Neural Processing Letters最新文献

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.

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.

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.

Pruning convolutional neural networks offers a promising solution to mitigate the computational complexity challenges encountered during application deployment. However, prevalent pruning techniques primarily concentrate on model parameters or feature mapping analysis to devise static pruning strategies, often overlooking the underlying feature extraction capacity of convolutional kernels. To address this, the study first quantitatively expresses the feature extraction capability of convolutional channels from three aspects: global features, distribution metrics, and directional metrics. It explores the multi-dimensional information of the channels, calculates the overall expectation, variance, and cosine distance from the unit vector as the quantitative results of the channels. Subsequently, a clustering algorithm is employed to categorize the multidimensional information. This approach ensures that convolutional channels grouped within each cluster possess similar feature extraction capabilities. An enhanced differential evolutionary algorithm is utilized to optimize the number of clustering centers across all convolutional layers, ensuring optimal grouping. The final step involves achieving channel sparsification through the calculation of crowding distances for each sample within its designated cluster. This preserves a diverse subset of channels that are critical for maintaining model accuracy. Extensive empirical evaluations conducted on three benchmark image classification datasets demonstrate the efficacy of this method. For instance, on the ImageNet dataset, the ResNet-50 model experiences a substantial reduction in FLOPs by 58.43% while incurring a minimal decrease in TOP-1 accuracy of only 1.15%.

The pressure Poisson equation is usually the most time-consuming problem in fluid simulation. To accelerate its solving process, we propose a deep neural network-based numerical method, termed Deep Residual Iteration Method (DRIM), in this paper. Firstly, the global equation is decomposed into multiple independent tridiagonal sub-equations, and DRIM is capable of solving all the sub-equations simultaneously. Moreover, we employed Residual Network and a correction iteration method to improve the precision of the solution achieved by the neural network in DRIM. The numerical results, including the Poiseuille flow, the backwards-facing step flow, and driven cavity flow, have proven that the numerical precision of DRIM is comparable to that of classic solvers. In these numerical cases, the DRIM-based algorithm is about 2–10 times faster than the conventional method, which indicates that DRIM has promising applications in large-scale problems.

Dynamic node classification aims to predict the labels of nodes in the dynamic networks. Existing methods primarily utilize the graph neural networks to acquire the node features and original graph structure features. However, these approaches ignore the high-order relationships between nodes and may lead to the over-smoothing issue. To address these issues, we propose a distance enhanced hypergraph learning (DEHL) method for dynamic node classification. Specifically, we first propose a time-adaptive pre-training component to generate the time-aware representations of each node. Then we utilize a dual-channel convolution module to construct the local and global hypergraphs which contain the corresponding local and global high-order relationships. Moreover, we adopt the K-nearest neighbor algorithm to construct the global hypergraph in the embedding space. After that, we adopt the node convolution and hyperedge convolution to aggregate the features of neighbors on the hypergraphs to the target node. Finally, we combine the temporal representations and the distance enhanced representations of the target node to predict its label. In addition, we conduct extensive experiments on two public dynamic graph datasets, i.e., Wikipedia and Reddit. The experimental results show that DEHL outperforms the state-of-the-art baselines in terms of AUC.

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.

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.

Although Deep Learning networks generally outperform traditional machine learning approaches based on tailored features, they often lack explainability. To address this issue, numerous methods have been proposed, particularly for image-related tasks such as image classification or object segmentation. These methods generate a heatmap that visually explains the classification problem by identifying the most important regions for the classifier. However, these explanations remain purely visual. To overcome this limitation, we introduce a novel CNN explainability method that identifies the most relevant regions in an image and generates a decision tree based on meaningful regional features, providing a rule-based explanation of the classification model. We evaluated the proposed method on a synthetic blob’s dataset and subsequently applied it to two cell image classification datasets with healthy and pathological patterns.

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.