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Interoperability and security challenges of Industrie 4.0 47

Interoperability and security challenges of Industrie 4.0

47. Jahrestagung der Gesellschaft fur Informatik, Informatik 2017 - 47th Annual Meeting of the German Informatics Society (GI), Informatics 2017

作者： Watson, Venesa Tellabi, Asmaa Sassmannshausen, Jochen Lou, Xinxin University of Siegen Faculty of Science and Engineering Chair for Data Communication Systems Hölderlinstraße 3 Siegen57068 Germany Bielefeld University Department of Computer Networks and Distributed Systems Universitätsstraße 25 Bielefeld33615 Germany

ISBN: (纸本)9783885796695

Industrie 4.0 (I4.0) is the fourth industrial revolution, which will see the digital transformation of manufacturing, through the integration on Industrial Internet of Things (IIoT), data and Services, and the convergence of Information Technology (IT) and Operational Technology (OT). With any such transformation, there exists challenges that must first be addressed for a successful outcome. For I4.0, these are interoperability and security, which respectively arise from the interconnection of devices from different manufacturers and/or with different communication protocols;and the interconnection of networks with competing critical and non-critical traffic, and the increased access to initially isolated networks. This paper presents an overview of standards, such as IEC 62443, the ISO 27000 series, IEC 62541 Open Connectivity Unified Architecture (OPC UA) and Time Sensitive Networks (TSN) (IEEE 1722-2016), which addresses these challenges. © 2017 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Interoperability

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

Nature Water

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Nature Water 2025年 1-11页

作者： Eva Aßmann Christopher Irrgang Hugues Richard Martin Hölzer Timo Greiner Alexander Ullrich Matthew Wade Shelesh Agrawal Susanne Lackner Fabian Amman Sindy Böttcher Markus Landthaler Emanuel Wyler Serghei Mangul Viorel Munteanu Fotis Psomopoulos Maureen Smith Maria Trofimova Max von Kleist Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London UK Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA USA Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE.

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Author Correction: Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

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Nature biotechnology 2023年第7期41卷 1026页

作者： Rosa Lundbye Allesøe Agnete Troen Lundgaard Ricardo Hernández Medina Alejandro Aguayo-Orozco Joachim Johansen Jakob Nybo Nissen Caroline Brorsson Gianluca Mazzoni Lili Niu Jorge Hernansanz Biel Cristina Leal Rodríguez Valentas Brasas Henry Webel Michael Eriksen Benros Anders Gorm Pedersen Piotr Jaroslaw Chmura Ulrik Plesner Jacobsen Andrea Mari Robert Koivula Anubha Mahajan Ana Vinuela Juan Fernandez Tajes Sapna Sharma Mark Haid Mun-Gwan Hong Petra B Musholt Federico De Masi Josef Vogt Helle Krogh Pedersen Valborg Gudmundsdottir Angus Jones Gwen Kennedy Jimmy Bell E Louise Thomas Gary Frost Henrik Thomsen Elizaveta Hansen Tue Haldor Hansen Henrik Vestergaard Mirthe Muilwijk Marieke T Blom Leen M 't Hart Francois Pattou Violeta Raverdy Soren Brage Tarja Kokkola Alison Heggie Donna McEvoy Miranda Mourby Jane Kaye Andrew Hattersley Timothy McDonald Martin Ridderstråle Mark Walker Ian Forgie Giuseppe N Giordano Imre Pavo Hartmut Ruetten Oluf Pedersen Torben Hansen Emmanouil Dermitzakis Paul W Franks Jochen M Schwenk Jerzy Adamski Mark I McCarthy Ewan Pearson Karina Banasik Simon Rasmussen Søren Brunak Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. Department of Health Technology Technical University of Denmark Kongens Lyngby Denmark. Copenhagen Research Centre for Mental Health Mental Health Centre Copenhagen Copenhagen University Hospital Copenhagen Denmark. Department of Immunology and Microbiology Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. C.N.R. Institute of Neuroscience Padova Italy. Wellcome Centre for Human Genetics University of Oxford Oxford UK. Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Research Unit of Molecular Epidemiology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Bavaria Germany. Institute of Epidemiology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Bavaria Germany. Chair of Food Chemistry and Molecular and Sensory Science Technical University of Munich Freising Germany. Metabolomics and Proteomics Core Helmholtz Zentrum Muenchen German Research Center for Environmental Health Neuherberg Germany. Affinity Proteomics Science for Life Laboratory School of Engineering Sciences in Chemistry Biotechnology and Health KTH Royal Institute of Technology Solna Sweden. Research and Development Global Development Translational Medicine and Clinical Pharmacology Sanofi-Aventis Deutschland Frankfurt Germany. Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. University of Exeter Medical School Exeter UK. The Immunoassay Biomarker Core Laboratory School of Medicine University of Dundee Dundee UK. Research Centre for Optimal Health Department of Life Sciences University of Westminster London UK. Section

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Quantum computing at the frontiers of biological sciences

arXiv

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arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Worldwide Soundscapes: A Synthesis of Passive Acoustic Monitoring Across Realms

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Global Ecology and Biogeography 2025年第5期34卷

作者： Kevin F. A. Darras Rodney A. Rountree Steven L. Van Wilgenburg Anna F. Cord Frederik Pitz Youfang Chen Lijun Dong Agnès Rocquencourt Camille Desjonquères Patrick Mauritz Diaz Tzu-Hao Lin Théophile Turco Louise Emmerson Tom Bradfer-Lawrence Amandine Gasc Sarah Marley Marcus Salton Laura Schillé Paul J. Wensveen Shih-Hung Wu Adriana C. Acero-Murcia Orlando Acevedo-Charry Matyáš Adam Jacopo Aguzzi Irmak Akoglu M. Clara P. Amorim Mina Anders Michel André Alexandre Antonelli Leandro Aparecido Do Nascimento Giulliana Appel Stephanie Archer Christos Astaras Andrey Atemasov Jamieson Atkinson Joël Attia Emanuel Baltag Luc Barbaro Fritjof Basan Carly Batist Julio Ernesto Baumgarten Just T. Bayle Sempere Kristen Bellisario Asaf Ben David Oded Berger-Tal Frédéric Bertucci Matthew G. Betts Iqbal S. Bhalla Thiago Bicudo Marta Bolgan Sara Bombaci Gerard Bota Martin Boullhesen Robert A. Briers Susannah Buchan Michal Budka Katie Burchard Giuseppa Buscaino Alice Calvente Marconi Campos-Cerqueira Maria Isabel Carvalho Gonçalves Maria Ceraulo Maite Cerezo-Araujo Gunnar Cerwén Adams A. Chaskda Maria Chistopolova Christopher W. Clark Kieran D. Cox Benjamin Cretois Chapin Czarnecki Luis P. da Silva Wigna da Silva Laurence H. De Clippele David de la Haye Ana Silvia de Oliveira Tissiani Devin de Zwaan M. Eugenia Degano Jessica Deichmann Joaquin del Rio Christian Devenish Ricardo Díaz-Delgado Pedro Diniz Dorgival Diógenes Oliveira-Júnior Thiago Dorigo Saskia Dröge Marina Duarte Adam Duarte Kerry Dunleavy Robert Dziak Simon Elise Hiroto Enari Haruka S. Enari Florence Erbs Britas Klemens Eriksson Pınar Ertör-Akyazi Nina C. Ferrari Luane Ferreira Abram B. Fleishman Paulo J. Fonseca Bárbara Freitas Nicholas R. Friedman Jérémy S. P. Froidevaux Svetlana Gogoleva Carolina Gonzaga José Miguel González Correa Eben Goodale Benjamin Gottesman Ingo Grass Jack Greenhalgh Jocelyn Gregoire Samuel Haché Jonas Hagge William Halliday Antonia Hammer Tara Hanf-Dressler Sylvain Haupert Samara Haver Becky Heath Daniel Hending Jose Hernandez-Blanco Dennis Higgs EFNO ECODIV INRAE Domaine des Barres Nogent-sur-Vernisson France Sustainable Agricultural Systems & Engineering Lab School of Engineering Westlake University Hangzhou Zhejiang China Chair of Computational Landscape Ecology Faculty of Environmental Sciences Dresden University of Technology Dresden Germany Biology Department University of Victoria Victoria British Columbia Canada The Fish Listener Waguoit Massachusetts USA Terrestrial Unit Prairie Region Canadian Wildlife Service Environment & Climate Change Canada Prairie & Northern Wildlife Research Centre Saskatoon Saskatchewan Canada Agro-ecological Modeling Group Faculty of Agriculture University of Bonn Bonn Germany Marine Mammal and Marine Bioacoustics Laboratory Department of Deep Sea Science Institute of Deep-sea Science and Engineering Chinese Academy of Sciences Sanya Hainan China Institut de Systématique Évolution Biodiversité Muséum National d’Histoire Naturelle Paris France Université Grenoble Alpes Université Savoie Mont Blanc CNRS LECA Grenoble France Climate Change and Biodiversity Hanns R. Neumann Stiftung Indonesia Muaradua Indonesia Biodiversity Research Center Academia Sinica Nankang Taipei Taiwan ENES Bioacoustics Research Laboratory University of Saint-Etienne CRNL CNRS UMR 5292 Inserm UMR_S 1028 Saint-Etienne France Australian Antarctic Division Science Branch Department of Climate Change Energy Environment and Water Channel Highway Kingston Tasmania Australia Biological and Environmental Sciences University of Stirling Stirling Scotland Centre for Conservation Science RSPB Edinburgh UK IMBE Aix Marseille Univ Avignon Univ CNRS IRD Aix-en-Provence France Scotland's Rural College Craibstone Estate Aberdeen UK BIOGECO INRAE University of Bordeaux Cestas France Westman Islands Research Centre University of Iceland Vestmannaeyjar Iceland Qigu Research Center Taiwan Biodiversity Research Institute Nantou County Taiwan Programa de Pós-graduação em Ecologia

Aim The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean). Location Worldwide, 12,343 sites, all ecosystem types. Time Period 1991 to present. Major Taxa Studied All soniferous taxa. Methods We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms. Results Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm 2 ) than aquatic sampling (0.3 and 1.8 sites/Mkm 2 in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds. Main Conclusions PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio-temporal replication in freshwater habitats. Overall, this

关键词： ARU automated sound recorder biodiversity conservation biology ecoacoustics IUCN GET realm Passive Acoustic Monitoring phenology soundscape ecology

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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

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NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

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Stealth databases: Ensuring User-Controlled Queries in Untrusted Cloud Environments 15

Stealth Databases: Ensuring User-Controlled Queries in Untru...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Josef Spillner Martin Beck Alexander Schill Thomas Michael Bohnert Zurich University of Applied Sciences School of Engineering Service Prototyping Lab Winterthur Switzerland Faculty of Computer Science Cloud Storage Lab Dresden Germany Faculty of Computer Science Chair of Privacy and Data Security & BIOTEC Dresden Germany

ISBN: (纸本)9780769556970

Sensitive data is increasingly being hosted online in ubiquitous cloud storage services. Recent advances in multi-cloud service integration through provider multiplexing and data dispersion have alleviated most of the associated risks for hosting files which are retrieved by users for further processing. However, for structured data managed in databases, many issues remain, including the need to perform operations directly on the remote data to avoid costly transfers. In this paper, we motivate the need for distributed stealth databases which combine properties from structure-preserving dispersed file storage for capacity-saving increased availability with emerging work on structure-preserving encryption for on-demand increased confidentiality with controllable performance degradation. We contribute an analysis of operators executing in map-reduce or map-carry-reduce phases and derive performance statistics. Our prototype, StealthDB, demonstrates that for typical amounts of personal structured data, stealth databases are a convincing concept for taming untrusted and unsafe cloud environments.

关键词： Encoding Cloud computing Encryption Distributed databases Redundancy

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A Comparative Study of Autonomous Learning Outlier Detection Methods Applied to Fault Detection

A Comparative Study of Autonomous Learning Outlier Detection...

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IEEE International Conference on Fuzzy Systems

作者： Clauber Gomes Bezerra Bruno Sielly Jales Costa Luiz Affonso Guedes Plamen Parvanov Angelov Campus EaD Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Campus Natal - Zona Norte Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Lancaster University Data Science Group School of Computing and Communications Lancaster LA1 4WA United Kingdom Chair of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781467374293

Outlier detection is a problem that has been largely studied in the past few years due to its great applicability in real world problems (e.g. financial, social, climate, security). Fault detection in industrial processes is one of these problems. In that context, several methods have been proposed in literature to address fault detection. In this paper we propose a comparative analysis of three recently introduced outlier detection methods: RDE, RDE with Forgetting and TEDA. Such methods were applied to the data set provided in DAMADICS benchmark, a very well-known real data tool for fault detection applications. The results, however, can be extended to similar problems of the area. Therewith, in this work we compare the main features of each method as well as the results obtained with them.

关键词： Comparative Study Autonomous Learning Outlier Detection Methods Applied Fault Detection

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