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Lecture Notes on Data engineering and Communications Technologies 2024年 222卷 v-viii页

作者： Faure, Emil Tryus, Yurii Vartiainen, Tero Danchenko, Olena Bondarenko, Maksym Bazilo, Constantine Zaspa, Grygoriy Department of Information Security and Computer Engineering Cherkasy State Technological University Cherkasy Ukraine Department of Computer Science and Systems Analysis Cherkasy State Technological University Cherkasy Ukraine Department of Computing Sciences University of Vaasa Vaasa Finland Department of Instrumentation Mechatronics and Computerized Technologies Cherkasy State Technological University Cherkasy Ukraine Department of Software of Automated System Cherkasy State Technological University Cherkasy Ukraine

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The Role of Masks in Mitigating Viral Spread on Networks

arXiv

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arXiv 2021年

作者： Tian, Yurun Sridhar, Anirudh Wu, Chai Wah Levin, Simon A. Carley, Kathleen M. Poor, H. Vincent Yağan, Osman Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Thomas J. Watson Research Center IBM Yorktown HeightsNY10598 United States Department of Ecology & Evolutionary Biology Princeton University PrincetonNJ08544 United States Software and Societal Systems School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.g., cloth or surgical). We derive analytical expressions for three key epidemiological quantities: the probability of emergence, the epidemic threshold, and the expected epidemic size. In particular, we show how the aforementioned quantities depend on the structure of the contact network, viral transmission dynamics, and the distribution of the different types of masks within the population. Through extensive simulations, we then investigate the impact of different allocations of masks within the population and trade-offs between the outward efficiency and inward efficiency of the masks. Interestingly, we find that masks with high outward efficiency and low inward efficiency are most useful for controlling the spread in the early stages of an epidemic, while masks with high inward efficiency but low outward efficiency are most useful in reducing the size of an already large spread. Lastly, we study whether degree-based mask allocation is more effective in reducing the probability of epidemic as well as epidemic size compared to random allocation. The result echoes the previous findings that mitigation strategies should differ based on the stage of the spreading process, focusing on source control before the epidemic emerges and on self-protection after the emergence. Copyright © 2021, The Authors. All rights reserved.

关键词： Efficiency

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks

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Procedia computer Science 2021年 191卷 328-336页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications’ Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings.

关键词： Cloud computing Computation offloading Edge computing Energy-efficiency Machine learning Queuing theory Support Vector Machine (SVM) Vehicular network

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Towards a circular economy of industrial software 27

Towards a circular economy of industrial software

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27th CIRP Life Cycle engineering Conference, LCE 2020

作者： Kutscher, Vladimir Ruland, Sebastian Müller, Patrick Wasser, Nathan Lochau, Malte Anderl, Reiner Schürr, Andy Mezini, Mira Hähnle, Reiner Department of Computer Integrated Design TU Darmstadt Otto-Berndt-Str. 2 Darmstadt64287 Germany Real-Time Systems Lab TU Darmstadt Magdalenenstr. 4 Darmstadt64289 Germany Software Technology Group TU Darmstadt Hochschulstr. 10 Darmstadt64287 Germany Software Engineering Group TU Darmstadt Hochschulstr. 10 Darmstadt64287 Germany

software has become an indispensable part of industrial production and thus influences the life cycle of manufacturing systems, as many of these systems have to be replaced or evolved due to changing requirements. software adaptation through continuous evolution extends the service time of these systems and thus saves valuable resources. In this paper we present an interdisciplinary methodology for reengineering legacy software to increase the productive lifetime. Our proposed approach systematically reuses implicit domain knowledge to evolve, validate and commission new software from legacy code. The approach is evaluated on a CNC machine as a special type of industrial system. © 2020 The Author(s). Published by Elsevier B.V.

关键词： Reengineering

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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

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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...

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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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Evidence-Based Quality-Aware Agile software Development Process: Design and Evaluation

SSRN

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SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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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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