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arXiv 2022年

作者： Aldulijan, Ibrahim Beal, Jacob Billerbeck, Sonja Bouffard, Jeff Chambonnier, Gaël Delkis, Nikolaos Guerreiro, Isaac Holub, Martin Ross, Paul Selvarajah, Vinoo Sprent, Noah Vidal, Gonzalo Vignoni, Alejandro Systems Engineering Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States Intelligent Software & Systems Raytheon BBN Technologies 10 Moulton Street CambridgeMA02138 United States Molecular Microbiology Groningen Biomolecular Sciences and Biotechnology Institute University of Groningen Nijenborgh 7 Groningen9747 AG Netherlands Centre for Applied Synthetic Biology Department of Biology Concordia University 7141 Sherbrooke Street West MontréalQCH4B 1R6 Canada Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Plant and Environmental Biotechnology Laboratory Department of Biochemistry and Biotechnology University of Thessaly Viopolis Mezourlo Larissa41500 Greece iGEM Foundation 45 Prospect Street CambridgeMA02139 United States Delft University of Technology Van der Maasweg 9 2629 HZ Netherlands BioStrat Marketing 9965 Harbour Lake Circle Boynton BeachFL33437 United States Department of Chemical Engineering Imperial College London South Kensington Campus Exhibition Road SW7 2AZ United Kingdom Research Group School of Computing Newcastle University Devonshire Building Devonshire Terrace Newcastle Upon TyneNE1 7RU United Kingdom Synthetic Biology and Biosystems Control Lab Instituto de Automatica e Informatica Industrial Universitat Politecnica de Valencia Camino de Vera s/n Valencia46022 Spain

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility, and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability, and reductions in technical risk and cost. © 2022, CC BY.

关键词： Biological systems

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Extracting Semantics of Indoor Places based on Context Recognition

Extracting Semantics of Indoor Places based on Context Recog...

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IEEE International Conference on Pervasive Computing and Communications

作者： Georgios Pipelidis Frederik Fraaz Christian Prehofer Faculty of Informatics Research Group for Software & Systems Engineering Technische Universit?t München Munich Germany

ISBN: (纸本)9781538632284

In this paper we present our work in progress for the dynamic extraction of semantics in a subway station, from smart-phone Inertial Motion Unit (IMU) data. For this, we use machine learning for context recognition and fuzzy set theory for quantification of the uncertainty. Our architecture is client-server, where the clients collect data from sensors, extract features and use classification to identify seven different activities, while these activities are then aggregated on the server with other user activities, from the same regions, to enable the semantic identification of places.

关键词： Semantics Elevators Feature extraction Sensors Legged locomotion Microsoft Windows Semantics Elevators Biped locomotion Microsoft Windows(software) Sensors Feature extraction semiotics subway station Client-server work in progress

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Safe food for infants: An EU-China project to enhance the control of safety risks raised by microbial and chemical hazards all along the infant food chains

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Global Pediatrics 2022年 2卷

作者： Engel, Erwan Rivière, Gilles Kemmer, Diana Deusch, Oliver Fuchsbauer, Norbert Biesterveld, Steven Krystalli, Evangelia Bondoux, Marion Li, Guang Yang, Weikang Hou, Jianbo Liang, Ying Yang, Hua Fang, Weihuan Pettoello-Mantovani, Massimo Flynn, Brian Rantsiou, Kalliopi Van der Burg, Bart Bover-Cid, Sara Zwietering, Marcel H. French National Research Institute for Agriculture Food and Environment (INRAE) UR QuaPA MASS Group Saint-Genès-Champanelle France French Agency for Food Environmental and Occupational Health & Safety (ANSES) Maisons-Alfort France Fraunhofer Institute for Process Engineering and Packaging (IVV) Freising Germany Computomics GmbH Tübingen Germany HiPP-Werk Georg Hipp OHG Pfaffenhofen Germany Friesland Campina Nederland BV Amersfoort Netherlands Yiotis Anonimos Emporiki & Viomixaniki Etaireia Athens Greece INRAE Transfert Clermont-Ferrand France Beingmate (Hangzhou) Food Research Institute Co. Ltd Hangzhou China Yangzhou Fangguang Food Co. Ltd. Yangzhou China Zhejiang Academy of Science & Technology for Inspection & Quarantine (ZAIQ Hangzhou China Jiangsu Academy of Agricultural Sciences (JAAS) Nanjing China Zhejiang Academy of Agricultural Sciences (ZAAS) Hangzhou China Zhejiang University (ZJU) Hangzhou China EPA-UNEPSA Berlin Germany Creme software LTD (Creme Global) Dublin Ireland University of Turin Department of Agricultural Forest and Food Sciences Turin Italy BioDetection Systems B.V. Amsterdam Netherlands Institut de Recerca i Tecnologia Agroalimentàries (IRTA) Food Safety and Functionality Program Monells Spain Wageningen University & Research Food Microbiology Wageningen Netherlands

The EU-project SAFFI targets food for EU's 15 million and China's 45 million children under the age of three. It aims at developing an integrated approach to enhance the identification, assessment, detection and mitigation of health risks raised by microbial and chemical hazards along EU and China infant food chains. SAFFI will benchmark the main risks through an extensive hazard identification system based on multiple data sources and a risk ranking procedure. It will also develop procedures to enhance top-down and bottom-up hazard control by combining management options with a panel of technologies for the detection and mitigation of priority hazards. Furthermore, it will explore unexpected contaminants by predictive toxicology and improve risk-based food safety management of biohazards by omics and predictive microbiology. SAFFI will co-develop with and deliver to stakeholders a decision-support system (DSS) to enhance safety control all along the food chain. This DSS will integrate the databases, procedures and methods described above and will be a framework for a generic DSS dedicated to other food. This overall methodology will also be implemented in a complementary Chinese side of the project, and exemplified for each side, with four case studies that were selected to cover priority hazards, main ingredients, processes and control steps of the infant food chain. Resulting databases, tools and procedures will be shared, cross-validated, concatenated, benchmarked and finally harmonized for further use in the EU and China. This EU-China multi-actor consortium of 20 partners involves academia, food safety authorities, infant food companies, a paediatrics association and technological and data-science SMEs. © 2022

关键词： Baby food Chemical contaminants Decision support system Food safety Foodborne hazards Hazard control Hazard detection Risk assessment

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Creating Meta Attack Language Instances using ArchiMate: Applied to Electric Power and Energy System Cases

Creating Meta Attack Language Instances using ArchiMate: App...

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International Conference on Enterprise Distributed Object Computing (EDOC)

作者： Simon Hacks Alexander Hacks Sotirios Katsikeas Benedikt Klaer Robert Lagerström Research Group Software Construction RWTH Aachen University Aachen Germany Network and Systems Engineering KTH Royal Institute of Technology Stockholm Sweden Universität Duisburg-Essen Duisburg Germany Institute for High Voltage Technology RWTH Aachen University Aachen Germany KTH Royal Institute of Technology Stockholm Sweden

Cyber-attacks on power assets can have disastrous consequences for individuals, regions, and whole nations. In order to respond to these threats, the assessment of power grids' and plants' cyber security can foster a higher degree of safety for the whole infrastructure dependent on power. Hitherto, we propose the use of attack simulations based on system architecture models. To reduce the effort of creating new attack graphs for each system of a given type, domain-specific attack languages may be employed. They codify common attack logics of the considered domain. Previously, MAL (the Meta Attack Language) was proposed, which serves as a framework to develop domain specific attack languages. We extend the tool set of MAL by developing an approach to model security domains in ArchiMate notation. Next, those models are used to create a MAL instance, which reflects the concepts modeled in ArchiMate. These instances serve as input to simulate attacks on certain systems. To show the applicability of our approach, we conduct two case studies in the power domain. On the one hand, we model a thermal power plant and possible attacks on it. On the other hand, we use the attack on the Ukrainian power grid for our case study.

关键词： Security Power grids DSL Tools Power generation Analytical models

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Mixing the Light Spin with Plasmon Orbit by Nonlinear Light-Matter Interaction in Gold

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Physical Review X 2019年第2期9卷 021031-021031页

作者： G. Spektor D. Kilbane A. K. Mahro M. Hartelt E. Prinz M. Aeschlimann M. Orenstein Department of Electrical Engineering Technion-Israel Institute of Technology 32000 Haifa Israel Department of Physics and Research Center OPTIMAS University of Kaiserslautern Erwin Schroedinger Strasse 46 67663 Kaiserslautern Germany School of Physics University College Dublin Belfield Dublin 4 Ireland Telecommunications Software & Systems Group Waterford Institute of Technology Waterford Ireland Graduate School Materials Science in Mainz Staudingerweg 9 55128 Mainz Germany

Transformation of light carrying spin angular momentum (SAM) to optical field vortices carrying orbital angular momentum (OAM) has been of wide interest in recent years. The interactions between two optical fields, each carrying one of those degrees of freedom, and furthermore, the transfer of the resulting angular momentum product to matter are seldom discussed. Here, we measure the interaction between 3D light carrying axial SAM and 2D plasmon-polariton vortices carrying high-order transverse OAM. The interaction is mediated by two-photon absorption within a gold surface, imprinting the resulting angular-momentum mixing into matter by excitation of electrons that are photo-emitted into vacuum. Interestingly, the spatial distribution of the emitted electrons carries the signature of a subtraction of the spin from the orbit angular momenta. We show experimentally and theoretically that the absorptive nature of this interaction leads to both single and double photon-plasmon angular momentum mixing processes by one- and two- photon interactions. Our results demonstrate high order angular momenta light-matter interactions, provide a glimpse into specific electronic excitation routes, and may be applied in future electronic sources and coherent control.

关键词： Angular momentum of light Light-matter interaction Nonlinear optics Plasmonics Quantum description of light-matter interaction Time-resolved two-photon photoemission spectroscopy

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Validation of Pervasive Cloud Task Migration with Colored Petri Net

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Tsinghua Science and Technology 2016年第1期21卷 89-101页

作者： Lianzhang Zhu Shouchao Tan Weishan Zhang Yong Wang Xiwei Xu Department of Software Engineering China University of Petroleum Software Systems Research Group NICTA

Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.

关键词： colored Petri net task migration pervasive cloud context information validation

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A Review on Type of Attacks on Fingerprint Image and Watermarking Techniques

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IOP Conference Series: Materials Science and engineering 2019年第1期551卷

作者： Mohamed Lebcir Suryanti Awang Soft Computing & Intelligent Systems Research Group (SPINT) Faculty of Computer Systems & Software Engineering University Malaysia Pahang UMP Lebuhraya Tun Razak 26300 Kuantan Pahang Malaysia

Fingerprint image watermarking techniques have several advantages over traditional security systems that based on token or knowledge. However, they are also vulnerable to attacks that can decrease their security. In this regard, this paper presents a study of a number of research studies on the attacks of fingerprint images watermarking techniques. The attacks can be categorized into intentional and unintentional. We study several attacks based on that category and several fingerprint image watermarking methods. We produce a comprehensive comparison of those methods to show how the methods able to prevent the attacks. We also discuss the advantages and disadvantages of the methods. Therefore, based on that, we can see what are the common attacks on the fingerprint image and what are the methods that efficient in preventing the attacks.

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Predicting Churn: How Multilayer Perceptron Method Can Help with Customer Retention in Telecom Industry

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IOP Conference Series: Materials Science and engineering 2020年第1期864卷

作者： NNA Sjarif NF Azmi HM Sarkan SM Sam MZ Osman Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra 54100 Kuala Lumpur Malaysia Soft Computing Research & Intelligent System Group (SPINT) Faculty of Systems Computer & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Pahang Malaysia

Customer churn prediction has been used widely in various kind of domain especially subscription-basis industries. With the rapid growth of telecommunication industry over the last decade, this industry not only focuses on providing numerous products, but also satisfying the customers as it is one of the key solutions to remain competitive. This research proposed MultiLayer Perceptron Method for churn prediction. The evaluation is compared with three classifiers which includes are Support Vector Machine, Naïve Bayes and Decision Tree in term of several aspects. In preprocessing phase, we employed Principal Component Analysis and normalization to find the correlation among all the variables. For the postprocessing, InfoGainAttribute is used to identify the highest factor attribute that leads to customer retention. It is found that MultiLayer Perceptron outperforms other classifiers and international plan plays important role to retain customer from leaving organization.

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