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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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Automatic Personality Prediction;an Enhanced Method Using Ensemble Modeling

arXiv

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arXiv 2020年

作者： Ramezani, Majid Feizi-Derakhshi, Mohammad-Reza Balafar, Mohammad-Ali Asgari-Chenaghlu, Meysam Feizi-Derakhshi, Ali-Reza Nikzad-Khasmakhi, Narjes Ranjbar-Khadivi, Mehrdad Jahanbakhsh-Nagadeh, Zoleikha Zafarani-Moattar, Elnaz Rahkar-Farshi, Taymaz Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering University of Tabriz Iran Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Altinbas University Istanbul Turkey

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP. © 2020, CC BY-NC-SA.

关键词： Forecasting

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A Raspberry Pi-based Traumatic Brain Injury detection system for single-channel Electroencephalogram

arXiv

引用

arXiv 2021年

作者： Dhillon, Navjodh Singh Sutandi, Agustinus Vishwanath, Manoj Lim, Miranda M. Cao, Hung Si, Dong Computing and Software Systems University of Washington BothellWA United States Department of Electrical Engineering and Computer Science University of California IrvineCA United States VA Portland Health Care System PortlandOR United States Department of Neurology Oregon Health & Science University PortlandOR United States Department of Biomedical Engineering University of California IrvineCA United States

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16 s - 64 s epochs for TBI vs control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems. © 2021, CC BY.

关键词： Brain

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Highway Traffic Classification for the Perception Level of Situation Awareness 15th

Highway Traffic Classification for the Perception Level of S...

引用

15th International Symposium on Visual Computing, ISVC 2020

作者： Nine, Julkar Manoharan, Shanmugapriyan Sapkota, Manoj Saleh, Shadi Hardt, Wolfram Department of Computer Engineering Technische Universität Chemnitz Straße der Nationen 62 Chemnitz09111 Germany Embedded Systems Technische Universität Chemnitz Straße der Nationen 62 Chemnitz09111 Germany Automotive Software Engineering Technische Universität Chemnitz Straße der Nationen 62 Chemnitz09111 Germany

ISBN: (纸本)9783030645588

The automotive industry is rapidly moving towards the highest level of autonomy. However, one of the major challenges for highly autonomous vehicles is the differentiation between driving modes according to different driving situations. Different driving zones have different driving safety regulations. For example, German traffic regulations require a higher degree of safety measurements for highway driving. Therefore, a classification of the different driving scenarios on a highway is necessary to regulate these safety assessments. This paper presents a novel vision-based approach to the classification of German highway driving scenarios. We develop three different and precise algorithms utilizing image processing and machine learning approaches to recognize speed signs, traffic lights, and highway traffic signs. Based on the results of these algorithms, a weight-based classification process is performed, which determines the current driving situation either as a highway driving mode or not. The main goal of this research work is to maintain and to ensure the high safety specifications required for the German highway. Finally, the result of this classification process is provided as an extracted driving scenario-based feature on the perceptual level of a system known as situation awareness to provide a high level of driving safety. This study was realized on a custom-made hardware unit called "CE-Box", which was developed at the department of computer engineering at TU Chemnitz as an automotive test solution for testing automotive software applications on an embedded hardware unit. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

arXiv

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arXiv 2021年

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar Xue, Ruini Song, Liang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Laboratory Department of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning (DL) and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling. © 2021, CC BY.

关键词： Deep reinforcement learning

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Decentralized blockchain-based model for edge computing

arXiv

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arXiv 2021年

作者： Al-Rakhami, Mabrook S. Gumaei, Abdu Rahman, Sk.Md. Mizanur Al-Amri, Atif King Saud University Riyadh11543 Saudi Arabia Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Centennial College TorontoONM1G 3T8 Canada Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Blockchain technology is among the fastest-growing technologies in the world today. It has been adopted in diverse areas but mostly in financial systems, such as Bitcoin cryptocurrency. Therefore, it is a niche that has attracted interest from researchers from various fields, including computer science. Other areas where Blockchain is being embraced are the Smart Grid and Internet of Things (IoT) technologies, among others. While it is all good and improving many areas of applications, Blockchain still has some shortcomings. For example, it is not designed for high scalability when accommodating normal transactions. On the other hand, a parallel technology that has diverse applications in distributednetworks better known as edge computing has emerged. Its main advantage is that it increases the speed of pf processes within those networks. However, like Blockchain, edge computing has its shortcomings. Its security systems and management systems have been foundto be wanting. Hence the idea to integrate the two technologies and take advantage of their strengths. A blend of the two would lead to advanced network servers, huge data storage, and heightened security in transactions. However, this integration will best happen when some measures are taken. For example, there is a need to address scalability, resource management satisfactorily, and the security of the systems. To solve the integration problem, a decentralized Blockchain-based model of Edge computing is proposed in this paper. © 2021, CC BY.

关键词： Blockchain

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A human vision system-based approach to improve computing efficiency in information systems

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International Journal of Information systems and Change Management 2019年第1期11卷 44-55页

作者： Li, Jianxun Chen, Jim Q. Li, Weiqian Department of Management Science and Engineering School of Economics and Management Xi'an University of Technology Xi'an China Department of Information Systems Herberger Business School St. Cloud State University St. CloudMN United States Department of Software Engineering School of Computer Science Xi'an Polytechnic University Xi'an China

While a lot of research has been done in system design, reducing computational complexity is still a core problem in information systems. In order to break through the bottleneck of computing efficiency, this paper draws on the powerful processing capability of the human vision system (HVS) to propose a pixel domain computing framework. It converts the original problem into pixel operations, and pixels are taken as the basic unit to change the construction process of data analysis. The framework significantly decreases computational complexity, simplifies programming logic, and enhances the automaticity of information system. Our results demonstrate that the efficiency of the containment test is improved by 30 times, the logic complexity of the intersection area measurement is reduced by a factor of five, and the automaticity of root finding is significantly heightened. © 2019 Inderscience Enterprises Ltd.

关键词： Information systems

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Enhancing data harvesting systems: Performance quantification of Cloud–Edge-sensor networks using queueing theory

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ICT Express 2025年

作者： Rocha, Jose Wanderlei Gomes, Eder Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Callou, Gustavo Silva, Francisco Airton Choi, Eunmi Nguyen, Tuan Anh Min, Dugki Lee, Jae-Woo Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piaui (UFPI) Picos Piaui R. Cicero Duarte No. 905 - Junco 64607-670 Brazil School of Software College of Computer Science Kookmin University Seoul 02707 South Korea Department of Software Engineering Faculty of Information Technology Ho Chi Minh City University of Industry and Trade (HUIT) Ho Chi Minh City 700000 Viet Nam Department of Artificial Intelligence Graduate School Konkuk University Seoul 05029 South Korea Department of Computer Science and Engineering College of Engineering Konkuk University Seoul 05029 South Korea Department of Aerospace Engineering Konkuk University Seoul 05029 South Korea

This study investigates a Cloud–Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configuration impacts on performance. The model significantly enhances efficiency and scalability, minimizing the need for extensive physical infrastructure. Analysis shows over 90% utilization in both layers, highlighting the model's applicability to various IoT applications. The M/M/c/K queuing model addresses scalability and real-time data processing challenges in agricultural cloud–edge-sensor networks, improving over traditional methods lacking dynamic scalability. Designed for optimized resource use and reduced data handling delays, this model proves crucial in precision agriculture, where timely data is essential for decision-making. Its versatility extends to various agricultural applications requiring efficient real-time analysis and resource management. © 2025 The Authors

关键词： Agricultural monitoring system Data harvesting Performance Queuing model Sensors

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Comparative analysis of time series model and machine testing systems for crime forecasting

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Neural Computing and Applications 2020年第17期33卷 10621-10636页

作者： Jha, Sudan Yang, Eunmok Almagrabi, Alaa Omran Bashir, Ali Kashif Joshi, Gyanendra Prasad School of Computer Science and Engineering Lovely Professional University Phagwara India School of Software Soongsil University Seoul South Korea Department of Information Systems King Abdul Aziz University Jeddah Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University Manchester UK School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Computer Science and Engineering Sejong University Seoul South Korea

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

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Machine Knowledge and Human Cognition

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Big Data Mining and Analytics 2020年第4期3卷 292-299页

作者： Fashen Li Lian Li Jianping Yin Liang Huang Qingguo Zhou Ning An Yong Zhang Li Liu Jialin Zhang Kun Kuang Lei Yang Zhixi Wu Lianchun Yu the School of Physical Science and Technology Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China the School of Cyber Science and Engineering Dongguan University of TechnologyDongguan 523808China the Institute of Computational Physics and Complex Systems Lanzhou UniversityLanzhou 730000China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China the Department of Physics Xiamen UniversityXiamen 361005China the School of Big Data&Software Engineering Chongqing UniversityChongqing 400044China the Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China the College of Computer Science and Technology Zhejiang UniversityHangzhou 310000China the Institute ofModern Physics Chinese Academy of SciencesLanzhou 730000China

Intelligent machines are knowledge systems with unique knowledge structure and *** this paper,we discuss issues including the characteristics and forms of machine knowledge,the relationship between knowledge and human cognition,and the approach to acquire machine *** issues are of great significance to the development of artificial intelligence.

关键词： intelligent machine machine knowledge human cognition knowledge interpretation principle of functional similarity Probable Approximative Correction(PAC)model

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