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18th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned 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 Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 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. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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Microgrid Design Toolkit Evaluation and Trade-offs Analysis for Rural Community in Cayey Puerto Rico

Microgrid Design Toolkit Evaluation and Trade-offs Analysis ...

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IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America)

作者： Rolando J. Tremont-Brito Erick Aponte-Bezares Lionel R. Orama-Exclusa Rachid Darbali-Zamora Amaury J. Malave-Sanabria Department of Electrical and Computer Engineering University of Puerto Rico-Mayagüez Mayagüez Puerto Rico USA Renewable Energy and Distributed Systems Integration Sandia National Laboratories Albuquerque New Mexico USA Department of Engineering University Ana G. Méndez-Gurabo Gurabo Puerto Rico USA

Puerto Rico's power infrastructure was devastated by the passage of Hurricanes Irma and Maria in 2017. It highlighted the lack of resilience of the system and left the island with unreliable electric service for years after. Community leaders are looking for reliable alternatives to supply their energy needs. Three microgrid design alternatives are evaluated for a rural community in a mountainous region in Puerto Rico. The three alternatives consider centralized and decentralized options for diesel generation and energy storage in lithium-ion batteries, while having distributed roof-mounted photovoltaic generation. The trade-off space of solutions for the three alternatives is presented in the form of a Pareto frontier. Optimization results for the centralized alternative slightly favor diesel and cost seems attractive. Less aggregated alternatives favor solar generation, making it the preferred alternative, especially to take advantage of Puerto Rico's net energy metering program.

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Code Smell Detection Using Whale Optimization Algorithm

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computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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Accelerating Wireless Federated Learning via Nesterov's Momentum and distributed Principle Component Analysis

arXiv

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arXiv 2023年

作者： Dong, Yanjie Wang, Luya Chi, Yuanfang Wang, Jia Zhang, Haijun Yu, Fei Richard Leung, Victor C.M. Hu, Xiping The Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China The College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China The Department of Systems and Computer Engineering Carleton University Ottawa Canada The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks. © 2023, CC BY.

关键词： Momentum

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Groove Gap Waveguide Crossover for Butler Matrices and Beamforming in Millimeter-Wave Satellite Antenna systems

Groove Gap Waveguide Crossover for Butler Matrices and Beamf...

引用

International Conference on Radioelektronika

作者： Mohammad Alibakhshikenari Peiman Parand Ali Zidour Bal S. Virdee Lida Kouhalvandi Patrick Longhi Takfarinas Saber Ernesto Limiti Electronics Engineering Department University of Rome “Tor Vergata” Rome Italy Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara Boumerdes Algeria Center for Communications Technology London Metropolitan University London UK Department of Electrical and Electronics Engineering Dogus University Istanbul Turkiye Lero Science Foundation Ireland Centre for Software School of Computer Science University of Galway Galway Ireland

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

This paper presents an innovative H-plane crossover based on groove gap-waveguide (GGW) technology for high-performance millimeter-wave (mm-wave) circuits. The design facilitates the development of key transmission components, such as Butler matrices (BMs) and beamforming feeding networks (BFNs), for multi-beam antenna systems operating in the V-band spectrum (40–50 GHz). The proposed crossover is built by cascading two identical 3-dB/90° hybrid couplers. Each coupler is designed with GGW unit-cells constructed from metallic pins spaced less than a quarter-wavelength apart. This configuration creates a wide stopband of 20–57 GHz, ensuring minimal signal interference and strong impedance matching. The coupler achieves 90° phase shift, 50 dB isolation, and low insertion loss of 0.02 dB at 45 GHz, with a fractional bandwidth of 22.22%. The crossover demonstrates excellent performance over the entire V-band, making it suitable for advanced antenna systems in satellite communications and space applications. The design reduces complexity, cost, and losses typically associated with 3D and multilayer crossover technologies, providing a compact and efficient solution for mm-wave networks.

关键词： Butler matrices Satellite antennas Millimeter wave circuits Three-dimensional displays Array signal processing Impedance matching Transmitting antennas Gap waveguide Couplers Millimeter wave communication

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A New Application of Machine Learning: Detecting Errors in Network Simulations

A New Application of Machine Learning: Detecting Errors in N...

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Simulation Winter Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University St. Oxford OH USA

ISBN: (纸本)9781665476621

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

关键词： Training Machine learning algorithms Computational modeling Aggregates Stochastic processes Machine learning Transforms

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A Proof of Kirchhoff's First Law for Hyperbolic Conservation Laws on Networks

arXiv

引用

arXiv 2022年

作者： Bayen, Alexandre M. Keimer, Alexander Müller, Nils Department of Electrical Engineering and Computer Sciences Department of Civil and Environmental Engineering University of California Berkeley United States Germany Max Planck Institute for Software Systems Saarland Informatics Campus Germany

Networks are essential models in many applications such as information technology, chemistry, power systems, transportation, neuroscience, and social sciences. In light of such broad applicability, a general theory of dynamical systems on networks may capture shared concepts, and provide a setting for deriving abstract properties. To this end, we develop a calculus for networks modeled as abstract metric spaces and derive an analog of Kirchhoff’s first law for hyperbolic conservation laws. In dynamical systems on networks, Kirchhoff’s first law connects the study of abstract global objects, and that of a computationally-beneficial edgewise-Euclidean perspective by stating its equivalence. In particular, our results show that hyperbolic conservation laws on networks can be stated without explicit Kirchhoff-type boundary *** Codes 35R02, 35C99 (Primary) 35L65, 00A71 (Secondary) © 2022, CC BY.

关键词： Dynamical systems

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Role of masks in mitigating viral spread on networks

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Physical Review E 2023年第1期108卷 014306-014306页

作者： Yurun Tian Anirudh Sridhar Chai Wah Wu Simon A. Levin Kathleen M. Carley H. Vincent Poor Osman Yağan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08544 USA Thomas J. Watson Research Center IBM Yorktown Heights New York 10598 USA Department of Ecology & Evolutionary Biology Princeton University Princeton New Jersey 08544 USA Software and Societal Systems School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA

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 tradeoffs 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. Last, 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.

关键词： Cascades Epidemic Percolation phase transition Spreading Multilayer & multiplex networks Poisson degree distribution networks Random graphs Social networks SIR model Spreading models Uncorrelated percolation

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Impact of Key Technological Parameters on Sulfur Production Processes

Impact of Key Technological Parameters on Sulfur Production ...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Batyr Orazbayev Ainur Zhumadillayeva Kulman Orazbayeva Nazgul Kurbangalieva Department of Systems Analysis and Control L.NGumilyov Eurasian National University Astana Kazakhstan Department of Computer and Software Engineering L.NGumilyov Eurasian National University Astana Kazakhstan Department of Management Esil University Astana Kazakhstan L.NGumilyov Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The key technological parameters of the sulfur production unit have been identified, and their influence on the sulfur production processes has been investigated. In this process, the released hydrogen can be used as hydrogen energy. Based on the results of the experimental studies, graphs have been constructed showing the effects of the unit's parameters that significantly impact the processes of converting hydrogen sulfide into produced sulfur. By approximating the obtained graphical dependencies, polynomial equations have been derived that describe the established relationships of the key technological parameters of the sulfur production unit on the sulfur production process. The derived equations describing the relationships between the main parameters of the installation and the degree of hydrogen sulfide conversion into sulfur form the basis of mathematical models that enable computer simulation of various operating modes of the installation and the selection of its optimal operating modes. The originality of the obtained results lies in the development of graphical dependencies and mathematical models that describe the influence of input parameters of the studied object on the sulfur production processes.

关键词： Temperature dependence Renewable energy sources Computational modeling Hydrogen Production Mathematical models Raw materials Polynomials Sulfur Inductors

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Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction

Comparative Analysis of Machine Learning Models for Diabetes...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Ismail, Leila Materwala, Huned United Arab Emirates University College of Information Technology Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu-Dhabi Al-Ain15551 United Arab Emirates

ISBN: (纸本)9781728176246

Diabetes is one of the top 10 causes of death worldwide. Health professionals are aiming for machine learning models to support the prognosis of diabetes for better healthcare and to put in place an effective prevention plan. In this paper, we conduct a comparative analysis of the most used machine learning models in the literature to predict the prevalence of diabetes mellitus type 2. We evaluate the models in terms of accuracy, F-measure and execution time with and without feature selection using a real-life diabetes dataset. The detailed analysis is in the paper. © 2020 IEEE.

关键词： Medical informatics

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