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4th World Symposium on Software Engineering, WSSE 2022

作者： Wu, Zhuoyi Ma, Zhiqiang Bao, Caijilahu Li, Leixiao Zhang, Xiaoxu Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Region Engineering Technology Research Centre of Big Data Based Software Service China

ISBN: (纸本)9781450396950

Vehicle behavior recognition is one of the most important research fields in intelligent transportation. In the field of traffic surveillance videos, target vehicles only occupy small proportion of the video frame,making it difficult for the network to extract key features of small target vehicles. To solve this problem, this paper constructed vehicle-3C, a video dataset for vehicle behavior recognition, and was combined with deep learning theory. As a result, it proposed a vehicle behavior recognition method based on the theory of Two-Stream with Attention Network (TSAN). For this method, the two-stream convolutional network is used as a basic framework and attention units are embedded in it to extract temporal (motion) features and spatial features of target vehicles in traffic surveillance videos, and then the temporal and spatial features are fused for category judgment. The results showed that TSAN could achieve 81.8% identification accuracy in vehicle-3C dataset, which was better than other behavior recognition methods based on deep learning. In addition, TSAN also achieves a recognition accuracy of 77.2% in dataset UCF-101, which verifies the generalization performance of the network. Experimental results showed that TSAN could accurately extract and effectively fuse the temporal and spatial features of the foreground target vehicle in the video and achieve high recognition accuracy in the vehicle behavior recognition task. © 2022 ACM.

关键词： Vehicles

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Solidification modes and delta-ferrite of two types of 316L stainless steels:a combination of as-cast microstructure and HT-CLSM research

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Journal of Iron and Steel Research International 2025年第2期32卷 426-436页

作者： Yang Wang Chao Chen Xiao-yu Yang Zheng-rui Zhang Jian Wang Zhou Li Lei Chen Wang-zhong Mu College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Department of Materials Science and Engineering KTH Royal Institute of TechnologyStockholm 10044Sweden College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University and Shandong Academy of Medical SciencesJinan 250117ShandongChina College of Mechanical Engineering Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Key Laboratory of Electromagnetic Processing of Materials(Ministry of Education) Northeastern UniversityShenyang 110819LiaoningChina

In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been *** microstructure and solidification kinetics of the two as-cast grades were in situ observed by high temperature confocal laser scanning microscopy(HT-CLSM).There are significant differences in the as-cast microstructures of the two 316L stainless steel *** L-316L steel,ferrite morphology appears as the short rods with a ferrite content of 6.98%,forming a dual-phase microstructure consisting of austenite and ***,in H-316L steel,the ferrite appears as discontinuous network structures with a content of 4.41%,forming a microstructure composed of austenite and sigma(σ)*** alloying elements in H-316L steel exhibit a complex distribution,with Ni and Mo enriching at the austenite grain ***-CLSM experiments provide the real-time observation of the solidification processes of both 316L specimens and reveal distinct solidification modes:L-316L steel solidifies in an FA mode,whereas H-316L steel solidifies in an AF *** differences result in ferrite and austenite predominantly serving as the nucleation and growth phases,*** solidification mode observed by experiments is similar to the thermodynamic calculation *** L-316L steel solidified in the FA mode and showed minimal element segregation,which lead to a direct transformation of ferrite to austenite phase(δ→γ)during phase transformation after ***,the H-316L steel solidified in the AF mode and showed severe element segregation,which lead to Mo enrichment at grain boundaries and transformation of ferrite into sigma and austenite phases through the eutectoid reaction(δ→σ+γ).

关键词： 316L austenitic stainless steel As-cast microstructure High-temperature confocal laser scanning microscopy Solidification mode Ferrite Characterization

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HMvisor: dynamic hybrid memory management for virtual machines

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science China(Information sciences) 2021年第9期64卷 74-89页

作者： Dang YANG Haikun LIU Hai JIN Yu ZHANG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and Technology Huazhong University of Science and Technology

Emerging non-volatile memory(NVM) technologies promise high density, low cost and dynamic random access memory(DRAM)-like performance, at the expense of limited write endurance and high write energy consumption. It is more practical to use NVM combining with the traditional DRAM. However, the hybrid memory management such as page migration becomes more challenging in a virtualization environment because virtual machines(VMs) are unaware of the memory heterogeneity. In this paper, we propose HMvisor, a hypervisor and VM coordinated hybrid memory management mechanism to better utilize DRAM and NVM resources. HMvisor exposes the memory heterogeneity to VMs by mapping virtual NUMA nodes to different physical NUMA nodes. We propose a lightweight and efficient page migration mechanism by decoupling page hotness tracking from page migration. HMvisor performs those operations in the hypervisor and VMs separately, without disrupting the execution of VMs. We also propose a memory resource trading policy to adjust the capacity of DRAM and NVM for each VM, with the monetary cost unchanged. We implement our prototype system based on QEMU/KVM and evaluate it with several benchmarks. Experimental results show that HMvisor can reduce 50% of write traffic to NVM with less than 5% performance overhead. Moreover, the hybrid memory adjustment scheme in HMvisor can significantly improve application performance by up to 30×.

关键词： hypervisor virtual machine non-volatile memory hybrid memory management

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Multi-objective multi-population Genetic Programming for feature selection and classification to high-dimensional data

Multi-objective multi-population Genetic Programming for fea...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Li, Qiaoman Gao, Xiaoying Meng, Wenyang Ma, Jianbin Hebei Agricultural University College of Information Science and Technology Baoding China Victoria University of Wellington School of Engineering and Computer Science Wellington New Zealand College of Information Science and Technology Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Baoding China

ISBN: (纸本)9798400704956

Classification for high-dimensional data is a challenging task due to a great number of redundant and irrelevant features. Genetic Programming (GP) has its built-in feature selection characteristics and is suitable for processing high-dimensional data. However, if the number of features in GP individual is not restricted, the bloat phenomenon will occur, which affects the generalization of the training model. Moreover, GP is easy to fall into local search in the evolutionary process. In this paper, a multi-objective multi-population GP classifier construction method is proposed, which uses a multipopulation co-evolution strategy to divide the evolving population into the main and auxiliary populations with different evolutionary strategies and different evaluation criteria, and employs a multiobjective strategy to ensure classification performance and restrict the number of features at the same time. Experiments on seven high-dimensional datasets show that our proposed multi-population co-evolution strategy and multi-objective strategy are all effective to improve the classification performance of GP classifiers. Comparisons with three state-of-the-art GP classifier construction methods show that our proposed methods achieve better or comparable classification performance on most cases. © 2024 held by the owner/author(s).

关键词： Spatio-temporal data

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Hash Function Based on Quantum Walks with Two-Step Memory

Hash Function Based on Quantum Walks with Two-Step Memory

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2024 International Conference on Computing, Machine Learning and data science, CMLDS 2024

作者： Zhou, Qing Lu, Songfeng Yang, Hao Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9798400716393

We propose a new quantum-walk-based hash function QHF2M by combining two types of quantum walks with two-step memory and numerically test its statistical performance. The test result shows that QHF2M is on a par with the state-of-the-art hash functions based on discrete quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property. Moreover, both unequal and equivalent coin angles used by the underlying quantum walks can lead to near-ideal statistical performance. This result indicates that neither using quantum walks with different memory length nor adopting distinct coin angles is a necessary condition for constructing good quantum-walk-with-memory-based hash functions. © 2024 ACM.

关键词： Hash functions

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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Incremental Learning Algorithms for Broad Learning System with Node and Input Addition

Incremental Learning Algorithms for Broad Learning System wi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Guang-Ze Jin, Junwei Sun, Hai-Wei Philip Chen, C.L. Faculty of Science and Technology University of Macau Department of Mathematics 99999 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 510335 China

ISBN: (纸本)9781665410205

The Broad Learning System (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Dynamical analysis of a network-based SIR model with saturated incidence rate and nonlinear recovery rate: an edge-compartmental approach

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Mathematical Biosciences and Engineering 2024年第4期21卷 5430-5445页

作者： Wang, Fang Zhang, Juping Liu, Maoxing School of Big Data North University of China Taiyuan030051 China College of General Education Shanxi College of Technology Shuozhou036000 China Complex Systems Research Center Shanxi University Taiyuan030006 China School of Science Beijing University of Civil Engineering and Architecture Beijing102616 China

A new network-based SIR epidemic model with saturated incidence rate and nonlinear recovery rate is proposed. We adopt an edge-compartmental approach to rewrite the system as a degree-edge-mixed model. The explicit formula of the basic reproduction number R0 is obtained by renewal equation and Laplace transformation. We find that R0 0 > 1, the system can exist multiple endemic equilibria. When the number of hospital beds is small enough, the system will undergo backward bifurcation at R0 = 1. Moreover, it is proved that the stability of feasible endemic equilibrium is determined by signs of tangent slopes of the epidemic curve. Finally, the theoretical results are verified by numerical simulations. This study suggests that maintaining sufficient hospital beds is crucial for the control of infectious diseases. © 2024 the Author(s)

关键词： Hospital beds

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A Bi-selection Method Based on Consistent Matrix for Large-Scale datasets

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1992-2005页

作者： Quan, Jinsheng Qiao, Fengcai Yang, Tian Shen, Shuo Qian, Yuhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Institute of Interdisciplinary Studies Changsha410081 China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410081 China Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematics model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a β-consistent granulation method to generate more accurate and concise fuzzy information granules, reducing the impact of noisy samples. In addition, a novel membership function is employed to distinguish noise while simultaneously evaluating both features and samples. As a result, a biselection algorithm with linear computation complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency. © 2025 IEEE.

关键词： Rough sets Noise Feature extraction Classification algorithms Accuracy Machine learning Information systems data models Computational efficiency Approximation algorithms

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Feature extraction and clustering of electricity market data based on GCN and graph filtering

Feature extraction and clustering of electricity market data...

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2024 International Conference on Smart Grid and Artificial Intelligence, SGAI 2024

作者： Huang, Longda Xu, Pan Weng, Liguo China Electric Power Research Institute Co. Ltd Jiangsu Nanjing210000 China Jiangsu Key Laboratory of Big Data Analysis Technology CICAEET Nanjing University of Information Science and Technology Nanjing210044 China

As the power grid undergoes transformation and the Internet's influence grows, the electricity market is evolving towards informatization. Traditional power marketing methods struggle to cope with the vast and intricate data of the modern power market. To address this challenge, this study employs random forest and PBF models for processing electricity market data. Additionally, it introduces the spectral clustering algorithm for a comprehensive analysis. The experimental results successfully extract diverse electricity consumption characteristics. This approach aids in advancing the informatization of power grid enterprises, boosting their capacity to perceive power data, and furnishing decision-makers with dependable support. © Published under licence by IOP Publishing Ltd.

关键词： Power markets

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