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

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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ROLE OF SIMULATION IN RAPID PROTOTYPING FOR CONCEPT DEVELOPMENT

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 204-211页

作者： KING, JF BARTON, DE J. Fred King:is the manager of the Advanced Technology Department for Unisys in Reston Virginia. He earned his Ph.D. in mathematics from the University of Houston in 1977. He has been principal investigator of research projects in knowledge engineering pattern recognition and heuristic problem-solving. Efforts include the development of a multi-temporal multispectral classifier for identifying graincrops using LANDSAT satellite imagery data for NASA. Also as a member of the research team for a NCI study with Baylor College of Medicine and NASA he helped develop techniques for detection of carcinoma using multispectral microphotometer scans of lung tissue. He established and became technical director of the AI Laboratory for Ford Aerospace where he developed expert scheduling modeling and knowledge acquisition systems for NASA. Since joining Unisys in 1985 he has led the development of object-oriented programming environments blackboard architectures data fusion techniques using neural networks and intelligent data base systems. Douglas E. Barton:is manager of Logistics Information Systems for Unisys in Reston Virginia. He earned his B.A. degree in computer science from the College of William and Mary in 1978 and did postgraduate work in London as a Drapers Company scholar. Since joining Unisys in 1981 his work has concentrated on program management and software engineering of large scale data base management systems and design and implementation of knowledge-based systems in planning and logistics. As chairman of the Logistics Data Subcommittee of the National Security Industrial Association (NSIA) he led an industry initiative which examined concepts in knowledge-based systems in military logistics. His responsibilities also include evaluation development and tailoring of software engineering standards and procedures for data base and knowledge-based systems. He is currently program manager of the Navigation Information Management System which provides support to the Fleet Ballistic Missile Progr

A valuable technique during concept development is rapid prototyping of software for key design components. This approach is particularly useful when the optimum design approach is not readily apparent or several known alternatives need to be rapidly evaluated. A problem inherent in rapid prototyping is the lack of a "target system" with which to interface. Some alternatives are to develop test driver libraries, integrate the prototype with an existing working simulator, or build one for the specific problem. This paper presents a unique approach to concept development using rapid prototyping for concept development and scenario-based simulation for concept verification. The rapid prototyping environment, derived from artificial intelligence technology, is based on a blackboard architecture. The rapid prototype simulation capability is provided through an object-oriented modeling environment. It is shown how both simulation and blackboard technologies are used collectively to rapidly gain insight into a tenacious problem. A specific example will be discussed where this approach was used to evolve the logic of a mission controller for an autonomous underwater vehicle.

关键词： Military engineering

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Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data

arXiv

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arXiv 2020年

作者： Jiang, Lingjing Haiminen, Niina Carrieri, Anna-Paola Huang, Shi Vázquez-Baeza, Yoshiki Parida, Laxmi Kim, Ho-Cheol Swafford, Austin D. Knight, Rob Natarajan, Loki Division of Biostatistics University of California San Diego San diegoCA92093 United States IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States IBM Research UK Hartree Center Warrington United Kingdom Center for Microbiome Innovation Jacobs School of Engineering UC San Diego San diegoCA92093 United States Department of Pediatrics University of California San Diego San diegoCA92093 United States Scalable Knowledge Intelligence IBM Research-Almaden San JoseCA95120 United States Department of Computer Science and Engineering University of California San Diego San diegoCA92093 United States Department of Bioengineering University of California San Diego San diegoCA92093 United States

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high-dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the training data would lead to large changes in the chosen feature subset, then many of the biological features that an algorithm has found are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metric MSE and proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications. We conclude that Stability is a preferred feature selection criterion over MSE because it better quantifies the reproducibility of the feature selection method. © 2020, CC BY.

关键词： Forecasting

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Slow Heat-Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

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Computational intelligence and neuroscience 2023年第1期2023卷 9918748页

作者： Pavan Kumar Pagadala P Lalitha Surya Kumari Deepak Thakur Vivek Bhardwaj Mohammad Shahid Abdulrajak Buradi Abdul Razak Abiot Ketema Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Hyderabad Telangana India. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India. School of Computer Science and Engineering Lovely Professional University Jalandhar 144411 India. Department of Electrical Engineering Galgotias College of Engineering and Technology 1 Knowledge Park Phase II Noida 201306 UP India. Department of Mechanical Engineering Nitte Meenakshi Institute of Technology Bangalore India. Department of Mechanical Engineering P. A. College of Engineering (Affiliated to Visvesvaraya Technological University Belagavi) Mangaluru 574153 India. Department of Biosystems Engineering Institute of Technology Hawassa University Hawassa Ethiopia.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

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[Retracted] Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy

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Journal of Healthcare engineering 2023年第1期2023卷

作者： Jaskirat Kaur Deepti Mittal Sharaf Malebary Soumya Ranjan Nayak Devendra Kumar Manoj Kumar Gagandeep Simrandeep Singh Department of Electronics and Communication Engineering Punjab Engineering College (Deemed to be University) Sector 12 Chandigarh 160012 India Electrical and Instrumentation Engineering Department Thapar Institute of Engineering and Technology Patiala 147004 Indiathapar.edu Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah 21911 Saudi Arabiakau.edu.sa School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 Odisha India Department of Computer Science Wachemo University Hosaena Ethiopiawcu.edu.et Faculty of Engineering and Information Sciences University of Wollongong in Dubai Dubai Knowledge Park UAEuowdubai.ac.ae MEU Research Unit Middle East University Amman 11831 Jordanmeu.edu.jo Computer Science Engineering Department Chandigarh Engineering College Mohali *** Electronics and Communication Engineering Department UCRD Chandigarh University Mohali Indiachandigarhuniversity.ac.in

Exudate, an asymptomatic yellow deposit on retina, is among the primary characteristics of background diabetic retinopathy. Background diabetic retinopathy is a retinopathy related to high blood sugar levels which slowly affects all the organs of the body. The early detection of exudates aids doctors in screening the patients suffering from background diabetic retinopathy. A computer-aided method proposed in the present work detects and then segments the exudates in the images of retina acquired using a digital fundus camera by (i) gradient method to trace the contour of exudates, (ii) marking the connected candidate pixels to remove false exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. The method is tested on 1307 retinal fundus images with varying characteristics. Six hundred and forty-nine images were acquired from hospital and the remaining 658 from open-source benchmark databases, namely, STARE, DRIVE MESSIDOR, DiaretDB1, and e-Ophtha. The exudates segmentation method proposed in this research work results in the retinal fundus image-based (i) accuracy of 98.04%, (ii) sensitivity of 95.345%, and (iii) specificity of 98.63%. The segmentation results for a number of exudates-based evaluations depict the average (i) accuracy of 95.68%, (ii) sensitivity of 93.44%, and (iii) specificity of 97.22%. The substantial combined performance at image and exudates-based evaluations proves the contribution of the proposed method in mass screening as well as treatment process of background diabetic retinopathy.

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Web 3.0-Enabled Microservice Re-Scheduling for Heterogenous Resources Co-Optimization in Metaverse-Integrated Edge Networks

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Yihong Yang Zhangbing Zhou Lei Shu Feng Zhou Walid Gaaloul Arif Ali Khan School of Information Engineering China University of Geosciences (Beijing) China School of Information Engineering Frontiers Science Center for Deep-time Digital Earth China University of Geosciences (Beijing) China Technology Innovation Center of Geoscience Knowledge and Intelligent Service China Geological Survey China and Computer Science Department TELECOM SudParis France College of Artificial Intelligence Nanjing Agricultural University China National Geological Library of China China Computer Science Department TELECOM SudParis France M3S Empirical Software Engineering Research Unit University of Oulu Finland

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User's requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this paper proposes a Web 3.0-enabled Microservice Re-Scheduling approach (called MRS), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the microservice re-scheduling task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4% and 2.2% in terms of network throughput, latency and energy consumption.

关键词： Web 3.0 Microservice Re-Scheduling Heterogenous Resources Co-Optimization Metaverse Connected Autonomous Vehicles (CAVs)

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Preface

Communications in Computer and Information Science

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Communications in computer and Information science 2017年 705卷 V-VIII页

作者： Cazenave, Tristan Winands, Mark H. M. Edelkamp, Stefan Schiffel, Stephan Thielscher, Michael Togelius, Julian LAMSADE Universitè Paris-Dauphine Paris Cedex 16 France Department of Data Science and Knowledge Engineering Maastricht University Maastricht Limburg Netherlands Institute for Artificial Intelligence Universität Bremen TAB Bremen Bremen Germany Reykjavik University Reykjavik Iceland The University of New South Wales SydneyNSW Australia Department of Computer Science and Engineering New York University BrooklynNY United States

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Preface

Communications in Computer and Information Science

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Communications in computer and Information science 2024年 2008卷 v-vi页

作者： Dhar, Suparna Goswami, Sanjay Dinesh Kumar, U. Bose, Indranil Dubey, Rameshwar Mazumdar, Chandan NSHM Institute of Computing & Analytics NSHM Knowledge Campus Kolkata Kolkata India NSHM Knowledge Campus Kolkata India Indian Institute of Management Bangalore Bengaluru India Indian Institute of Management Ahmedabad Gujarat Ahmedabad India Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

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Middle East and North African Health Informatics Association (MENAHIA): Building Sustainable Collaboration

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Yearbook of Medical Informatics 2018年第1期27卷 286-291页

作者： Najeeb Al-Shorbaji Mowafa Househ Adel Taweel Abdullah Alanizi Bennani Othmani Mohammed Haitham Abaza Hala Bawadi Hamayon Rasuly Khalid Alyafei Luis Fernandez-Luque Mohamed Shouman Osama El-Hassan Rada Hussein Riyad Alshammari Salah Mandil Sarah Shouman Shahrad Taheri Tamer Emara Wasmiya Dalhem Zaid Al-Hamdan Zineb Serhier eHealth and Knowledge Management Consultant Retired Director of Knowledge Ethics and Research Department WHO/HQ Amman Jordan Department of Health Informatics College of Public Health and Health Informatics King Saud Bin Abdulaziz University for Health Sciences Ministry of National Guard-Health Affairs Riyadh Saudi Arabia Department of Computer Science BirZeit University Ramallah Palestine Head of Medical Informatics Unit 20 August Hospital Ibn Rochd University Hospital Casablanca Head of Casablanca Medical Informatics Laboratory Medical Faculty of Casablanca Morocco Department of Biomedical Engineering Misr University for Science & Technology Cairo Egypt Associate Professor College of Nursing University of Jordan Amman Jordan eHealth Program Coordinator Bamyan Provincial Hospital Bamyan City Afghanistan Chief Medical Informatics Officer (CMIO) - SIDRA Assistant Professor Clinical Pediatrics at Weill Cornell Medicine Qatar Qatar Computing Research Institute Hamad Bin Khalifa University Doha Qatar Faculty of Medicine Ain Shams University Cairo Egypt Vice-Chair of Emirates Health Informatics Society Head of eHealth Section at Dubai Health Authority Dubai United Arab Emirates Founder and Former Director of the Biomedical Informatics Center of Excellence (BMICoE) Information Technology Institute (ITI) Ministry of Communications and Information Technology (MCIT) Egypt President of Saudi Association for Health Informatics Riyadh Saudi Arabia Senior Consultant on eStrategies and eHealth to WHO and ITU Geneva Switzerland Department of Medicine Weill Cornell Medical College in Qatar Qatar Foundation Doha Qatar Executive Director of Nursing Informatics Hamad Medical Corporation Qatar Associate Professor of Nursing Management Faculty of Nursing Jordan University of Science and Technology Irbid Jordan Professor at Medical Informatics Laboratory Faculty of Medicine and Pharmacy Hassan II University Casablanca Morocco

There has been a growing interest in Health Informatics applications, research, and education within the Middle East and North African Region over the past twenty years. People of this region share similar cultural and religious values, primarily speak the Arabic language, and have similar health care related issues, which are in dire need of being addressed. Health Informatics efforts, organizations, and initiatives within the region have been largely under-represented within, but not ignored by, the International Medical Informatics Association (IMIA). Attempts to create bonds and collaboration between the different organizations of the region have remained scattered, and often, resulted in failure despite the fact that the need for a united health informatics collaborative within the region has never been more crucial than today. During the 2017 MEDINFO, held in Hangzhou, China, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was conceived as a regional non-governmental organization to promote and facilitate health informatics uptake within the region endorsing health informatics research and educational initiatives of the 22 countries represented within the region. This paper provides an overview of the collaboration and efforts to date in forming MENAHIA and displays the variety of initiatives that are already occurring within the MENAHIA region, which MENAHIA will help, endorse, support, share, and improve within the international forum of health informatics.

关键词： eHealth Middle East and North Africa Health Informatics collaboration MENAHIA Universal Health Coverage Bioinformatics International Medical Informatics Association

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CTS 2014 panel sessions

CTS 2014 panel sessions

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International Symposium on Collaborative Technologies and Systems

作者： Obinna B. Anya John V. Carlis Brent J. Hecht Daniel Keefe Obinna Anya Neale R. Chumbler Saif Khairat Joseph A. Konstan Danilo Pani Jason Evgenides Janet Girton Arlene King Joseph Zahn Gregory Padula Mary Galvin Accelerated Discovery Lab Almaden Research Center California USA University of Minnesota - Twin Cities Minnesota USA Department of Health Policy and Management College of Public Health University of Georgia - Athens Georgia USA Institute for Health Informatics University of Minnesota - Twin Cities Minnesota USA Department of Computer Science and Engineering University of Minnesota - Twin Cities Minnesota USA EOLAB - Microelectronics and Bioengineering Lab Dept. of Electrical and Electronic Engineering University of Cagliari Italy Organizational Enablement Shiloh Illinois USA Cyber Semantics Shiloh Illinois USA Strategic Communication C5T Shiloh Illinois USA Knowledge Management Specialist C5T Shiloh Illinois USA Vice President-Operations C5T Shiloh Illinois USA LexisNexis Special Services Inc. (LNSSI) Washington D.C. USA

This panel explores the many roles of data analytics in today's cross-domain collaborations. In some instances, cross-domain analytics are required to understand big data. In others, big data holds the key to understanding and evaluating how people collaborate across domains. Panelists will present their experiences with big data and collaboration, and discussion will be guided by the following questions: How does “understanding data through collaboration” relate to “understanding collaboration through data”? What modeling tools, visualizations, abstractions, and metrics can help facilitate collaborative discovery from big data? To what extent should these tools be tailored to specific domains or groups? How much do we need to know about another domain or group before we can intersect domain specific activities that constitute the context to various analytical processes, and cross-pollinate ideas for deeper analytics? What optimally would that other domain or group need to know about us? How much of this knowledge can we extract from data?

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