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A computer-aided feature-based encryption model with concealed access structure for medical Internet of Things

Decision Analytics Journal 2023年 7卷

作者： Vaidya, Sumit Suri, Ashish Batla, Vishnu Keshta, Ismail Ajibade, Samuel-Soma M. Safarov, Giyosiddin Department of Electronics and Communication Engineering Medicaps University AB Road Pigdambar Rau Madhya Pradesh Indore 453331 India School of Electronics and Communication Engineering Shri Mata Vaishno Devi University Katra India Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad (MNNIT Allahabad) Uttar Pradesh Prayagraj India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Tashkent Institute of Finance Tashkent Uzbekistan

One of the Internet of Things (IoT) security issues is the secure sharing and granular management of data access. This study recommends a feature-based encryption scheme with a hidden access structure for medical IoT data security. While establishing fine-grained access control of ciphertext data, the system can guarantee clinical client data privacy. First, it is recommended to convert identity-based encryption (IBE) into a feature-based encryption model (FBEM) using a universal conversion technique that supports multi-valued attributes and gates. IBE characteristics could be inherited by the converted FBEM. The conversion method is then used to change the receiver anonymous IBE scheme into the FBEM scheme with concealed access structure. The FBEM model is then used to construct the IoT scenario for the smart medical application. Theoretical analysis and experimental findings reveal that the suggested system provides advantages over prominent systems regarding computing efficiency, storage load, and security when the access structure is disguised. © 2023 The Author(s)

关键词： Clinical consumer Data privacy Feature-based encryption model Internet of Things (IoT) Smart healthcare

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Reliable Extrapolation of Deep Neural Operators Informed by Physics or Sparse Observations

SSRN

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SSRN 2023年

作者： Zhu, Min Zhang, Handi Jiao, Anran Karniadakis, George Em Lu, Lu Department of Chemical and Biomolecular Engineering University of Pennsylvania PhiladelphiaPA19104 United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States School of Engineering Brown University ProvidenceRI02912 United States

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias-variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information-the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed. © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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Tunable Source of Quantum-Correlated Photons with Integrated Pump Rejection in a Silicon CMOS Platform

Tunable Source of Quantum-Correlated Photons with Integrated...

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Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Fargas Cabanillas, Josep M. Kramnik, Danielius Ramesh, Anirudh Gentry, Cale M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

ISBN: (纸本)9781557528209

A wavelength-tunable, silicon photon-pair source based on spontaneous four-wave mixing, integrated with a pump rejection filter in a single, flip-chip packaged CMOS chip, is demonstrated with a coincidence-to-accidentals ratio of 9.1 with no off-chip pump filtering. Frontiers in Optics / Laser science © Optica Publishing Group 2021.

关键词： CMOS integrated circuits

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Exploring ququart computation on a transmon using optimal control

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Physical Review A 2023年第6期108卷 062609-062609页

作者： Lennart Maximilian Seifert Ziqian Li Tanay Roy David I. Schuster Frederic T. Chong Jonathan M. Baker Department of Computer Science University of Chicago Chicago Illinois 60637 USA James Franck Institute University of Chicago Chicago Illinois 60637 USA Department of Physics University of Chicago Chicago Illinois 60637 USA Department of Applied Physics Stanford University Stanford California 94305 USA Pritzker School of Molecular Engineering University of Chicago Chicago Illinois 60637 USA Duke Quantum Center Duke University Durham North Carolina 27701 USA

Contemporary quantum computers encode and process quantum information in binary qubits (d=2). However, many architectures include higher energy levels that are left as unused computational resources. We demonstrate a superconducting ququart (d=4) processor and combine quantum optimal control with efficient gate decompositions to implement high-fidelity ququart gates. We distinguish between viewing the ququart as a generalized four-level qubit and an encoded pair of qubits, and characterize the resulting gates in each case. In randomized benchmarking experiments we observe gate fidelities ≥95% and identify coherence as the primary limiting factor. Our results validate ququarts as a viable tool for quantum information processing.

关键词： Quantum benchmarking Quantum control Quantum gates Quantum information processing Quantum information with solid state qubits Quantum tomography Qudits Superconducting qubits

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The Adaptive Spectral Koopman Method for Dynamical Systems

arXiv

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arXiv 2022年

作者： Li, Bian Ma, Yi-An Kutz, J. Nathan Yang, Xiu Department of Industrial and Systems Engineering Lehigh University BethlehemPA United States Halicioğlu Data Science Institute Department of Computer Science and Engineering University of California San Diego San DiegoCA United States Department of Applied Mathematics University of Washington SeattleWA United States

Dynamical systems have a wide range of applications in mechanics, electrical engineering, chemistry, and so on. In this work, we propose the adaptive spectral Koopman (ASK) method to solve nonlinear autonomous dynamical systems. This novel numerical method leverages the spectral-collocation (i.e., pseudo-spectral) method and properties of the Koopman operator to obtain the solution of a dynamical system. Specifically, this solution is represented as a linear combination of the multiplication of Koopman operator’s eigenfunctions and eigenvalues, and these eigenpairs are approximated by the spectral method. Unlike conventional time evolution algorithms such as Euler’s scheme and the Runge-Kutta scheme, ASK is mesh-free, and hence is more flexible when evaluating the solution. Numerical experiments demonstrate high accuracy of ASK for solving one-, two- and three-dimensional dynamical *** Codes 37L65 (primary) 65L60 (secondary) © 2022, CC BY.

关键词： Dynamical systems

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

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Ultra-small amorphous MoS2 decorated reduced graphene oxide for supercapacitor application

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Journal of Materials science & Technology 2020年第5期40卷 196-203页

作者： Poulami Hota Milon Miah Saptasree Bose Diptiman Dinda Uttam K.Ghorai Yan-Kuin Su Shyamal K.Saha Department of Materials Science Indian Association for the Cultivation of ScienceJadavpurKolkata700032India Department of Materials Science and Engineering Seoul National UniversitySeoul151-744Republic of Korea Department of Industrial Chemistry and Applied Chemistry Ramakrishna Mission Vidyamandira and Swami Vivekananda Research CenterBelur MathHowrah711202India Green Energy Technology Research Center Kun Shan UniversityTainanTaiwanChina

Amorphous materials have recently gained much attention as electrode materials in supercapacitor application due to the presence of larger amount active sites which can efficiently increase the storage capacity of the *** engineering is an elegant approach to fully utilize the advantages of the amorphous ***,large surface area and high conductivity of reduced graphene oxide(RGO)can efficiently increase the storage capacity of the *** this idea,in the present work,we have successfully synthesized amorphous MoS2 of two different sizes on reduced graphene oxide and thoroughly investigated the supercapacitor behavior of the *** specific capacitance of the composite structures has been found to be largely increased with decreasing size of the amorphous nano *** specific capacitance of amorphous MoS2-RGO composite containing nearly 50 nm of MoS2 found to be 270 F/g whereas when the particle size is reduced to 5–7 nm,value of specific capacitance increases to 460 F/*** large increase in specific capacitance with the tuning of the size of amorphous nano particle has been explained by the presence of a large number of active sulfur edges of ultra-small MoS2 nano structure along with the better charge transport which can effectively increase the storage capacity of the overall *** retention in the capacitance of the material has been found to be 90%after 5000 cycles.

关键词： Supercapacitor Ultrasmall amorphous MoS2 nanoparticles Sulfur edge sites Reduced graphene oxide(RGO) Specific capacitance

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An Implementation of Internet of Things-based Live Temperature and Humidity Monitoring System

An Implementation of Internet of Things-based Live Temperatu...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rubina Liyakat Khan Deepa Priyanshu Danish Ather Haneen Allataifeh Computer Science Department The Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Administrative Sciences Department The Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of IT & Engineering Amity University in Tashkent Uzbekistan Team University Uzbekistan

ISBN: (纸本)9781665487351

Weather monitoring is an essential component of human existence; hence, collecting data about the temporal dynamics of weather shifts is of the utmost significance. The primary objective of this study is to create an embedded system to design a weather monitoring system that permits the monitoring of various weather parameters for academic research in a laboratory setting. A pair of sensors, including those for monitoring temperature and humidity, are included in such a system. The microcontroller gathers the data from the sensors, and then the microcontroller sends the data from the sensors into the google cloud application (google sheets) using Serial Communication. This module will store the data on the cloud, and information can also be received on mobile with the help of the GSM module if the values are outside the defined boundary range. The values obtained from the device are compared with those obtained from the device and checked with the data available at the national center for metrology to determine the degree to which the intended system accurately represents reality.

关键词： Temperature sensors Cloud computing Temperature distribution Temperature dependence Microcontrollers Humidity Control systems

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Classification of Soil Images using Convolution Neural Networks

Classification of Soil Images using Convolution Neural Netwo...

引用

2021 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2021

作者： Chatterjee, Kajal Obaidat, Mohammad S. Samanta, Debabrata Sadoun, Balqies Islam, Sk Hafizul Chatterjee, Rajdeep University Department of Computer Science Karnataka Bangalore India University of Sharjah College of Computing and Informatics Sharjah27272 United Arab Emirates Al-Balqa' Applied University College of Engineering Al Salt Jordan Indian Institute of Information Technology Kalyani Department of CSE West Bengal 741235 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9781665432085

Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. © 2021 IEEE.

关键词： Soils

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Fine-Tuning Optimization of Small Language Models: A Novel Graph-Theoretical Approach for Efficient Prompt engineering

Fine-Tuning Optimization of Small Language Models: A Novel G...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Venkata Gadiraju Hao-Yu Tsai Hsiao-Chun Wu Manali Singha C.-H. Scott Huang Guannan Liu Shih Yu Chang Yiyan Wu School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge USA Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Innovation Center for AI Applications Yuan Ze University Chungli Taiwan Institute for Genomic Medicine (IGM) Nationwide Children’s Hospital Columbus USA Department of Applied Data Science San Jose State University San Jose USA Communications Research Centre Ottawa Canada

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In the realm of fine-tuning pre-trained language models in modern prompt engineering, we introduce a novel graph-theoretical approach to address the resource-intensive challenges to the conventional data fine-tuning methods for prompt engineering. Leveraging semantic and contextual prompt relationships, we propose to form a novel prompt graph, which facilitates a new comprehensive representation of prompt similarities. Building upon this new graph structure, our proposed approach can minimize the training time during the fine-tuning process for small language models by identifying and utilizing cliques corresponding to condensed subsets of highly similar prompts. This new strategic reduction in training data can greatly reduce the training time, particularly for resource-constrained applications in practice. Our proposed new approach leads to a significant reduction in the original prompt-graph order and a more focused and streamlined fine-tuning process. This data-reduction strategy demonstrates the potential to enable finetuning language models for prompt engineering with smaller datasets subject to less computational resource. The real run-time analysis for the training process of a small language model GPT2 have been undertaken to show the advantage of our proposed new approach.

关键词： Training Computational modeling Simulation Semantics Training data Data models Prompt engineering

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