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IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification

arXiv

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arXiv 2020年

作者： Mahbod, Amirreza Ecker, Rupert Schaefer, Gerald Dorffner, Georg Wang, Chunliang Ellinger, Isabella Institute for Pathophysiology and Allergy Research Medical University of Vienna Vienna Austria Research and Development Department TissueGnostics GmbH Vienna Austria Department of Computer Science Loughborough University Loughborough United Kingdom Section for Artificial Intelligence Decision Support Medical University of Vienna Vienna Austria Department of Biomedical Engineering and Health Systems KTH Royal Institute of Technology Stockholm Sweden

Skin cancer is among the most common cancer types. Dermoscopic image analysis improves the diagnostic accuracy for detection of malignant melanoma and other pigmented skin lesions when compared to unaided visual inspection. Hence, computer-based methods to support medical experts in the diagnostic procedure are of great interest. Fine-tuning pre-trained convolutional neural networks (CNNs) has been shown to work well for skin lesion classification. Pre-trained CNNs are usually trained with natural images of a fixed image size which is typically significantly smaller than captured skin lesion images and consequently dermoscopic images are downsampled for fine-tuning. However, useful medical information may be lost during this transformation. In this paper, we explore the effect of input image size on skin lesion classification performance of fine-tuned CNNs. For this, we resize dermoscopic images to different resolutions, ranging from 64 × 64 to 768 × 768 pixels and investigate the resulting classification performance of three well-established CNNs, namely DenseNet-121, ResNet-18, and ResNet-50. Our results show that using very small images (of size 64×64 pixels) degrades the classification performance, while images of size 128×128 pixels and above support good performance with larger image sizes leading to slightly improved classification. We further propose a novel fusion approach based on a three-level ensemble strategy that exploits multiple fine-tuned networks trained with dermoscopic images at various sizes. When applied on the ISIC 2017 skin lesion classification challenge, our fusion approach yields an area under the receiver operating characteristic curve of 89.2% and 96.6% for melanoma classification and seborrheic keratosis classification, respectively, outperforming state-of-the-art algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Dermatology

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Early detection of the advanced persistent threat attack using performance analysis of deep learning

arXiv

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arXiv 2020年

作者： Joloudari, Javad Hassannataj Haderbadi, Mojtaba Mashmool, Amir GhasemiGol, Mohammad Shahab, S. Mosavi, Amir Computer Engineering Department Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Ro ad Section 3 Douliou Yunlin64002 Taiwan Institute of Automation Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences Ås1430 Norway Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany Department of Informatics J. Selye University Komarno94501 Slovakia

One of the most common and important destructive attacks on the victim system is Advanced Persistent Threat (APT)-attack. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. One of the solutions to detect a secret APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the network may crash because of high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods such as C5.0 decision tree, Bayesian network and deep neural network are used for timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, 10-fold cross validation method is used to experiment these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 95.64%, 88.37% and 98.85%, respectively, and also, in terms of the important criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 2.56, 10.47 and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false negative rate and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Security analysis of a three-factor anonymous authentication scheme for wireless sensor networks in internet of things environments

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International Journal of Network Security 2019年第6期21卷 1014-1020页

作者： Tai, Wei-Liang Chang, Ya-Fen Hou, Po-Lin Department of Information Communications Chinese Culture University Department of Computer Science and Information Engineering Taiwan National Taichung University of Science and Technology No. 129 Section 3 Sanmin Road Taichung Taiwan

The Internet of Things (IoT) can be applied to applications in various fields such as industry, medical care, and public security because IoT enables remote sensing and control in heterogeneous environments. Wireless sensor networks (WSNs) are an important infrastructure in IoT, where a sensor node provides the collected data to authorized users. Because of the resource-onstrained nature of sensor nodes such as transmission and computational capabilities and the limited energy, how to ensure both security and efficiency of WSNs in IoT environments becomes a challenge. Recently, Li et al. proposed a threefactor anonymous authentication scheme by adopting a fuzzy commitment scheme and an error correction code to handle the user0s biometric data for WSNs in IoT environments. They claimed their scheme could ensure computational efficiency and achieve more security and functional features. After analyzing their authentication scheme, we find that it cannot ensure security. First, a malicious user can retrieve a sensor node0s secret and impersonate the sensor node. Second, a malicious user can acquire the sensory data without the gateway node even with a forged identity. Third, the malicious user can retrieve another legal user0s essential information for authentication and impersonate this innocent user. In this paper, how these security aws damage Li et al.0s authentication scheme and further discussions will be shown in detail. © 2019 Femto Technique Co., Ltd.

关键词： Internet of things

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Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models

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NEW JOURNAL OF PHYSICS 2021年第6期23卷

作者： Tewarie, Prejaas Prasse, Bastian Meier, Jil Byrne, Aine De Domenico, Manlio Stam, Cornelis J. Brookes, Matthew J. Hillebrand, Arjan Daffertshofer, Andreas Coombes, Stephen Van Mieghem, Piet Sir Peter Mansfield Imaging Centre School of Physics and Astronomy University of Nottingham Nottingham United Kingdom Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Charité – Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin Humboldt-Universität zu Berlin and Berlin Institute of Health Department of Neurology Brain Simulation Section Berlin Germany School of Mathematics and Statistics University College Dublin Dublin Ireland Fondazione Bruno Kessler Povo (TN) Italy Amsterdam UMC Vrije Universiteit Amsterdam Department of Clinical Neurophysiology and MEG Center Amsterdam Neuroscience Amsterdam The Netherlands Amsterdam Movement Science & Institute for Brain and Behavior Amsterdam Faculty of Behavioural and Movement Sciences Vrije Universiteit Amsterdam The Netherlands School of Mathematical Sciences University of Nottingham Nottingham United Kingdom

Large-scale neurophysiological networks are often reconstructed from band-pass filtered time series derived from magnetoencephalography (MEG) data. Common practice is to reconstruct these networks separately for different frequency bands and to treat them independently. Recent evidence suggests that this separation may be inadequate, as there can be significant coupling between frequency bands (interlayer connectivity). A multilayer network approach offers a solution to analyze frequency-specific networks in one framework. We propose to use a recently developed network reconstruction method in conjunction with phase oscillator models to estimate interlayer connectivity that optimally fits the empirical data. This approach determines interlayer connectivity based on observed frequency-specific time series of the phase and a connectome derived from diffusion weighted imaging. The performance of this interlayer reconstruction method was evaluated in-silico. Our reconstruction of the underlying interlayer connectivity agreed to very high degree with the ground truth. Subsequently, we applied our method to empirical resting-state MEG data obtained from healthy subjects and reconstructed two-layered networks consisting of either alpha-to-beta or theta-to-gamma band connectivity. Our analysis revealed that interlayer connectivity is dominated by a multiplex structure, i.e. by one-to-one interactions for both alpha-to-beta band and theta-to-gamma band networks. For theta-gamma band networks, we also found a plenitude of interlayer connections between distant nodes, though weaker connectivity relative to the one-to-one connections. Our work is an stepping stone towards the identification of interdependencies across frequency-specific networks. Our results lay the ground for the use of the promising multilayer framework in this field with more-informed and justified interlayer connections.

关键词： multiplex networks magnetoencephalography multilayer brain networks phase oscillators Kuramoto model neural mass model multilayer networks

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An approximation method to solve Atangana-Baleanu FPDEs

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AIP Conference Proceedings 2024年第1期3219卷

作者： Rasool Shah Hassan K. Jassim Hijaz Ahmad Muslim Yusif Zayir Safaa H. Mahdi Ali T. Salman Ali K. Jassim Sahib A. Sachit Hussein G. Taher Hossein A. Eaued Mohammed A. Hussein Tarig M. Elzaki Mohamed R. Ali Kadhim A. Jabbar Saad A. Khafif Department of Mathematics Abdul Wali Khan University Mardan Mardan - Pakistan Department of Mathematics University of Thi-Qar Thi-Qar-Iraq Near East University Operational Research Center in Healthcare Nicosia-Turkey Department of Computer Science and Mathematics Lebanese American University Beirut-Lebanon Section of Mathematics International Telematic University Uninettuno Roma-Italy Educational Directorate of Thi-Qar Nasiriyah-Iraq Scientific Research Center AL-Ayen University Nassreya-Iraq Mathematics Department Faculty of Sciences and Arts University of Jeddah Jeddah-Saudi Arabia Department of Mathematics Benha Faculty of Engineering Benha University Benha-Egypt College of Technical Engineering National University of Science and Technology Thi-Qar-Iraq Educational Directorate of Diwaniyah Diwaniyah - Iraq

In this study, the homotopy perturbation approach (HPM) combined with the natural transform (NT) is used to obtain analytical solutions to fractional differential equations (FDEs) involving the Atangana-Baleanu deriva...

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The Halo Occupation Distribution Modeling of the X-ray-selected AGNs at 0.6 < z < 2.6 in the COSMOS field

arXiv

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arXiv 2025年

作者： Ikeda, Hiroyuki Miyaji, Takamitsu Oogi, Taira Toba, Yoshiki Aceves, Héctor Marchesi, Stefano Allevato, Viola Viitanen, Akke Civano, Francesca National Institute of Technology Wakayama College Wakayama Gobo644-0023 Japan Instituto de Astronomía Universidad Nacional Autónoma de México AP 106 Baja California Ensenada22800 Mexico Department of Electrical and Computer Engineering National Institute of Technology Asahikawa College Shunkodai 2-2-1-6 Hokkaido Asahikawa071-8142 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Academia Sinica Institute of Astronomy and Astrophysics 11F of Astronomy-Mathematics Building AS/NTU No.1 Section 4 Roosevelt Road Taipei10617 Taiwan Research Center for Space and Cosmic Evolution Ehime University 2-5 Bunkyo-cho Matsuyama Ehime790-8577 Japan Università di Bologna via Gobetti 93/2 BolognaI-40129 Italy Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy INAF Osservatorio Astronomico di Capodimonte Salita Moiariello 16 Napoli80131 Italy INAF Osservatorio Astronomico di Roma via Frascati 33 Monteporzio Catone00040 Italy Department of Physics University of Helsinki PO Box 64 HelsinkiFI-00014 Finland Astrophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States

We conducted precise measurements of Active Galactic Nuclei (AGNs) clustering at z ∼ 1 and z ∼ 2 by measuring the two-point cross-correlation function (CCF) between galaxies and X-ray-selected AGNs, and the two-point auto-correlation function (ACF) of galaxies in the COSMOS field to interpret the CCF results. The galaxy sample was selected from the COSMOS2015 catalog, while the AGN sample was chosen from the Chandra COSMOS-Legacy survey catalog. For the AGN samples at z ∼ 1 and z ∼ 2, we calculated AGN bias values of b = 1.16 (1.16;1.31) and b = 2.95 (2.30;3.55), respectively. These values correspond to typical host dark matter halo (DMH) masses of log(Mtyp/M☉) = 11.82 (11.82;12.12) and log(Mtyp/M☉) = 12.80 (12.38;13.06), respectively. Subsequently, we performed Halo Occupation Distribution (HOD) modeling of X-ray-selected AGNs using the CCF and ACF of galaxies. We have found a significant satellite AGN population at z ∼ 1 all over the DMH mass (MDMH) range occupied by AGNs. While z ∼ 2 AGNs in our sample are associated with higher mass DMHs and smaller satellite fractions. The HOD analysis suggests a marginal tendency of increasing satellite slope with redshift, but larger samples are needed to confirm this with sufficient statistical significance. We find that the best-fit values of satellite slope in both redshift bins are greater than 0, suggesting tendencies of increasing satellite AGN number with MDMH Copyright © 2025, The Authors. All rights reserved.

关键词： Active Galactic Nuclei

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Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems

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Journal of Diabetes Science and technology 2018年第5期12卷 953-966页

作者： Hajizadeh, Iman Rashid, Mudassir Turksoy, Kamuran Samadi, Sediqeh Feng, Jianyuan Sevil, Mert Hobbs, Nicole Lazaro, Caterina Maloney, Zacharie Littlejohn, Elizabeth Cinar, Ali Department of Chemical and Biological Engineering Illinois Institute of Technology Chicago IL United States Department of Biomedical Engineering Illinois Institute of Technology Chicago IL United States Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL United States Department of Pediatrics and Medicine Section of Endocrinology Kovler Diabetes Center University of Chicago Chicago IL United States

Background: Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. Method: An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm. Results: The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions. Conclusions: The approach presented is able to identify reliable time-varying individualized glucose-insulin models. © 2018 Diabetes technology Society.

关键词： artificial pancreas biomedical system linear systems modeling and identification recursive identification subspace methods

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A Perspective on Quantum Sensors from Basic Research to Commercial Applications

arXiv

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arXiv 2024年

作者： Oh, Eun Gregoire, Maxwell D. Black, Adam T. Hughes, K. Jeramy Kunz, Paul D. Larsen, Michael Lautier-Gaud, Jean Lee, Jongmin Schwindt, Peter D.D. Mouradian, Sara L. Narducci, Frank A. Sackett, Charles A. Laboratory for Physical Sciences College ParkMD20740 United States Air Force Research Laboratory AlbuquerqueNM87117 United States Naval Research Laboratory WashingtonDC20375 United States Fieldline Inc. BoulderCO80301 United States Army Research Laboratory AdelphiMD20783 United States Northrup Grumman Woodland HillsCA91367 United States Rue François Mitterrand Talence33400 France Sandia National Laboratories AlbuquerqueNM87123 United States AFRL/RVBYT Space Vehicles Directorate Section Head Quantum Optics Optical Sciences Division Fieldline Industries Inc. Quantum Science & Technology Branch Chief DEVCOM Army Research Laboratory Department of Physics The University of Texas at Austin AustinTX78712 United States Quantum Science Architect Mission Systems Sector Emerging Capabilities Development iXblue Quantum Sensors The Technical Staff Atom-Optic Sensing Department The Technical Staff Atom-Optic Sensing Department Electrical and Computer Engineering Department Department of Physics Physics Department University of Washington SeattleWA98105 United States Naval Postgraduate School MontereyCA93943 United States University of Virginia CharlottesvilleVA22904 United States

Quantum sensors represent a new generation of sensors with improved precision, accuracy, stability, and robustness to environmental effects compared to their classical predecessors. After decades of laboratory development, several types of quantum sensors are now commercially available or are part-way through the commercialization process. This article provides a brief description of the operation of a selection of quantum sensors that employ the principles of atom-light interactions and discusses progress toward packaging those sensors into products. This article covers quantum inertial and gravitational sensors, including gyroscopes, accelerometers, gravimeters, and gravity gradiometers that employ atom interferometry, nuclear magnetic resonance gyroscopes, atomic and spin-defect magnetometers, and Rydberg electric field sensors. © 2024, CC BY-NC-ND.

关键词： Gyroscopes

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Hidden figures underlying quality measures: revealing hidden racial inequalities in end-of-life cancer care delivery: a cohort study

The Lancet Regional Health - Americas

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The Lancet Regional Health - Americas 2025年 47卷

作者： Inas S. Khayal John J. Shin Gabriel A. Brooks Amber E. Barnato Ellesse-Roselee Akré Terri Lewinson Amro M. Farid A. James O'Malley The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon NH 03756 USA Biomedical Data Science Geisel School of Medicine at Dartmouth Lebanon NH 03756 USA Department of Computer Science Dartmouth College Hanover NH 03755 USA Cancer Population Sciences Program Dartmouth Cancer Center Lebanon NH 03756 USA Department of Medicine Dartmouth Hitchcock Medical Center Lebanon NH 03756 USA Section of Palliative Care Department of Medicine Dartmouth Hitchcock Medical Center Lebanon NH 03756 USA Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA Department of Systems Engineering School of Engineering and Science Stevens Institute of Technology Hoboken NJ 07030 USA Department of Mathematics Dartmouth College Hanover NH 03755 USA

Background Calls for healthcare systems to reduce disparities in cancer care access and outcomes draw on comparisons of existing measures across race and ethnicity subgroups. This approach may hide inequities driven by systematic bias in the timing of care delivery. The goals of this study were to: (1) identify differences in the timing of care delivery between racial groups, and (2) determine whether these differences could be identified from quality measures. Methods Retrospective decedent follow-back study of hospitals treating Medicare fee-for-service beneficiaries with advanced cancer aged 65–99 who died April–December 2016. Among hospitals serving at least 11 decedents of color (including Black or African-American, Asian/Pacific Islander, Hispanic, American Indian/Alaska Native, and Other) and 11 White decedents, we calculated hospital-level differences between White decedents and decedents of color for 1) any use of palliative care and hospice (Measures) and 2) daily counts of palliative care and hospice use for each day in the 6 months before death (Signatures). Findings The cohort included 30,319 decedents across 217 hospitals, of whom 7,852 (25.9%) were people of color (POC). The median of the hospital-specific aggregate measure difference was −5.35% (IQR = 12.83) for palliative care, indicating more POC received any palliative care, and 3.66% (IQR = 12.45) for hospice care, indicating more White people (WP) received any hospice care. We identified 5 high-level cluster-group descriptions of inequality from signatures. Inequality information from signatures matched those from measures in only 46.5% and 39.2% of hospitals for palliative and hospice care, respectively. Interpretation Signatures incorporating timing of care delivery using longitudinal data revealed patterns of racial-ethnic inequalities in end-of-life cancer care otherwise missed by traditional aggregate quality measures. Funding This work was supported by the American Cancer Society Award (RS

关键词： Racial/ethnic healthcare inequities Palliative care Hospice care Healthcare quality indicators

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