Neural Processing Letters最新文献

Label-Only Membership Inference Attack Based on Model Explanation 基于模型解释的仅标签成员推理攻击

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-09-18 DOI: 10.1007/s11063-024-11682-1

Yao Ma, Xurong Zhai, Dan Yu, Yuli Yang, Xingyu Wei, Yongle Chen

{"title":"Label-Only Membership Inference Attack Based on Model Explanation","authors":"Yao Ma, Xurong Zhai, Dan Yu, Yuli Yang, Xingyu Wei, Yongle Chen","doi":"10.1007/s11063-024-11682-1","DOIUrl":"https://doi.org/10.1007/s11063-024-11682-1","url":null,"abstract":"It is well known that machine learning models (e.g., image recognition) can unintentionally leak information about the training set. Conventional membership inference relies on posterior vectors, and this task becomes extremely difficult when the posterior is masked. However, current label-only membership inference attacks require a large number of queries during the generation of adversarial samples, and thus incorrect inference generates a large number of invalid queries. Therefore, we introduce a label-only membership inference attack based on model explanations. It can transform a label-only attack into a traditional membership inference attack by observing neighborhood consistency and perform fine-grained membership inference for vulnerable samples. We use feature attribution to simplify the high-dimensional neighborhood sampling process, quickly identify decision boundaries and recover a posteriori vectors. It also compares different privacy risks faced by different samples through finding vulnerable samples. The method is validated on CIFAR-10, CIFAR-100 and MNIST datasets. The results show that membership attributes can be identified even using a simple sampling method. Furthermore, vulnerable samples expose the model to greater privacy risks.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"21 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269527","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 Robot Ground Medium Classification Algorithm Based on Feature Fusion and Adaptive Spatio-Temporal Cascade Networks 基于特征融合和自适应时空级联网络的机器人地面介质分类算法

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-09-17 DOI: 10.1007/s11063-024-11679-w

Changqun Feng, Keming Dong, Xinyu Ou

{"title":"A Robot Ground Medium Classification Algorithm Based on Feature Fusion and Adaptive Spatio-Temporal Cascade Networks","authors":"Changqun Feng, Keming Dong, Xinyu Ou","doi":"10.1007/s11063-024-11679-w","DOIUrl":"https://doi.org/10.1007/s11063-024-11679-w","url":null,"abstract":"With technological advancements and scientific progress, mobile robots have found widespread applications across various fields. To enable robots to perform tasks safely and effectively in diverse and unknown environments, this paper proposes a ground medium classification algorithm for robots based on feature fusion and an adaptive spatio-temporal cascade network. Specifically, the original directional features in the dataset are first transformed into quaternion form. Then, spatio-temporal forward and reverse neighbors are identified using KD trees, and their connection strengths are evaluated via a kernel density estimation algorithm to determine the final set of neighbors. Subsequently, based on the connection strengths determined in the previous step, we perform noise reduction on the features using discrete wavelet transform. The noise-reduced features are then weighted and fused to generate a new feature representation.After feature fusion, the Adaptive Dynamic Convolutional Neural Network (ADC) proposed in this paper is cascaded with the Long Short-Term Memory (LSTM) network to further extract hybrid spatio-temporal feature information from the dataset, culminating in the final terrain classification. Experiments on the terrain type classification dataset demonstrate that our method achieves an average accuracy of 97.46% and an AUC of 99.80%, significantly outperforming other commonly used algorithms in the field. Furthermore, the effectiveness of each module in the proposed method is further demonstrated through ablation experiments.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"31 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266230","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 Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation 用于位置感知网络服务推荐的基于深度学习的混合 CNN-LSTM 模型

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-09-16 DOI: 10.1007/s11063-024-11687-w

Ankur Pandey, Praveen Kumar Mannepalli, Manish Gupta, Ramraj Dangi, Gaurav Choudhary

{"title":"A Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation","authors":"Ankur Pandey, Praveen Kumar Mannepalli, Manish Gupta, Ramraj Dangi, Gaurav Choudhary","doi":"10.1007/s11063-024-11687-w","DOIUrl":"https://doi.org/10.1007/s11063-024-11687-w","url":null,"abstract":"Advertising is the most crucial part of all social networking sites. The phenomenal rise of social media has resulted in a general increase in the availability of customer tastes and preferences, which is a positive development. This information may be used to improve the service that is offered to users as well as target advertisements for customers who already utilize the service. It is essential while delivering relevant advertisements to consumers, to take into account the geographic location of the consumers. Customers will be ecstatic if the offerings displayed to them are merely available in their immediate vicinity. As the user’s requirements will vary from place to place, location-based services are necessary for gathering this essential data. To get users to stop thinking about where they are and instead focus on an ad, location-based advertising (LBA) uses their mobile device’s GPS to pinpoint nearby businesses and provide useful information. Due to the increased two-way communication between the marketer and the user, mobile consumers’ privacy concerns and personalization issues are becoming more of a barrier. In this research, we developed a collaborative filtering-based hybrid CNN-LSTM model for recommending geographically relevant online services using deep neural networks. The proposed hybrid model is made using two neural networks, i.e., CNN and LSTM. Geographical information systems (GIS) are used to acquire initial location data to collect precise locational details. The proposed LBA for GIS is built in a Python simulation environment for evaluation. Hybrid CNN-LSTM recommendation performance beats existing location-aware service recommender systems in large simulations based on the WS dream dataset.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"96 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266229","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

An Adaptive Missing Data Restoration Method for UAV Confrontation Based on Deep Regression Model 基于深度回归模型的无人机对抗自适应缺失数据恢复方法

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-09-05 DOI: 10.1007/s11063-024-11690-1

Huan Wang, Xu Zhou, Xiaofeng Liu

{"title":"An Adaptive Missing Data Restoration Method for UAV Confrontation Based on Deep Regression Model","authors":"Huan Wang, Xu Zhou, Xiaofeng Liu","doi":"10.1007/s11063-024-11690-1","DOIUrl":"https://doi.org/10.1007/s11063-024-11690-1","url":null,"abstract":"Completing missions with autonomous decision-making unmanned aerial vehicles (UAV) is a development direction for future battlefields. UAV make decisions based on battlefield situation information collected by sensors and can quickly and accurately perform complex tasks such as path planning, cooperative reconnaissance, cooperative pursuit and attacks. Obtaining real-time situation information of enemy is the basis for realizing autonomous decision-making of the UAV. However, in practice, due to internal sensor failure or interference of enemy, the acquired situation information is prone to be missing, which affects the training and decision-making of autonomous UAV. In this paper, an adaptive missing situation data restoration method for UAV confrontation is proposed. The UAV confrontation situation data are acquired through JSBSim, an open-source UAV simulation platform. By fusing temporal convolutional network and long short-term memory sequences, we establish a deep regression method for missing data restoration and introduce an adaptive mechanism to reduce the training time of the restoration model in response to dynamic changes in the enemy’s strategy during UAV confrontation. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models under different degrees of data missing conditions. The performance of our method is quantified by five metrics. The performance of our proposed method is better than the other benchmark algorithms. The experimental results show that the proposed method can solve the missing data restoration problem and provide reliable situation data while effectively reducing the training time of the restoration model.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"28 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210821","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

Microblog Negative Comments Data Analysis Model Based on Multi-scale Convolutional Neural Network and Weighted Naive Bayes Algorithm 基于多尺度卷积神经网络和加权 Naive Bayes 算法的微博负面评论数据分析模型

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-09-05 DOI: 10.1007/s11063-024-11688-9

Chunliang Zhou, XiangPei Meng, Zhaoqiang Shen

{"title":"Microblog Negative Comments Data Analysis Model Based on Multi-scale Convolutional Neural Network and Weighted Naive Bayes Algorithm","authors":"Chunliang Zhou, XiangPei Meng, Zhaoqiang Shen","doi":"10.1007/s11063-024-11688-9","DOIUrl":"https://doi.org/10.1007/s11063-024-11688-9","url":null,"abstract":"As a form of public supervision, Microblog’s negative reviews allow people to share their opinions and experiences and express dissatisfaction with unfair and unreasonable phenomena. This form of supervision has the potential to promote social fairness, drive governments, businesses, and individuals to correct mistakes and enhance transparency. To characterize the sentiment trend and determine the influence of Microblog negative reviews, we propose a multi-scale convolutional neural network and weighted naive bayes algorithm (MCNN–WNB). We define the feature vector characterization index for Microblog negative review data and preprocess the data accordingly. We quantify the relationship between attributes and categories using the weighted Naive Bayes method and use the quantification value as the weighting coefficient for the attributes, addressing the issue of decreased classification performance in traditional methods. We introduce a sentiment classification model based on word vector representation and a multi-scale convolutional neural networks to filter out Microblog negative review data. We conduct simulation experiments using real data, analyzing key influencing parameters such as convergence time, training set sample size, and number of categories. By comparing with K-means, Naive Bayes algorithm, Spectral Clustering algorithm and Autoencoder algorithm, we validate the effectiveness of our proposed method. We discover that the convergence time of the MCNN–WNB algorithm increases as the number of categories increases. The average classification accuracy of the algorithm remains relatively stable with varying test iterations. The algorithm’s precision increases with the number of training set samples and eventually stabilizes.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"11 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210819","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

Within-Class Constraint Based Multi-task Autoencoder for One-Class Classification 基于类内约束的单类分类多任务自动编码器

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-09-02 DOI: 10.1007/s11063-024-11681-2

Guojie Xie, Tianlei Wang, Dekang Liu, Wandong Zhang, Xiaoping Lai

{"title":"Within-Class Constraint Based Multi-task Autoencoder for One-Class Classification","authors":"Guojie Xie, Tianlei Wang, Dekang Liu, Wandong Zhang, Xiaoping Lai","doi":"10.1007/s11063-024-11681-2","DOIUrl":"https://doi.org/10.1007/s11063-024-11681-2","url":null,"abstract":"Autoencoders (AEs) have attracted much attention in one-class classification (OCC) based unsupervised anomaly detection. The AEs aim to learn the unity features on targets without involving anomalies and thus the targets are expected to obtain smaller reconstruction errors than anomalies. However, AE-based OCC algorithms may suffer from the overgeneralization of AE and fail to detect anomalies that have similar distributions to target data. To address these issues, a novel within-class constraint based multi-task AE (WC-MTAE) is proposed in this paper. WC-MTAE consists of two different task: one for reconstruction and the other for the discrimination-based OCC task. In this way, the encoder is compelled by the OCC task to learn the more compact encoded feature distribution for targets when minimizing OCC loss. Meanwhile, the within-class scatter based penalty term is constructed to further regularize the encoded feature distribution. The aforementioned two improvements enable the unsupervised anomaly detection by the compact encoded features, thereby addressing the issue of the overgeneralization in AEs. Comparisons with several state-of-the-art (SOTA) algorithms on several non-image datasets and an image dataset CIFAR10 are provided where the WC-MTAE is conducted on 3 different network structures including the multilayer perception (MLP), LeNet-type convolution network and full convolution neural network. Extensive experiments demonstrate the superior performance of the proposed WC-MTAE. The source code would be available in future.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"25 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210822","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

Training Artificial Neural Network with a Cultural Algorithm 用文化算法训练人工神经网络

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-08-27 DOI: 10.1007/s11063-024-11636-7

Kübra Tümay Ateş, İbrahim Erdem Kalkan, Cenk Şahin

{"title":"Training Artificial Neural Network with a Cultural Algorithm","authors":"Kübra Tümay Ateş, İbrahim Erdem Kalkan, Cenk Şahin","doi":"10.1007/s11063-024-11636-7","DOIUrl":"https://doi.org/10.1007/s11063-024-11636-7","url":null,"abstract":"Artificial neural networks are amongst the artificial intelligence techniques with their ability to provide machines with some functionalities such as decision making, comparison, and forecasting. They are known for having the capability of forecasting issues in real-world problems. Their acquired knowledge is stored in the interconnection strengths or weights of neurons through an optimization system known as learning. Several limitations have been identified with commonly used gradient-based optimization algorithms, including the risk of premature convergence, the sensitivity of initial parameters and positions, and the potential for getting trapped in local optima. Various meta-heuristics are proposed in the literature as alternative training algorithms to mitigate these limitations. Therefore, the primary aim of this study is to combine a feed-forward artificial neural network (ANN) with a cultural algorithm (CA) as a meta-heuristic, aiming to establish an efficient and dependable training system in comparison to existing methods. The proposed artificial neural network system (ANN-CA) evaluated its performance on classification tasks over nine benchmark datasets: Iris, Pima Indians Diabetes, Thyroid Disease, Breast Cancer Wisconsin, Credit Approval, Glass Identification, SPECT Heart, Wine and Balloon. The overall experimental results indicate that the proposed method outperforms other methods included in the comparative analysis by approximately 12% in terms of classification error and approximately 7% in terms of accuracy.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"44 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210619","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

Lagrange Stability of Competitive Neural Networks with Multiple Time-Varying Delays 具有多重时变延迟的竞争神经网络的拉格朗日稳定性

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-08-26 DOI: 10.1007/s11063-024-11667-0

Dandan Tang, Baoxian Wang, Jigui Jian, Caiqing Hao

引用次数: 0

Improving Neural Radiance Fields Using Near-Surface Sampling with Point Cloud Generation 利用点云生成近表面采样改进神经辐射场

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-07-22 DOI: 10.1007/s11063-024-11654-5

Hye Bin Yoo, Hyun Min Han, Sung Soo Hwang, Il Yong Chun

{"title":"Improving Neural Radiance Fields Using Near-Surface Sampling with Point Cloud Generation","authors":"Hye Bin Yoo, Hyun Min Han, Sung Soo Hwang, Il Yong Chun","doi":"10.1007/s11063-024-11654-5","DOIUrl":"https://doi.org/10.1007/s11063-024-11654-5","url":null,"abstract":"Neural radiance field (NeRF) is an emerging view synthesis method that samples points in a three-dimensional (3D) space and estimates their existence and color probabilities. The disadvantage of NeRF is that it requires a long training time since it samples many 3D points. In addition, if one samples points from occluded regions or in the space where an object is unlikely to exist, the rendering quality of NeRF can be degraded. These issues can be solved by estimating the geometry of 3D scene. This paper proposes a near-surface sampling framework to improve the rendering quality of NeRF. To this end, the proposed method estimates the surface of a 3D object using depth images of the training set and performs sampling only near the estimated surface. To obtain depth information on a novel view, the paper proposes a 3D point cloud generation method and a simple refining method for projected depth from a point cloud. Experimental results show that the proposed near-surface sampling NeRF framework can significantly improve the rendering quality, compared to the original NeRF and three different state-of-the-art NeRF methods. In addition, one can significantly accelerate the training time of a NeRF model with the proposed near-surface sampling framework.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"45 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741210","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

On Stage-Wise Backpropagation for Improving Cheng’s Method for Fully Connected Cascade Networks 关于分阶段反向传播改进全连接级联网络的程氏方法

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-07-11 DOI: 10.1007/s11063-024-11655-4

Eiji Mizutani, Naoyuki Kubota, Tam Chi Truong

{"title":"On Stage-Wise Backpropagation for Improving Cheng’s Method for Fully Connected Cascade Networks","authors":"Eiji Mizutani, Naoyuki Kubota, Tam Chi Truong","doi":"10.1007/s11063-024-11655-4","DOIUrl":"https://doi.org/10.1007/s11063-024-11655-4","url":null,"abstract":"In this journal, Cheng has proposed a backpropagation (BP) procedure called BPFCC for deep fully connected cascaded (FCC) neural network learning in comparison with a neuron-by-neuron (NBN) algorithm of Wilamowski and Yu. Both BPFCC and NBN are designed to implement the Levenberg-Marquardt method, which requires an efficient evaluation of the Gauss-Newton (approximate Hessian) matrix (nabla textbf{r}^textsf{T} nabla textbf{r}), the cross product of the Jacobian matrix (nabla textbf{r}) of the residual vector (textbf{r}) in nonlinear least squares sense. Here, the dominant cost is to form (nabla textbf{r}^textsf{T} nabla textbf{r}) by rank updates on each data pattern. Notably, NBN is better than BPFCC for the multiple (q~!(>!1))-output FCC-learning when q rows (per pattern) of the Jacobian matrix (nabla textbf{r}) are evaluated; however, the dominant cost (for rank updates) is common to both BPFCC and NBN. The purpose of this paper is to present a new more efficient stage-wise BP procedure (for q-output FCC-learning) that reduces the dominant cost with no rows of (nabla textbf{r}) explicitly evaluated, just as standard BP evaluates the gradient vector (nabla textbf{r}^textsf{T} textbf{r}) with no explicit evaluation of any rows of the Jacobian matrix (nabla textbf{r}).","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"64 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585797","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