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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

computer Modeling in engineering & sciences 2023年第5期135卷 1209-1228页

作者： Chuan Wang Ruoyu Zhu Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang College of Software Henan Normal UniversityXinxiang453007China College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Department of Military Information Engineering Hanyang UniversityAnsan15588South Korea College of Engineering and Science Institute for Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneVIC 8001Australia

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to *** paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP *** work is novel and contributes to three aspects:problemmodel,optimization framework,and ***,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over ***,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic *** framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous *** way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme *** the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational ***,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search *** evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic *** experimental results show that ACO with 2-opt can solve the DTSPs effectively.

关键词： Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy

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Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4738-4749页

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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Image-based Criticality-Aware Fire Detection

Image-based Criticality-Aware Fire Detection

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Arshad, Alina Shamsi, Jawwad Ahmed Khan, Muhammad Burhan Bawany, Narmeen Zakaria Department of Software Engineering National University of Computer and Emerging Sciences Karachi Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Department of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan Faculty of Science Jinnah University for Women Karachi Pakistan

ISBN: (纸本)9798331510503

Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machine-learning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on real-time data collected from surveillance cameras and achieves an accuracy of 86.67% in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment. © 2024 IEEE.

关键词： Fire detectors

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No-reference Image Contrast Quality Assessment based on the Degree of Uniformity in Probability Distribution

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IEIE Transactions on Smart Processing and Computing 2022年第2期11卷 85-91页

作者： Kim, Wonvin Yim, Changhoon Department of Computer Software Korean Bible University Seoul Korea Republic of Department of Computer Science and Engineering Konkuk University Seoul Korea Republic of

We propose a novel contrast-based no-reference image quality assessment (NR-IQA) method. The proposed method quantifies the degree of uniformity in the probability distribution of the pixel values of an image to measure contrast quality without the need for a reference image. To quantify the degree of symmetric uniformity in the distribution of pixel values for image contrast assessment, we divide the intensity levels into left and right ranges recursively at each depth as a binary tree. This symmetric hierarchical recursive calculation of the degree of uniformity for contrast measurement provides a good metric for NR-IQA. Experimental results show that the proposed method is more consistent with the levels of contrast decrement and is superior in assessing contrast-enhanced and dehazed images compared with state-of-the-art and classic NR-IQA methods. The proposed method is more suited to real-time applications due to its lower computational complexity compared with previous methods. © 2022 The Institute of Electronics and Information Engineers.

关键词： Probability distributions

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EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG

EEG-EMG FAConformer: Frequency Aware Conv-Transformer for th...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, ZhengXiao Cai, Minghong Li, Letian Tian, Siyuan Dai, Ren-Jie Tongji University College of Electronic and Information Engineering Shanghai China Tongji University School of Software Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9798350386226

Motor pattern recognition paradigms are the main forms of Brain-computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What's more, modules like Multi-Scale Fusion Module, Independent ChannelSpecific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEGEMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability. © 2024 IEEE.

关键词： Electromyography

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SO(3) Equivariant Framework for Spatial Networks

SO(3) Equivariant Framework for Spatial Networks

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Xia, Tian Aykent, Sarp Microsoft CambridgeMA United States Auburn University Computer Science and Software Engineering Department AuburnAL United States

ISBN: (纸本)9798350362480

Representation learning on spatial networks is emerging as a distinct area in machine learning and is attracting much attention in diverse domains. Some applications include molecular graphs for drug discovery and screening, brain networks for neuroscience, social networks for recommender systems, etc. However, most proposed approaches either use only spatial or network data that cannot distinguish certain types of graphs and limit their network expressivity or are tied to specific input data domains and network architectures. To address this gap, we introduce an equivariant message-passing network architecture that simultaneously leverages the spatial and network properties. In addition, we propose to take advantage of the geometric representations and extend the classical scalar features with 3D vectors. To exploit the spatial and network features, we present a Spatial Vector Neuron, which can be easily incorporated into the existing graph neural network architectures and allow the model to scale up by stacking more layers for larger receptive fields. A comprehensive set of experiments on both synthetic and real-world datasets demonstrate the strength of our proposed method and the potential of geometric representation learning for Spatial Networks. © 2024 IEEE.

关键词： Adversarial machine learning

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SE(3) Equivariant Neural Network for 3D Graphs

SE(3) Equivariant Neural Network for 3D Graphs

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Aykent, Sarp Xia, Tian Auburn University Computer Science and Software Engineering Department AuburnAL United States Microsoft CambridgeMA United States

ISBN: (纸本)9798350362480

Three-dimensional (3D) graph representations have gained significant importance in numerous scientific domains, such as molecular dynamics and astrophysics. In these applications, a precise and effective representation of 3D graphs is crucial. Although various neural network architectures have been proposed to encapsulate the intricate relationships inherent to these graphs, many remain sensitive to challenges such as reflection variances observed in molecular structures. In this paper, we present an SE(3) equivariant neural network architecture tailored for 3D graphs. Our approach uniquely integrates equivariance to both rotations and translations, ensuring robustness against these spatial variances. Distinctively, our model leverages an innovative message passing mechanism, endowing it with an intrinsic understanding of reflection variances. This mechanism not only facilitates superior 3D graph representation but also promotes computational efficiency. Unlike many existing methods, our architecture sidesteps the need for computationally demanding higher-order representations in intermediary layers while still achieving competitive or superior performance metrics. Empirical evaluations conducted on synthetic and real-world datasets underscore the superior performance of our approach in comparison to prevalent models in terms of both accuracy and efficiency. © 2024 IEEE.

关键词： Graph neural networks

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Towards Safe Online Machine Learning Model Training and Inference on Edge Networks 22

Towards Safe Online Machine Learning Model Training and Infe...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Maruf, Md Al Azim, Akramul Auluck, Nitin Sahi, Mansi Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada Indian Institute of Technology Departmcnt of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350345346

With the increasing demand for edge computing in cyber-physical system (CPS) applications, ensuring the safety and reliability of machine learning models running on edge devices during online model training and inference is essential. Although data and model parallelism offer significant advantages for large machine learning model training, adopting parallel computing architecture in edge networks is challenging. It introduces safety concerns while splitting and integrating machine learning models over different computing nodes, which can pose risks to the integrity and reliability of the system. Therefore, online model training and inference in edge networks require a safe parallel computing architecture to achieve improved performance with optimal resource utilization. To address this challenge, we propose an efficient machine learning model partitioning algorithm that considers the safety constraint and requirements of edge networks and includes the triple-modular redundancy (TMR) technique for trusted computation. Our proposed approach achieves a significant speedup of approximately 56.3% in net training time compared to the non-partitioning approach, making it more efficient and suitable for real-time applications in edge networks. © 2023 IEEE.

关键词： Edge Networks Machine Learning Model Partitioning Triple Modular Redundancy (TMR)

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A First Look at the Inheritance-Induced Redundant Test Execution 24

A First Look at the Inheritance-Induced Redundant Test Execu...

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44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Kim, Dong Jae Chen, Tse-Hsun Peter Yang, Jinqiu Lab Concordia University MontrealQC Canada Concordia University Department of Computer Science and Software Engineering MontrealQC Canada

ISBN: (纸本)9798400702174

Inheritance, a fundamental aspect of object-oriented design, has been leveraged to enhance code reuse and facilitate efficient software development. However, alongside its benefits, inheritance can introduce tight coupling and complex relationships between classes, posing challenges for software maintenance. Although there are many studies on inheritance in source code, there is limited study on using inheritance in test code. In this paper, we take the first step by studying inheritance in test code, with a focus on redundant test executions caused by inherited test cases. We empirically study the prevalence of test inheritance and its characteristics. We also propose a hybrid approach that combines static and dynamic analysis to identify and locate inheritance-induced redundant test cases. Our findings reveal that (1) inheritance is widely utilized in the test code, (2) inheritance-induced redundant test executions are prevalent, accounting for 13% of all execution test cases, (3) bypassing these redundancies can help reduce 14% of the test execution time, and finally, (4) our study highlights the need for careful refactoring decisions to minimize redundant test cases and identifies the need for further research on test code quality. © 2024 ACM.

关键词： software testing

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Fair and Communication-Efficient Personalized Federated Learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer Theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. Then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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