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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Jeongsoo Kim Suhyeon Jeon Jeongho Kwak Department of Electrical Engineering and Computer Science DGIST Korea

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

The satellite edge computing (SEC) has recently received considerable attention thanks to its wide area service around the world. However, this also creates a risk of exposing private user data to eavesdroppers. Physical layer security can help prevent this, yet it requires extra usage of network resources. Hence, efficient management of these resources is essential for saving power and ensuring secure code offloading. Moreover, from the perspective of mobile devices that request services, the level of security demands is quite different for various services, yet current studies have not fully considered this aspect. In this paper, we propose a secure code offloading framework for an SEC system with a jamming strategy in the existence of eavesdropping satellite. We formulate an average power minimization problem of an LEO satellite, a gateway, and a mobile device while ensuring security and the stability of queues. This includes making decisions of code offloading, computing/network resource allocation, and jamming unit selection. As a solution of this problem, we propose an SOS algorithm by invoking stochastic optimization theory. Finally, via extensive simulations, we demonstrate that the proposed SOS algorithm can save up to 60% of average power compared to existing algorithms while maintaining the same delay and zero leakage of information toward eavesdropper.

关键词： Satellites Codes Heuristic algorithms Low earth orbit satellites Minimization Mobile handsets Security Jamming Optimization Edge computing

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Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems

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Neural Computing and Applications 2024年第26期36卷 16195-16212页

作者： González-Afanador, Ian Chen, Claudia Morales-Torres, Gerardo Meihls, Scott Shi, Hongyang Tan, Xiaobo Sepúlveda, Nelson Department of Electrical and Computer Engineering Michigan State University 426 Auditorium Road East LansingMI48824 United States Great Lakes Science Center U.S. Geological Survey MillersburgMI49759 United States

Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86%. The models were deployed on an Arduino microcontroller platform and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65 and 75% were observed during validation. Models demonstrated good capture rates for lamprey attachments (63–85%), and average detection delays ranging from 9 to 36 s. A video demonstrating the operation of the system during a real-time validation test is also included in this work. While there is room for improving the accuracy of the system, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity enhancing control and conservation efforts across its entire range. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision trees

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A Liquid-Based Reconfigurable Fabry-Perot Cavity Antenna for Beam Steering 11

A Liquid-Based Reconfigurable Fabry-Perot Cavity Antenna for...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Ashfaq, Mubashir Khan, Muhammad U. Farooqi, Tayyaba Arya, Ravi Kumar Dong, Junwei Mittra, Raj Islamabad Pakistan Zhongshan Institute of Changchun University of Science and Technology Xiangshan Laboratory China University of Central Florida Department of Electrical and Computer Engineering FL United States

ISBN: (纸本)9798350326277

In this work, a liquid-based reconfigurable beam steerable antenna is presented. The proposed antenna is a Fabry-Pérot cavity antenna (FPCA) wherein the metasurface forming the cavity has four-quadrant 3D-printed liquid chambers. The chambers are sandwiched between two 16×16 conductive cross grid and square patch resonant element array layers making the metasurface. The source antenna is a planar coaxial-fed patch antenna radiating at 10 GHz. The proposed metasurface along with the source antenna forms a FPCA having a gain of 7 dBi. Beam steering is achieved and controlled by filling water chambers in different combinations. Beam steering of ±30° is achieved in two orthogonal planes i.e., xz-plane and yz-plane. © 2023 IEEE.

关键词： 3D printing Beam steering meta-surface water

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Revisiting Kernel Attention with Correlated Gaussian Process Representation 40

Revisiting Kernel Attention with Correlated Gaussian Process...

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40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Bui, Long Minh Huu, Tho Tran Dinh, Duy Nguyen, Tan Minh Hoang, Trong Nghia FPT Software AI Center Department of Mathematics National University of Singapore Singapore School of Electrical Engineering and Computer Science Washington State University United States

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP’s kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Distribution transformers

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Secure Multi-Party Computation in Distributed Deep Learning Networks 3

Secure Multi-Party Computation in Distributed Deep Learning ...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Nigam, Nishesh Goswami, Sudarshan Agarwal, Manish Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Electrical & Electronics Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

The goal of this study is to present a novel strategy for improving the data privacy and security of distributed deep learning networks through the use of secure multi-party computation (SMPC). The approach in issue employs three key processes: privacy-preserving gradient descent (PPGD), secure aggregation (SA), and homomorphic encryption (HE). Several criteria are utilized to compare the efficacy of our proposed strategy to six industry-standard techniques, including accuracy, precision, recall, F1-score, training time, computational complexity, memory utilization, scalability, resilience, and privacy protection. The findings show that our method surpasses standard strategies in terms of protecting private information and sensitive data while retaining competitive model accuracy and training efficiency. This paper offers a complete architecture for secure collaborative model training and underlines the need for privacy-preserving strategies in distributed deep learning. © 2023 IEEE.

关键词： Sensitive data

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Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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Effects of External Archives on the Performance of Multi-Objective Evolutionary Algorithms on Real-World Problems

Effects of External Archives on the Performance of Multi-Obj...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Nan, Yang Shu, Tianye Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350314588

External archives have attracted more and more attention in the evolutionary multi-objective optimization (EMO) community. This is because a solution set selected from an external archive is usually better than the final population of an EMO algorithm. Whereas the effects of subset selection from external archives have already been investigated on artificial test problems, its effects on real-world problems have not been examined. In this paper, we examine the effects of subset selection from external archives for ten EMO algorithms on two real-world problem suites. Experimental results show that the performance improvement by subset selection is large for most algorithms and many problems but small for a few algorithms and a few problems (i.e., algorithm dependent and problem dependent). © 2023 IEEE.

关键词： Multiobjective optimization

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Aerial-ground collaborative routing with time constraints

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Chinese Journal of Aeronautics 2023年第2期36卷 270-283页

作者： Jiahao XING Lichen SU Wenjing HONG Lu TONG Renli LYU Wenbo DU School of Electronics and Information Engineering Beihang UniversityBeijing 100091China School of Future Aerospace Technology/Shen Yuan Honors College Beihang UniversityBejing 100091China School of Automation Science and Electrical Engineering Beihang UniversityBejing 100091China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Shenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Research Institute of Frontier Science Beihang UniversityBeijing 100083China CAAC Key Laboratory of General Aviation Operation Civil Aviation Management Institute of ChinaBeijing 100102China

The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery *** reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily *** this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion ***,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality *** numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.

关键词： Aerial-ground collaborative delivery Mixed-integer programming Space-time hybrid heuristic algorithm Vehicle routing problem with drones Time-dependent travel times

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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An Improved Fuzzy Classifier-Based Evolutionary Algorithm for Expensive Multiobjective Optimization Problems with Complicated Pareto Sets 12th

An Improved Fuzzy Classifier-Based Evolutionary Algorithm fo...

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12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Zhang, Jinyuan He, Linjun Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

Various surrogate-based multiobjective evolutionary algori-thms (MOEAs) have been proposed to solve expensive multiobjective optimization problems (MOPs). However, these algorithms are usually examined on test suites with unrealistically simple Pareto sets (e.g., ZDT and DTLZ test suites). Real-world MOPs usually have complicated Pareto sets, such as a vehicle dynamic design problem and a power plant design optimization problem. Such MOPs are challenging to construct reliable surrogates for surrogate-based MOEAs. Constructed surrogates with low accuracy are likely to make incorrect predictions and even mislead the search direction. In this paper, we propose an improved fuzzy classifier-based MOEA by leveraging the accuracy information of the classifier. The proposed algorithm is compared with five state-of-the-art algorithms on two well-known test suites with complicated Pareto sets and four real-world problems. Experimental results demonstrate the effectiveness of the proposed algorithm in solving realistic MOPs with complicated Pareto sets when only a limited number of function evaluations are available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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