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arXiv 2021年

作者： Troop, Dylan Godin, Frédéric Yu, Jia Yuan Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Mathematics and Statistics Concordia University Montreal Canada Concordia Institute of Information Systems Engineering Concordia University Montreal Canada Quantact Actuarial and Financial Mathematics Laboratory Montreal Canada

The conditional value-at-risk (CVaR) is a useful risk measure in fields such as machine learning, finance, insurance, energy, etc. When measuring very extreme risk, the commonly used CVaR estimation method of sample averaging does not work well due to limited data above the value-at-risk (VaR), the quantile corresponding to the CVaR level. To mitigate this problem, the CVaR can be estimated by extrapolating above a lower threshold than the VaR using a generalized Pareto distribution (GPD), which is often referred to as the peaks-over-threshold (POT) approach. This method often requires a very high threshold to fit well, leading to high variance in estimation, and can induce significant bias if the threshold is chosen too low. In this paper, we derive a new expression for the GPD approximation error of the CVaR, a bias term induced by the choice of threshold, as well as a bias correction method for the estimated GPD parameters. This leads to the derivation of a new estimator for the CVaR that we prove to be asymptotically unbiased. In a practical setting, we show through experiments that our estimator provides a significant performance improvement compared with competing CVaR estimators in finite samples. As a consequence of our bias correction method, it is also shown that a much lower threshold can be selected without introducing significant bias. This allows a larger portion of data to be be used in CVaR estimation compared with the typical POT approach, leading to more stable estimates. As secondary results, a new estimator for a second-order parameter of heavy-tailed distributions is derived, as well as a confidence interval for the CVaR which enables quantifying the level of variability in our estimator. Copyright © 2021, The Authors. All rights reserved.

关键词： Value engineering

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

arXiv

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arXiv 2023年

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023, CC BY-NC-ND.

关键词： Edge computing

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Offsite Evaluation of Localization systems: Criteria, systems, and Results From IPIN 2021 and 2022 Competitions

IEEE Journal on Indoor and Seamless Positioning and Navigati...

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IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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Spatial Computing: Concept, Applications, Challenges and Future Directions

arXiv

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arXiv 2024年

作者： Yenduri, Gokul Ramalingam, M. Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Bandi, Ajay Chen, Junxin Wang, Wei Shirawalmath, Adarsh Arunkumar Ravishankar, Raghav Wang, Weizheng School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu 632014 India Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China College of Information Science and Engineering Jiaxing University Jiaxing314001 China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Computer Science and Information Systems Northwest Missouri State University 800 University Dr MaryvilleMO64468 United States School of Software Dalian University of Technology Dalian116621 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Vellore Institute of Technology TamilNadu 632014 India Department of Computer Science City University of Hong Kong Hong Kong

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2024, CC BY.

关键词： Augmented reality

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Endogenous Security Techniques for Safeguarding Next-Generation Spectrum Usage at the Physical Layer

引用

IEEE Network 2025年

作者： Li, Guyue Zhou, Yajie Sun, Yingjie Wen, Shiping Wang, Shuai Hu, Aiqun Wen, Guanghui Southeast University School of Cyber Science and Engineering Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China University of Technology Sydney Australian AI Institute Faculty of Engineering and Information Technology NSW2007 Australia Beihang University School of Computer Science and Engineering Beijing100191 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University School of Mathematics Department of Systems Science Nanjing211189 China

The rapid increase in the data transmission rates for next generation communication systems has intensified the demand for spectrum, straining limited resources under traditional management. This has spurred a move toward dynamic spectrum sharing and exploration of higher frequency bands. Although spectrum sharing enables other users to access the underutilized whitespace spectrum, it also raises additional security concerns. The possibility of attacks at higher carrier frequencies has not yet been well characterized. To address these challenges, endogenous properties of wireless communication systems can be explored as new opportunities for security design. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art wireless endogenous security techniques that guarantee wireless security and privacy for spectrum usage at the physical layer, along with the principles, solutions, and future research trends. © 1986-2012 IEEE.

关键词： next-generation spectrum usage physical layer security spectrum sharing wireless endogenous security

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Novel symmetric numerical methods for solving symmetric mathematical problems

引用

International Journal of Circuits, systems and Signal Processing 2021年 15卷 1545-1557页

作者： Ibrahimov, V.R. Mehdiyeva, G.Y.U. Yue, Xiao-Guang Kaabar, Mohammed K.A. Noeiaghdam, Samad Juraev, Davron Aslonqulovich Institute of Control Systems named after Academician A.Huseynov BakuAZ1141 Azerbaijan Computational mathematics Baku State University BakuAZ1148 Azerbaijan Department of Computer Science and Engineering School of Sciences European University Cyprus Nicosia1516 Cyprus Institute of Mathematical Sciences Faculty of Science University of Malaya Kuala Lumpur50603 Malaysia Department of Mathematics and Statistics Washington State University PullmanWA99163 United States Industrial Mathematics Laboratory Baikal School of BRICS Irkutsk National Research Technical University Irkutsk664074 Russia Department of Applied Mathematics and Programming South Ural State University Lenin Prospect 76 Chelyabinsk454080 Russia Department of Natural Science Disciplines Higher Military Aviation School of the Republic of Uzbekistan Karshi City180100 Uzbekistan

—The mathematical model for many problems is arising in different industries of natural science, basically formulated using differential, integral and integro-differential equations. The investigation of these equations is conducted with the help of numerical integration theory. It is commonly known that a class of problems can be solved by applying numerical integration. The construction of the quadrature formula has a direct relation with the computation of definite integrals. The theory of definite integrals is used in geometry, physics, mechanics and in other related subjects of science. In this work, the existence and uniqueness of the solution of above-mentioned equations are investigated. By this way, the domain has been defined in which the solution of these problems is equivalent. All proposed four problems can be solved using one and the same methods. We define some domains in which the solution of one of these problems is also the solution of the other problems. Some stable methods with the degree p © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： Integrodifferential equations

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Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing

Multi-Relational Graph Neural Architecture Search with Fine-...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Xin Zheng Miao Zhang Chunyang Chen Chaojie Li Chuan Zhou Shirui Pan Department of Software Systems and Cybersecurity Monash University Australia School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China School of Electrical Engineering and Telecommunications University of New South Wales Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Australia

Graph neural architecture search (NAS) has gained great popularity in automatically designing powerful graph neural networks (GNNs) with superior learning abilities, significantly relieving human effort and expertise reliance. Despite the advanced performance of automated learning, existing graph NAS models mainly work on single-relational graphs, while the widespread multi-relational graphs in real-world applications, are not well addressed. Moreover, current search spaces of automated GNNs are generally coarse-grained by simply integrating typical GNN layers and hyper-parameters, resulting in severe limitations on search capacities and scopes for creating innovative GNN architectures. To tackle the limitations of single-relational setting and coarse-grained search space design in existing graph NAS, in this paper, we propose a novel framework of multi-relational graph neural architecture search, dubbed MR-GNAS, to automatically develop innovative and excellent multi-relational GNN architectures. Specifically, to enlarge search capacities and improve search flexibility, MR-GNAS contains a fine-grained search space that embraces the full-pipe multi-relational message passing schema, enabling expressive architecture search scopes. With the well-designed fine-grained search space, MR-GNAS constructs a relation-aware supernet with a tree topology, to jointly learn discriminative node and relation representations. By searching with a gradient-based strategy in the supernet, the proposed MR-GNAS could derive excellent multi-relational GNN architectures in multi-relational graph analysis. Extensive experiments on entity classification and link prediction tasks over multi-relational graphs illustrate the effectiveness and superiority of the proposed method.

关键词： Message passing Graph neural networks Topology Data mining Task analysis

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A Numerical Method to Analyze Electromagnetic Scattering from Composite Plasmonic Structures Involving a Metal-Metal Interface

A Numerical Method to Analyze Electromagnetic Scattering fro...

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Antennas and Propagation Society International Symposium

作者： Doolos Aibek Uulu Rui Chen Liang Chen Ping Li Hakan Bagci Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Mathematics and Natural Sciences University of Central Asia (UCA) Naryn Kyrgyzstan Key Laboratory of Ministry of Education of Design and Electromagnetic Compatibility of High-Speed Electronic Systems Shanghai Jiao Tong University Shanghai China

A coupled system of volume integral and hydrodynamic equations is formulated to analyze electromagnetic scattering from composite plasmonic structures involving a metal-metal interface. This coupled system in unknowns electric flux and free electron velocity is discretized using Schaubert-Wilton-Glisson basis functions. Inserting these expansions into the coupled system of equations and applying Galerkin testing yield a matrix equation. Two-level iterative scheme is used to solve this matrix equation for the unknown expansion coefficients. To demonstrate the accuracy of the proposed method, numerical results are compared to those obtained using an extended Mie series approach that takes into account the hydrodynamic equation and the boundary conditions it requires.

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

arXiv

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arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms

arXiv

引用

arXiv 2023年

作者： Ponce, Moises Herrman, Rebekah Lotshaw, Phillip C. Powers, Sarah Siopsis, George Humble, Travis Ostrowski, James Department of Industrial and Systems Engineering University of Tennessee KnoxvilleTN37996 United States Quantum Computational Science Group Oak Ridge National Laboratory Oak RidgeTN37830 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Physics Astronomy University of Tennessee KnoxvilleTN37996-1200 United States Quantum Science Center Oak Ridge National Laboratory Oak RidgeTN37830 United States

The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current noisy quantum devices cannot solve large problems due to hardware constraints. In this work, we develop an algorithm that decomposes the QAOA input problem graph into a smaller problem and solves MaxCut using QAOA on the reduced graph. The algorithm requires a subroutine that can be classical or quantum–in this work, we implement the algorithm twice on each graph. One implementation uses the classical solver Gurobi in the subroutine and the other uses QAOA. We solve these reduced problems with QAOA. On average, the reduced problems require only approximately 1/10 of the number of vertices than the original MaxCut instances. Furthermore, the average approximation ratio of the original MaxCut problems is 0.75, while the approximation ratios of the decomposed graphs are on average of 0.96 for both Gurobi and QAOA. With this decomposition, we are able to measure optimal solutions for ten 100-vertex graphs by running single-layer QAOA circuits on the Quantinuum trapped-ion quantum computer H1-1, sampling each circuit only 500 times. This approach is best suited for sparse, particularly k-regular graphs, as k-regular graphs on n vertices can be decomposed into a graph with at most knk+1 vertices in polynomial time. Further reductions can be obtained with a potential trade-off in computational time. While this paper applies the decomposition method to the MaxCut problem, it can be applied to more general classes of combinatorial optimization problems. © 2023, CC BY.

关键词： Economic and social effects

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