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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This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief.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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End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Optimal control of linear Gaussian quantum systems via quantum learning control

arXiv

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arXiv 2024年

作者： Liu, Yu-Hong Zeng, Yexiong Tan, Qing-Shou Dong, Daoyi Nori, Franco Liao, Jie-Qiao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wakoshi Saitama351-0198 Japan Quantum Computing Center RIKEN Wakoshi Saitama351-0198 Japan Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang414000 China School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Department of Physics The University of Michigan Ann ArborMI48109-1040 United States Institute of Interdisciplinary Studies Hunan Normal University Changsha410081 China

Efficiently controlling linear Gaussian quantum (LGQ) systems is a significant task in both the study of fundamental quantum theory and the development of modern quantum technology. Here, we propose a general quantum-learning-control method for optimally controlling LGQ systems based on the gradient-descent algorithm. Our approach flexibly designs the loss function for diverse tasks by utilizing first- and second-order moments that completely describe the quantum state of LGQ systems. We demonstrate both deep optomechanical cooling and large optomechanical entanglement using this approach. Our approach enables the fast and deep ground-state cooling of a mechanical resonator within a short time, surpassing the limitations of sideband cooling in the continuous-wave driven strong-coupling regime. Furthermore, optomechanical entanglement could be generated remarkably fast and surpass several times the corresponding steady-state entanglement, even when the thermal phonon occupation reaches one hundred. This work will not only broaden the application of quantum learning control, but also open an avenue for optimal control of LGQ systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Cooling

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Autonomous Water Surface Vehicle Metaheuristic Mission Planning using Self-generated Goals and Environmental Forecasts

Autonomous Water Surface Vehicle Metaheuristic Mission Plann...

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American control Conference

作者： Evan Krell Scott A. King Luis Rodolfo Garcia Carrillo Control of Robots and Autonomous Agents Laboratory (CORAL) Department of Computing Sciences Texas A&M University Corpus Christi CORAL and the Unmanned Systems Laboratory (TAMUCC-USL) Department of Electrical Engineering Texas A&M University Corpus Christi

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

An unmanned surface vehicle takes on the role of an autonomous science agent, generating missions based on data. The vehicle explores over long durations and large regions, continually selecting and visiting target survey locations. Select system components are implemented and evaluated for automatic target generation, onboard mapping and path planning that optimizes energy use and data collection reward.

关键词： Planning History Cameras Satellites Path planning Collision avoidance Prototypes

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Mobile robot navigation for complete coverage of an environment

Mobile robot navigation for complete coverage of an environm...

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作者： Šelek, Ana Seder, Marija Petrović, Ivan Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Croatia

Inspired by the Spanning Tree Covering (STC) algorithm of Gabriely and Rimon, a novel algorithm of complete coverage for known environments is developed. Unlike the original STC algorithm, we detect dynamic elements of the environment and efficiently update the solution when changes are observed. The contribution is a path replanning algorithm that reduces overlapping when the part of the environment is changed. Experiments show that the proposed algorithm is capable of planning complete coverage robot paths with 70.27% coverage and without overlapping. © 2018

关键词： Mobile robots

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An IoT Edge computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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Stable recovery of entangled weights: Towards robust identification of deep neural networks from minimal samples

arXiv

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arXiv 2021年

作者： Fiedler, Christian Fornasier, Massimo Klock, Timo Rauchensteiner, Michael Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute for Data Science in Mechanical Engineering RWTH-Aachen University Dennewartstraße 27 Aachen50868 Germany Department of Mathematics Bolzmannstraße 3 85748 Garching Germany Department of Numerical Analysis and Scientific Computing Simula Research Laboratory Oslo Norway Department of Mathematics University of San Diego California San Diego United States

In this paper we approach the problem of unique and stable identifiability of generic deep artificial neural networks with pyramidal shape and smooth activation functions from a finite number of input-output samples. More specifically we introduce the so-called entangled weights, which compose weights of successive layers intertwined with suitable diagonal and invertible matrices depending on the activation functions and their shifts. We prove that entangled weights are completely and stably approximated by an efficient and robust algorithm as soon as O(D2 × m) nonadaptive input-output samples of the network are collected, where D is the input dimension and m is the number of neurons of the network. Moreover, we empirically observe that the approach applies to networks with up to O(D × mL) neurons, where mL is the number of output neurons at layer L. Provided knowledge of layer assignments of entangled weights and of remaining scaling and shift parameters, which may be further heuristically obtained by least squares, the entangled weights identify the network completely and uniquely. To highlight the relevance of the theoretical result of stable recovery of entangled weights, we present numerical experiments, which demonstrate that multilayered networks with generic weights can be robustly identified and therefore uniformly approximated by the presented algorithmic pipeline. In contrast backpropagation cannot generalize stably very well in this setting, being always limited by relatively large uniform error. In terms of practical impact, our study shows that we can relate input-output information uniquely and stably to network parameters, providing a form of explainability. Moreover, our method paves the way for compression of overparametrized networks and for the training of minimal complexity *** Codes 65D15, 68T07, 90C26 Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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A self-supervised mixed-curvature graph neural network

arXiv

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arXiv 2021年

作者： Sun, Li Zhang, Zhongbao Ye, Junda Peng, Hao Zhang, Jiawei Su, Sen Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Ifm Lab Department of Computer Science University of California DavisCA United States Department of Computer Science University of Illinois at Chicago IL United States

Graph representation learning received increasing attentions in recent years. Most of the existing methods ignore the complexity of the graph structures and restrict graphs in a single constant-curvature representation space, which is only suitable to particular kinds of graph structure indeed. Additionally, these methods follow the supervised or semisupervised learning paradigm, and thereby notably limit their deployment on the unlabeled graphs in real applications. To address these aforementioned limitations, we take the first attempt to study the self-supervised graph representation learning in the mixed-curvature spaces. In this paper, we present a novel Self-supervised Mixed-curvature Graph Neural Network (SELFMGNN). To capture the complex graph structures, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces, and design hierarchical attention mechanisms for learning and fusing graph representations across these component spaces. To enable the self-supervisd learning, we propose a novel dual contrastive approach. The constructed mixed-curvature space actually provides multiple Riemannian views for the contrastive learning. We introduce a Riemannian projector to reveal these views, and utilize a welldesigned Riemannian discriminator for the single-view and cross-view contrastive learning within and across the Riemannian views. Finally, extensive experiments show that SELFMGNN captures the complex graph structures and outperforms state-of-the-art baselines. © 2021, CC BY-NC-SA.

关键词： Graph neural networks

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

引用

Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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A real neural network state for quantum chemistry

arXiv

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arXiv 2023年

作者： Wu, Yangjun Xu, Xiansong Poletti, Dario Fan, Yi Guo, Chu Shang, Honghui Institute of Computing Technology Chinese Academy of Sciences Beijing China Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China EPD Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore University of Science and Technology of China Hefei China Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

The restricted Boltzmann machine (RBM) has been successfully applied to solve the many-electron Schrödinger equation. In this work we propose a single-layer fully connected neural network adapted from RBM and apply it to study ab initio quantum chemistry problems. Our contribution is two-fold: 1) our neural network only uses real numbers to represent the real electronic wave function, while we obtain comparable precision to RBM for various prototypical molecules;2) we show that the knowledge of the Hartree-Fock reference state can be used to systematically accelerate the convergence of the variational Monte Carlo algorithm as well as to increase the precision of the final energy. © 2023, CC BY.

关键词： Wave functions

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