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Semantic scattering graph structure alignment for cross-sensor SAR image target detection 认领 引用
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作者 Xianghui ZHANG Siqian ZHANG +4 位作者 Zhongzhen SUN Chenfang LIU Yuli SUN Kefeng JI Gangyao KUANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期639-658,共20页
Cross-sensor Synthetic Aperture Radar(SAR)target detection suffers from severe performance degradation due to the inconsistency of sensors between training and testing data.Structural characteristics of SAR targets,as... Cross-sensor Synthetic Aperture Radar(SAR)target detection suffers from severe performance degradation due to the inconsistency of sensors between training and testing data.Structural characteristics of SAR targets,as important intrinsic properties,play a crucial role in robust detection.However,variations in sensor types often cause inconsistencies in structural details,leading to distribution differences and domain shifts.To address these challenges,we propose a cross-sensor SAR target detection method based on semantic scattering graph structure alignment.First,a semantic scattering graph is constructed from sampled scattering points to characterize the target’s intrinsic structure.Meanwhile,the semantic node associations are enhanced with cross-domain statistical distributions to enrich node prior information and a graph convolutional network to strengthen contextual awareness.Then,a hierarchical structure alignment mechanism is introduced to calibrate structural consistency across domains.This includes the cross-domain perceptual interaction to narrow the semantic distribution differences.Furthermore,the hierarchical alignment is operated at both the local node level and the global structure level,achieving the alignment of target structural consistency.Finally,the experiments conducted on cross-sensor tasks demonstrate that our method significantly outperforms the state-of-the-art methods,achieving improvements of 5%–40%in mAP and F1-score,which highlights the effectiveness of the proposed approach. 展开更多
关键词 Synthetic aperture radar Target detection Graph structures Semantic scattering point Domain adaptation
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Multivariate Data Anomaly Detection Based on Graph Structure Learning 认领 引用
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作者 Haoxiang Wen Zhaoyang Wang +2 位作者 Zhonglin Ye Haixing Zhao Maosong Sun 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1174-1206,共33页
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co... Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment. 展开更多
关键词 Multivariate data anomaly detection graph structure learning coupled network
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Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention 认领 引用 被引量：1
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作者 Yifan Gao Jieming Zhang +1 位作者 Zhanchen Chen Xianchao Chen 《Computers, Materials & Continua》 SCIE EI 2024年第4期493-507,共15页
In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower s... In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection. 展开更多
关键词 Anomaly detection transformer graph structure
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PGSLM:Edge-Enabled Probabilistic Graph Structure Learning Model for Traffic Forecasting in Internet of Vehicles 认领 引用
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作者 Xiaozhu Liu Jiaru Zeng +1 位作者 Rongbo Zhu Hao Liu 《China Communications》 SCIE CSCD 2023年第4期270-286,共17页
With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simu... With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simultaneously.To enhance the forecasting performance,a novel edge-enabled probabilistic graph structure learning model(PGSLM)is proposed,which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road network.To obtain the spatio-temporal dependencies of traffic data,the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution module.During the training process,a new graph training loss is introduced,which is composed of the K nearest neighbor(KNN)graph constructed by the traffic feature tensors and the graph structure.Detailed experimental results show that,compared with existing models,the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV. 展开更多
关键词 edge computing traffic forecasting graph convolutional network graph structure learning Internet of Vehicles
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Graph Structure Learning for Robust Recommendation 认领 引用 被引量：4
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作者 Lei Sang Hang Yuan +1 位作者 Yuee Huang Yiwen Zhang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2025年第4期1617-1635,共19页
Recommendation systems play a crucial role in uncovering concealed interactions among users and items within online social networks.Recently,Graph Neural Network(GNN)-based recommendation systems exploit higher-order ... Recommendation systems play a crucial role in uncovering concealed interactions among users and items within online social networks.Recently,Graph Neural Network(GNN)-based recommendation systems exploit higher-order interactions within the user-item interaction graph,demonstrating cutting-edge performance in recommendation tasks.However,GNN-based recommendation models are susceptible to different types of noise attacks,such as deliberate perturbations or false clicks.These attacks propagate through the graph and adversely affect the robustness of recommendation results.Conventional two-stage method that purifies the graph before training the GNN model is suboptimal.To strengthen the model’s resilience to noise,we propose Graph Structure Learning for Robust Recommendation(GSLRRec),a joint learning framework that integrates graph structure learning and GNN model training for recommendation.Specifically,GSLRRec considers the graph adjacency matrix as adjustable parameters,and simultaneously optimizes both the graph structure and the representations of user/item nodes for recommendation.During the joint training process,the graph structure learning employs low-rank and sparse constraints to effectively denoise the graph.Our experiments illustrate that the simultaneous learning of both structure and GNN parameters can provide more robust recommendation results under various noise levels. 展开更多
关键词 robust recommendation Graph Neural Network(GNN) Graph Structure Learning(GSL)
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DHSEGATs:distance and hop-wise structures encoding enhanced graph attention networks 认领 引用 被引量：1
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作者 HUANG Zhiguo 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2023年第2期350-359,共10页
Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can signi... Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result. 展开更多
关键词 graph attention network(GAT) graph structure information label propagation
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Chinese Semantic Parsing Based on Feature Structure with Recursive Directed Graph 认领 引用
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作者 CHEN Bo Lü Chen +1 位作者 WEI Xiaomei JI Donghong 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2015年第4期318-322,共5页
It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semanti... It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations. 展开更多
关键词 recursive directed graph feature structure semantic annotation Chinese special sentence patterns
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A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults 认领 引用
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作者 Bin Wang Manyi Wang +3 位作者 Yadong Xu Liangkuan Wang Shiyu Chen Xuanshi Chen 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2025年第8期364-373,共10页
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o... Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems. 展开更多
关键词 Fault diagnosis Graph neural networks Graph topological structure Intrinsic mode functions Feature learning
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Erratum:Data-Driven Prediction of Thermal Conductivity from Short MD Trajectories:A GCN-LSTM Approach [Chin.Phys.Lett.43 020801 (2026)] 认领 引用
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作者 Shihao Feng Haifeng Chen +2 位作者 Jian Zhang Meng An Gang Zhang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第3期380-380,共1页
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu... In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted. 展开更多
关键词 typesetting error production processfigure short MD trajectories GCN LSTM molecular dynamics simulation thermal conductivity graph structured representations data driven prediction
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Ponzi Scheme Detection for Smart Contracts Based on Oversampling 认领 引用
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作者 Yafei Liu Yuling Chen +2 位作者 Xuewei Wang Yuxiang Yang Chaoyue Tan 《Computers, Materials & Continua》 SCIE EI 2026年第1期1065-1085,共21页
As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ... As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods. 展开更多
关键词 Blockchain smart contracts Ponzi schemes class imbalance graph structure construction
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NCRT:A Noise-Tolerant Label Recognition and Correction Framework for Network Traffic Detection 认领 引用
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作者 Yu Yang Yuheng Gu +1 位作者 Zhuoyun Yang Shangjun Wu 《Computers, Materials & Continua》 SCIE EI 2026年第6期2181-2199,共19页
The performance of network traffic detection heavily relies on high-quality annotated data,yet the widespread presence of noisy labels in real-world scenarios severely undermines model reliability and generalization.E... The performance of network traffic detection heavily relies on high-quality annotated data,yet the widespread presence of noisy labels in real-world scenarios severely undermines model reliability and generalization.Existing methods predominantly rely on training dynamics signals and struggle to distinguish between noisy labels and valuable hard samples,leading to diminished model sensitivity to emerging threats.To address this fundamental challenge,this paper proposes a noise-tolerant label recognition and correction framework based on graph-structured neighbor consistency(NCRT).The framework leverages the inherent clustering characteristics of network traffic in feature space,constructs an adaptive K-nearest neighbor graph to model behavioral similarities among samples,and employs multi-dimensional consistency evaluation metrics to accurately differentiate between noisy labels and hard samples.Furthermore,a feature-space-based label propagation algorithm is introduced,which utilizes reliable anchor samples in the graph to guide noisy labels toward correction based on the consensus of their semantic neighbors.The core innovation lies in leveraging graph-structured neighbor consistency to fundamentally distinguish noisy labels from hard samples,and correcting them via label propagation over an adaptive K-nearest neighbor graph.Experiments on real-world datasets such as CICIDS2017 and NSL-KDD demonstrate that the proposed method significantly outperforms mainstream baseline approaches in terms of noise identification,label correction,and final classification performance.It exhibits exceptional robustness and stability,particularly under high-ratio and asymmetric noise settings,providing an effective solution for building reliable traffic detection models adapted to complex real-world network environments. 展开更多
关键词 Network traffic detection noisy labels graph structure neighbor consistency label propagation
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Application of topology-based structure features for machine learning in materials science 认领 引用 被引量：4
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作者 Shisheng Zheng Haowen Ding +2 位作者 Shunning Li Dong Chen Feng Pan 《Chinese Journal of Structural Chemistry》 SCIE CAS CSCD 2023年第7期47-53,共7页
Structure features play an important role in machine learning models for the materials investigation.Here,two topology-based features for the representation of material structure,specifically structure graph and algeb... Structure features play an important role in machine learning models for the materials investigation.Here,two topology-based features for the representation of material structure,specifically structure graph and algebraic topology,are introduced.We present the fundamental mathematical concepts underlying these techniques and how they encode material properties.Furthermore,we discuss the practical applications and enhancements of these features made in specific material predicting tasks.This review may provide suggestions on the selection of suitable structural features and inspire creativity in developing robust descriptors for diverse applications. 展开更多
关键词 Machine learning Structure feature Structure graph Algebraic topology
Smoother manifold for graph meta-learning 认领 引用
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作者 ZHAO Wencang WANG Chunxin XU Changkai 《High Technology Letters》 EI CAS 2022年第1期48-55,共8页
Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain d... Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain differences among them.These factors often result in poor generalization in existing meta-learning models.In this work,a novel smoother manifold for graph meta-learning(SGML)is proposed,which derives the similarity parameters of node features from the relationship between nodes and edges in the graph structure,and then utilizes the similarity parameters to yield smoother manifold through embedded propagation module.Smoother manifold can naturally filter out noise from the most important components when generalizing the local mapping relationship to the global.Besides suiting for generalizing on unseen low data issues,the framework is capable to easily perform transductive inference.Experimental results on MiniImageNet and TieredImageNet consistently show that applying SGML to supervised and semi-supervised classification can improve the performance in reducing the noise of domain shift representation. 展开更多
关键词 meta-learning smoother manifold similarity parameter graph structure
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A Secure Microgrid Data Storage Strategy with Directed Acyclic Graph Consensus Mechanism 认领 引用
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作者 Jian Shang Runmin Guan Wei Wang 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2609-2626,共18页
The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to ... The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks.To meet the high hardware resource requirements,address the vulnerability to network attacks and poor reliability in the tradi-tional centralized data storage schemes,this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph(DAG)consensus mechanism.Firstly,the microgrid data storage model is designed based on the edge computing technology.The blockchain,deployed on the edge computing server and combined with cloud storage,ensures reliable data storage in the microgrid.Secondly,a blockchain consen-sus algorithm based on directed acyclic graph data structure is then proposed to effectively improve the data storage timeliness and avoid disadvantages in traditional blockchain topology such as long chain construction time and low consensus efficiency.Finally,considering the tolerance differences among the candidate chain-building nodes to network attacks,a hash value update mechanism of blockchain header with node trust identification to ensure data storage security is proposed.Experimental results from the microgrid data storage platform show that the proposed method can achieve a private key update time of less than 5 milliseconds.When the number of blockchain nodes is less than 25,the blockchain construction takes no more than 80 mins,and the data throughput is close to 300 kbps.Compared with the traditional chain-topology-based consensus methods that do not consider node trust,the proposed method has higher efficiency in data storage and better resistance to network attacks. 展开更多
关键词 Microgrid data security storage node trust degree directed acyclic graph data structure consensus mechanism secure multi-party computing blockchain
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Causal Representation Enhances Cross-Domain Named Entity Recognition in Large Language Models 认领 引用 被引量：2
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作者 Jiahao Wu Jinzhong Xu +2 位作者 Xiaoming Liu Guan Yang Jie Liu 《Computers, Materials & Continua》 SCIE EI 2025年第5期2809-2828,共20页
Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information ... Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations. 展开更多
关键词 Large language model entity bias causal graph structure
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Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering 认领 引用 被引量：1
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作者 Kai Zhou Yanan Bai +1 位作者 Yongli Hu Boyue Wang 《Computers, Materials & Continua》 SCIE EI 2025年第3期3873-3890,共18页
Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin s... Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at http://gffzz188fe103f8f1460asu0nu0556noop6xqk.ffgz.tsg.suse.edu.cn/songzuolong/MNF-MDSC(accessed on 25 December 2024). 展开更多
关键词 Multi-view subspace clustering subspace clustering deep clustering multi-order graph structure
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Guided Proximal Policy Optimization with Structured Action Graph for Complex Decision-making 认领 引用 被引量：1
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作者 Yiming Yang Dengpeng Xing +1 位作者 Wannian Xia Peng Wang 《Machine Intelligence Research》 EI CSCD 2025年第4期797-816,共20页
Reinforcement learning encounters formidable challenges when tasked with intricate decision-making scenarios,primarily due to the expansive parameterized action spaces and the vastness of the corresponding policy land... Reinforcement learning encounters formidable challenges when tasked with intricate decision-making scenarios,primarily due to the expansive parameterized action spaces and the vastness of the corresponding policy landscapes.To surmount these difficulties,we devise a practical structured action graph model augmented by guiding policies that integrate trust region constraints.Based on this,we propose guided proximal policy optimization with structured action graph(GPPO-SAG),which has demonstrated pronounced efficacy in refining policy learning and enhancing performance across sophisticated tasks characterized by parameterized action spaces.Rigorous empirical evaluations of our model have been performed on comprehensive gaming platforms,including the entire suite of StarCraft II and Hearthstone,yielding exceptionally favorable outcomes.Our source code is at http://gffzz188fe103f8f1460asu0nu0556noop6xqk.ffgz.tsg.suse.edu.cn/sachiel321/GPPO-SAG. 展开更多
关键词 Reinforcement learning trust region policy optimization complex decision-making policy guiding structured action graph
Graph Games with Component-Based Control Structures 认领 引用
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作者 Letian Chen Guang Zhang 《Journal of Applied Mathematics and Physics》 2025年第11期3989-4001,共13页
This paper investigates allocation rules in graph-structured cooperative games(hereinafter referred to as graph games)by integrating the notion of network control.A component-restricted game and the component control ... This paper investigates allocation rules in graph-structured cooperative games(hereinafter referred to as graph games)by integrating the notion of network control.A component-restricted game and the component control value are proposed through treating each connected component as a virtual player(termed a component player),under the stipulation that only coalitions attended by component players are eligible to obtain coalitional worth.The component control value initially assigns a Shapley payoff(Shapley value,SV)to every player,and the SV of each component player is subsequently redistributed equally among all original players belonging to its corresponding connected component.Thereby,an axiomatic characterization of this allocation rule is established,demonstrating that the component control value constitutes the unique solution satisfying component efficiency and component-restricted fairness in graph games. 展开更多
关键词 Cooperative Game Graph Structure Component Restriction Allocation Rule
GSPT-CVAE: A New Controlled Long Text Generation Method Based on T-CVAE 认领 引用
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作者 Tian Zhao Jun Tu +1 位作者 Puzheng Quan Ruisheng Xiong 《Computers, Materials & Continua》 SCIE EI 2025年第7期1351-1377,共27页
Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper propo... Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper proposes a new GSPT-CVAE model(Graph Structured Processing,Single Vector,and Potential Attention Com-puting Transformer-Based Conditioned Variational Autoencoder model).The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text,and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation.In the process of potential representation guiding text generation,the model adopts a combination of traditional embedding and potential attention calculation to give full play to the guiding role of potential representation for generating text,to improve the controllability and effectiveness of text generation.The experimental results show that the model has excellent representation learning ability and can learn rich and useful textual relationship representations.The model also achieves satisfactory results in the effectiveness and controllability of text generation and can generate long texts that match the given constraints.The ROUGE-1 F1 score of this model is 0.243,the ROUGE-2 F1 score is 0.041,the ROUGE-L F1 score is 0.22,and the PPL-Word score is 34.303,which gives the GSPT-CVAE model a certain advantage over the baseline model.Meanwhile,this paper compares this model with the state-of-the-art generative models T5,GPT-4,Llama2,and so on,and the experimental results show that the GSPT-CVAE model has a certain competitiveness. 展开更多
关键词 Controllable text generation textual graph structuring text relationships potential characterization
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NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT 认领 引用 被引量：4
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作者 Chaopeng Guo Zhaojin Zhong +1 位作者 Zexin Zhang Jie Song 《Digital Communications and Networks》 SCIE CSCD 2024年第2期439-449,共11页
A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction... A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at http://gffzz188fe103f8f1460asu0nu0556noop6xqk.ffgz.tsg.suse.edu.cn/NEUSoftGreenAI/NeurstrucEnergy.git. 展开更多
关键词 Internet of things Neural network energy prediction Graph neural networks Graph structure embedding Multi-head attention
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