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Inductive Lottery Ticket Learning for Graph Neural Networks

Journal of computer science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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Edge Caching Placement Optimization for Improving User Experience 24

Edge Caching Placement Optimization for Improving User Exper...

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2024 2nd International Conference on Internet of Things and Cloud Computing technology, IoTCCT 2024

作者： Luo, Jian Tang, Bing Liu, Yu Yang, Qing School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China School of Computer Science Guangzhou Maritime University Guangdong Guangzhou China

ISBN: (纸本)9798400710148

In recent years, the rapid expansion of short video services has led to a surge in internet traffic from mobile devices. Transferring a large volume of popular videos from centralized cloud servers to edge data centers or edge servers can incur significant expenses for these providers, highlighting the importance of a well-defined edge cache placement strategy. This paper explores into the cache placement problem of popular videos on short video platforms within edge environments, aiming to optimize user experience while minimizing cache deployment costs. Initially, we assess user experience holistically, considering both user preferences for popular videos and the latency involved in fetching requested content. Subsequently, we quantify cache placement expenses, accounting for both video transmission and storage costs. Ultimately, we formulate the placement problem as a single objective optimization problem maximizing overall user experience. A genetic algorithm is proposed to solve the problem. Simulation results indicate that, compared to traditional edge caching placement algorithms, our proposed strategy can reduce the average response time by 17% and improve the cache hit rate by 21%. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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A Survey of Deep Learning-Based Multimodal Vehicle Trajectory Prediction Methods 17th

A Survey of Deep Learning-Based Multimodal Vehicle Trajecto...

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17th China Conference on Wireless Sensor Networks, CWSN 2023

作者： Wang, Xiaoliang Zhou, Lian Liu, Yuzhen School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9789819710096

With the rapid development of deep learning, an increasing number of researchers have paid attention to and engaged in research in the field of autonomous driving, achieving numerous remarkable achievements. The paper aims to explore the current status and methods of trajectory prediction for autonomous vehicles, and reviews some commonly used trajectory prediction techniques and algorithms. The existing vehicle trajectory prediction tasks mainly involve extracting and modeling the historical trajectory sequences of the target vehicle and the surrounding environmental information features, to infer multimodal trajectories for a certain future time duration. The paper focuses on deep learning-based multimodal trajectory prediction methods, including traditional convolutional neural networks, recurrent neural networks, graph neural networks, attention mechanisms, and the latest research results of mixed models composed of multiple network structures. Additionally, two large-scale publicly available datasets in the field of autonomous driving, the Argoverse dataset and the nuScenes dataset are introduced, and the performance of existing evaluation metrics is analyzed. Finally, through the analysis and comparison of these methods, the paper summarizes and deduces the possible future directions of vehicle trajectory prediction and the existing technological bottlenecks, aiming to provide useful assistance and prospects for future research and application of autonomous vehicle trajectory prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Trajectories

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Crystalline/Non-Crystalline Carbon Co-Modified Strategy to Construct N,S Co-Doped Carbon Layer Wrapped Fe_(0.95)S_(1.05)/Carbon Nanotubes for Enhanced Lithium Storage Property

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Acta Metallurgica Sinica(English Letters) 2025年第1期38卷 93-101页

作者： Liang Chen Lan−Yun Yang Li−Ting Zeng Xu Liu Xin−Rui Li Yu−Shan Tian Wei Wang Gang−Yong Li Chen−Xi Xu Zhao−Hui Hou Key Laboratory of Hunan Province for Advanced Carbon‒based Functional Materials School of Chemistry and Chemical EngineeringHunan Institute of Science and TechnologyYueyang414006China College of Materials Science and Engineering Central South University of Forestry and TechnologyChangsha410004China

Due to their high theoretical capacity and abundant resources,transition metal sulfides are regarded as a prospering alternative to replace the commercial graphite anode in lithium-ion batteries(LIBs),particularly for large-scale energy storage and conversion ***,low conductivity,easy agglomeration and obvious volume change greatly impede their practical *** this work,a novel crystalline/non-crystalline carbon co-modified strategy is proposed to fabricate N,S co-doped carbon(NSC)layer wrapped Fe_(0.95)S_(1.05)/carbon nanotubes(CNTs)composite(Fe_(0.95)S_(1.05)/CNTs@NSC)through a simple Fenton reaction followed by a sulfurization *** characterizations and analyses reveal that this strategy well combines the advantages of crystalline CNTs and non-crystalline NSC,ensuring good conductivity and a high contribution to capacity from the carbon ***,the joint encapsulation of Fe_(0.95)S_(1.05)by both CNTs and NSC can significantly mitigate the agglomeration and volume change of Fe_(0.95)S_(1.05)during the continuous charge/discharge *** from these advantageous features,the resultant Fe_(0.95)S_(1.05)/CNTs@NSC composite displays much improved cycling stability and rate capability when compared to the ***,our research offers a distinct and innovative approach to design and construct advanced transition metal sulfides/carbon composite anodes for LIBs.

关键词： Lithium-ion batteries Crystalline/non-crystalline carbon Joint encapsulation Fe_(0.95)S_(1.05)/CNTs@NSC Lithium storage property

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai China Ant Group Shanghai China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Fudan University School of Computer Science Shanghai China Tongji University Department of Control Science and Engineering Shanghai China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning from Heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the- art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Refinement modeling and verification of secure operating systems for communication in digital twins

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Digital Communications and Networks 2024年第2期10卷 304-314页

作者： Zhenjiang Qian Gaofei Sun Xiaoshuang Xing Gaurav Dhiman School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India

In traditional digital twin communication system testing,we can apply test cases as completely as possible in order to ensure the correctness of the system implementation,and even then,there is no guarantee that the digital twin communication system implementation is completely *** verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented *** this paper,we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture,and to model the related assembly *** verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states,indicating that the system meets the design expectations.

关键词： Theorem proving Isabelle/HOL Formal verification System modeling Correctness verification

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A decentralised asynchronous federated learning framework for autonomous driving

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International Journal of Vehicle Autonomous Systems 2024年第3-4期17卷 133-149页

作者： Li, Xiaoli Cai, Ting Xiong, Wei Xu, Degang Computer School HuBei University of Arts and Science Xiangyang China School of Computer Science and Engineering Hubei University of Technology Wuhan China

Traditional autonomous driving usually requires a large number of vehicles to upload data to a central server for training. However, collecting data from vehicles may violate personal privacy as road environmental information contains geographic location. Federated learning can achieve multi-vehicle collaborative sensing of the road environment while protecting data privacy. However, the existing centralised federated learning architecture faces some challenges, such as credibility, fairness, and real-time. To address the above issues, we propose a decentralised asynchronous federated learning framework based on blockchain. Firstly, using blockchain to replace the central server of traditional federated learning architecture avoids the untrustworthy issues caused by the central architecture. Secondly, the blockchain module includes scoring contract units and incentive contract units to prevent malicious vehicle attacks and designs fair incentive mechanisms to ensure the ecological health and sustainable development of federated learning. Thirdly, using the asynchronous federated learning algorithm, blockchain can immediately aggregate model updates from vehicles, greatly improving the overall training flexibility and real-time performance. Experimental results demonstrate the effectiveness of the proposed framework. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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Revealing the Trends in the Academic Landscape of the Health Care System Using Contextual Topic Modeling

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Data Intelligence 2023年第4期5卷 923-946页

作者： Muhammad Inaam ul haq Qianmu Li School of computer science and Engineering Nanjing University of Science and Technology

The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of *** to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare *** gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of *** research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the ***,thirty-three topics are showing in-significant *** further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific *** current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary *** further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based *** also utilized corpus tuning,the mean pooling technique,and the hugging face *** method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).

关键词： Contextual topic modeling health care Bert content analysis health care system

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A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

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computers, Materials & Continua 2024年第8期80卷 2363-2385页

作者： Rong Wang Xinxin Xu Zijia Wang Fei Ji Nankun Mu School of Electronic and Information Engineering South China University of TechnologyGuangzhou510641China School of Computer Science and Technology Ocean University of ChinaQingdao266100China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China School of Computer Science Chongqing UniversityChongqing400044China

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics ***,the efficiency of resource scheduling significantly influences the operation performance of *** solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm ***,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated *** new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the ***,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection *** the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible *** the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality *** evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is *** results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

关键词： Resource scheduling problem(RSP) ant colony system(ACS) marine container terminal(MCT) pre-selection strategy

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