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A hierarchical and interlamination graph self-attention mechanism-based knowledge graph reasoning architecture

INFORMATION SCIENCES 2025年 686卷

作者： Wu, Yuejia Zhou, Jian-tao Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Minist Educ Engn Res Ctr Ecol Big Data Hohhot Peoples R China Inner Mongolia Engn Lab Cloud Comp & Serv Softwar Hohhot Peoples R China Inner Mongolia Key Lab Social Comp & Data Proc Hohhot Peoples R China Inner Mongolia Key Lab Discipline Inspect & Super Hohhot Peoples R China Inner Mongolia Engn Lab Big Data Anal Technol Hohhot Peoples R China

Knowledge Graph (KG) is an essential research field in graph theory, but its inherent incompleteness and sparsity influence its performance in several fields. Knowledge Graph Reasoning (KGR) aims to ameliorate those problems by mining new knowledge from subsistent knowledge. As one of the downstream tasks of KGR, link prediction is of great significance for improving the quality of KG. Recently, the Graph Neural Network (GNN)-based method became the most effective way to achieve the link prediction task. However, it still suffers from problems such as incomplete neighbor and relation-level information aggregation and unstable learning of the entity's features. To improve those issues, a Hierarchical and Interlamination Graph Self-attention Mechanism- based (HIGSM) plug-and-play architecture is proposed for KGR in this paper. It is composed of three-level layers: feature extractor, encoder, and decoder. The feature extractor makes our architecture more effective and stable for the retrieval of new features. The encoder is equipped with a two-stage encoding mechanism accompanied by two mixture-of-expert strategies, which enables our architecture to capture more practical reasoning information to improve prediction accuracy and generalization of the model. The decoder can use existing KGR models and compute the scores of triples in KG. The extensive experimental results and ablation studies on four KGs unambiguously demonstrate the state-of-the-art prediction performance of the proposed HIGSM architecture compared to current GNN-based methods.

关键词： Knowledge graph reasoning Graph self-attention mechanism variational autoencoder Mixture-of-expert Link prediction

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Application of Artificial Intelligence Virtual Image Technology in Photography Art Creation Under Deep Learning

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IEEE ACCESS 2025年 13卷 14542-14556页

作者： Yao, Qiong Mahasarakham Univ Fac Fine Appl Arts & Cultural Sci Maha Sarakham 44150 Thailand

With the continuous advancement of artificial intelligence (AI) and deep learning technologies, virtual image generation exhibits significant potential for application in photographic art creation. The primary objective of this study is to investigate the use of AI virtual image technology in photography, particularly focusing on achieving creative expression and artistic style transfer through deep learning models. Consequently, this study proposes a novel model that integrates conditional generative adversarial networks (cGANs) with variational autoencoders (VAEs). This model aims to effectively address the challenges associated with image generation and style conversion in photographic art by leveraging the realistic generation capabilities of cGANs alongside the diversity maintenance features of VAEs. In the experimental section, the proposed cGANs + VAEs model is systematically compared with traditional Deep Convolutional GANs (DCGAN) and Pix2Pix models through empirical analysis. The experimental results indicate that the cGANs + VAEs model significantly outperforms traditional models in terms of image quality, artistic expression, and user satisfaction. Expert reviews further confirm the model's superiority in artistic style imitation and creative generation. Additionally, user surveys reveal that most participants are highly satisfied with the images generated by the model, particularly regarding artistic perception and visual effects. Moreover, the cGANs + VAEs model demonstrates strong performance in Frechet Inception Distance (FID) and Inception Score (IS) across multiple datasets, yielding FID values of 13.67, 9.45, and 11.90 on the COCO, CelebA, and WikiArt datasets, respectively. In summary, the proposed cGANs + VAEs model not only achieves remarkable advancements in the technical performance of image generation but also exhibits considerable potential for practical applications in photographic art creation.

关键词： Image synthesis Deep learning Art Artificial intelligence Photography Training Diversity reception Data models Analytical models Translation conditional generative adversarial networks variational autoencoder photography artistic creation virtual image technology

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Asynchronous multimodal PINN pre-train framework based on TransVNet(MPP-TV) and its application in numerical solutions of the Cauchy problem for the Hamilton-Jacobi equation

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2025年 187卷 203-230页

作者： Chen, Tianhao Li, Zeyu Xu, Pengbo Zheng, Haibiao East China Normal Univ Sch Math Sci Key Lab MEA Shanghai Key Lab PMMPMinist Educ Shanghai 200241 Peoples R China Sanda Univ Innovat Res Ctr AI Finance Shanghai 201209 Peoples R China Shanghai Zhangjiang Inst Math Shanghai 201203 Peoples R China

The Hamilton-Jacobi(HJ) equation represents a class of highly nonlinear partial differential equations. Classical numerical techniques, such as finite element methods, face significant challenges when addressing the numerical solutions of such nonlinear HJ equations. However, recent advances in neural network-based approaches, particularly Physics-Informed Neural Networks (PINNs) and neural operator methods, have ushered in innovative paradigms for numerically solving HJ equations. In this work, we leverage the PINN approach, infused with the concept of neural operators. By encoding and extracting features from the discretized images of functions through TransVNet, which is a novel autoencoder architecture proposed in this paper, we seamlessly integrate Hamiltonian information into PINN training, thereby establishing a novel scientific computation framework. Additionally, we incorporate the vanishing viscosity method, introducing viscosity coefficients in our model, which equips our model to tackle potential singularities in nonlinear HJ equations. These attributes signify that our MPP-TV framework paves new avenues and insights for the generalized solutions of nonlinear HJ equations.

关键词： Physics-informed neural network Numerical methods for PDE Hamilton-Jacobi equation variational autoencoder

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Counterfactual experience augmented off-policy reinforcement learning

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NEUROCOMPUTING 2025年 637卷

作者： Lee, Sunbowen Gong, Yicheng Deng, Chao Wuhan Univ Sci & Technol Coll Sci Wuhan 430065 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Syst Sci Met Proc Wuhan 430065 Hubei Peoples R China Wuhan Univ Sci & Technol Intelligent Automobile Engn Res Inst Sch Automobile & Traff Engn Wuhan 430065 Hubei Peoples R China

Reinforcement learning control algorithms face significant challenges due to out-of-distribution and inefficient exploration problems. While model-based reinforcement learning enhances the agent's reasoning and planning capabilities by constructing virtual environments, training such virtual environments can be very complex. In order to build an efficient inference model and enhance the representativeness of learning data, we propose the Counterfactual Experience Augmentation (CEA) algorithm. CEA leverages variational autoencoders to model the dynamic patterns of state transitions and introduces randomness to model non-stationarity. This approach focuses on expanding the learning data in the experience pool through counterfactual inference and performs exceptionally well in environments that follow the bisimulation assumption. Environments with bisimulation properties are usually represented by discrete observation and action spaces, we propose a sampling method based on maximum kernel density estimation entropy to extend CEA to various environments. By providing reward signals for counterfactual state transitions based on real information, CEA constructs a complete counterfactual experience to alleviate the out-of-distribution problem of the learning data, and outperforms general SOTA algorithms in environments with difference properties. Finally, we discuss the similarities, differences and properties of generated counterfactual experiences and real experiences. The code is available at https://***/Aegis1863/CEA.

关键词： Reinforcement learning variational autoencoder Counterfactual inference Bisimulation

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Multi-agent reinforcement learning for cooperative search under aperiodically intermittent communication

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Fu, Longyue Wang, Jiao Luo, Hongchen Northeastern Univ Coll Informat Sci & Engn 3-11 Wenhua Rd Shenyang 110819 Liaoning Peoples R China

Aperiodically Intermittent Communication (AIC) refers to the unpredictable irregular disruptions in exchange of information among agents. These disruptions pose significant challenges to multi-agent cooperative search tasks. The challenges are information loss, increased uncertainty, and reduced collaboration efficiency, thus limiting overall search performance. The Aperiodically Intermittent Communication Reinforcement Learning (AICoRL) algorithm is proposed to achieve multi-agent cooperative search under AIC to address these challenges. Firstly, AICoRL integrates an Adaptive Predictive variational autoencoder (AP-VAE) to reconstruct lost information during communication interruptions. Predicting uncertain information through adaptive communication states and global features enables agents to maintain an approximate global perspective. Secondly, to enhance agents' adaptability in complex dynamic environments, the policy network employs multi-agent reinforcement learning with noisy parameterization. It integrates individual observations with reconstructed features and encourages a more diverse exploration of the action space. Extensive experimental results demonstrate that AICoRL significantly outperforms traditional methods in the search task under AIC. Our algorithm achieves higher search efficiency, better reward performance, and stronger robustness. This framework demonstrates its potential for enhancing the performance of multi-agent systems operating under AIC.

关键词： Aperiodically intermittent communication Multi-agent reinforcement learning Multi-agent cooperative search variational autoencoder

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The Application of Lite-GRU Embedding and VAE-Augmented Heterogeneous Graph Attention Network in Friend Link Prediction for LBSNs

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4585-4585页

作者： Yang, Ziteng Li, Boyu Wang, Yong Liu, Aoxue China Univ Geosci Wuhan Sch Comp Sci Wuhan 430078 Peoples R China

Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behaviors, along with the high dimensionality, sparsity, and complex noise in the data. To address these issues, this paper proposes a Heterogeneous Graph Attention Network (GEVEHGAN) model based on Lite Gate Recurrent Unit (Lite-GRU) embedding and variational autoencoder (VAE) enhancement. The model constructs a heterogeneous graph with two types of nodes and three types of edges;combines Skip-Gram and Lite-GRU to learn Point of Interest (POI) and user node embeddings;introduces VAE for dimensionality reduction and denoising of the embeddings;and employs edge-level attention mechanisms to enhance information propagation and feature aggregation. Experiments are conducted on the publicly available Foursquare dataset. The results show that the GEVEHGAN model outperforms other comparative models in evaluation metrics such as AUC, AP, and Top@K accuracy, demonstrating its superior performance in the friend link prediction task.

关键词： friend link prediction gated recurrent unit variational autoencoder heterogeneous graph attention mechanism

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A semi-supervised multiscale generalized-VAE framework for one-class classification

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NEUROCOMPUTING 2025年 620卷

作者： Sharma, Renuka Awate, Suyash P. Commonwealth Sci & Ind Res Org CSIRO Data61 Brisbane Australia Indian Inst Technol IIT Bombay Comp Sci & Engn CSE Dept Mumbai India IITB Monash Res Acad Mumbai India

Deep-learning based approaches for unsupervised anomaly detection typically learn either a generative model of the inlier class or a decision boundary to encapsulate the inlier class. In addition to the training data from the inlier class, the availability of a small amount of training data from the outlier class can aid in refining the classifier model using principles of semi-supervised learning. This paper proposes a novel end-to-end deep semi-supervised variational framework for one-class classification of images, leveraging data-adaptive generalized- Gaussian (GG) models leading to effective modeling of distributions in both latent space and image space. The framework proposes a novel variational encoder that models a distribution on a multiscale (here, "scale"refers to spatial resolution) latent-space encoding, together with a generalized reparameterization scheme for the GG model's sampling at each such scale. While the multiscale latent-space helps effective feature learning at coarse and fine spatial scales, the semi-supervision helps tune the feature learning to improve separability between the inliers and the outliers. Results on several publicly available industrial-imaging and medical-imaging datasets show the benefits of our framework's novel components over existing approaches.

关键词： One-class classification variational autoencoder Semi-supervision Multiscale latent space Generalized-Gaussian Robustness Uncertainty

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Synthetic data for enhanced privacy: A VAE-GAN approach against membership inference attacks

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KNOWLEDGE-BASED SYSTEMS 2025年 309卷

作者： Yan, Jian'en Huang, Haihui Yang, Kairan Xu, Haiyan Li, Yanling Harbin Inst Technol Fac Comp Harbin 150001 Heilongjiang Peoples R China Inner Mongolia Normal Univ Coll Comp Sci & Technol Hohhot 010022 Peoples R China

The raw data utilized in training machine learning models faces a potential threat from membership inference attacks. To mitigate this risk, employing synthetic data instead of real data is proved effective in desensitizing the information. We introduce a novel generative model, combining variational autoencoder and Generative Adversarial Network, to enhance privacy protection by generating synthetic data. In our approach, discrete variables are encoded by conditional generators, and sampling training is employed to ensure the distribution of synthetic data closely aligning with the real data. The modification of the model structure prompts a refinement of the loss function. We leverage Wasserstein distance with gradient penalty and SNorm to keep the stability of the model training process. Experimental results demonstrate that the efficacy of our model surpasses existing state-of-the-art models in terms of data utility metrics. Notably, in the face of membership inference attacks, the similarity from the results indicates the difficulty when distinguish the real data from synthetic data. It means our model have highlighting capabilities for the privacy protection.

关键词： Membership privacy Synthetic data variational autoencoder Generative Adversarial Network Tabular data

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Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2025年

作者： Yang, Wenjie Chu, Wenchao Wu, Xingfu Zhou, Lianlin Wang, Jiayi Yang, Hua Li, Zirui Hebei Univ Technol Sch Artificial Intelligence State Key Lab Reliabil & Intelligence Elect Equipm Tianjin Peoples R China AECC South Ind Co Ltd Blade Machining Ctr Zhuzhou Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha Peoples R China Suzhou Shushe Technol Co Ltd Suzhou Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China

To address the challenges faced in industrial anomaly detection, including data sample imbalance, lack of anomaly labels, and complex spatiotemporal relationships in high-dimensional data, this paper proposes a novel multi-modal time-series anomaly detection model that combines attention mechanisms and adversarial training. In this model, the first step involves utilizing graph attention mechanisms to extract sequence correlation features from multi-modal time-series data, which are then summed with the original data to form a dual-feature-based data representation. Subsequently, a self-supervised learning approach is employed to input this data representation into a variational autoencoder's encoding-decoding network for reconstruction. Anomaly detection is performed by analyzing the error between the input and reconstructed data. The model also employs spatiotemporal attention mechanisms and adversarial training during reconstruction to enhance feature extraction and model generalization. By comparing our proposed model to five commonly used baseline models, we demonstrate its effectiveness in detecting anomalies in scenarios involving high-dimensional data and imbalanced abnormal samples, demonstrating superior anomaly detection performance, as well as excellent performance on real industrial production and processing datasets.

关键词： Anomaly detection deep learning multivariate time series variational autoencoder adversarial learning Self-supervised learning

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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment

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ENERGIES 2025年第7期18卷 1637-1637页

作者： Goncalves, Rafael Magalhaes, Diogo Teixeira, Rafael Antunes, Mario Gomes, Diogo Aguiar, Rui L. Inst Telecomunicacoes P-3810193 Aveiro Portugal Univ Aveiro Dept Eletron Telecomunicacoes & Informat P-3810193 Aveiro Portugal

The non-stationary nature of energy data is a serious challenge for energy forecasting methods. Frequent model updates are necessary to adapt to distribution shifts and avoid performance degradation. However, retraining regression models with lookback windows large enough to capture energy patterns is computationally expensive, as increasing the number of features leads to longer training times. To address this problem, we propose an approach that guarantees fast convergence through dimensionality reduction. Using a synthetic neighborhood dataset, we first validate three deep learning models-an artificial neural network (ANN), a 1D convolutional neural network (1D-CNN), and a long short-term memory (LSTM) network. Then, in order to mitigate the long training time, we apply principal component analysis (PCA) and a variational autoencoder (VAE) for feature reduction. As a way to ensure the suitability of the proposed models for a residential context, we also explore the trade-off between low error and training speed by considering three test scenarios: a global model, a local model for each building, and a global model that is fine-tuned for each building. Our results demonstrate that by selecting the optimal dimensionality reduction method and model architecture, it is possible to decrease the mean squared error (MSE) by up to 63% and accelerate training by up to 80%.

关键词： residential energy forecasting machine learning deep learning artificial neural network convolutional neural network long short-term memory dimensionality reduction principal component analysis variational autoencoder

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