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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Federated learning

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Sequential Fusion of Text-close and Text-far Representations for Multimodal Sentiment Analysis 31

Sequential Fusion of Text-close and Text-far Representations...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sun, Kaiwei Tian, Mi Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798891761964

Multimodal Sentiment Analysis (MSA) aims to identify human attitudes from diverse modalities such as visual, audio and text modalities. Recent studies suggest that the text modality tends to be the most effective, which has encouraged models to consider text as its core modality. However, previous methods primarily concentrate on projecting modalities other than text into a space close to the text modality and learning an identical representation, which does not fully make use of the auxiliary information provided by audio and visual modalities. In this paper, we propose a framework, Sequential Fusion of Text-close and Text-far Representations (SFTTR), aiming to refine multimodal representations from multimodal data which should contain both representations close to and far from the text modality. Specifically, we employ contrastive learning to sufficiently explore the information similarities and differences between text and audio/visual modalities. Moreover, to fuse the extracted representations more effectively, we design a sequential cross-modal encoder to sequentially fuse representations that are close to and far from the text modality. Experiments on three public benchmark datasets, MOSI, MOSEI, and CH-SIMS, demonstrate the superiority of the proposed method over the state-of-the-arts. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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On the optimality of uniform velocity-deceleration separation scheme for tethered satellite systems

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Control Theory and Technology 2024年第4期22卷 503-516页

作者： Peng Yu Ji-Feng Zhang Shuping Tan Jin Guo Yanlong Zhao School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China Beijing Institute of Control Engineering Beijing100094China

For mitigating the libration angle fluctuation of the tethered satellite system,this paper discusses how to make the uniform velocity-deceleration separation scheme achieve the best ***,a judgment condition is established to determine the tether state by comparing the tether length and the relative distance of the sub-satellite and the parent *** on the tethered satellite system dynamics equation and Clohessy-Wiltshire equation,dynamic models are given for four cases of tether ***,the influence of the uniform velocity-deceleration separation scheme on the libration angle is analyzed by taking the libration angle at the separation ending time and the mean absolute value of the libration angle as index ***,the optimality problem of the uniform velocity-deceleration separation scheme is formulated as an optimization problem with constraints,and an approximate solution algorithm is given by combining the back propagation neural network and Newton-Raphson method of multiple initial ***,the effectiveness of the proposed method is verified by a numerical simulation.

关键词： Tethered satellite systems Libration angle Uniform velocity-deceleration separation Neural network

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

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Chinese Journal of Chemical engineering 2023年第3期55卷 266-276页

作者： Yiming Bai Shuaiyu Xiang Feifan Cheng Jinsong Zhao Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China Sinopec Engineering Incorporation Beijing 100084China

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

关键词： Fault prognosis Process systems Safety Prediction Principal component analysis Long short term memory

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Performance optimization of aqueous Zn/MnO^(2)batteries through the synergistic effect of PVP intercalation and GO coating

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Chinese Chemical Letters 2025年第3期36卷 488-493页

作者： Shilong Li Ming Zhao Yefei Xu Zhanyi Liu Mian Li Qing Huang Xiang Wu School of Materials Science and Engineering Shenyang University of TechnologyShenyang 110870China Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China

Manganese dioxide(MnO_(2))electrode material possesses the advantages of high energy density,structural diversity and high modification *** allows it become one of the important cathodes for aqueous zinc ion ***,the applications are limited by the poor electrical conductivity,narrow layer spacing and the ease of ***,we prepare MnO_(2)-PVP@0.03GO composites by the co-modification of polyvinylpyrrolidone(PVP)pre-insertion layer and graphene oxide(GO)self-assembly *** Zn//MnO_(2)-PVP@0.03GO cells deliver a discharge specific capacity of 442 mAh/g at a current density of 0.2 A/*** also maintains 100%capacity for 1000 times cycling at 1 A/*** assembled soft package batteries demonstrate superior flexibility and adaptability under different bending conditions.

关键词： Aqueous zinc ion batteries Cathode MnO_(2) Cycle stability High capacity

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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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Computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured *** extraction of encrypted traffic attributes and their subsequent identification presents a formidable *** existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s *** the present study,a new model,referred to as UD-VLD(Unbalanced dataset-VAE-LSTM-DRN),was proposed to address above *** proposed model is an encrypted traffic identification model for handling unbalanced *** encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced *** enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related ***,the data is classified and *** UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced *** UD-VLD model was tested using the publicly available Tor dataset and VPN *** UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC *** results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced ***,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic *** can serve as a novel solution for encrypted traffic identification.

关键词： data enhancement LSTM deep residual network VAE

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Adaptive multiscale convolutional neural network model for chemical process fault diagnosis

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Chinese Journal of Chemical engineering 2022年第10期50卷 398-411页

作者： Ruoshi Qin Jinsong Zhao State Key Laboratory of Chemical Engineering Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China

Intelligent fault recognition techniques are essential to ensure the long-term reliability of *** to the variations in material,equipment and environment,the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating *** much progress in statistical learning and deep learning for fault recognition,most models are constrained by abundant diagnostic expertise,inefficient multiscale feature extraction and unruly multimode *** overcome the above issues,a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the selection of relevant fault pattern *** triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode *** benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed *** with other common models,AMCNN shows its outstanding fault diagnosis performance and great generalization ability.

关键词： Neural networks Multiscale Adaptive attentionmodule Triplet lossoptimization Fault diagnosis Chemical processes

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data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

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Science China(Technological Sciences) 2023年第5期66卷 1304-1316页

作者： YANG Lei LI ShaoBo LI ChuanJiang ZHU CaiChao ZHANG AnSi LIANG GuoQiang School of Mechanical Engineering Guizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China State Key Laboratory of Mechanical Transmission Chongqing UniversityChongqing400044China

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from ***-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous ***,flight data with random noise pose a significant challenge for anomaly *** work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight ***,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the ***-based feature subset selection is subsequently performed to reduce the reliance on expert ***,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised ***,the method's effectiveness is validated on real UAV flight data.

关键词： unmanned aerial vehicle(UAV) anomaly detection spatiotemporal correlation based on long short-term memory and autoencoder(STC-LSTM-AE) Savitzky-Golay feature selection

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