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4th Computational Methods in systems and Software, CoMeSySo 2020

作者： Almomani, Malek A. Basri, Shuib Almomani, Omar Capretz, Luiz Fernando Balogun, Abdullateef Husni, Moath Gilal, Abdul Rehman Department of Software Engineering The World Islamic Sciences and Education University Amman11947 Jordan Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia Department of Computer Network and Information Systems The World Islamic Sciences and Education University Amman11947 Jordan Department of Electrical and Computer Engineering Western University LondonON Canada Department of Computer Science University of Ilorin PMB 1515 Ilorin Nigeria Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

ISBN: (纸本)9783030633219

This paper presents the components of a newly developed Malaysian SMEs - Software Process Improvement model (MSME-SPI) that can assess SMEs software development industry in managing and improving their software processes capability. The MSME-SPI is developed in response to practitioner needs that were highlighted in an empirical study with the Malaysian SME software development industry. After the model development, there is a need for independent feedback to show that the model meets its objectives. Consequently, the validation phase is performed by involving a group of software process improvement experts in examining the MSME-SPI model components. Besides, the effectiveness of the MSME-SPI model is validated using an expert panel. Three criteria were used to evaluate the effectiveness of the model namely: usefulness, verifiability, and structure. The results show the model effective to be used by SMEs with minor modifications. The validation phase contributes towards a better understanding and use of the MSME-SPI model by the practitioners in the field. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software design

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The Outline and the Current Status of the Power Transmission System Development Project for the Realization of the SSPS 73

The Outline and the Current Status of the Power Transmission...

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73rd International Astronautical Congress, IAC 2022

作者： Sasaki, Kenji Machida, Hirotaka Ijichi, Koichi Kashimura, Osamu Ishimura, Kosei Ishikawa, Ryo Honjo, Kazuhiko Ozawa, Yuichiro Tanaka, Koji Japan Space Systems 3-5-8 Shibakoen Minato-ku Tokyo105-0011 Japan Waseda University Dept. of Modern Mechanical Engineering School of Creative Science and Engineering 30401 Okubo Shinjuku-ku Tokyo169-8555 Japan Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Electronics Technologies Office Technologies Development Department IHI Aerospace Co. Ltd. 900 Fujiki Gunma-ken Tomioka-shi370-2398 Japan Japan Aerospace Exploration Agency Institute of Space and Astronautical Science 3-1-1 Yoshinodai Chuo-ku Kanagawa Sagamihara252-5210 Japan

The outline and the status of the current development project towards the realization of the operational SSPS which is the final object of the development will be presented. The current project under development consists of three major tasks. First one is to develop so called power generation and transmission panel whose size is about 50 cm square and 10 cm thick, and the target weight should be less than 9 kg. The second is to improve the conversion efficiency from DC to microwave to exceed 60%. The third is to demonstrate power transmission from the ground to the target on unmanned vehicle high up about one kilo-meter in the air. All these three development activities are for the realization of the operational SSPS toward the final operational system on the Geostationary Orbit (GEO) Space Solar Power System (SSPS) after the one step of the power beaming experiment from the Low Earth Orbit (LEO) satellite to the ground as the next logical development step. © 2022 International Astronautical Federation, IAF. All rights reserved.

关键词： Power transmission

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Educational Programme Management Methodology for Research Projects

Educational Programme Management Methodology for Research Pr...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Alkistis Kontou Kai Heussen Leonard Ramos Perez Panagiotis A. Karafotis Mazheruddin Syed Alexandros Paspatis Evangelos Rikos School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Wind and Energy Systems Technical University of Denmark Roskilde Denmark European Distributed Energy Resources Laboratories (DERlab) e.V. Kassel Germany Hellenic Electricity Distribution Network Operator (HEDNO) Athens Greece Energy Advisory WSP UK Glasgow United Kingdom Department of Engineering Manchester Metropolitan University Manchester United Kingdom Centre for Renewable Energy Sources and Saving Athens Greece

Smart grids and intelligent energy systems play a pivotal role in fostering the sustainable advancement of our civilization. Over the past few decades, power systems, like many other sectors, have undergone a rapid digital transformation. This rapid development necessitates a proactive response from universities, research institutes, industry stakeholders in relation to educational programmes. Educators must rapidly adapt their curricula and teaching methodologies to effectively train the next generation of engineering professionals. While curriculum crafting for new educational programs is inherently challenging, another layer of complexity arises when research collaborations in large consortia are tasked with delivering high-quality education within a given project scope and time frame. This paper outlines a methodology for establishing an educational strategy for such research projects. This approach takes into account the available resources and expertise of the project participants, while embracing modern, learner-centric educational methodologies. It also ensures alignment with broader objectives or frameworks. Furthermore, the strategy incorporates a dynamic evaluation process that runs concurrently with the educational activities. Finally, the ERIGrid 2.0 H2020 project upon which the proposed methodology was developed, is presented as a case study.

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The nature and nurture of network evolution

arXiv

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arXiv 2023年

作者： Zhou, Bin Holme, Petter Gong, Zaiwu Zhan, Choujun Huang, Yao Lu, Xin Meng, Xiangyi School of Management Science and Engineering Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Department of Computer Science Aalto University AaltoFI-00076 Finland Center for Computational Social Science Kobe University Hyogo Kobe657-8501 Japan School of Computer South China Normal University Guangdong Guangzhou510631 China College of Systems Engineering National University of Defense Technology Hunan Changsha410073 China Network Science Institute Department of Physics Northeastern University BostonMA02115 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States

Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node's weight, reflecting its innate attractiveness (nature), and the node's degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks. © 2023, CC BY.

关键词： Complex networks

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Optimal vaccine allocation for pandemic stabilization

arXiv

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arXiv 2021年

作者： Ma, Qianqian Liu, Yang-Yu Olshevsky, Alex Department of Electrical and Computer Engineering Boston University BostonMA United States Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States

How to strategically allocate the available vaccines is a crucial issue for pandemic control. In this work, we propose a mathematical framework for optimal stabilizing vaccine allocation, where our goal is to send the infections to zero as soon as possible with a fixed number of vaccine doses. This framework allows us to efficiently compute the optimal vaccine allocation policy for general epidemic spread models including SIS/SIR/SEIR and a new model of COVID-19 transmissions. By fitting the real data in New York State to our framework, we found that the optimal stabilizing vaccine allocation policy suggests offering vaccines priority to locations where there are more susceptible people and where the residents spend longer time outside the home. Besides, we found that offering vaccines priority to young adults (20-29) and middle-age adults (20-44) can minimize the cumulative infected cases and the death cases. Moreover, we compared our method with five age-stratified strategies in [11] based on their epidemics model. We also found it's better to offer vaccine priorities to young people to curb the disease and minimize the deaths when the basic reproduction number R0 is moderately above one, which describes the most world during COVID-19. Such phenomenon has been ignored in [11]. Copyright © 2021, The Authors. All rights reserved.

关键词： Vaccines

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An avoiding overlap method between acoustic shadow and organ for automated ultrasound diagnosis and treatment

An avoiding overlap method between acoustic shadow and organ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Momoko Matsuyama Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Kazushi Numata Department of Mechanical and Intelligent Systems Engineering The University of Electro-Communications Chofu Japan Department of Computer and Network Engineering The University of Electro-Communications Chofu Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology line 4 Tsukuba Japan Department of Cardiothoracic Surgery The University of Tokyo Bunnkyo-Ku Japan Gastroenterological Center Yokohama City University Medical Center Yokohama Japan

ISBN: (纸本)9781665492331

Ultrasound diagnosis and treatment have attracted attention because they are noninvasive and allow real-time observation of lesions. However, it is difficult to accurately estimate the location of the treatment area because of information loss due to acoustic shadows caused by the reflection of sound waves from hard tissues such as bone. In this study, we propose a probe manipulation method that uses deep learning to avoid overlap between organs and shadows for automated diagnosis.

关键词： Deep learning Ultrasonic imaging Acoustics Real-time systems Reflection Reliability Lesions

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Categorizing learning analytics models according to their goals and identifying their relevant components: A review of the learning analytics literature from 2011 to 2019

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computers and Education: Artificial Intelligence 2021年 2卷

作者： Quadir, Benazir Chang, Maiga Yang, Jie Chi Information Management Department School of Business. Shandong University of Technology China School of Computing and Information Systems Athabasca University Canada Graduate Institute of Network Learning Technology. &. Department of Computer Science and Information Engineering National Central University Taiwan

This study aimed to categorize learning analytics (LA) models and identify their relevant components by analyzing LA-related articles published between 2011 and 2019 in international journals. A total of 101 articles discussing various LA models were selected. These models were characterized according to their goals and components. A qualitative content analysis approach was used to develop a coding scheme for analyzing the aforementioned models. The results reveal that the studied LA models belong to five categories, namely performance, meta-cognitive, interactivity, communication, and data models. The majority of the selected LA-related articles were data models, followed by performance models. This review also identified 16 components that were commonly used in the studied models. The results indicate that analytics was the most common component in the studied models (used in 10 LA models). Furthermore, visualization was the most relevant component in the studied communication models. © 2021 The Authors

关键词： Learning systems

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network consensus with privacy: A secret sharing method

arXiv

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arXiv 2021年

作者： Zhang, Silun Timoudas, Thomas Ohlson Dahleh, Munther Massachusetts Institute of Technology CambridgeMA02139 United States Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Electrical Engineering and Computer Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States

In this work, inspired by secret sharing schemes, we introduce a privacy-preserving approach for network consensus, by which all nodes in a network can reach an agreement on their states without exposing the individual state to neighbors. With the privacy degree defined for the agents, the proposed method makes the network resistant to the collusion of any given number of neighbors, and protects the consensus procedure from communication eavesdropping. Unlike existing works, the proposed privacy-preserving algorithm is resilient to node failures. When a node fails, the method offers the possibility of rebuilding the lost node via the information kept in its neighbors, even though none of the neighbors knows the exact state of the failing node. Moreover, it is shown that the proposed method can achieve consensus and average consensus almost surely, when the agents have arbitrary privacy degrees and a common privacy degree, respectively. To illustrate the theory, two numerical examples are presented. © 2021, CC BY.

关键词： Privacy-preserving techniques

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Hardware-Aware In Situ Learning Based on Stochastic Magnetic Tunnel Junctions

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Physical Review Applied 2022年第1期17卷 014016-014016页

作者： Jan Kaiser William A. Borders Kerem Y. Camsari Shunsuke Fukami Hideo Ohno Supriyo Datta Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47906 USA Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Sendai Japan Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara California 93106 USA Center for Innovative Integrated Electronic Systems Tohoku University Sendai Japan Center for Spintronics Research Network Tohoku University Sendai Japan Center for Science and Innovation in Spintronics Tohoku University Sendai Japan WPI-Advanced Institute for Materials Research Tohoku University Sendai Japan

One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties is a serious concern. In this paper, we show an autonomously operating circuit that performs hardware-aware machine learning utilizing probabilistic neurons built with stochastic magnetic tunnel junctions. We show that in situ learning of weights and biases in a Boltzmann machine can counter device-to-device variations and learn the probability distribution of meaningful operations such as a full adder. This scalable autonomously operating learning circuit using spintronics-based neurons could be especially of interest for standalone artificial-intelligence devices capable of fast and efficient learning at the edge.

关键词： Spintronics Stochastic inference Synapses Artificial neural networks Magnetic tunnel junctions Stochastic dynamical systems Machine learning

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