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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.

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Fast Image matting based on matting pyramids and surrogate models

Fast Image matting based on matting pyramids and surrogate m...

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Robotics, Artificial Intelligence and Intelligent Control (RAIIC), International Conference on

作者： Yihui Liang Hongshan Gou Guisong Liu Sibo Huang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China College of Big data and Information Engineering Guiyang Institute of Humanities and Technology Guiyang China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China Network and Information Centre Huizhou University Huizhou China

Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time.

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UAV-Aided Covert Federated Learning Networks

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IEEE Network 2025年

作者： Wang, Chao Guo, Shaoyong Xiong, Zehui Xing, Chengwen Zhao, Nan Niyato, Dusit Karagiannidis, George Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Beijing Institute of Technology School of Information and Electronics Beijing100081 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

With the increasing emphasis on data privacy, federated learning (FL) networks show great potential through distributed training without directly sharing the raw data. However, the coverage of FL terrestrial servers is usually limited. Therefore, we propose leveraging the unmanned aerial vehicle (UAV) as a mobile flying server to further improve the wireless coverage and training efficiency of FL. Nevertheless, frequent exchanges of model parameters in UAV-assisted FL can result in serious security risks. In order to achieve this, we propose a covert FL scheme assisted by the UAV, which can protect the transmission key features of local models from being detected by wardens. Furthermore, we discuss the FL network and covert communications, as well as the exceptional features of UAV-assisted covert FL. Finally, we present two case studies, one of which focuses on minimising the network latency, while the other concentrates on reducing the energy consumption of covert FL. The simulation results demonstrate the efficacy of the proposed schemes in addressing future challenges. © 1986-2012 IEEE.

关键词： data privacy

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Filter-Enhanced Hypergraph Transformer for Multi-Behavior Sequential Recommendation

Filter-Enhanced Hypergraph Transformer for Multi-Behavior Se...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhufeng Shao Shoujin Wang Wenpeng Lu Weiyu Zhang Hongjiao Guan Long Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Data Science Institute University of Technology Sydney Sydney Australia

Sequential recommendation has been developed to predict the next item in which users are most interested by capturing user behavior patterns embedded in their historical interaction sequences. However, most existing methods appear to exhibit limitations in modeling fine-grained dependencies embedded in users’ various periodic behavior patterns and heterogeneous dependencies across multi-behaviors. Towards this end, we propose a Filter-enhanced Hypergraph Transformer framework for Multi-Behavior Sequential Recommendation (FHT-MB) to address the above challenges. Specifically, a multi-scale filter layer equipped with multi-learnable filters is devised to encode behavior-aware sequential patterns emerging from different periodic trends (e.g., daily or weekly routines), and then a hypergraph structure is devised to extract heterogeneous dependencies across users’ multiple types of behaviors. Extensive experiments on two real-world e-commerce datasets show the superiority of our proposed FHT-MB over various state-of-the-art methods. 1

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Feature Selection Based on Consistent Granulation

SSRN

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SSRN 2024年

作者： Yang, Tian Shen, Shuo Deng, Jinsheng Liang, Jie Qian, Yuhua Dai, Jianhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Institute of Big Data Science and Industry Shanxi university Shanxi Taiyuan030006 China

Classifying large-scale data is a challenging task in machine learning. Feature selection and feature construction can improve the classification performances of classifiers. However, existing feature selections struggle to handle large-scale data. Granulation is the key process for generating accurate and efficient models. In this paper, we propose a novel granulation approach based on rough sets, consistent granulation, and a novel feature construction based on consistent granulation to improve the computation speed, scale, and classification performance of feature selection. As a result, the time and space complexities are reduced to a linear level in terms of sample scales. Moreover, consistent granulation can precisely represent the topological structure of the data and reduce the parameter space from [0, 1] to a finite set lowering the difficulty of optimal parameter searching. Experiments show that the proposed algorithm, FCG, outperforms four classical feature selection algorithms in handling datasets with millions of samples or tens of thousands of dimensions. Additionally, on the first thirteen datasets, FCG reduces the average runtime by 1145.68 times (NARNeMF), 883.11 times (HANDI), 1333.10 times (GBNRS), and 3.73 times (RMDPS), while increasing the average classification accuracy by 2.73% (FARNeMF), 2.30% (HANDI), 4.61% (GBNRS) and 14.33% (RMDPS) compared to these methods. © 2024, The Authors. All rights reserved.

关键词： Granulation

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Hamiltonian characterisation of multi-time processes with classical memory

arXiv

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arXiv 2024年

作者： Goswami, Kaumudibikash Roy, Abhinash Kumar Srivastava, Varun Perez, Barr Giarmatzi, Christina Gilchrist, Alexei Costa, Fabio QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong Department of Physical and Mathematical Sciences Macquarie University SydneyNSW Australia School of Computer Science University of Technology Sydney SydneyNSW Australia Stockholm University KTH Royal Institute of Technology Stockholm Sweden

A central problem in the study of open quantum systems is the characterisation of non-Markovian processes, where an environment retains memory of its interaction with the system. A key distinction is whether or not this memory can be simulated classically, as this can lead to efficient modelling and noise mitigation. Powerful tools have been developed recently within the process matrix formalism, a framework that conveniently characterises all multi-time correlations through a sequence of measurements. This leads to a detailed classification of classical and quantum-memory processes and provides operational procedures to distinguish between them. However, these results leave open the question of what type of system-environment interactions lead to classical memory. More generally, process-matrix methods lack a direct connection to joint system-environment evolution, a cornerstone of open-system modelling. In this work, we characterise Hamiltonian and circuit-based models of system-environment interactions leading to classical memory. We show that general time-dependent Hamiltonians with product eigenstates, and where the environment’s eigenstates form a time-independent, orthonormal basis, always produce a particular type of classical memory: probabilistic mixtures of unitary processes. Additionally, we show that the most general type of classical-memory processes can be generated by a quantum circuit in which system and environment interact through a specific class of controlled unitaries. Our results establish the first strong link between process-matrix methods and traditional Hamiltonian-based approaches to open quantum systems. © 2024, CC BY.

关键词： Markov processes

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Development of a Sensor-Based Wearable Fetal and Maternal Health Monitoring System

Development of a Sensor-Based Wearable Fetal and Maternal He...

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Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Yugashi Jayalath MWP Maduranga Valmik Tilwari WMSRB Wijayarathna WAAM Wanniarachchi Debashree Sharma School Of Pharmacy & Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Department of Data Science Faculty of Computing and IT Sri Lanka Technology Campus Padukka Sri Lanka Department of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India Department of Information Technology General Sir Jhon Kotelawala Defence University Colombo Sri Lanka

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

According to the World Health Organization, approximately 360,000 pregnant women are registered annually in Sri Lanka. Among these, about 328,000 babies are born healthy, while 5,800 are born with birth defects, some of which are severe and impact daily life. To address the Sri Lankan stillbirth rate of 6.01%, continuous monitoring of fetal and maternal health is essential. Although commercial devices exist to fulfill this requirement, their high costs and safety issues often preclude regular use. To overcome these challenges, there is a need for a passive, wearable, and portable monitoring system for fetal and maternal health. This system should track key parameters such as fetal heart rate, fetal movements, and maternal heart rate to mitigate health risks for both the fetus and the mother. In this study, we developed a device using the Arduino, appropriate sensors, and a microcontroller to monitor these vital parameters. The device is user-friendly and suitable for regular use. MATLAB was utilized for the signal processing requirements of the proposed device. Test results demonstrated that fetal movement detection was accurate, although potential errors may arise during real-time testing. For pulse sensor readings, an average percentage difference of 3.025% was observed compared to pulse oximeter readings, likely due to placement errors and sensor instability in the proposed device.

关键词： Pregnancy Pulse oximeter Tracking Signal processing Real-time systems Sensors Safety Biomedical monitoring Monitoring Testing

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Automatic Sinhala News Classification Approach for News Platforms 7

Automatic Sinhala News Classification Approach for News Plat...

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7th IEEE International Conference on Engineering Technologies and Applied sciences, ICETAS 2020

作者： Kirindage, Gayashan Godewithana, Navod Sri Lanka Institute of Information Technology Faculty of Computing Department of Data Science Colombo Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Colombo Sri Lanka

ISBN: (纸本)9780738105048

Because of generating various news articles in large scale, online sources moved into an automatic categorization mechanism. This research has been conducted using LDA topic modeling approach and using other classification algorithms to establish a news categorization solution. Sinhala news websites have only few news categories and do not have any relationships or hierarchies between the categories. Therefore, some users require to search manually and find the necessary articles which are in those categories. Purpose of this study is to build a news categorization model with categorization hierarchies for Sinhala news articles. The goals of the models are to identify the most suitable news category for a related news article and develop hierarchies using generated news categories and assign the news articles according to the hierarchical structure. The final experiments and evaluations show that the solution performs well to solve the automatic categorization problem in Sinhala news platforms. © 2020 IEEE.

关键词： Machine learning

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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