Neurocomputing最新文献_第8页

Finite-time-convergent support vector neural dynamics for classification

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-19 DOI: 10.1016/j.neucom.2024.128810

Mei Liu , Qihai Jiang , Hui Li , Xinwei Cao , Xin Lv

{"title":"Finite-time-convergent support vector neural dynamics for classification","authors":"Mei Liu , Qihai Jiang , Hui Li , Xinwei Cao , Xin Lv","doi":"10.1016/j.neucom.2024.128810","DOIUrl":"10.1016/j.neucom.2024.128810","url":null,"abstract":"<div><div>Support vector machine (SVM) is a popular binary classification algorithm widely utilized in various fields due to its accuracy and versatility. However, most of the existing research involving SVMs stays at the application level, and there is few research on optimizing the support vector solving process. Therefore, it is an alternative way to optimize the support vector solving process for improving the classification performance via constructing new solving methods. Recent research has demonstrated that neural dynamics exhibit robust solving performance and high accuracy. Motivated by this inspiration, this paper leverages neural dynamics to improve the accuracy and robustness of SVM solutions. Specifically, this paper models the solving process of SVM as a standard quadratic programming (QP) problem. Then, a support vector neural dynamics (SVND) model is specifically developed to provide the optimal solution to the aforementioned QP problem, with theoretical analysis confirming its ability to achieve global convergence. Datasets of varying sizes from various sources are employed to validate the effectiveness of the designed SVND model. Experimental results show that the designed SVND model demonstrates superior classification accuracy and robustness compared to other classical machine learning algorithms. The source code is available at <span><span>https://github.com/LongJin-lab/NC_SVND</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 128810"},"PeriodicalIF":5.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745440","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

Temporal convolution derived multi-layered reservoir computing

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-19 DOI: 10.1016/j.neucom.2024.128938

Johannes Viehweg , Dominik Walther , Patrick Mäder

{"title":"Temporal convolution derived multi-layered reservoir computing","authors":"Johannes Viehweg , Dominik Walther , Patrick Mäder","doi":"10.1016/j.neucom.2024.128938","DOIUrl":"10.1016/j.neucom.2024.128938","url":null,"abstract":"<div><div>The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological processes. Especially chaotic time series that depend on a long history pose an exceptionally difficult problem. While machine learning has shown to be a promising approach for predicting such time series, it either demands long training time and much training data when using deep Recurrent Neural Networks. Alternative, when using a Reservoir Computing approach it comes with high uncertainty and typically a high number of random initializations and extensive hyper-parameter tuning. In this paper, we focus on the Reservoir Computing approach and propose a new mapping of input data into the reservoir’s state space. Furthermore, we incorporate this method in two novel network architectures increasing parallelizability, depth and predictive capabilities of the neural network while reducing the dependence on randomness. For the evaluation, we approximate a set of time series from the Mackey–Glass equation, inhabiting non-chaotic as well as chaotic behavior as well as the SantaFe Laser dataset and compare our approaches in regard to their predictive capabilities to Echo State Networks, Autoencoder connected Echo State Networks and Gated Recurrent Units. For the chaotic time series, we observe an error reduction of up to 85.45% compared to Echo State Networks and 90.72% compared to Gated Recurrent Units. Furthermore, we also observe tremendous improvements for non-chaotic time series of up to 99.99% in contrast to the existing approaches.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 128938"},"PeriodicalIF":5.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745443","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

VIWHard: Text adversarial attacks based on important-word discriminator in the hard-label black-box setting

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-19 DOI: 10.1016/j.neucom.2024.128917

Hua Zhang , Jiahui Wang , Haoran Gao , Xin Zhang , Huewei Wang , Wenmin Li

{"title":"VIWHard: Text adversarial attacks based on important-word discriminator in the hard-label black-box setting","authors":"Hua Zhang , Jiahui Wang , Haoran Gao , Xin Zhang , Huewei Wang , Wenmin Li","doi":"10.1016/j.neucom.2024.128917","DOIUrl":"10.1016/j.neucom.2024.128917","url":null,"abstract":"<div><div>In the hard-label black-box setting, the adversary only obtains the decision of the target model, which is more practical. Both the perturbed words and the sets of substitute words affect the performance of adversarial attack. We propose a hard-label black-box adversarial attack framework called VIWHard, which takes important words as perturbed words. In order to verify the words which highly impact on the classification of the target model, we design an important-word discriminator consisting of a binary classifier and a masked language model as an important component of VIWHard. Meanwhile, we use a masked language model to construct the context-preserving sets of substitute words for important words, which further improves the naturalness of the adversarial texts. We conduct experiments by attacking WordCNN, WordLSTM and BERT on seven datasets, which contain text classification, toxic information, and sensitive information datasets. Experimental results show that our method achieves powerful attacking performance and generates natural adversarial texts. The average attack success rate on the seven datasets reaches 98.556%, and the average naturalness of the adversarial texts reaches 7.894. Specially, on the four security datasets Jigsaw2018, HSOL, EDENCE, and FAS, our average attack success rate reaches 97.663%, and the average naturalness of the adversarial texts reaches 8.626. In addition, we evaluate the attack performance of VIWHard on large language models (LLMs), the generated adversarial examples are effective for LLMs.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"616 ","pages":"Article 128917"},"PeriodicalIF":5.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743511","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

Hyperspectral image denoising via cooperated self-supervised CNN transform and nonconvex regularization

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-19 DOI: 10.1016/j.neucom.2024.128912

Ruizhi Hou , Fang Li

{"title":"Hyperspectral image denoising via cooperated self-supervised CNN transform and nonconvex regularization","authors":"Ruizhi Hou , Fang Li","doi":"10.1016/j.neucom.2024.128912","DOIUrl":"10.1016/j.neucom.2024.128912","url":null,"abstract":"<div><div>Methods that leverage the sparsity and the low-rankness in the transformed domain have gained growing interest for hyperspectral image (HSI) denoising. Recently, many researches simultaneously utilizing low-rankness and local smoothness have emerged. Although these approaches achieve great denoising performance, they exhibit several limitations. First, the widely adopted <span><math><msub><mrow><mi>l</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> norm is a biased function, potentially leading to blurring edges. Second, employing tensor singular value decomposition (T-SVD) to ensure low-rankness brings a heavy computational burden. Additionally, the manually designed regularization norm is fixed for all testing data, which may cause a generalization problem. To address these challenges, this work proposes a novel optimization model for HSI denoising that incorporates the self-supervised CNN transform and TV regularization (CTTV) with the nonconvex function induced norm. The CNN-based transform could implicitly ensure the low-rankness of the tensor and learn the potential information in the noisy data. Furthermore, we exploit the unbiased nonconvex minimax concave penalty (MCP) to enforce the local smoothness of the extracted features while preserving sharp edges. We design an algorithm to solve the proposed model built on the hybrid of the half-quadratic splitting (HQS) and the alternating direction method of multipliers (ADMM), in which the network parameter and the denoised image are separately optimized. Extensive experiments on various datasets indicate that our proposed method can achieve state-of-the-art performance in HSI denoising.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"616 ","pages":"Article 128912"},"PeriodicalIF":5.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742967","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

Identifying local useful information for attribute graph anomaly detection

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-18 DOI: 10.1016/j.neucom.2024.128900

Penghui Xi , Debo Cheng , Guangquan Lu , Zhenyun Deng , Guixian Zhang , Shichao Zhang

{"title":"Identifying local useful information for attribute graph anomaly detection","authors":"Penghui Xi , Debo Cheng , Guangquan Lu , Zhenyun Deng , Guixian Zhang , Shichao Zhang","doi":"10.1016/j.neucom.2024.128900","DOIUrl":"10.1016/j.neucom.2024.128900","url":null,"abstract":"<div><div>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 a new anomaly detection method called LIAD (Identifying <u>L</u>ocal Useful <u>I</u>nformation for <u>A</u>ttribute Graph Anomaly <u>D</u>etection), 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.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"617 ","pages":"Article 128900"},"PeriodicalIF":5.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745436","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

Learning dual-pixel alignment for defocus deblurring

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-17 DOI: 10.1016/j.neucom.2024.128880

Yu Li, Yaling Yi, Xinya Shu, Dongwei Ren, Qince Li, Wangmeng Zuo

{"title":"Learning dual-pixel alignment for defocus deblurring","authors":"Yu Li, Yaling Yi, Xinya Shu, Dongwei Ren, Qince Li, Wangmeng Zuo","doi":"10.1016/j.neucom.2024.128880","DOIUrl":"10.1016/j.neucom.2024.128880","url":null,"abstract":"<div><div>It is a challenging task to recover sharp image from a single defocus blurry image in real-world applications. On many modern cameras, dual-pixel (DP) sensors create two-image views, based on which stereo information can be exploited to benefit defocus deblurring. Despite the impressive results achieved by existing DP defocus deblurring methods, the misalignment between DP image views is still not studied, leaving room for improving DP defocus deblurring. In this work, we propose a Dual-Pixel Alignment Network (DPANet) for defocus deblurring. Generally, DPANet is an encoder–decoder with skip-connections, where two branches with shared parameters in the encoder are employed to extract and align deep features from left and right views, and one decoder is adopted to fuse aligned features for predicting the sharp image. Due to that DP views suffer from different blur amounts, it is not trivial to align left and right views. To this end, we propose novel encoder alignment module (EAM) and decoder alignment module (DAM). In particular, a correlation layer is suggested in EAM to measure the disparity between DP views, whose deep features can then be accordingly aligned using deformable convolutions. DAM can further enhance the alignment of skip-connected features from encoder and deep features in decoder. By introducing several EAMs and DAMs, DP views in DPANet can be well aligned for better predicting latent sharp image. Experimental results on real-world datasets show that our DPANet is notably superior to state-of-the-art deblurring methods in reducing defocus blur while recovering visually plausible sharp structures and textures.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"616 ","pages":"Article 128880"},"PeriodicalIF":5.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742860","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

Deep multi-similarity hashing via label-guided network for cross-modal retrieval

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-17 DOI: 10.1016/j.neucom.2024.128830

Lei Wu , Qibing Qin , Jinkui Hou , Jiangyan Dai , Lei Huang , Wenfeng Zhang

{"title":"Deep multi-similarity hashing via label-guided network for cross-modal retrieval","authors":"Lei Wu , Qibing Qin , Jinkui Hou , Jiangyan Dai , Lei Huang , Wenfeng Zhang","doi":"10.1016/j.neucom.2024.128830","DOIUrl":"10.1016/j.neucom.2024.128830","url":null,"abstract":"<div><div>Due to low storage cost and efficient retrieval advantages, hashing technologies have gained broad attention in the field of cross-modal retrieval in recent years. However, most current cross-modal hashing usually employs random sampling or semi-hard negative mining to construct training batches for model optimization, which ignores the distribution relationships between raw samples, generating redundant and unbalanced pairs, and resulting in sub-optimal embedding spaces. In this work, we address this dilemma with a novel deep cross-modal hashing framework, called Deep Multi-similarity Hashing via Label-Guided Networks (DMsH-LN), to learn a high separability public embedding space and generate discriminative binary descriptors. Specifically, by utilizing pair mining and weighting to jointly calculate self-similarity and relative similarity between pairs, the multi-similarity loss is extended to cross-modal hashing to alleviate the negative impacts caused by redundant and imbalanced samples on hash learning, enhancing the distinguishing ability of the obtained discrete codes. Besides, to capture fine-grained semantic supervised signals, the Label-guided Network is proposed to learn class-specific semantic signals, which could effectively guide the parameter optimization of the Image Network and Text Network. Extensive experiments are conducted on four benchmark datasets, which demonstrate that the DMsH-LN framework achieves excellent retrieval performance. The source codes of DMsH-LN are downloaded from <span><span>https://github.com/QinLab-WFU/DMsH-LN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"616 ","pages":"Article 128830"},"PeriodicalIF":5.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742966","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

DoA-ViT: Dual-objective Affine Vision Transformer for Data Insufficiency DoA-ViT：针对数据不足的双目标仿射视觉变换器

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-17 DOI: 10.1016/j.neucom.2024.128896

Qiang Ren, Junli Wang

{"title":"DoA-ViT: Dual-objective Affine Vision Transformer for Data Insufficiency","authors":"Qiang Ren, Junli Wang","doi":"10.1016/j.neucom.2024.128896","DOIUrl":"10.1016/j.neucom.2024.128896","url":null,"abstract":"<div><div>Vision Transformers (ViTs) excel in large-scale image recognition tasks but struggle with limited data due to ineffective patch-level local information utilization. Existing methods focus on enhancing local representations at the model level but often treat all features equally, leading to noise from irrelevant information. Effectively distinguishing between discriminative features and irrelevant information helps minimize the interference of noise at the model level. To tackle this, we introduce Dual-objective Affine Vision Transformer (DoA-ViT), which enhances ViTs for data-limited tasks by improving feature discrimination. DoA-ViT incorporates a learnable affine transformation that associates transformed features with class-specific ones while preserving their intrinsic features. Additionally, an adaptive patch-based enhancement mechanism is designed to assign importance scores to patches, minimizing the impact of irrelevant information. These enhancements can be seamlessly integrated into existing ViTs as plug-and-play components. Extensive experiments on small-scale datasets show that DoA-ViT consistently outperforms existing methods, with visualization results highlighting its ability to identify critical image regions effectively.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"615 ","pages":"Article 128896"},"PeriodicalIF":5.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703000","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

CNN explanation methods for ordinal regression tasks 用于序数回归任务的 CNN 解释方法

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-17 DOI: 10.1016/j.neucom.2024.128878

Javier Barbero-Gómez , Ricardo P.M. Cruz , Jaime S. Cardoso , Pedro A. Gutiérrez , César Hervás-Martínez

引用次数: 0

Fooling human detectors via robust and visually natural adversarial patches

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-17 DOI: 10.1016/j.neucom.2024.128915

Dawei Zhou , Hongbin Qu , Nannan Wang , Chunlei Peng , Zhuoqi Ma , Xi Yang , Xinbo Gao

{"title":"Fooling human detectors via robust and visually natural adversarial patches","authors":"Dawei Zhou , Hongbin Qu , Nannan Wang , Chunlei Peng , Zhuoqi Ma , Xi Yang , Xinbo Gao","doi":"10.1016/j.neucom.2024.128915","DOIUrl":"10.1016/j.neucom.2024.128915","url":null,"abstract":"<div><div>DNNs are vulnerable to adversarial attacks. Physical attacks alter local regions of images by either physically equipping crafted objects or synthesizing adversarial patches. This design is applicable to real-world image capturing scenarios. Currently, adversarial patches are typically generated from random noise. Their textures are different from image textures. Also, these patches are developed without focusing on the relationship between human poses and adversarial robustness. The unnatural pose and texture make patches noticeable in practice. In this work, we propose to synthesize adversarial patches which are visually natural from the perspectives of both poses and textures. In order to adapt adversarial patches to human pose, we propose a patch adaption network PosePatch for patch synthesis, which is guided by perspective transform with estimated human poses. Meanwhile, we develop a network StylePatch to generate harmonized textures for adversarial patches. These networks are combined together for end-to-end training. As a result, our method can synthesize adversarial patches for arbitrary human images without knowing poses and localization in advance. Experiments on benchmark datasets and real-world scenarios show that our method is robust to human pose variations and synthesized adversarial patches are effective, and a user study is made to validate the naturalness.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"616 ","pages":"Article 128915"},"PeriodicalIF":5.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742858","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