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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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Occupational health and safety risk management

Occupational health and safety risk management

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2018 Annual Session of Scientific Papers IMT ORADEA 2018

作者： Oarga, Ioan-Florin Rat Iu, Mariana Oarga, Ioan-Tudor Technical University of Cluj-Napoca Faculty of Machine Building Cluj-Napoca400641 Romania University of Oradea Department of Mechanical Engineering and Automotive Oradea410087 Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca400027 Romania

The ongoing industrial revolution also includes the health and safety domain, which assists this development process. The participants and beneficiaries of this revolution don't have to endure a lowering of the protection levels from the beginning of it. Consequently, the health and safety domain needs to play its role in this historical stage. The risks of injury and illness are managed from the beginning, when the technological processes are being developed, on each production stage, until the end of every technological process. In order to achieve this, the professional risks must be identified, quantified, ranked and evaluated such that, the elaborated measures will eliminate or decrease the risks from the source. This control process of occupational health and safety risks of injury or illness uses documents which are applicable to any organization. These documents are legally required in each state, being instruments of the organization management, working like a system along with all other documents. © The Authors, published by EDP Sciences, 2018.

关键词： Risk management

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Preconditioning for the integration of a spatiotemporal pharmacodynamic system

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PAMM 2023年第1期22卷

作者： Jurjen Duintjer Tebbens Martin Lanzendörfer Ctirad Matonoha Štěpán Papáček Department of Computational Mathematics Institute of Computer Science of the Czech Academy of Sciences Pod Vodárenskou věží 271/2 Praha - Libeň 182 07 Czech Republic Department of Biophysics and Physical Chemistry Faculty of Pharmacy in Hradec Kralove Charles University Heyrovského 1203 Hradec Králové 500 05 Czech Republic Department of Computational Mathematics Institute of Computer Science of the Czech Academy of Sciences Pod Vodárenskou věží 271/2 Praha - Libeň 182 07 Czech Republic Department of Biophysics and Physical Chemistry Faculty of Pharmacy in Hradec Kralove Charles University Heyrovského 1203 Hradec Králové 500 05 Czech Republic Institute of Hydrogeology Engineering Geology and Applied Geophysics Faculty of Science Charles University Albertov 6 128 43 Praha 2 Czech Republic Department of Control Theory Institute of Information Theory and Automation of the Czech Academy of Sciences Pod Vodárenskou věží 4 Praha - Libeň 182 00 Czech Republic

We address efficient modeling of the intracellular action of a drug binding to the nuclear pregnane X receptor. The binded complex enters the cell nucleus and acts on DNA, resulting in enhanced production of an enzyme which metabolizes, among others, co-administered drugs. Pharmacodynamic modeling is traditionally based on a compartmental approach, which simplifies the complex processes in the human body through the definition of a small number of compartments representing organs, tissues, cells, cytoplasm or abstract units. Inside a compartment, the concentrations of all substances are assumed to be distributed homogeneously, i.e. there is no spatial dependence. They lead to a system of ODEs for the time-dependence of the concentrations of the active substances. We will present an extension of a model from the literature for the action of the tuberculose-drug Rifampicin. The extension consists of a first attempt to add spatial resolution for substances that are active in the cytoplasm. Spatial resolution can be benecifial to analyze important issues like, among others, local exceeding of toxic drug levels, delay of transport and drug-drug interactions. We address efficient solution of the linear systems arising when numerically integrating the resulting PDE's and consider some preconditioning techniques based on properties of the underlying biochemical network.

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Cross-ethnicity face anti-spoofing recognition challenge: A review

arXiv

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arXiv 2020年

作者： Liu, Ajian Li, Xuan Wan, Jun Liang, Yanyan Escalera, Sergio Escalante, Hugo Jair Madadi, Meysam Jin, Yi Wu, Zhuoyuan Yu, Xiaogang Tan, Zichang Yuan, Qi Yang, Ruikun Zhou, Benjia Guo, Guodong Li, Stan Z. Faculty of Information Technology Avenida WaiLong Taipa Macau China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico School of Software Beihang University Beijing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Beijing Westlake University Hangzhou China

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1;607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face *** organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Preface

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2021年 62卷 ix-x页

作者： Balas, Valentina E. Hassanien, Aboul Ella Chakrabarti, Satyajit Mandal, Lopa Department of Automation and Applied Informatics and Department of International Relations Programs and Projects Intelligent Systems Research Centre Aurel Vlaicu University of Arad Arad Romania Department of Information Technology Faculty of Computer and Artificial Intelligence Cairo University Giza Egypt Institute of Engineering and Management Kolkata West Bengal India Department of Information Technology Institute of Engineering and Management Kolkata West Bengal India

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Modelling and Essential Control of an Oceanographic Monitoring Remotely Operated Underwater Vehicle

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IFAC-PapersOnLine 2018年第29期51卷 213-219页

作者： Rojas, Jorge Baatar, Ganzorig Cuellar, Francisco Eichhorn, Mike Glotzbach, Thomas Institute for Automation and Systems Engineering Technische Universität Ilmenau P.O.Box 10 05 65 Ilmenau98684 Germany Branch of Fraunhofer IOSB Ilmenau98693 Germany Centro de Tecnologías Avanzadas de Manufactura Pontificia Universidad Católica del Perú Lima Peru Faculty Electrical Engineering & Computer Science Aalen University Aalen Germany

Remotely Operated Vehicles (ROVs) are of great importance for a wide range of underwater missions. Although being controlled by a human operator, there is yet the desire to enable some basic autonomous behavior to relieve the operator from controlling every single movement. An accurate model is the basis for any reasonable controller design, not only to find suitable controller parameters, but also to test and optimize controllers in simulation before using them at the vehicle in reality. This paper discusses the procedures of modelling, parameter identification and the design of essential control algorithms for an existing ROV. The model considers kinematics, kinetics and thruster allocation. Two used procedures for parameter identification are presented; a third one as well was recently published in another paper. Finally, we discuss the controller design and show the simulative validation for set-point regulation and waypoint tracking. © 2018

关键词： Remotely operated underwater vehicles Actuators Controllers Electronic guidance systems Identification (control systems) Marine navigation Models Navigation Parameter estimation Propulsion Remote control Remotely operated vehicles Marine systems Marine vehicles Propulsion system simulation thrusters

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Transportation, Germs, Culture: A Dynamic Graph Model of COVID-19 Outbreak

SSRN

引用

SSRN 2020年

作者： Yang, Xiaofei Xu, Tun Jia, Peng Xia, Han Guo, Li Zhang, Lei Ye, Kai School of Computer Science and Technology Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks & Networks Security Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Hugobiotech Co. Ltd. Beijing100000 China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong University Xi’an710061 China The School of Life Science and Technology Xi’an Jiaotong University Xi’an710049 China China-Australia Joint Research Center for Infectious Diseases School of Public Health Xi’an Jiaotong University Health Science Center Shaanxi Xi’an710061 China Melbourne Sexual Health Centre Alfred Health Melbourne Australia Central Clinical School Faculty of Medicine Nursing and Health Sciences Monash University MelbourneVIC Australia Department of Epidemiology and Biostatistics College of Public Health Zhengzhou University Henan Zhengzhou450001 China

Background: Various forms of model have been applied to predict the trend of the epidemic since the outbreak of COVID-19 at the hardest-hit city of Wuhan. Methods: In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. Findings: We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city. We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system, and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality. Interpretation: Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions. We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS-CoV-2 from spreading globally. Funding Statement: This study was supported by the National Science Foundation of China (grand NO. 61702406 31671372, 31701739, and 8191101420), the National Key R&D Program of China (grand NO. 2018YFC0910400, 2017YFC0907500), the National Science and Technology Major Project of China (grand NO. 2018ZX10302205), the 'World-Class Universities and the Characteristic Development Guidance Funds for the Central Universities', Thousand Talents Plan Professorship for Young Scholars (3111500001), Xi'an Jiaotong University Young Talent Support Program and Xi’an Jiaotong University Basic Research and Profession Grant (xtr022019003). Declaration of Interests: None. © 2020, The Authors. All rights reserved.

关键词： SARS

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A comparative study above two self-tuning controllers with aplication to the control of synchronous generator excitation system

arXiv

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arXiv 2018年

作者： Filip, Ioan Curiac, Daniel Prostean, Octavian Szeidert, Iosif "Politehnica" University of Timisoara Faculty of Control Engineering and Computer Sciences Department of Automation and Industrial Informatics

This paper presents two self-tuning control structures synthesized through the minimization of two criterion functions. It is described the computation methodology of the control laws, both being particularized for the case of the synchronous generator's excitation control. The parameters estimator is considered the recursive least square error (RLSE) algorithm. In order to validate the considered control structures, two comparative study cases by computer simulation are presented. Copyright © 2018, The Authors. All rights reserved.

关键词： Adaptive control systems

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Correction: The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial

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Journal of medical Internet research 2020年第3期22卷 e18362页

作者： Deborah L Johanson Ho Seok Ahn Bruce A MacDonald Byeong Kyu Ahn JongYoon Lim Euijun Hwang Craig J Sutherland Elizabeth Broadbent Department of Psychological Medicine Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand. Centre for Automation and Robotic Engineering Science The University of Auckland Auckland New Zealand. Department of Electrical Computer and Software Engineering Faculty of Engineering The University of Auckland Auckland New Zealand.

[This corrects the article DOI: 10.2196/13667.].

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