Neural Processing Letters最新文献_第10页

Extended dissipative criteria for delayed semi-discretized competitive neural networks 延迟半离散竞争神经网络的扩展耗散标准

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-25 DOI: 10.1007/s11063-024-11583-3

B. Adhira, G. Nagamani

引用次数: 0

Neural Data Augmentation for Legal Overruling Task: Small Deep Learning Models vs. Large Language Models 法律推翻任务的神经数据增强：小型深度学习模型与大型语言模型的对比

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-23 DOI: 10.1007/s11063-024-11574-4

{"title":"Neural Data Augmentation for Legal Overruling Task: Small Deep Learning Models vs. Large Language Models","authors":"","doi":"10.1007/s11063-024-11574-4","DOIUrl":"https://doi.org/10.1007/s11063-024-11574-4","url":null,"abstract":"<h3>Abstract</h3> Deep learning models produce impressive results in any natural language processing applications when given a better learning strategy and trained with large labeled datasets. However, the annotation of massive training data is far too expensive, especially in the legal domain, due to the need for trained legal professionals. Data augmentation solves the problem of learning without labeled big data. In this paper, we employ pre-trained language models and prompt engineering to generate large-scale pseudo-labeled data for the legal overruling task using 100 data samples. We train small recurrent and convolutional deep-learning models using this data and fine-tune a few other transformer models. We then evaluate the effectiveness of the models, both with and without data augmentation, using the benchmark dataset and analyze the results. We also test the performance of these models with the state-of-the-art GPT-3 model under few-shot setting. Our experimental findings demonstrate that data augmentation results in better model performance in the legal overruling task than models trained without augmentation. Furthermore, our best-performing deep learning model trained on augmented data outperforms the few-shot GPT-3 by 18% in the F1-score. Additionally, our results highlight that the small neural networks trained with augmented data achieve outcomes comparable to those of other large language models.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Causality-Driven Intra-class Non-equilibrium Label-Specific Features Learning 因果关系驱动的类内非平衡标签特定特征学习

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-21 DOI: 10.1007/s11063-024-11439-w

Wenxin Ge, Yibin Wang, Yuting Xu, Yusheng Cheng

{"title":"Causality-Driven Intra-class Non-equilibrium Label-Specific Features Learning","authors":"Wenxin Ge, Yibin Wang, Yuting Xu, Yusheng Cheng","doi":"10.1007/s11063-024-11439-w","DOIUrl":"https://doi.org/10.1007/s11063-024-11439-w","url":null,"abstract":"In multi-label learning, label-specific feature learning can effectively avoid some ineffectual features that interfere with the classification performance of the model. However, most of the existing label-specific feature learning algorithms improve the performance of the model for classification by constraining the solution space through label correlation. The non-equilibrium of the label distribution not only leads to some spurious correlations mixed in with the calculated label correlations but also diminishes the performance of the classification model. Causal learning can improve the classification performance and robustness of the model by capturing real causal relationships from limited data. Based on this, this paper proposes a causality-driven intra-class non-equilibrium label-specific features learning, named CNSF. Firstly, the causal relationship between the labels is learned by the Peter-Clark algorithm. Secondly, the label density of all instances is calculated by the intra-class non-equilibrium method, which is used to relieve the non-equilibrium distribution of original labels. Then, the correlation of the density matrix is calculated using cosine similarity and combined with causality to construct the causal density correlation matrix, to solve the problem of spurious correlation mixed in the label correlation obtained by traditional methods. Finally, the causal density correlation matrix is used to induce label-specific feature learning. Compared with eight state-of-the-art multi-label algorithms on thirteen datasets, the experimental results prove the reasonability and effectiveness of the algorithms in this paper.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"2015 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140196409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Robustness analysis of exponential stability of fuzzy inertial neural networks through the estimation of upper limits of perturbations 通过估计扰动上限对模糊惯性神经网络指数稳定性的稳健性分析

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-20 DOI: 10.1007/s11063-024-11587-z

Wenxiang Fang, Tao Xie

引用次数: 0

Hyperspectral Image Classification Based on 3D–2D Hybrid Convolution and Graph Attention Mechanism 基于三维-二维混合卷积和图注意机制的高光谱图像分类

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-20 DOI: 10.1007/s11063-024-11584-2

Hui Zhang, Kaiping Tu, Huanhuan Lv, Ruiqin Wang

{"title":"Hyperspectral Image Classification Based on 3D–2D Hybrid Convolution and Graph Attention Mechanism","authors":"Hui Zhang, Kaiping Tu, Huanhuan Lv, Ruiqin Wang","doi":"10.1007/s11063-024-11584-2","DOIUrl":"https://doi.org/10.1007/s11063-024-11584-2","url":null,"abstract":"Convolutional neural networks and graph convolutional neural networks are two classical deep learning models that have been widely used in hyperspectral image classification tasks with remarkable achievements. However, hyperspectral image classification models based on graph convolutional neural networks using only shallow spectral or spatial features are insufficient to provide reliable similarity measures for constructing graph structures, limiting their classification performance. To address this problem, we propose a new end-to-end hyperspectral image classification model combining 3D–2D hybrid convolution and a graph attention mechanism (3D–2D-GAT). The model utilizes the collaborative work of hybrid convolutional feature extraction module and GAT module to improve classification accuracy. First, a 3D–2D hybrid convolutional network is constructed and used to quickly extract the discriminant deep spatial-spectral features of various ground objects in hyperspectral image. Then, the graph is built based on deep spatial-spectral features to enhance the feature representation ability. Finally, a network of graph attention mechanism is adopted to learn long-range spatial relationship and distinguish the intra-class variation and inter-class similarity among different samples. The experimental results on three datasets, Indian Pine, the University of Pavia and Salinas Valley show that the proposed method can achieve higher classification accuracy compared with other advanced methods.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"52 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Novel Distributed Process Monitoring Framework of VAE-Enhanced with Deep Neural Network 利用深度神经网络增强 VAE 的新型分布式过程监控框架

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-20 DOI: 10.1007/s11063-024-11577-1

Ming Yin, Jiayi Tian, Yibo Wang, Jijiao Jiang

{"title":"A Novel Distributed Process Monitoring Framework of VAE-Enhanced with Deep Neural Network","authors":"Ming Yin, Jiayi Tian, Yibo Wang, Jijiao Jiang","doi":"10.1007/s11063-024-11577-1","DOIUrl":"https://doi.org/10.1007/s11063-024-11577-1","url":null,"abstract":"Intelligent manufacturing process needs to adopt distributed monitoring scenario due to its massive, high-dimensional and complex data. Distributed process monitoring has been introduced into global monitoring and local monitoring to analyze the characteristic relationship between process data. However, the existing framework methods ignore or suppress the fault information and thus cannot effectively identify the local faults and the time sequence characteristics between units in the batch production system. This paper proposes a novel distributed process monitoring framework based on Girvan-Newman algorithm modular subunit partitioning and probabilistic learning model with deep neural networks. First, Girvan-Newman algorithm is used to divide the complex manufacturing system modularized to reduce the latitude of data processing. Second, variational autoencoder (VAE) is adopted to ensure the stability of local analysis, and long short-term memory is adopted to improve the VAE model to detect global multi-time scale anomalies. Finally, distributed process fault detection is carried out for each subunit in a separate and integrated manner, and the performance of the framework in distributed process monitoring is analyzed through two fault detection indicators T2 and SPE statistics. A case study of the Tennessee Eastman Process is used to demonstrate the performance and applicability of the proposed framework. Results show that the proposed VAE enhancement framework based on the DNN could accurately identify faults in distributed process monitoring and locate the specific sub-units where the fault occurs. Compared with VAE-DNN method and traditional process monitoring methods, the framework proposed in this paper has higher fault detection rate and lower false alarm rate, and the detection rate of some faults can reach 100%.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"58 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140196757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Intuitionistic Fuzzy Extreme Learning Machine with the Truncated Pinball Loss 带有截断弹球损失的直觉模糊极限学习机

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-20 DOI: 10.1007/s11063-024-11492-5

Qingyun Gao, Qing Ai, Wenhui Wang

{"title":"Intuitionistic Fuzzy Extreme Learning Machine with the Truncated Pinball Loss","authors":"Qingyun Gao, Qing Ai, Wenhui Wang","doi":"10.1007/s11063-024-11492-5","DOIUrl":"https://doi.org/10.1007/s11063-024-11492-5","url":null,"abstract":"Fuzzy extreme learning machine (FELM) is an effective algorithm for dealing with classification problems with noises, which uses a membership function to effectively suppress noise in data. However, FELM has the following drawbacks: (a) The membership degree of samples in FELM is constructed by considering only the distance between the samples and the class center, not the local information of samples. It is easy to mistake some boundary samples for noises. (b) FELM uses the least squares loss function, which leads to sensitivity to feature noise and instability to re-sampling. To address the above drawbacks, we propose an intuitionistic fuzzy extreme learning machine with the truncated pinball loss (TPin-IFELM). Firstly, we use the K-nearest neighbor (KNN) method to obtain local information of the samples and then construct membership and non-membership degrees for each sample in the random mapping feature space based on valuable local information. Secondly, we calculate the score value of samples based on the membership and non-membership degrees, which can effectively identify whether the boundary samples are noises or not. Thirdly, in order to maintain the sparsity and robustness of the model, and enhance the stability of the resampling of the model, we introduce the truncated pinball loss function into the model. Finally, in order to solve more efficiently, we employ the concave-convex procedure (CCCP) to solve TPin-IFELM. Extensive comparative experiments are conducted on the benchmark datasets to verify the superior performance of TPin-IFELM.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"31 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals 利用深度学习、误差水平和噪声残差检测图像篡改

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-19 DOI: 10.1007/s11063-024-11448-9

Sunen Chakraborty, Kingshuk Chatterjee, Paramita Dey

{"title":"Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals","authors":"Sunen Chakraborty, Kingshuk Chatterjee, Paramita Dey","doi":"10.1007/s11063-024-11448-9","DOIUrl":"https://doi.org/10.1007/s11063-024-11448-9","url":null,"abstract":"Images once were considered a reliable source of information. However, when photo-editing software started to get noticed it gave rise to illegal activities which is called image tampering. These days we can come across innumerable tampered images across the internet. Software such as Photoshop, GNU Image Manipulation Program, etc. are applied to form tampered images from real ones in just a few minutes. To discover hidden signs of tampering in an image deep learning models are an effective tool than any other methods. Models used in deep learning are capable of extracting intricate features from an image automatically. Here we proposed a combination of traditional handcrafted features along with a deep learning model to differentiate between authentic and tampered images. We have presented a dual-branch Convolutional Neural Network in conjunction with Error Level Analysis and noise residuals from Spatial Rich Model. For our experiment, we utilized the freely accessible CASIA dataset. After training the dual-branch network for 16 epochs, it generated an accuracy of 98.55%. We have also provided a comparative analysis with other previously proposed work in the field of image forgery detection. This hybrid approach proves that deep learning models along with some well-known traditional approaches can provide better results for detecting tampered images.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"10 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Enhanced Coalescence Backdoor Attack Against DNN Based on Pixel Gradient 基于像素梯度的 DNN 增强凝聚后门攻击

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-19 DOI: 10.1007/s11063-024-11469-4

Jianyao Yin, Honglong Chen, Junjian Li, Yudong Gao

{"title":"Enhanced Coalescence Backdoor Attack Against DNN Based on Pixel Gradient","authors":"Jianyao Yin, Honglong Chen, Junjian Li, Yudong Gao","doi":"10.1007/s11063-024-11469-4","DOIUrl":"https://doi.org/10.1007/s11063-024-11469-4","url":null,"abstract":"Deep learning has been widely used in many applications such as face recognition, autonomous driving, etc. However, deep learning models are vulnerable to various adversarial attacks, among which backdoor attack is emerging recently. Most of the existing backdoor attacks use the same trigger or the same trigger generation approach to generate the poisoned samples in the training and testing sets, which is also commonly adopted by many backdoor defense strategies. In this paper, we develop an enhanced backdoor attack (EBA) that aims to reveal the potential flaws of existing backdoor defense methods. We use a low-intensity trigger to embed the backdoor, while a high-intensity trigger to activate it. Furthermore, we propose an enhanced coalescence backdoor attack (ECBA) where multiple low-intensity incipient triggers are designed to train the backdoor model, and then, all incipient triggers are gathered on one sample and enhanced to launch the attack. Experiment results on three popular datasets show that our proposed attacks can achieve high attack success rates while maintaining the model classification accuracy of benign samples. Meanwhile, by hiding the incipient poisoned samples and preventing them from activating the backdoor, the proposed attack exhibits significant stealth and the ability to evade mainstream defense methods during the model training phase.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"30 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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-based Methods for Offline Arabic Handwriting Recognition: A Review 基于 CNN 的离线阿拉伯语手写识别方法：综述

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-03-19 DOI: 10.1007/s11063-024-11544-w

Mohsine El Khayati, Ismail Kich, Youssef Taouil

{"title":"CNN-based Methods for Offline Arabic Handwriting Recognition: A Review","authors":"Mohsine El Khayati, Ismail Kich, Youssef Taouil","doi":"10.1007/s11063-024-11544-w","DOIUrl":"https://doi.org/10.1007/s11063-024-11544-w","url":null,"abstract":"Arabic Handwriting Recognition (AHR) is a complex task involving the transformation of handwritten Arabic text from image format into machine-readable data, holding immense potential across various applications. Despite its significance, AHR encounters formidable challenges due to the intricate nature of Arabic script and the diverse array of handwriting styles. In recent years, Convolutional Neural Networks (CNNs) have emerged as a pivotal and promising solution to address these challenges, demonstrating remarkable performance and offering distinct advantages. However, the dominance of CNNs in AHR lacks a dedicated comprehensive review in the existing literature. This review article aims to bridge the existing gap by providing a comprehensive analysis of CNN-based methods in AHR. It covers both segmentation and recognition tasks, delving into advancements in network architectures, databases, training strategies, and employed methods. The article offers an in-depth comparison of these methods, considering their respective strengths and limitations. The findings of this review not only contribute to the current understanding of CNN applications in AHR but also pave the way for future research directions and improved practices, thereby enriching and advancing this critical domain. The review also aims to uncover genuine challenges in the domain, providing valuable insights for researchers and practitioners.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"116 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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