New-Type DTZ Model for Solving Discrete Time-Dependent Nonlinear Equation System With Robotic-Arm Application

New-Type DTZ Model for Solving Discrete Time-Dependent Nonli...

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International Conference on Information science and Technology (ICIST)

作者： Jinjin Guo Binbin Qiu Yunong Zhang School of Data and Computer Science Sun Yat-sen University Guangzhou China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (数字)9781728155586

ISBN: (纸本)9781728155593

In this study, a cubic-precision discretization (CPD) formula is utilized to approximate the first-order time derivative and discretize the continuous-time zeroing model, which is designed via zeroing neural dynamics. Then, a new-type discrete-time zeroing (N-DTZ) model is proposed to solve a special kind of discrete time-dependent problems, that is, discrete time-dependent nonlinear equation system with future unknownness (DT-NES-FU). Comparative numerical experiments, including an application example to the real-time motion generation of a universal two-link robotic arm, are performed to illustrate the superior computational performance of the proposed N-DTZ model for solving the DT-NES-FU, as compared with the presented other models. Finally, detailed comparisons with other types of approximation formulas and the resultant DTZ models further verify the validity and superiority of the presented CPD formula as well as the N-DTZ model for solving the DT-NES-FU.

关键词： Mathematical model Numerical models Computational modeling Nonlinear equations Robots Jacobian matrices Real-time systems

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