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arXiv 2023年

作者： Feng, Fang Yu, Yue School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Hunan Xiangtan411105 China

This paper is dedicated to the numerical solution of a fourth-order singular perturbation problem using the interior penalty virtual element method (IPVEM) proposed in [42]. The study introduces modifications to the jumps and averages in the penalty term, as well as presents an automated mesh-dependent selection of the penalty parameter. Drawing inspiration from the modified Morley finite element methods, we leverage the conforming interpolation technique to handle the lower part of the bilinear form. Through our analysis, we establish optimal convergence in the energy norm and provide a rigorous proof of uniform convergence concerning the perturbation parameter in the lowest-order case. © 2023, CC BY-NC-ND.

关键词： Numerical methods

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Performance Analysis of OMA/NOMA-aided Satellite Communication Networks: A Stochastic Geometry Approach

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IEEE Transactions on Communications 2024年

作者： Li, Kecheng Wang, Jun Hou, Tianwei Li, Anna Yue, Xinwei Liu, Yuanwei Chen, Wei School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom KU Leuven Leuven3001 Belgium School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes;2) fading factors have negligible effects on OP and spectral efficiency;and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Satellite communication systems

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Hyper-Parameter Optimization and Proxy Re-encryption for Federated Learning 6th

Hyper-Parameter Optimization and Proxy Re-encryption for F...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Du, Wanrong Wen, Bin Ma, Mengshuai Liu, Wenlong Wei, Xiaoxun Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University Haikou571158 China School of Information Science and Technology Hainan Normal University Haikou571158 China Cloud Computing and Big Data Research Center Hainan Normal University Haikou571158 China

ISBN: (纸本)9789819614103

Federated learning is a distributed machine learning paradigm that allows multiple participants to collaborate on training a shared model without revealing their own data. However, federated learning faces challenges such as data heterogeneity, communication overhead, security attacks, etc., which result in low efficiency of data training. To address these issues, this paper proposes a strategy to enhance the training efficiency of federated learning data through hyperparameter optimization and proxy re-encryption. A semi-supervised model of federated learning based on generative adversarial network is designed so that each participant can automatically adjust and optimize their own training parameters according to their own data distribution and environment in order to adapt to the data heterogeneity and dynamics of federated learning. Finally, experiments demonstrate that the proposed method can significantly improve the efficiency of data training and model performance compared with traditional federated learning methods across different datasets and scenarios. Experimental results show that the proposed strategy can achieve the same or even better accuracy than centralized machine learning while safeguarding data privacy and enabling ciphertext conversion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Intelligent Autonomous-Robot Control for Medical Applications

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Computers, Materials & Continua 2021年第8期68卷 2189-2203页

作者： Rihem Farkh Haykel Marouani Khaled Al Jaloud Saad Alhuwaimel Mohammad Tabrez Quasim Yasser Fouad College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsDepartment of Electrical EngineeringNational Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Riyadh12354Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a *** has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected *** COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a *** artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic *** officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent *** propose an advanced controller for a service robot to be used in *** type of robot is deployed to deliver food and dispense medications to individual *** autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its *** criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

关键词： Autonomous medic robots PID control neural network control system real-time implementation navigation environment differential drive system

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Blockchain Applicability for the Internet of Things: Performance and Scalability Challenges and Solutions

arXiv

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arXiv 2022年

作者： Rahman, Ziaur Yi, Xun Mehedi, Sk. Tanzir Islam, Rafiqul Kelarev, Andrei School Science Rmit University Melbourne3001 Australia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail1902 Bangladesh School of Computing Mathematics and Engineering Wagga WaggaNSW2678 Australia

Blockchain has recently drawn wide attention in the research community. Since its emergence, the world has seen the expansion of this new technology, which was initially developed as a digital currency more than a decade ago. A self-administering ledger that ensures extensive data immutability over a peer-to-peer network has made it attractive for cybersecurity applications, including sensor-enabled Internet of Things (IoT) systems. Brand new challenges and questions now demand solutions, as IoT devices are now online in a distributed fashion to simplify our everyday lives. Motivated by those challenges, the work here has detailed issues from which an IoT infrastructure can suffer if the wrong blockchain technology is chosen. Unlike a typical review, this paper focuses on security challenges of the blockchain-IoT eco-system through critical findings and applicable use cases. The contribution directs Blockchain architects, designers, and researchers in the domain to select an unblemished combination of Blockchain-powered IoT applications. In addition, the paper provides insight into the state-of-the-art Blockchain platforms, namely Ethereum, Hyperledger, and IOTA, to exhibit their respective challenges, their constraints, and their prospects in terms of performance and scalability. © 2022, CC BY-NC-SA.

关键词： Scalability

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Coherence fraction in Grover's search algorithm

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Physical Review A 2024年第6期110卷 062429-062429页

作者： Si-Qi Zhou Hai Jin Jin-Min Liang Shao-Ming Fei Yunlong Xiao Zhihao Ma School of Mathematical Sciences MOE-LSC Shanghai Jiao Tong University Shanghai 200240 China Shanghai Seres Information Technology Co. Ltd Shanghai 200040 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-Optoelectronics Peking University Beijing 100871 China School of Mathematical Sciences Capital Normal University Beijing 100048 China Max Planck Institute for Mathematics in the Sciences - 04103 Leipzig Germany Quantum Innovation Centre (Q.InC) Agency for Science Technology and Research (ASTAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Republic of Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (ASTAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Republic of Singapore

The question of which resources drive the advantages in quantum algorithms has long been a fundamental challenge. While entanglement and coherence are critical to many quantum algorithms, our results indicate that they do not fully explain the quantum advantage achieved by the Grover search algorithm. By introducing a generalized Grover search algorithm, we demonstrate that the success probability depends not only on the querying number of oracles but also on the coherence fraction, which quantifies the fidelity between an arbitrary initial quantum state and the equal superposition state. Additionally, we explore the role of the coherence fraction in the quantum minimization algorithm, which offers a framework for solving complex problems in quantum machine learning. These findings offer insights into the origins of quantum advantage and open pathways for the development of new quantum algorithms.

关键词： Quantum entanglement

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TGNet: Learning 3D Shape from Sparse and Incomplete Point Cloud

TGNet: Learning 3D Shape from Sparse and Incomplete Point Cl...

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Virtual Reality (ICVR), IEEE International Conference on

作者： Yushi Li Jia Wang Yunzhe Wang Rong Xiang Yihong Wang Yushan Pan Department of Intelligent Science Xi’an Jiaotong-Liverpool University Suzhou China School of Electronic & Information Engineering Suzhou University of Science and Technology Suzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong China

Learning accurate 3D shapes from sparse and incomplete point clouds is challenging and meaningful, on account that the point clouds with low resolution always lack representative and informative details. This paper presents a novel deep auto-encoder called TGNet, which is formulated based on a tree-based generative adversarial network (GAN), to address self-supervised learning tasks on the point cloud with low sparsity. On the encoder side, we employ a PointNet-based framework to intensively capture the global representations. To better infer the spatial information in latent space, we propose a spectral graph learning module in with due consideration to graph topology. Further, we present a new loss that combines Wasserstein metric and multi-resolution Chamfer distance to better estimate global 3D geometry and structural details. The proposed TGNet achieves state-of-the-art performance for various point cloud learning tasks. Qualitative and quantitative evaluations demonstrate the novelty of the proposed model.

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Application for Decision Making by Controlling Chaotic Mode Competition Dynamics in Multi-Modex Semiconductor Laser

Application for Decision Making by Controlling Chaotic Mode ...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Ryugo Iwami Takatomo Mihana Kazutaka Kanno Makoto Naruse Atsushi Uchida Department of Information and Computer Sciences Saitama University Saitama City Saitama Japan Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

We numerically demonstrate decision making for solving the multi-armed bandit problem by controlling chaotic mode competition dynamics in a multi-mode semiconductor laser. The proposed method is effective when the num...

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Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

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Computers, Materials & Continua 2021年第9期68卷 3979-3995页

作者： Imam Yuadi Myrtati D.Artaria Sakina A.Taufiq Asyhari Department of Information and Library Science Airlangga UniversitySurabaya60286Indonesia Department of Anthropology Airlangga UniversitySurabaya60286Indonesia Department of Anatomy and Histology Airlangga UniversitySurabaya60286Indonesia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUnited Kingdom

The size,shape,and physical characteristics of the human skull are distinct when considering individual *** physical anthropology,the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective *** example,labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of *** the multiple issues associated with the manual identification of skulls,we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features,Gabor features,fractal features,discrete wavelet transforms,and combinations of *** underlying facial bone exhibits unique characteristics essential to the face’s physical structure that could be exploited for ***,we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification *** our proposed approach,we were able to achieve an accuracy of 92.3–99.5%in the classification of human skulls with mandibles and an accuracy of 91.4–99.9%in the classification of human skills without *** study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

关键词： Discrete wavelet transform Gabor gray-level co-occurrence matrix human skulls physical anthropology support vector machine

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Hyperbolic feature augmentation via distribution estimation and infinite sampling on manifolds 22

Hyperbolic feature augmentation via distribution estimation ...

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Proceedings of the 36th International Conference on Neural information Processing Systems

作者： Zhi Gao Yuwei Wu Yunde Jia Mehrtash Harandi Beijing Lab of Intellegent Information Technology School of Computer Science Beijing Institute of Technology China Beijing Lab of Intellegent Information Technology School of Computer Science Beijing Institute of Technology China and Guangdong Lab of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Guangdong Lab of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China and Beijing Lab of Intellegent Information Technology School of Computer Science Beijing Institute of Technology China Department of Electrical and Computer Systems Eng. Monash University and Data61 Australia

ISBN: (纸本)9781713871088

Learning in hyperbolic spaces has attracted growing attention recently, owing to their capabilities in capturing hierarchical structures of data. However, existing learning algorithms in the hyperbolic space tend to overfit when limited data is given. In this paper, we propose a hyperbolic feature augmentation method that generates diverse and discriminative features in the hyperbolic space to combat overfitting. We employ a wrapped hyperbolic normal distribution to model augmented features, and use a neural ordinary differential equation module that benefits from meta-learning to estimate the distribution. This is to reduce the bias of estimation caused by the scarcity of data. We also derive an upper bound of the augmentation loss, which enables us to train a hyperbolic model by using an infinite number of augmentations. Experiments on few-shot learning and continual learning tasks show that our method significantly improves the performance of hyperbolic algorithms in scarce data regimes.

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