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Evolution and contributions of internet of medical things aided emergency COVID-19 health monitoring and quarantine management

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AIP Conference Proceedings 2023年第1期2603卷

作者： Shahanawaj Ahamad Melanie Elizabeth Lourens Shvets Yuriy Yurievich Chandra Kumar Dixit Ruchi Kalra Swaminathan Ramamurthy Department of Information and Computer Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Department of Human Resources Management Durban University of Technology South Africa Department of Economical Development Institute of Control of Science Trapeznikov. RAS Financial University under the Government of Russion Federation Russia Department of Physics Dr. Shakuntala Misra National Rehabilitation University Lucknow 226017 Uttar Pradesh India IIS (Deemed to Be University) Jaipur 302 020 Rajasthan India Department of Home Science Guru Nanak College Sri Muktsar Sahib 152026 Punjab India Department of Electronics and Communication Engineering Saveetha school of Engineering Chennai 602105 Tamilnadu India

The present global situation of COVID-19 pandemic has broke through regional, revolutionary, philosophical, spiritual, social, and educational barriers. By utilizing an interconnection of network, an Internet of Things enabled medical system is useful for regular monitoring of COVID-19 patients. It also aids in improving patient experience and reducing re-hospitalizations. The arrival of the IoT has effect on reducing healthcare costs and enhancing the treatment outcome of affected patients. As a result, the goal of this study is to analyze and emphasize the overall applicability of the well-established IoT concept by providing a conceptual outlay to combat the COVID-19 pandemic.

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A two-stage feature weighting method for naive bayes and its application in software defect prediction

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International Journal of Performability engineering 2018年第7期14卷 1468-1480页

作者： Ji, Haijin Huang, Song Lv, Xuewei Wu, Yaning Hui, Zhanwei Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China School of Computer Science and Technology Huaiyin Normal University Huai'an223300 China

Software defect prediction (SDP) models facilitate software practitioners to find out defect-prone software modules in software. Software practitioners can then test these defect-prone software modules with limited testing resources to minimize software defects. Among various SDP models, Naive Bayes (NB) has been widely used in SDP because of its simplicity, effectiveness and robustness. The NB classifier is an effective classification approach, especially for data sets with discrete attributes. In NB, the attributes are assumed to be independent and thus equally important. However, in common practice, the attributes of software defect data sets are usually continuous or numeric, and because they are designed for different purposes, their contributions to prediction are different. Therefore, this paper proposes a new NB method called TSWNB, which contains two stages: Feature (i.e. attribute) discretization and feature weighting. More specifically, for the stage of feature discretization, we make the comparison between two discretization methods i.e. equal-width discretization method and equal-frequency discretization method, and identify the most appropriate one. For the stage of feature weighting, we use the feature weighting technique to alleviate the equal importance assumption, which combines the obtained feature weights into the NB formula and its likelihood estimations. To evaluate the proposed method, we carry out experiments on 5 software defect data sets of NASA MDP provided by PROMISE repository. Three well-known classification algorithms and two feature weighting techniques are included for comparison. The experimental results reveal the effectiveness and practicability of the two-stage feature weighting method TSWNB. © 2018 Totem Publishers Ltd. All rights reserved.

关键词： Defects

Rely-Guarantee Reasoning about Messaging System for Autonomous Vehicles

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Rely-Guarantee Reasoning about Messaging System for Autonomo...

Theoretical Aspects of Software engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning System) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning System

Wavelength Conversion Efficiency Enhancement in Modal Phase Matched χ(2) Nonlinear Waveguides

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arXiv 2021年

作者： Kang, Dongpeng Zhang, Weiqi Helmy, Amr S. Yu, Siyuan Tan, Liying Ma, Jing School of Astronautics National Key Laboratory of Science and Technology on Tunable Laser Harbin Institute of Technology 92 West Dazhi Street Harbin150001 China The Edward S. Rogers Department of Electrical and Computer Engineering Centre for Quantum Information and Quantum Control University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada

Modal phase matching (MPM) is a widely used phase matching technique in AlxGa1−xAs and other χ(2) nonlinear waveguides for efficient wavelength conversions. The use of a non-fundamental spatial mode compensates the material dispersion but also reduces the spatial overlap of the three interacting waves and therefore limits the conversion efficiency. In this work, we develop a technique to increase the nonlinear overlap by modifying the material nonlinearity, instead of the traditional method of optimizing the modal field profiles. This could eliminate the limiting factor of low spatial overlap inherent to MPM and significantly enhance the conversion efficiency. Among the design examples provided, this technique could increase the conversion efficiency by a factor of up to ∼290 in an AlxGa1−xAs waveguide. We further show that this technique is applicable to all χ(2) material systems that utilize MPM for wavelength conversion. © 2021, CC BY.

关键词： Optical frequency conversion

Spin-phonon interaction in yttrium iron garnet

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Physical Review B 2021年第2期104卷 L020401-L020401页

作者： Kevin S. Olsson Jeongheon Choe Martin Rodriguez-Vega Guru Khalsa Nicole A. Benedek Jiaming He Bin Fang Jianshi Zhou Gregory A. Fiete Xiaoqin Li Department of Physics Center of Complex Quantum Systems The University of Texas at Austin Austin Texas 78712 USA Department of Computer and Electrical Engineering University of Maryland College Park Maryland 20740 USA Center for Dynamics and Control of Materials The University of Texas at Austin Austin Texas 78712 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA Department of Materials Science and Engineering Cornell University Ithaca New York 14853 USA Department of Mechanical Engineering The University of Texas at Austin Austin Texas 78712 USA Texas Materials Institute The University of Texas at Austin Austin Texas 78712 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Spin-phonon interaction is an important channel for spin and energy relaxation in magnetic insulators. Understanding this interaction is critical for developing magnetic insulator-based spintronic devices. Quantifying this interaction in yttrium iron garnet (YIG), one of the most extensively investigated magnetic insulators, remains challenging because of the large number of atoms in a unit cell. Here, we report temperature-dependent and polarization-resolved Raman measurements in a YIG bulk crystal. We first classify the phonon modes based on their symmetry. We then develop a modified mean-field theory and define a symmetry-adapted parameter to quantify spin-phonon interaction in a phonon-mode specific way in YIG. Based on this improved mean-field theory, we discover a positive correlation between the spin-phonon interaction strength and the phonon frequency.

关键词： Phonons Spin caloritronics Spintronics Magnetic insulators Density functional calculations Landau-Ginzburg-Wilson theory Mean field theory Pseudopotentials Raman spectroscopy

Existence theorem of finite krasner hyperfields

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arXiv 2021年

作者： Tsafack, Surdive Atamewoue Amassayoga, Ogadoa Onasanya, Babatunde O. Feng, Yuming School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing404100 China Department of Mathematics Higher Teacher Training College University of Yaounde 1 Cameroon Department of Mathematics Faculty of Science University of Yaounde 1 Cameroon Key Laboratory of Intelligent Information Processing and Control Chongqing Three China Gorges University Chongqing404100 China Department of Mathematics Uni-versity of Ibadan Nigeria School of Computer Science and Engineering Chongqing Three Gorges University China

关键词： Algebra

Master Equation Emulation and Coherence Preservation with Classical control of a Superconducting Qubit

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arXiv 2022年

作者： Vlachos, Evangelos Zhang, Haimeng Maurya, Vivek Marshall, Jeffrey Albash, Tameem Levenson-Falk, E.M. Department of Physics & Astronomy Dornsife College of Letters Arts & Sciences University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Electrical Engineering Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Physics and Astronomy Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States

Open quantum systems are a topic of intense theoretical research. The use of master equations to model a system's evolution subject to an interaction with an external environment is one of the most successful theoretical paradigms. General experimental tools to study different open system realizations have been limited, and so it is highly desirable to develop experimental tools which emulate diverse master equation dynamics and give a way to test open systems theories. In this paper we demonstrate a systematic method for engineering specific system-environment interactions and emulating master equations of a particular form using classical stochastic noise in a superconducting transmon qubit. We also demonstrate that non-Markovian noise can be used as a resource to extend the coherence of a quantum system and counteract the adversarial effects of Markovian environments. © 2022, CC BY.

关键词： Stochastic systems

Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications

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arXiv 2019年

作者： Duan, Moming Liu, Duo Chen, Xianzhang Tan, Yujuan Ren, Jinting Qiao, Lei Liang, Liang College of Computer Science Chongqing University Chongqing China Beijing Institute of Control Engineering Beijing China

Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while their private training data remains in local devices. This distributed approach is promising in the edge computing system where have a large corpus of decentralized data and require high privacy. However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications. In this paper, we demonstrate that the imbalanced distributed training data will cause accuracy degradation in FL. To counter this problem, we build a self-balancing federated learning framework call Astraea, which alleviates the imbalances by 1) Global data distribution based data augmentation, and 2) Mediator based multi-client rescheduling. The proposed framework relieves global imbalance by runtime data augmentation, and for averaging the local imbalance, it creates the mediator to reschedule the training of clients based on KullbackLeibler divergence (KLD) of their data distribution. Compared with FedAvg, the state-of-the-art FL algorithm, Astraea shows +5.59% and +5.89% improvement of top-1 accuracy on the imbalanced EMNIST and imbalanced CINIC-10 datasets, respectively. Meanwhile, the communication traffic of Astraea can be 92% lower than that of FedAvg. Copyright © 2019, The Authors. All rights reserved.

关键词： Edge computing

Astraea: Self-Balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications

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Astraea: Self-Balancing Federated Learning for Improving Cla...

IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Moming Duan Duo Liu Xianzhang Chen Yujuan Tan Jinting Ren Lei Qiao Liang Liang College of Computer Science Chongqing University Chongqing China Beijing Institute of Control Engineering Beijing China

Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while their private training data remains in local devices. This distributed approach is promising in the edge computing system where have a large corpus of decentralized data and require high privacy. However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications. In this paper, we demonstrate that the imbalanced distributed training data will cause accuracy degradation in FL. To counter this problem, we build a self-balancing federated learning framework call Astraea, which alleviates the imbalances by 1) Global data distribution based data augmentation, and 2) Mediator based multi-client rescheduling. The proposed framework relieves global imbalance by runtime data augmentation, and for averaging the local imbalance, it creates the mediator to reschedule the training of clients based on Kullback-Leibler divergence (KLD) of their data distribution. Compared with FedAvg, the state-of-the-art FL algorithm, Astraea shows +5.59% and +5.89% improvement of top-1 accuracy on the imbalanced EMNIST and imbalanced CINIC-10 datasets, respectively. Meanwhile, the communication traffic of Astraea can be 92% lower than that of FedAvg.

关键词： Training Training data Distributed databases Deep learning Mobile handsets Neural networks Data models