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Innovation, Research and Development in engineering Education. Activities of the Spanish Chapter of the IEEE Education Society: Directive Board of the IEEE Education Society Spanish Chapter 2020-2021

Innovation, Research and Development in Engineering Educatio...

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Technologies Applied to Electronics Teaching (TAEE)

作者： Ó scar Martí nez Bonastre Cristina Ferná ndez Lluí s Vicent Francisco Mur Manuel Castro Gabriel Diaz Jose Carpio Elio Sancristobal Sergio Martin Jose Sá nchez Francisco Javier Arcega Solsona Juan Suardiaz Muro Manuel Caeiro-Rodrí guez Martí n Llamas Inmaculada Plaza Baltasar Ferná ndez Manjó n Pedro Muñ oz-Merino Edmundo Tovar Telmo Zarraonandia Ayo Javier Garcia Zubia José Antonio Delgado Pení n Department of Statistics Mathematics and Computing Science Operations Research Centre Universidad Miguel Hern&#x00E1 ndez Elche Spain Department of Electronic Technology Universidad Carlos III de Madrid Madrid Spain UPF Barcelona School of Management Barcelona Spain Department of Electrical Electronic and Control Engineering UNED Madrid Spain Department of Electrical Engineering Universidad de Zaragoza Zaragoza Spain Department of Automatics Electrical Engineering and Electronic Technology Technical University of Cartagena Cartagena Spain atlanTTic Research Center University of Vigo Vigo Spain Department of Electronics and Communication Universidad de Zaragoza Zaragoza Spain Department of Software Engineering and Artificial Intelligence Complutense University of Madrid Madrid Spain Department of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain Department of Computer Languages and Systems and Software Engineering Universidad Polit&#x00E9 cnica de Madrid Madrid Spain Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain Department of Electronic Engineering Universidad de Deusto Bilbao Spain Universitat Polit&#x00E8 cnica de Catalunya Barcelona Spain

ISBN: (数字)9781665421614

ISBN: (纸本)9781665421621

The Technical Committee of the CESEI (Spanish Chapter of the IEEE Education Society) is focused on the Innovation, Research and Development (I+R+D) of the education in engineering, mainly Electric and computer engineering. As in other interdisciplinary fields, the I+R+D involves several sources of information and different stakeholders, each one with its own focus and purpose. As a result, sometimes it may be complex for practitioners and researchers to identify the more appropriate entities for their interests. This paper shows how the CESEI Technical Committee tries to solve this difficulty by providing updated information regarding the main publications (journals), events (conferences) and awards to the best academic works. The paper focuses special attention on the activities held during 2020–2021.

关键词： Technological innovation Collaboration Stakeholders engineering education Research and development

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Diagnosing covid-19 lung inflammation using machine learning algorithms: A comparative study

Studies in Big Data

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Studies in Big Data 2020年 80卷 91-105页

作者： Ali, Abbas M. Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Mulahuwaish, Aos Department of Software Engineering Salahaddin University-Erbil Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Koysinjaq Iraq Department of Computer Science and Information Systems Saginaw Valley State University 7400 Bay Rd Science East 174 University Center Itta BenaMI48710 United States

In this paper, we performed a comparative analysis using machine learning algorithms named support vector machine (SVM), decision tree (DT), k-nearest neighbor (kNN), and convolution neural network (CNN) to classify pneumonia level (mild, progressive, and severe stage) of the COVID-19 confirmed patients. More precisely, the proposed model consists of two phases: first, the model computes the volume and density of lesions and opacities of the CT images using morphological approaches. In the second phase, we use machine learning algorithms to classify the pneumonia level of the confirmed COVID-19 patient. Extensive experiments have been carried out and the results show the accuracy of 91.304%, 91.4%, 87.5%, 95.622% for kNN, SVM, DT, and CNN, respectively. © Springer International Publishing AG 2018.

关键词： COVID-19

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On PID control for synchronization of complex dynamical network with delayed nodes

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science China(Technological sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Sparse Weighted Canonical Correlation Analysis

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Chinese Journal of Electronics 2018年第3期27卷 459-466页

作者： MIN Wenwen LIU Juan ZHANG Shihua State Key Laboratory of Software Engineering School of Computer Wuhan University National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics Sciences University of Chinese Academy of Sciences

Given two data matrices X and Y, Sparse canonical correlation analysis（SCCA） is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Yv. Classical and sparse Canonical correlation analysis（CCA） models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. We propose a novel Sparse weighted canonical correlation analysis（SWCCA）,where weights are used for regularizing different *** solve the L0-regularized SWCCA（L0-SWCCA） using an alternating iterative algorithm. We apply L0-SWCCA to synthetic data and real-world data to demonstrate its effectiveness and superiority compared to related methods. We consider also SWCCA with different penalties like Least absolute shrinkage and selection operator（LASSO）and Group LASSO, and extend it for integrating more than three data matrices.

关键词： Canonical correlation analysis(CCA) Sparse canonical correlation analysis(SCCA) Sparse weighted CCA(SWCCA) Group LASSO regularized SWCCA Multi-view SWCCA

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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COVID-19 pneumonia level detection using deep learning algorithm

TechRxiv

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TechRxiv 2020年

作者： Ghafoor, Kayhan Zrar Ali, Abbas M. Mulahuwaish, Aos Maghdid, Halgurd S. Hussein, Safwan Mawlood Mohammed, Marwan A. Department of Software Engineering Salahaddin University-Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Computer Science and Information Systems Saginaw Valley State University University Center 7400 Bay Rd Science East 174 MI48710 United States Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq Computer Engineering Department Tishk International University Iraq

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases;in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65% and 91.304 % of CNN and kNN respectively. © 2020, CC BY.

关键词： COVID-19

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Managing MyGRANTS Fragmented Database Using Binary Vote Assignment Grid Quorum with Association Rule (BVAGQ-AR) Replication Model 2nd

Managing MyGRANTS Fragmented Database Using Binary Vote Assi...

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Noraziah, Ahmad Fauzi, Ainul Azila Che Mohd, Wan Maseri Wan Herawan, Tutut Abdullah, Zailani Faculty of Computer Systems & Software Engineering University of Malaysia Pahang Gambang KuantanPahang26300 Malaysia Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia School of Informatics & Applied Mathematics Universiti Malaysia Terengganu Terengganu Malaysia

ISBN: (纸本)9789811317972

Replication is one of the mechanisms managing data since it improves data access and reliability. However, in recent years, with widely available, low-cost technology, the amount of various data grows rapidly. The problem is although we are packed with data, but we have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper propose a new algorithm namely Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) in order to handle fragmented database replication. BVAGQ-AR algorithm is capable of partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Handling fragmented database replication becomes challenging issue to administrator since the distributed database is scattered into split replica partitions or fragments. We address how to build reliable system by using the proposed BVAGQ-AR algorithm for distributed database fragmentation by using Malaysian Greater Research Network (MyGRANTS) data as a case study. The result shows that managing fragmented database replication through proposed BVAGQ-AR algorithm able to preserve MyGRANTS data consistency. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Association rules

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Correction to “Performance Evaluation of LoRaWAN for Green Internet of Things”

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IEEE Access 2022年 10卷 125400-125400页

作者： Zulfiqar Ali Shagufta Henna Adnan Akhunzada Mohsin Raza Sung Won Kim Department of Software Engineering Bahria University Islamabad Pakistan Department of Computer Science Bahria University Islamabad Pakistan Telecommunications Software and Systems Group (TSSG) Waterford Institute of Technology (WIT) Waterford Ireland Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Design Engineering and Mathematics Middlesex University London London U.K Department of Information and Communication Engineering Yeungnam University Gyeongsan South Korea

In the above article [1] , we want to change the affiliation of the first author to “Department of software engineering, Bahria University, Islamabad, 46000, Pakistan” and add a new affiliation for the second author, which is “Department of computer science, Bahria University, Islamabad, 46000, Pakistan.” The first author (ZULFIQAR ALI; zulfijobs@***) is a Ph.D. student at Bahria University Islamabad, Pakistan, and the second author (SHAGUFTA HENNA; shaguftahenna@***) is his Ph.D. Supervisor.

关键词： Performance evaluation computer science Internet of Things software engineering

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Fault Tolerance Impact on Near Field Communication for Data Storage of Mobile Commerce Technology in Cloud Computing 2nd

Fault Tolerance Impact on Near Field Communication for Data ...

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Noraziah, Ahmad Herawan, Tutut Rahman, Mohd Tarmizi Abd Abdullah, Zailani Mustafa, Balsam A. Fakharaldien, Mohammed Adam Ibrahim Faculty of Computer Systems and Software Engineering University Malaysia Pahang GambangPahang26300 Malaysia Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia School of Informatics & Applied Mathematics University Malaysia Terengganu Kuala Terengganu21030 Malaysia

ISBN: (纸本)9789811317972

Fault tolerance is a major concern to guarantee availability and reliability of critical services as well as application execution. Recently, due to rapid expansion of smart phone market and wireless communication environment, mobile devices are considered as resource for large scale distributed processing but there are several drawbacks, such as unstable wireless connection, limitation of data storage capacity, low communication bandwidth and frequent location changes. Cloud systems differ from these previous frameworks in that they are designed to support large numbers of customer-oriented applications, each with different quality of service (QoS) requirements and resource consumption characteristics. As resource providers, mobile devices can join and leave the distributed computing environment unpredictably. This interrupts the undergoing operation, and the delay or failure of completing the operation may cause a system failure. In this paper, we propose a monitoring technique based on Load Balancing. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Mean time between failures

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software module clustering: An in-depth literature analysis

arXiv

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arXiv 2020年

作者： Sarhan, Qusay I. Ahmed, Bestoun S. Bures, Miroslav Zamli, Kamal Z. Department of Computer Science University of Duhok Duhok Kurdistan Region Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam. 13 Praha 2121 35 Czech Republic IBM Center of Excellence Faculty of Computer Systems and Software Engineering University Malaysia Pahang 26 300 Gambang KuantanPahang Malaysia

software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities’ structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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