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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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programming Variational Quantum Circuits with Quantum-Train Agent

arXiv

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arXiv 2024年

作者： Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Research Institute Taipei Taiwan Wells Fargo New YorkNY United States Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

In this study, the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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Differential evolution with cluster-based external archive and local search for multimodal optimization 8

Differential evolution with cluster-based external archive a...

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8th Brazilian Conference on Intelligent Systems, BRACIS 2019

作者： Dominico, Gabriel Boiani, Mateus Stubs Parpinelli, Rafael Graduate Program in Applied Computing Santa Catarina State University Joinville Santa Catarina Brazil

ISBN: (纸本)9781728142531

One of the main objectives in multimodal optimization is to find multiple optima solutions in a search space. Hence, population-based metaheuristics are suitable for this class of problems but their loss of diversity while converging may become a problem when tracking multiple optima. In this paper, we propose the use of a cluster-based external archive maintenance strategy along with the jDE algorithm, namely NCjDE-HJar. The DBSCAN algorithm is employed to group candidate solutions in an external archive representing multiple peaks that will feed the Hooke-Jeeves local search algorithm. Also, the Michalewicz mutation strategy is applied to refine the solutions found by the jDE algorithm. The proposed approach is compared with five state-of-the-art algorithms in terms of peak ratio and the results obtained show that the proposed modifications favor the finding more multimodal peaks. © 2019 IEEE.

关键词： Evolutionary algorithms

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Comparative experimental analysis of Docker container networking drivers

Comparative experimental analysis of Docker container networ...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Lucas Litter Mentz Wilton Jaciel Loch Guilherme Piêgas Koslovski Graduate Program in Applied Computing (PPGCA) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (数字)9781728194868

ISBN: (纸本)9781728194875

Virtualization allows for more efficient hardware usage by allowing several instances of Operating Systems (OSs) or Virtual Machines (VMs) to run on a physical server. Containers are a subset of lightweight virtualization and reduce the overhead of virtualizing an entire OS by sharing the server's OS with the virtualized instances. Moreover, containers work closer to hardware than VMs and are similar to Linux processes, however, this limits connectivity freedom since processes do not have access to network addressing. To resemble container's communication to that of conventional networks we use network drivers. Studies show that in processing- or memory-bound scenarios, containers perform better than VMs, but in network-bound scenarios they achieve less performance. This work analyzes performance of networking implementations for Docker in different container allocations and workload scenarios.

关键词： Cloud computing Linux Bandwidth Containers Hardware Servers Virtualization

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A GPU-based hybrid jDE algorithm applied to the 2D-AB protein structure prediction problem 8

A GPU-based hybrid jDE algorithm applied to the 2D-AB protei...

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8th Brazilian Conference on Intelligent Systems, BRACIS 2019

作者： Boiani, Mateus Dominico, Gabriel Stubs Parpinelli, Rafael Graduate Program in Applied Computing Santa Catarina State University Joinville Santa Catarina Brazil

ISBN: (纸本)9781728142531

Protein Structure Prediction (PSP) problem is an open problem in bioinformatics and, as the problem scales, complexity and processing time increases. In this way, robust methods and massively parallel architectures are required. This paper presents a GPU-based hybrid algorithm, named cuHjDE, to handle the 2D-AB off-lattice PSP problem. The cuHjDE is composed of the jDE algorithm and the Hooke-Jeeves local search algorithm. An important feature present in the cuHjDE algorithm is the use of a crowding mechanism to avoid premature convergence promoting diversification in the search space. The proposed algorithm is compared with four state-of-the-art algorithms in both artificial and real proteins. Also, the impact of using GPU is analyzed. Experimental results point out that the proposed approach is competitive in artificial sequences and achieved new best results for all real sequence proteins. © 2019 IEEE.

关键词： Proteins

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Tutorial on communication between access networks and the 5G core

arXiv

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arXiv 2021年

作者： Baleeiro Dominato, Lucas Carvalho de Resende, Henrique Both, Cristiano Bonato Marquez-Barja, Johann M. Silvestre, Bruno O. Cardoso, Kleber V. Universidade Federal de Goiás GO Goiânia Brazil IDLab Department of Applied Engineering University of Antwerp IMEC Antwerp Belgium Applied Computing Graduate Program University of Vale do Rio dos Sinos RS Porto Alegre Brazil

Fifth-generation (5G) networks enable a variety of use cases, e.g., Ultra-Reliable and Low-Latency Communications, enhanced Mobile Broadband, and massive Machine Type Communication. To explore the full potential of these use cases, it is mandatory to understand the communication between User Equipment (UE), Radio Access Network (RAN), and 5G Core (5GC), which support new network concepts and paradigms. For example, network slicing plays a crucial role in the communication system to address the challenges expected by the 5G networks. 3rd Generation Partnership Project has recently published Release 16, including the protocols used to communicate between RANs and 5GC, i.e., Non-Access Stratum (NAS) and NG Application Protocol (NGAP). The main goal of this article is to present a comprehensive tutorial about NAS and NGAP specifications using a didactic and practical approach. The tutorial describes the protocol stacks and aspects of the functionality of these protocols in 5G networks, such as authentication and identification procedures, data session establishment, and allocation of resources. Moreover, we review message flows related to these protocols in UE and Next Generation Node B (gNodeB) registration. To illustrate the concepts presented in the tutorial, we introduce a 5GC tester that implements NAS and NGAP for availing of three open-source 5GC projects on a black-box testing methodology. © 2021, CC BY.

关键词： 5G mobile communication systems

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vailá: VERSATILE ANARCHO INTEGRATED LIBERATION ÁNALYSIS IN MULTIMODAL TOOLBOX

arXiv

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arXiv 2024年

作者： Santiago, Paulo Roberto Pereira Chinaglia, Abel Gonçalves Flanagan, Kira Bedo, Bruno L.S. Mochida, Ligia Yumi Aceros, Juan Bononi, Aline Cesar, Guilherme Manna Biomechanics and Motor Control Laboratory School of Physical Education and Sport USP Brazil Graduate Program in Rehabilitation and Functional Performance Medical School USP Brazil Technology and Sports Performance Analysis Laboratory School of Physical Education USP Brazil Laboratory of Applied Biomechanics and Engineering Brooks College of Health UNF United States Department of Physical Therapy Brooks College of Health UNF United States College of Computing Engineering and Construction UNF United States Municipal Pharmacy Ribeirão Preto Brazil

Human movement analysis is crucial in health and sports biomechanics for understanding physical performance, guiding rehabilitation, and preventing injuries. However, existing tools are often proprietary, expensive, and function as "black boxes", limiting user control and customization. This paper introduces vailá—Versatile Anarcho Integrated Liberation Ánalysis in Multimodal Toolbox—an open-source, Python-based platform designed to enhance human movement analysis by integrating data from multiple biomechanical systems. vailá supports data from diverse sources, including retroreflective motion capture systems, inertial measurement units (IMUs), markerless video capture technology, electromyography (EMG), force plates, and GPS/GNSS systems, enabling comprehensive analysis of movement patterns. Developed entirely in Python 3.11.9, which offers improved efficiency and long-term support, and featuring a straightforward installation process, vailá is accessible to users without extensive programming experience. In this paper, we also present several workflow examples that demonstrate how vailá allows the rapid processing of large batches of data, independent of the type of collection method. This flexibility is especially valuable in research scenarios where unexpected data collection challenges arise, ensuring no valuable data point is lost. We demonstrate the application of vailá in analyzing sit-to-stand movements in pediatric disability, showcasing its capability to provide deeper insights even with unexpected movement patterns. By fostering a collaborative and open environment, vailá encourages users to innovate, customize, and freely explore their analysis needs, potentially contributing to the advancement of rehabilitation strategies and performance optimization. For more details in GitHub repository: https://***/vaila-multimodaltoolbox/vaila and the online documentation: https://*** Codes 92C10, 68U10, 65D18, 65K10 © 2024, CC BY-SA.

关键词： Electromyography

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Investigation of factors regarding the effects of COVID-19 pandemic on college students’ depression by quantum annealer

arXiv

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arXiv 2023年

作者： Choi, Junggu Kim, Kion Park, Soohyun Hur, Juyoen Yang, Hyunjung Kim, Younghoon Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive Science Yonsei University Seoul03722 Korea Republic of Korea Quantum Computing© Seoul06164 Korea Republic of Department of Psychology Yonsei University Seoul03722 Korea Republic of University College Yonsei University Seoul03722 Korea Republic of Deparment of Applied Statistics Yonsei University Seoul03722 Korea Republic of Department of Statistics and Data science Yonsei University Seoul03722 Korea Republic of

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. College student groups have been frequently selected as the target population in previous studies because they are easily affected by pandemics. In this study, multivariable datasets were collected from 751 college students based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g., decision-making in uncertain situations) were more important in post-pandemic conditions. We believe that our study will serve as a reference for researchers studying similar topics. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiple linear regression

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