Neurocomputing最新文献_第2页

Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learning

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.129019

Jingni Song , Luyi Bai , Xuanxuan An , Longlong Zhou

{"title":"Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learning","authors":"Jingni Song , Luyi Bai , Xuanxuan An , Longlong Zhou","doi":"10.1016/j.neucom.2024.129019","DOIUrl":"10.1016/j.neucom.2024.129019","url":null,"abstract":"<div><div>Temporal Knowledge Graph Entity Alignment (TKGEA) aims to identify the equivalent entities between different Temporal Knowledge Graphs (TKGs), which is important to knowledge fusion. The current mainstream TKGEA models are supervised embedding-based models that rely on pre-aligned seeds and implicitly encode structural information into entity embedding space for identifying equivalent entities. To deal with the TKGs structural information, some models use Graph Neural Network (GNN) encoding. But they ignore the design of decoders, failing to fully leverage the TKGs structural information. In addition, they primarily focus on crisp TKGs with clear entity semantics. However, many real-world TKGs exhibit fuzzy semantics. This fuzzy information makes existing TKGEA models face the challenge of handling the fuzzy semantics when aligning the equivalent fuzzy entities. To solve the above problems, we propose a novel unsupervised <u>F</u>uzzy <u>T</u>emporal Knowledge Graphs Entity Alignment (EA) framework that jointly performs <u>F</u>uzzy Semantics Learning and Global <u>S</u>tructure Learning, namely FTFS. In this framework, we convert the EA task into an unsupervised optimal transport task between two intra-graph matrices, eliminating the necessity for pre-aligned seeds and thereby avoiding intensive labor. Since we further consider the relation between graph structure and entities during the optimal-transport-based decoder module, it can make better use of the global structural information rather than simply encoding it implicitly into the embedding space. Moreover, unlike TKGEA models, which use binary classification to represent temporal relational facts, we introduce fuzzy semantics learning to embed membership degrees of fuzzy temporal relational facts. Extensive experiments on five FTKG datasets show that our unsupervised method is superior to the state-of-the-art EA methods.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 129019"},"PeriodicalIF":5.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Depth-Wise Convolutions in Vision Transformers for efficient training on small datasets

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.128998

Tianxiao Zhang , Wenju Xu , Bo Luo , Guanghui Wang

{"title":"Depth-Wise Convolutions in Vision Transformers for efficient training on small datasets","authors":"Tianxiao Zhang , Wenju Xu , Bo Luo , Guanghui Wang","doi":"10.1016/j.neucom.2024.128998","DOIUrl":"10.1016/j.neucom.2024.128998","url":null,"abstract":"<div><div>The Vision Transformer (ViT) leverages the Transformer’s encoder to capture global information by dividing images into patches and achieves superior performance across various computer vision tasks. However, the self-attention mechanism of ViT captures the global context from the outset, overlooking the inherent relationships between neighboring pixels in images or videos. Transformers mainly focus on global information while ignoring the fine-grained local details. Consequently, ViT lacks inductive bias during image or video dataset training. In contrast, convolutional neural networks (CNNs), with their reliance on local filters, possess an inherent inductive bias, making them more efficient and quicker to converge than ViT with less data. In this paper, we present a lightweight Depth-Wise Convolution module as a shortcut in ViT models, bypassing entire Transformer blocks to ensure the models capture both local and global information with minimal overhead. Additionally, we introduce two architecture variants, allowing the Depth-Wise Convolution modules to be applied to multiple Transformer blocks for parameter savings, and incorporating independent parallel Depth-Wise Convolution modules with different kernels to enhance the acquisition of local information. The proposed approach significantly boosts the performance of ViT models on image classification, object detection, and instance segmentation by a large margin, especially on small datasets, as evaluated on CIFAR-10, CIFAR-100, Tiny-ImageNet and ImageNet for image classification, and COCO for object detection and instance segmentation. The source code can be accessed at <span><span>https://github.com/ZTX-100/Efficient_ViT_with_DW</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 128998"},"PeriodicalIF":5.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A neurodynamic approach with fixed-time convergence for complex-variable pseudo-monotone variational inequalities

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.128988

Jinlan Zheng , Xingxing Ju , Naimin Zhang , Dongpo Xu

引用次数: 0

Point cloud feature consistency learning for incomplete 3D face recognition

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.129000

Faxiu Huang , Yanqiong Guo , Zhu Xu , Zhisheng You , Xiao Yang

{"title":"Point cloud feature consistency learning for incomplete 3D face recognition","authors":"Faxiu Huang , Yanqiong Guo , Zhu Xu , Zhisheng You , Xiao Yang","doi":"10.1016/j.neucom.2024.129000","DOIUrl":"10.1016/j.neucom.2024.129000","url":null,"abstract":"<div><div>Point cloud-based 3D face recognition has emerged as an exciting research topic due to the availability of 3D facial structures and detailed surface information. Existing approaches have primarily focused on complete facial point clouds and have achieved remarkable results. However, in real-world applications, the collected facial point clouds are often incomplete due to factors such as various poses, occlusion, and noise, posing significant challenges to face recognition tasks. In this paper, a feature consistency learning framework is proposed to improve incomplete 3D face recognition. The feature gap between incomplete and complete data is filled through joint optimization of completion and supervised contrastive learning. Specifically, to maintain and enhance the structure of incomplete point clouds, we introduce a structure-enhanced representation method for neighboring points that incorporates positional information residuals during the formation of point proxies. Additionally, a simple and effective dynamic input approach within the point proxy completion process is designed to alleviate concerns related to density disparities and detail loss in point clouds that exhibit relatively minor degrees of incompleteness. Extensive experiments on four datasets demonstrate our proposed method outperforms state-of-the-art methods on both inherent and artificially constructed incomplete data. Moreover, it also achieves comparable results on complete 3D face recognition. Overall, this work represents an early exploration into the realm of point cloud-based incomplete 3D face recognition through feature consistency learning, providing a promising approach for practical applications.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 129000"},"PeriodicalIF":5.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards robust DeepFake distortion attack via adversarial autoaugment

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.129011

Qi Guo , Shanmin Pang , Zhikai Chen , Qing Guo

{"title":"Towards robust DeepFake distortion attack via adversarial autoaugment","authors":"Qi Guo , Shanmin Pang , Zhikai Chen , Qing Guo","doi":"10.1016/j.neucom.2024.129011","DOIUrl":"10.1016/j.neucom.2024.129011","url":null,"abstract":"<div><div>Face forgery by DeepFake is posing a potential threat to society. Previous studies have shown that adversarial examples can effectively disrupt DeepFake models. However, the practical application of adversarial examples to defend against DeepFake is limited due to the existence of various input transformations. To address this issue, we propose a <strong>R</strong>obust <strong>D</strong>eepFake <strong>D</strong>istortion <strong>A</strong>ttack (RDDA) method from the perspective of data augmentation, which uses adversarial autoaugment to generate robust and generalized adversarial examples to disrupt DeepFake. Specifically, we design an adversarial autoaugment module to synthesize diverse and challenging input transformations. Through coping with these transformations, the robustness and generalization ability of the adversarial examples in disrupting DeepFake models are greatly enhanced. In addition, we further improve the generalization ability of adversarial examples in handling specific input transformations by incremental learning. With RDDA and incremental learning, our generated adversarial examples can effectively protect personal privacy from being violated by DeepFake. Extensive experiments on public benchmarks demonstrate that our DeepFake defense method has better robustness and generalization ability than state-of-the-arts.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 129011"},"PeriodicalIF":5.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LGAT: A novel model for multivariate time series anomaly detection with improved anomaly transformer and learning graph structures

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.129024

Mi Wen , ZheHui Chen , Yun Xiong , YiChuan Zhang

{"title":"LGAT: A novel model for multivariate time series anomaly detection with improved anomaly transformer and learning graph structures","authors":"Mi Wen , ZheHui Chen , Yun Xiong , YiChuan Zhang","doi":"10.1016/j.neucom.2024.129024","DOIUrl":"10.1016/j.neucom.2024.129024","url":null,"abstract":"<div><div>Time series anomaly detection involves identifying data points in continuously collected datasets that deviate from normal patterns. Given that real-world systems often consist of multiple variables, detecting anomalies in multivariate datasets has become a key focus of current research. This challenge has wide-ranging applications across various industries for system maintenance, such as in water treatment and distribution networks, transportation, and autonomous vehicles, thus driving active research in the field of time series anomaly detection. However, traditional methods primarily address this issue by predicting and reconstructing input time steps, but they still suffer from problems of overgeneralization and inconsistency in providing high performance for reasoning about complex dynamics. In response, we propose a novel unsupervised model called LGAT, which can automatically learn graph structures and leverage an enhanced Anomaly Transformer architecture to capture temporal dependencies. Moreover, the model features a new encoder–decoder architecture designed to enhance context extraction capabilities. In particular, the model calculates anomaly scores for multivariate time series anomaly detection by combining the reconstruction of input time series with the model’s computed prior associations and sequential correlations. This model captures inter-variable relationships and exhibit stronger context extraction abilities, making it more sensitive to anomaly detection. Extensive experiments on six common anomaly detection benchmarks further demonstrate the superiority of our approach over other state-of-the-art methods, with an improvement of approximately 1.2% across various metrics.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 129024"},"PeriodicalIF":5.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new visual-inertial odometry scheme for unmanned systems in unified framework of zeroing neural networks

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.129017

Dechao Chen , Jianan Jiang , Zhixiong Wang , Shuai Li

{"title":"A new visual-inertial odometry scheme for unmanned systems in unified framework of zeroing neural networks","authors":"Dechao Chen , Jianan Jiang , Zhixiong Wang , Shuai Li","doi":"10.1016/j.neucom.2024.129017","DOIUrl":"10.1016/j.neucom.2024.129017","url":null,"abstract":"<div><div>In recent years, multi-sensor fusion has gained significant attention from researchers and is used extensively in simultaneous localization and mapping (SLAM) applications, such as visual-inertial odometry (VIO). This technology primarily utilizes visual and odometry measurements for unmanned aerial vehicles (UAVs) to estimate their position, orientation, and environment. However, in most previous works, the input error data of sensors in the system were considered independent. To improve system precision and fully utilize sensor data, a new method called Multi-State Constraint Kalman Filter with NearSAC (MSCKF-NearSAC), based on the MSCKF, is proposed. This method eliminates outliers by limiting the range of selected points, which significantly improves the success rate of feature point matching in the front-end. Furthermore, the MSCKF-ZNN method is proposed for the back-end, and combines zeroing neural network (ZNN) (originated from the Hopfield-type neural network) and error state, resulting in an exponentially converging output trajectory error, thus improving the trajectory precision of the SLAM system. The proposed algorithms, MSCKF-NearSAC and MSCKF-ZNN, are used in the excellent work of the stereo multi-state constraint Kalman filter system (S-MSCKF). A plethora of comparison experiments, utilizing precise measurement and calibration techniques, are conducted on open-source datasets and real-world environments. Experimental results demonstrate that the introduced approach exhibits higher stability in contrast to other algorithms.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 129017"},"PeriodicalIF":5.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards the characterization of representations learned via capsule-based network architectures

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.129027

Saja Tawalbeh, José Oramas

引用次数: 0

Accuracy-preassigned fixed-time synchronization of switched inertial neural networks with time-varying distributed, leakage and transmission delays

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-28 DOI: 10.1016/j.neucom.2024.128958

Shilei Yuan , Yantao Wang , Xiaona Yang , Xian Zhang

引用次数: 0

Prototype matching-based meta-learning model for few-shot fault diagnosis of mechanical system

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-27 DOI: 10.1016/j.neucom.2024.129012

Lin Lin, Sihao Zhang, Song Fu, Yikun Liu, Shiwei Suo, Guolei Hu

{"title":"Prototype matching-based meta-learning model for few-shot fault diagnosis of mechanical system","authors":"Lin Lin, Sihao Zhang, Song Fu, Yikun Liu, Shiwei Suo, Guolei Hu","doi":"10.1016/j.neucom.2024.129012","DOIUrl":"10.1016/j.neucom.2024.129012","url":null,"abstract":"<div><div>The efficacy of advanced deep-learning diagnostic methods is contingent mainly upon sufficient trainable data for each fault category. However, gathering ample data in real-world scenarios is often challenging, rendering these deep-learning techniques ineffective. This paper introduces a novel Prototype Matching-based Meta-Learning (PMML) approach to address the few-shot fault diagnosis under constrained data conditions. Initially, the PMML’s feature extractor is meta-trained within the Model-Agnostic Meta-Learning framework, utilizing multiple fault classification tasks from known operational conditions in the source domain to acquire prior meta-knowledge for fault diagnosis. Subsequently, the trained feature extractor is employed to derive meta-features from few-shot samples in the target domain, and metric learning is conducted to facilitate swift and precise few-shot fault diagnosis, leveraging meta-knowledge and similarity information across sample sets. Moreover, instead of utilizing all target domain samples, the prototype of each fault category is used to capture similarity information between support and query samples. Concurrently, BiLSTM is employed to selectively embed the meta-feature prototype, enabling the extraction of more distinguishable metric features for enhanced metric learning. Finally, the effectiveness of the proposed PMML is validated through a series of comparative experiments on two fault datasets, demonstrating its outstanding performance in addressing both zero-shot and few-shot fault diagnosis challenges.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 129012"},"PeriodicalIF":5.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0