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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Making failure the mother of success

Making failure the mother of success

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Frontiers in Education (FIE) Conference

作者： Xiang Fu Kai Qian Kent Palmer Boris Peltsverger Brian Campbell Billy Lim Paul Vogt Department of Computer Science Hofstra University USA School of Computing and Software Engineering Southern Polytechnic State University USA Goshen College USA School of Computing and Mathematics Georgia Southwestern State University USA Adjunct Faculty School of Computing and Mathematics Georgia Southwestern State University USA School of Information Technology Illinois State University USA Illinois State University USA

Do students really welcome failure, the mother of success? This paper reports the implementation of a trial-and-failure learning strategy, in both entry and senior level computer science classes. The idea is simple: given a sophisticated course project, let students try project submissions as many times as they want before the project deadline. For each submission, a thorough inspection is performed by an automated grading system called APOGEE. A student project has to accomplish not only the functional requirements but also many desired quality attributes such as robustness and security. In 2009, the trial-and-failure strategy has been adopted by four universities in five class sections. We report some interesting observations from student survey results, e.g., one can find out if factors like students' positive experiences of programming, choice of programming language, years of working experience, and instructors are predictive variables for positive attitudes toward the trial-and-failure learning experience as a whole.

关键词： Graphical user interfaces Educational institutions Servers Testing Engines Conferences

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Circular antenna array design for breast Cancer detection

arXiv

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arXiv 2018年

作者： Ouerghi, K. Fadlallah, N. Smida, A. Ghayoula, R. Fattahi, J. Boulejfen, N. Unit of Research in High Frequency Electronic Circuits and Systems Faculty of Mathematical Physical and Natural Sciences of Tunis Tunis El Manar University Tunisia RADIOCOM Institut of Technology saida P.O.Box 813 #36 Lebanon Department of Computer Science and Software Engineering Laval University Quebec Canada Research Center for Microelectronics and Nanotechnology Technopole Sousse Sousse4050 Tunisia

— —Microwave imaging for breast cancer detection is based on the contrast in the electrical properties of healthy fatty breast tissues. This paper presents an industrial, scientific and medical (ISM) bands comparative study of five microstrip patch antennas for microwave imaging at a frequency of 2.45 GHz. The choice of one antenna is made for an antenna array composed of 8 antennas for a microwave breast imaging system. Each antenna element is arranged in a circular configuration so that it can be directly faced to the breast phantom for better tumor detection. This choice is made by putting each antenna alone on the Breast skin to study the electric field, magnetic fields and current density in the healthy tissue of the breast phantom designed and simulated in Ansoft High Frequency Simulation software (HFSS). Copyright © 2018, The Authors. All rights reserved.

关键词： Microstrip antennas

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SusTrainable: Promoting Sustainability as a Fundamental Driver in software Development Training and Education Teacher Training November 1–5, Nijmegen, The Netherlands Revised lecture notes

arXiv

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arXiv 2022年

作者： Koopman, Pieter Lubbers, Mart Fernandes, João Paulo Korečko, Štefan Paquete, Luís Porkoláb, Zoltán Szalay, Richárd Mauša, Goran Oprescu, Ana Koedijk, Lukas van Oostveen, Sander Kok, Stephan Li, Jianhao Zsók, Viktória Grbac, Tihana Galinac Grbac, Neven Valkanov, Vladimir Petrov, Mihail Radboud University Nijmegen Netherlands University of Porto Portugal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia CISUC Department of Informatics Engineering University of Coimbra Portugal Department of Programming Languages and Compilers Institute of Computer Science Faculty of Informatics Eötvös Lorád University Budapest Hungary University of Rijeka Faculty of Engineering Vukovarska 58 Rijeka51000 Croatia Complex Cyber Infrastructure University of Amsterdam Netherlands KPMG Software Engineering Master University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Juraj Dobrila Univeristy of Pula Zagrebačka 30 PulaHR-52100 Croatia University Of Plovdiv "Paisii Hilendarski" Faculty of Mathematics and Informatics 24 Bulgaria Blvd. Plovdiv4027 Bulgaria

These are the proceedings of the first teacher training of the Erasmus+ project No. 2020-1-PT01-KA203-078646 -- Sustrainable. The full title of this project is Promoting Sustainability as a Fundamental Driver in software Development Training and Education and the interested reader may know more about it at: https://*** The flagship contribution is the organization of two summer schools on sustainable software production. The first summer school is moved due to the Corona pandemic. It is now scheduled for July 2022 in Rijeka, Croatia. This summer school will consist of lectures and practical work for master and PhD students in the area of computing science and closely related fields. There will be contributions from Plovdiv University, University of Ko\v{s}ice, University of Coimbra, University of Minho, E\"otv\"os Lor\'and University, University of Rijeka, Radboud University Nijmegen, University of Pula, University of Amsterdam and Babe\c{s}-Bolyai University. To prepare and streamline this summer school, the consortium organized a teacher training in Nijmegen, the Netherlands. This was an event of five full days on November 1-5, 2022 organized by Ingrid Berenbroek, Mart Lubbers and Pieter Koopman on the campus of the Radboud University Nijmegen, The Netherlands. Sustainability is one of the themes within the strategy of the Radboud University called `A Significant Impact', see https://***/sustainability/organisation/radboud-sustainable/. Sustainability plays an important role in education, research, and management. The contributions were reviewed and give a good overview of what can be expected from the summer school. Based on these papers and the very positive and constructive atmosphere, we expect a high quality and pleasant summer school. We are looking forward to contributing to a sustainable future. © 2022, CC BY-NC-ND.

关键词： Personnel training

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Resource-aware network topology management framework

arXiv

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arXiv 2020年

作者： Abbasi, Aaqif Afzaal Shamshirband, Shahaboddin Al-qaness, Mohammed A.A. Abbasi, Almas ALJallad, Nashat T. Mosavi, Amir Department of Software Engineering Foundation University Islamabad44000 Pakistan Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh758307 Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh758307 Viet Nam School of Computer Science Wuhan University Bayi Road No. 299 Wuhan430072 China Department of Computer Science International Islamic University Islamabad44000 Pakistan School of Computer Science and Technology Wuhan University of Technology Wuhan430079 China School of Computer Science Palestine Technical University Tulkarem44864 Palestine School of the Built Environment Oxford Brookes University No.5 Jack Straws Lane OxfordOX3 0BP United Kingdom Kalman Kando Faculty of Electrical Engineering Obuda University Becsi ut 94-96 Budapest1034 Hungary Institute of Structural Mechanics Bauhaus University Weimar Marienstraße 15 WeimarD-99423 Germany

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our *** Codes 68T05 Copyright © 2020, The Authors. All rights reserved.

关键词： Information management

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Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks

arXiv

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arXiv 2022年

作者： Grabowski, Bartosz Glomb, Przemyslaw Masarczyk, Wojciech Plawiak, Pawel Yıldırım, Özal Acharya, U. Rajendra Tan, Ru-San Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Warsaw University of Technology Pl. Politechniki 1 Warsaw00-661 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Department of Software Engineering Firat University Elazig Turkey School of Science and Technology Singapore University of Social Sciences Singapore Faculty of Business Education Law and Arts University of Southern Queensland Australia School of Engineering Ngee Ann Polytechnic 535 Clementi Road 599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan Kumamoto University Kumamoto Japan Department of Cardiology National Heart Centre Singapore169609 Singapore Duke-NUS Medical School 169857 Singapore

Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and classification tasks. Moreover, we combined the two approaches, using unlabeled and labeled data, to train the model. We tested the model on the MIT-BIH Arrhythmia database. Our method showed high effectiveness in detecting cardiac arrhythmia based on modified Lead II ECG records, as well as achieved high quality of ECG signal approximation. For the former, our method attained overall accuracy of 87.33% and balanced accuracy of 80.54%, on par with reference approaches. For the latter, application of self-supervised learning allowed for training without the need for expert labels. The regression model yielded satisfactory performance with fairly accurate prediction of QRS complexes. Transferring knowledge from regression to the classification task, our method attained higher overall accuracy of 87.78%. © 2022, CC BY.

关键词： Electrocardiography

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Line drawing technique for building images

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Ruan Jian Xue Bao/Journal of software 2012年第SUPPL.2期23卷 34-41页

作者： Liu, You-Quan Wu, Zong-Sheng Wang, Shan-Dong Xue, Ru Wu, En-Hua School of Information Engineering Chang'an University Xi'an 710064 China Faculty of Science and Technology University of Macau Macao China State Key Laboratory of Computer Science Institute of Software The Chinese Academy of Sciences Beijing 100190 China Tibet University for Nationalities Xianyang 712082 China

This paper presents an automatic line-drawing method designed for building images. The fast line segment detector with a false detection control and detects the outline and the door, or window edge features detected, which are used in the construction of edge tangent flow. Based on the modified ETF, the Difference-of-Gaussians is used to render the final line drawing styles. This scheme gives clear line features for building images without much noise, which is also simple and easy to implement. The test results verify the effectiveness of the presented algorithm. © Copyright 2012, Editorial Department of Journal of software.

关键词： Rendering (computer graphics)

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A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

arXiv

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arXiv 2023年

作者： Wang, Jiapu Wang, Boyue Qiu, Meikang Pan, Shirui Xiong, Bo Liu, Heng Luo, Linhao Liu, Tengfei Hu, Yongli Yin, Baocai Gao, Wen Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beacom College of Computer and Cyber Sciences Dakota State University MadisonSD United States Griffith University QLD Australia The Department of Computer Science University of Stuttgart Stuttgart70569 Germany The Department of Data Science and AI Monash University Melbourne Australia The Institute of Digital Media Peking University Beijing100871 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1) Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol;2) Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information;3) Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Some Generalized Normal Subgroups and p-nilpotency of Finite Groups

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Chinese Quarterly Journal of Mathematics 2018年第1期33卷 17-24页

作者： ZHONG Guo LIN Shi-Xun CHEN Qi-le QIN Li-hua Faculty of Science and Technology University of Macao Macao SAR China School of Mathematics and Statistics Zhaotong University Zhaotong 657000 China School of Software Engineering South China University of Technology Guangzhou Guangdong China College of Mathematics and Computer Science Guangxi Normal University for Nationlities Chongzuo Guangxi China

In this paper, the influence of s-semipermutable, c~#-normal, subnormally embedded and ss-quasinormal subgroups on the p-nilpotency of finite groups is investigated and some recent results are generalized.

关键词： s-semipermutable subgroups c#-normal subgroups subnormally embedded subgroups ss-quasinormal subgroups p-nilpotency

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Exact quantum sensing limits for bosonic dephasing channels

arXiv

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arXiv 2024年

作者： Huang, Zixin Lami, Ludovico Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia QuSoft Science Park 123 Amsterdam1098 XG Netherlands Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands Institute for Theoretical Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges towards realizing useful quantum computation, communication, and sensing. Here we consider discrimination and estimation of bosonic dephasing channels, when using the most general adaptive strategies allowed by quantum mechanics. We reduce these difficult quantum problems to simple classical ones based on the probability densities defining the bosonic dephasing channels. By doing so, we rigorously establish the optimal performance of various distinguishability and estimation tasks and construct explicit strategies to achieve this performance. To the best of our knowledge, this is the first example of a non-Gaussian bosonic channel for which there are exact solutions for these tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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