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arXiv 2020年

作者： Dev, Kapal Khowaja, Sunder Ali Jaiswal, Aman Bist, Ankur Singh Saini, Vaibhav Bhatia, Surbhi CONNECT Centre Trinity College Dublin Ireland Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan Department KIET Group of Institutions Ghaziabad India Signy Advanced Technologies. Gurugram Haryana India Dep Indian Institute of Technology Dehli India Department of Information Systems College of Computer Science and Information Technology King Faisal University Saudi Arabia

The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for potential alternatives to reverse transcriptionpolymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of largescale annotated data makes the clinical implementation of machine learning-based COVID detection methods difficult. Another important issue is the usage of ImageNet pretrained networks which does not guarantee to extract reliable feature representations. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from wellknown pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison to the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity. © 2020, CC-BY.

关键词： Convolution

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A novel approach for protection of accounts’ names against hackers combining cluster analysis and chaotic theory

arXiv

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arXiv 2019年

作者： Andreev, Desislav Petrakieva, Simona Taralova, Ina Dep. Computer Science Faculty of Computer Systems and Technologies Technical University of Sofia Dep. Theory of Electrical Engineering Faculty of Automation Technical University of Sofia Laboratoire des Sciences du Numérique de Nantes LS2N UMR 6004 Ecole Centrale de Nantes

The last years of the 20th century and the beginning of the 21th mark the facilitation trend of our real life due to the big development and progress of the computers and other intelligent devices. Algorithms based on artificial intelligence are basically a part of the software. The transmitted information by Internet or LAN arises continuously and it is expected that the protection of the data has been ensured. The aim of the present paper is to reveal false names of users’ accounts as a result of hackers’ attacks. The probability a given account to be either false or actual is calculated using a novel approach combining machine learning analysis (especially clusters’ analysis) with chaos theory. The suspected account will be used as a pattern and by classification techniques clusters will be formed with a respective probability this name to be false. This investigation puts two main purposes: First, to determine if there exists a trend of appearance of the similar usernames, which arises during the creation of new accounts. Second, to detect the false usernames and to discriminate those from the real ones, independently of that if two types of accounts are generated with the same speed. These security systems are applied in different areas, where the security of the data in users’ accounts is strictly required. For example, they can be used in on-line voting for balloting, in studying the social opinion by inquiries, in protection of the information in different user accounts of given system etc. Copyright © 2019, The Authors. All rights reserved.

关键词： Cluster analysis

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Modelling and control of a photovoltaic energy system with battery storage 6

Modelling and control of a photovoltaic energy system with b...

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6th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2018

作者： García-Ramos, Carlos Y. González-Cava, Jose M. González, José F. Gómez Pérez, Sara González Díaz, Benjamín González Méndez-Pérez, Juan Albino Dep. of Computer Science and Systems Engineering Universidad de la Laguna. APDO. 456 La Laguna Tenerife38200 Spain Dep. of Industrial Engineering Universidad de la Laguna. APDO. 456 La Laguna Tenerife38200 Spain

ISBN: (纸本)9788885741171

This work presents a simulated study of the energy management of an energy system connected to the grid with photovoltaic generation and battery storage. The work proposes a energy management system based on fuzzy logic. It is intended to be used in the hotel industry. The objective of the proposed controller is to maximise the renewable power source but including also economic criteria in the management. The proposal was implemented in simulation considering a 5,1kW peak photovoltaic installation and a set of batteries with a capacity of 384Ah. First results obtained show that the system achieves the specifications proposed. Thus, the study evidences the potential of the proposed control algorithm and demonstrate the suitability of the use of intelligent techniques for the energy management in hotels. © 2018 DIME Università di Genova, DIMEG University of Calabria.

关键词： Fuzzy logic

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Controlling symmetries and clustered dynamics of complex networks

arXiv

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arXiv 2020年

作者： Gambuzza, Lucia Valentina Frasca, M. Sorrentino, F. Pecora, L.M. Boccaletti, S. Dipartimento di Ingegneria Elettrica Elettronica e Informatica University of Catania Catania95029 Italy Dipartimento di Ingegneria Elettrica Elettronica e Informatica University of Catania Catania95029 Italy CNR-IASI Institute for Systems Analysis and Computer Science A. Ruberti Rome00185 Italy Department of Mechanical Engineering Univ. of New Mexico AlbuquerqueNM87131 United States Dep. of Electrical and Computer Engineering Univ. of New Mexico AlbuquerqueNM87131 United States U.S. Naval Research Laboratory WashingtonDC20375 United States CNR Institute of Complex Systems Via Madonna del Piano 10 Florence50019 Italy Unmanned Systems Research Institute Northwestern Polytechnical University Xi’an710072 China Moscow Institute of Physics and Technology Dolgoprudny Moscow Region141701 Russia

Symmetries are an essential feature of complex networks as they regulate how the graph collective dynamics organizes into clustered states. We here show how to control network symmetries, and how to enforce patterned states of synchronization with nodes clustered in a desired way. Our approach consists of perturbing the original network connectivity, either by adding new edges or by adding/removing links together with modifying their weights. By solving suitable optimization problems, we furthermore guarantee that changes made on the existing topology are minimal. The conditions for the stability of the enforced pattern are derived for the general case, and the performance of the method is illustrated with paradigmatic examples. Our results are relevant to all the practical situations in which coordination of the networked systems into diverse groups may be desirable, such as for teams of robots, unmanned autonomous vehicles, power grids and central pattern generators. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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Using Scrum for Implementing IT Governance with COBIT 5

Using Scrum for Implementing IT Governance with COBIT 5

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International Conference on Enterprise Distributed Object Computing (EDOC)

作者： Ana Cláudia Amorim Miguel Mira da Silva Rúben Pereira Margarida Gonçalves Dep. of Computer Science and Engineering Instituto Superior Técnico Lisbon Portugal Dep. of Information Systems ISCTE Lisbon Portugal Quasinfalival Lisbon Portugal

COBIT 5 is a widely-used framework for implementing sound governance of enterprise IT (GEIT). Despite the existence of official guidance, there are still several challenges that we can encounter. Currently, the ISACA's official implementation solution follows a sequentially ordered process corresponding to a traditional approach, however, organizations are increasingly embracing more agile ones for managing projects where the solution is not clear from the beginning. Using the Design science Research Methodology, the authors analyse the current state of art and provide a Scrum based methodology for managing at team level a COBIT 5 programme. With a hybrid agile-traditional approach, the authors aim to eliminate some known challenges of COBIT 5, such as lack of support from top management and misaligned scopes and solutions. Additionally, in this paper, the authors present the results obtained from applying the designed methodology in a COBIT 5 programme in the Portuguese Finance Ministry, as well as inspect two series of interviews: 10 performed with experts from both Scrum and COBIT 5 areas to evaluate the solution, and 6 others with the team involved in the demonstration programme, to understand if the objectives where achieved. The article ends with lessons learned, limitations and future work.

关键词： Organizations Software Stakeholders Resistance Interviews Finance

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A Gaussian Process Regression for Natural Gas Consumption Prediction Based on Time Series Data

A Gaussian Process Regression for Natural Gas Consumption Pr...

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International Conference on Information Fusion

作者： Oussama Laib Mohamed Tarek Khadir Lyudmila Mihaylova Dep. of Computer Science University of Badji Mokhtar Annaba (UBMA) Annaba Algeria Dep. of Automatic Control and Systems Engineering (ACSE) University of Sheffield Sheffield UK

For several economical, financial and operational reasons, forecasting energy demand becomes a key instrument in energy system management. This paper develops a natural gas forecasting approach, which consists of two major phases: 1) it classifies the natural gas consumption daily pattern sequences into different groups with similar attributes. 2) the design and training of multiple autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (temperature) and calendar (day of the week, hour indicator) factors. The main novelty in this work consists of the investigation of multiple different clustering techniques for better analysis and clustering of natural gas consumption data. The impact of the obtained clusters, by each technique, is then summarized and evaluated with respect to the prediction accuracy.

关键词： Natural gas Forecasting Gaussian processes Load modeling Predictive models Clustering methods Kernel

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Classification of Hand Movements from EMG Signals using Optimized MLP

Classification of Hand Movements from EMG Signals using Opti...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Lima, Aron A. Martins Araujo, Rafael M. Santos, Fábio A. Guidotti Dos Yoshizumi, Victor H. De Barros, Fábio K. H. Spatti, Danilo H. Liboni, Luisa H. B. Dajer, Maria E. Department of Electrical Engineering Federal University of Technology Paraná Cornélio Procópio Brazil Department of Computational Engineering Federal University of Technology Paraná Cornélio Procópio Brazil Department of Computational Systems University of São Paulo São Carlos Brazil Dep. Of Electrical and Computer Engineering Federal Institute of Education Science and Technology of São Paulo Sertãozinho Brazil

ISBN: (纸本)9781509060146

The emergence of artificial neural networks (ANNs) caused a great revolution in the knowledge area of pattern-classification and contributed to the fast development of intelligent systems. For an ANN to be effective, its topology parameters must be set up, such as learning rate, number of layers, and number of neurons. Therefore, optimization algorithms can be used to produce more efficient ANNs. In this paper, we propose a genetic algorithm (GA) to better estimate parameters of an ANN applied to the classification of the type, strength, and orientation of different hand movements from electromyography signals. Acquired data signals were first processed with the Wavelet Transform (WT) to extract the most important features. These features were presented to the optimized hybrid intelligent system (HIS) composed of ANNs and the optimization GA. Classification results are promising, presenting accuracy rates above 90% and reliability rate of 98%. These results support the use of HIS in practical applications such as smart prostheses and automatic medical evaluations. © 2018 IEEE.

关键词： Genetic algorithms

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Classification of Hand Movements from EMG Signals using Optimized MLP

Classification of Hand Movements from EMG Signals using Opti...

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International Joint Conference on Neural Networks (IJCNN)

作者： Aron A. Martins Lima Rafael M. Araujo Fábio A. Guidotti dos Santos Victor H. Yoshizumi Fábio K. H. de Barros Danilo H. Spatti Luisa H. B. Liboni Maria E. Dajer Department of Electrical Engineering Federal University of Technology Paraná Cornélio Procópio Brazil Department of Computational Systems University of São Paulo São Carlos Brazil Dep. of Electrical and Computer Engineering Federal Institute of Education Science and Technology of São Paulo Sertãozinho Brazil

关键词： Electromyography Artificial neural networks Topology Genetic algorithms Wrist Transforms Network topology

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Wavelet transform based technique for text image localization

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Karbala International Journal of Modern science 2016年第2期2卷 138-144页

作者： Ali, Suhad A. Hashim, Ashwaq T. Babylon University Science College for Women Computer Science Dep. Babylon Iraq Technology University Control and Systems Engineering Dep. Baghdad Iraq

In this paper, a robust technique based on discrete wavelet transform, edge detection, and morphology operation for scene text detection is proposed. There are several stages in the proposed method. In the first stage, a single wavelet decomposition LH, HL and HH subbands are applied for detecting edges in original scene text image. The projection technique is applied in the second stage to preliminary detect text and non-text pixels. In third stage, 4-connected components are applied, and then area geometric feature is used as threshold to remove non-text region. At last stage, morphological operations are applied to connect isolated text components and to remove non-text regions. The proposed method is applied on a various images such as images of low contrast, complex background images and images of different fonts and size of text. The experimental results show that the proposed method can detect regions of the text perfectly. © 2016 The Authors

关键词： Connected components DWT Image projection Morphological operator

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Distribution of load change in industrial demand: A DOE approach

Distribution of load change in industrial demand: A DOE appr...

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Innovative Smart Grid Technologies (ISGT)

作者： Mostafa G. Varzaneh Rupy Sawhney Hesam Shams Ailin Asadinejad Dep. of Industrial and Systems Engineering Dep. of Electrical Engineering and Computer Science University of Tennessee-Knoxville

ISBN: (纸本)9781509051687

Estimating distribution of load change in response to demand management programs has been targeted in many researches, mostly based on economical and business sale-price models. In this paper a bottom-up analysis approach will be considered. As the first step consumer's decision making process has been approximated using mathematical modeling. In this step a novel optimization model for production scheduling will be introduced which takes into account different energy consumption states. In the second step a design of experiment (DOE) model will be developed base on different energy and production factors. In the third step, two new distributions will be introduced based on a variable selected in DOE analysis. The proposed distributions can be obtained based on significant variables and be utilized in market analysis and more specifically, in stochastic analysis.

关键词： Production Energy consumption Mathematical model Biological system modeling Elasticity Load modeling

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