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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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Longitudinal Alzheimer's Disease Progression Prediction With Modality Uncertainty and Optimization of Information Flow

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第1期29卷 259-272页

作者： Dao, Duy-Phuong Yang, Hyung-Jeong Kim, Jahae Ho, Ngoc-Huynh Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju 61186 South Korea Chonnam Natl Univ Hosp Dept Nucl Med Gwangju 61469 South Korea

Alzheimer's disease (AD) is a global neurodegenerative disorder that affects millions of individuals worldwide. Actual AD imaging datasets challenge the construction of reliable longitudinal models owing to imaging modality uncertainty. In addition, they are still unable to retain or obtain important information during disease progression from previous to followup time points. For example, the output values of current gates in recurrent models should be close to a specific value that indicates the model is uncertain about retaining or forgetting information. In this study, we propose a model which can extract and constrain each modality into a common representation space to capture intermodality interactions among different modalities associated with modality uncertainty to predict AD progression. In addition, we provide an auxiliary function to enhance the ability of recurrent gate robustly and effectively in controlling the flow of information over time using longitudinal data. We conducted comparative analysis on data from the Alzheimer's Disease Neuroimaging Initiative database. Our model outperformed other methods across all evaluation metrics. Therefore, the proposed model provides a promising solution for addressing modality uncertainty challenges in multimodal longitudinal AD progression prediction.

关键词： Multimodal longitudinal data modality uncertainty variational autoencoder recurrent gate Alzheimer's disease progression

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Identifying local useful information for attribute graph anomaly detection

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NEUROCOMPUTING 2025年 617卷

作者： Xi, Penghui Cheng, Debo Lu, Guangquan Deng, Zhenyun Zhang, Guixian Zhang, Shichao Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Univ South Australia UniSA STEM Adelaide Australia Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Jiangsu Peoples R China

Graph anomaly detection primarily relies on shallow learning methods based on feature engineering and deep learning strategies centred on autoencoder-based reconstruction. However, these methods frequently fail to harness the local attributes and structural information within graph data, making it challenging to capture the underlying distribution in scenarios with class-imbalanced graph anomalies, which can result in overfitting. To deal with the above issue, this paper proposes anew anomaly detection method called LIAD (Identifying Local Useful Information for Attribute Graph Anomaly Detection), which learns the data's underlying distribution and captures richer local information. First, LIAD employs data augmentation techniques to create masked graphs and pairs of positive and negative subgraphs. Then, LIAD leverages contrastive learning to derive rich embedding representations from diverse local structural information. Additionally, LIAD utilizes a variational autoencoder (VAE) to generate new graph data, capturing the neighbourhood distribution within the masked graph. During the training process, LIAD aligns the generated graph data with the original to deepen its comprehension of local information. Finally, anomaly scoring is achieved by comparing the discrimination and reconstruction scores of the contrastive pairs, enabling effective anomaly detection. Extensive experiments on five real-world datasets demonstrate the effectiveness of LIAD compared to state-of-the-art methods. Comprehensive ablation studies and parametric analyses further affirm the robustness and efficacy of our model.

关键词： Graph neural networks Contrastive learning Graph anomaly detection variational autoencoder

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An intelligent sequential fraud detection model based on deep learning

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JOURNAL OF SUPERCOMPUTING 2024年第10期80卷 14824-14847页

作者： Zioviris, Georgios Kolomvatsos, Kostas Stamoulis, George Univ Thessaly Dept Elect & Comp Engn Glavani 37 Volos 38221 Thessaly Greece Univ Thessaly Dept Informat & Telecommun Papasiopoulou 2-4 Lamia 35131 Thessaly Greece

Fraud detection and prevention has received a lot of attention from the research community due to its high impact on financial institutions' revenues and reputation. The increased use of the web and the provision of online services open up the pathway for exposing these systems to numerous threats and jeopardizing their effective functioning. Naturally, financial frauds are increased in number and form imposing various requirements for their efficient and immediate detection. These requirements are related to the performance of the adopted models as well as the timely response of the decision-making mechanism. Machine learning and data mining are two research domains that can provide a number of techniques/algorithms for fraud detection and setup the road for mitigation actions. However, these methods still need to be improved with respect to the detection of unknown fraud patterns and the incorporation of big data processing mechanisms. This paper presents our attempt to build a hybrid system, i.e., a sequential scheme for combining two deep learning models and efficiently detecting potential financial frauds. We elaborate on the combination of an autoencoder and a Long Short-Term Memory Recurrent Neural Network trained upon datasets which are processed through the use of an oversampling technique. Oversampling is adopted to handle heavily imbalanced datasets which is the 'natural' scenario due to the limited number of frauds compared to the humongous volumes of transactions. The proposed approach tends to capture much more fraud events in comparison with other conventional ML techniques. Our experimental evaluation exposes that our model exhibits a good performance in terms of recall and precision.

关键词： Fraud detection autoencoder variational autoencoder Dimensionality reduction Oversampling Recurrent Neural Networks Long Short-Term Memory networks

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Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 912卷 169540页

作者： Shin, Jihoon Lee, Gunhyeong Kim, Taeho Cho, Kyung Hwa Hong, Seok Min Kwon, Do Hyuck Pyo, Jongcheol Cha, Yoonkyung Univ Seoul Sch Environm Engn Seoul 02504 South Korea Univ Michigan Dept Civil & Environm Engn Ann Arbor MI USA Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea Ulsan Natl Inst Sci & Technol Sch Urban & Environm Engn Ulsan 44919 South Korea Pusan Natl Univ Dept Environm Engn Busan 46241 South Korea

Recent advances in remote sensing techniques provide a new horizon for monitoring the spatiotemporal variations of harmful algal blooms (HABs) using hyperspectral data in inland water. In this study, a hierarchical concatenated variational autoencoder (HCVAE) is proposed as an efficient and accurate deep learning (DL) based bio-optical model. To demonstrate its usefulness in retrieving algal pigments, the HCVAE is applied to bloom -prone regions in Daecheong Lake, South Korea. By abstracting the similarity between highly related features using layer-wise clique-based latent-feature extraction, HCVAE reduces the computational loads in deriving outputs while preventing performance degradation. Graph-based clique-detection uses information theory-based criteria to group the related reflectance spectra. Consequently, six latent features were extracted from 79 spectral bands to consist of a multilevel hierarchy of HCVAE that can simultaneously estimate concentrations of chlorophyll-a (Chl-a) and phycocyanin (PC). Despite the parsimonious model architecture, the Chl-a and PC concentrations estimated by HCVAE closely agree with the measured concentrations, with test R2 values of 0.76 and 0.82, respectively. In addition, spatial distribution maps of algal pigments obtained from HCVAE using drone-borne reflectance successfully capture the blooming spots. Based on its multilevel hierarchical architecture, HCVAE can provide the importance of latent features along with their individual wavelengths using Shapley additive explanations. The most important latent features covered the spectral regions associated with both Chl-a and PC. The lightweight neural network DNNsel, which uses only the spectral bands of highest importance in latent-feature extraction, performed comparably to HCVAE. The study results demonstrate the utility of the multilevel hierarchical architecture as a comprehensive assessment model for near-real-time drone-borne sensing of HABs. Moreover, HCVAE is applica

关键词： Explainable artificial intelligence variational autoencoder Latent -feature extraction Hyperspectral imagery Drone -borne sensing Algal pigment

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Vehicular Social Dynamic Anomaly Detection With Recurrent Multi-Mask Aggregator Enabled VAE

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 21709-21722页

作者： Hu, Zehao He, Yuwei Shen, Yuqi Jo, Minho Collotta, Mario Shen, Guojiang Kong, Xiangjie Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Korea Univ Dept Comp Convergence Software Sejong City 30019 South Korea Kore Univ Enna Fac Engn & Architecture I-94100 Enna Italy

Vehicle driving behavior analysis and detection tasks have become an indispensable part of intelligent transportation systems. Accurate pattern recognition of potential anomalies during the movement of entities is crucial for improving transportation efficiency. Current methods typically analyze vehicle trajectories independently without considering potential interactions among vehicles. To address this limitation, some studies have integrated graph attention mechanisms to capture the influence of neighboring vehicles during the aggregation process. However, Graph Attention Networks (GATs) are constrained by the univariate nature of attention heads and coefficients, thus lacking flexibility. In this work, we not only consider the social dynamics among neighboring vehicles but also delve into the limitations of GAT models. We propose a Vehicular Social Dynamics Anomaly Detection (VSD-AD) model based on the Recurrent Multi-Mask Aggregator (MMA) enabled variational autoencoder (VAE) architecture to maximize the learning of relational embeddings among neighbors in a highway vehicle network. Furthermore, we apply Node Feature Quantisation (NFQ) to the encoder output to mitigate the complexity of neighbor relationships. Our model is flexible and customizable for different highway scenarios, suitable for large-scale highway vehicle video data. To validate real-world applicability, we further assess its performance on both the simulated dataset and real-world traffic dataset, where our model outperforms other mainstream methods in terms of detection performance.

关键词： Anomaly detection variational autoencoder graph aggregation intelligent transportation

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