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Experimental study reliability and functional stability of the social graph

Journal of Physics: Conference Series 2019年第1期1202卷

作者： E K Algazinov V V Garshina V A Stepantsov E N Desyatirikova Computer Science Department Voronezh State University Voronezh Russia Department Automatic Control Systems Department Voronezh State Technical University Voronezh Russia

The article is devoted to the results of an experimental study the integrity, reliability and efficiency of the social network graph as a complex network in terms of transferring messages between users. The computed network parameters that affect the evaluation of the efficiency of its functioning are determined; the integral coefficient of network stability loss is developed; formulated heuristic rules for assessing the functional stability of a social network based on the parameters of the social graph are formulated.

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Low cost air invitation remote lab

Low cost air invitation remote lab

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Experiment@ International Conference (exp.at)

作者： Jacobo Sáenz Jesús Chacón Luis de la Torre Sebastián Dormido Department of Computer Science and Automatic Control Computer Science School

ISBN: (纸本)9781538608111

Online experimentation have become a common resource in education since the virtual and remote labs (VRLs) allowed the users to interact with a system without been in a laboratory environment. The science and engineering areas needs to teach their student in experimental practices and this implies, in most cases, costly and complex systems. This work shows how new technologies allows a low cost online laboratory.

关键词： Graphical user interfaces Education Sensors Electron tubes Visualization Webcams Software

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Benchmarking top-k keyword and top-k document processing with T2K2and T2K2D2

arXiv

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arXiv 2018年

作者： Truicǎ, Ciprian-Octavian Darmont, Jérôme Boicea, Alexandru Rǎdulescu, Florin Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania Université de Lyon ERIC EA Lyon 23083 France

Top-k keyword and top-k document extraction are very popular text analysis techniques. Top-k keywords and documents are often computed on-the-fly, but they exploit weighted vocabularies that are costly to build. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present T2K2, a top-k keywords and documents benchmark, and its decision support-oriented evolution T2K2D2. Both benchmarks feature a real tweet dataset and queries with various complexities and selectivities. They help evaluate weighting schemes and database implementations in terms of computing performance. To illustrate our benchmarks' relevance and genericity, we successfully ran performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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Advanced Processing Techniques for Detection and Classification of Skin Lesions

Advanced Processing Techniques for Detection and Classificat...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Şerban-Radu-Ştefan Jianu Loretta Ichim Dan Popescu Oana Chenaru Faculty of Control and Computers University POLITEHNICA Bucharest Bucharest Romania Faculty of Control and Computers University POLITEHNICA Bucharest “St. S. Nicolau”Institute of Virology Bucharest Romania Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

The paper presents how the skin cancer in forms of melanoma can be identified based on the digital image processing of the Iesion. The solution is based on the extraction of seven features (deterministic and statistic type) from the image of a skin lesion: perimeter, area, diameter, fractal dimension, lacunarity, histogram of oriented gradients, and local binary patterns. Each feature has attached a specific classifier and the diagnosis is obtained by using a voting scheme in the final classifier. The experimental results on a free database demonstrate that the method provides a high accuracy.

关键词： Lesions Feature extraction Histograms Fractals Skin Cancer Image color analysis

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P1.11-08 Are Risk Prediction Models Superior Over Standard Criteria for Lung Cancer Screening in Europe? Macroscale Simulation on Large Polish Cohort

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Journal of Thoracic Oncology 2019年第10期14卷 S518-S518页

作者： M. Ostrowski T. Marjanski R. Dziedzic S. Kocot W. Rzyman Thoracic Surgery Department Medical University of Gdansk Gdańsk/PL Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice/PL

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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BCCNet: Bayesian classifier combination neural network

arXiv

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arXiv 2018年

作者： Isupova, Olga Li, Yunpeng Kuzin, Danil Roberts, Stephen J. Willis, Katherine Reece, Steven Department of Engineering Science University of Oxford United Kingdom Department of Computer Science University of Surrey United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Department of Zoology University of Oxford United Kingdom

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Developing countries

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Semantic Modelling of Hybrid controllers for Robotic Cells

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Procedia Manufacturing 2017年 11卷 292-299页

作者： Haage, Mathias Malec, Jacek Nilsson, Anders Stenmark, Maj Topp, Elin Anna Department of Computer Science Lund University Box 118 Lund 221 00 Sweden Department of Automatic Control Lund University Box 118 Lund 221 00 Sweden

Programmable Logic controllers (PLCs) play an important role for integration of hardware and software in industrial robot cells. In this paper we propose a semantic grounding of the Sequential Function Charts (SFC) notation for specification of PLC programs. Our semantic grounding is expressed in the OWL semantic language and forms part of our semantic robot framework, called KIF (Knowledge Integration Framework). KIF is a set of ontologies and associated tools to ensure interoperability between heterogeneous equipment making up a robot cell. We also present a tool set for manipulating SFC instances stored in RDF triple stores reachable through the RDF4J framework. The semantic grounding and tool set are evaluated in a teaching-by-demonstration experiment in a small parts assembly setup featuring a collaborative industrial robot, ABB YuMi, where the tool set is used to create and execute SFC descriptions on-the-fly based on data from human demonstrations. © 2017

关键词： IEC 61131-3 Knowledge Integration Framework Ontology PLC Programmable Logic controller Sequential Function Chart SFC

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Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning

Ensemble Kalman Filtering for Online Gaussian Process Regres...

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International Conference on Information Fusion

作者： Danil Kuzin Le Yang Olga Isupova Lyudmila Mihaylova Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK Department of Electrical and Computer Engineering University of Canterbury Christchurch New Zealand Department of Engineering Science The University of Oxford Oxford UK

Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices.

关键词： Kalman filters Gaussian processes Estimation Prediction algorithms Bayes methods Covariance matrices Noise measurement

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Implementation of Cryptographically Enforced RBAC

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2017年第2期79卷 93-102页

作者： Ghiţǎ, Valentin Costea, Sergiu Ţǎpuş, Nicolae Faculty Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania

RBAC (Role-based access control) is an efficient method for sharing objects between different groups of users. If RBAC is implemented using ABE (Attribute Based Encryption), then the resulting system enforces access control indirectly, through cryptography. We propose a new multi-user system based on Cryptographically Enforced RBAC. To improve performance, our system combines existing work on Cryptographically Enforced RBAC with symmetric cryptography. From the best of our knowledge, we are the first to implement and experimentally evaluate the feasibility of such a system, which was previously only analyzed theoretically. We describe the architecture of our system, its implementation and evaluate performance. Our solution can be used to implement secure storage in clouds where the service provider is untrusted.

关键词： Access control

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