Moving toward new and higher frequencies would bring the 6G communication network into practice. Using a new MAC mechanism will enhance and overcome the THz challenges. Our paper focused on analyzing the entropy inter...
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ISBN:
(纸本)9798350387896;9798350387889
Moving toward new and higher frequencies would bring the 6G communication network into practice. Using a new MAC mechanism will enhance and overcome the THz challenges. Our paper focused on analyzing the entropy interdependence between two subsystems, normal and lucky mobile terminals. The two introduced entropy metrics play a crucial role in interpreting the interdependence of the two subsystems. However, the lack of data obliged us to use GAN and its methods to generate similar data. Therefore, we used four different GAN methods plus the standard GAN to obtain the most significant data, and we determined the similarity using six different similarity metrics. The results showed that cosine and correlation similarities are not appropriate to capture the similarity, meanwhile, the rest;dynamic time warping, Frechet inception, root mean square error, and peak signal-to-noise ratio agreed that DCGAN was the one who generated the most accurate data series compared to the rest.
Detecting spammer groups is important for maintaining the normal operation of e-commerce platforms. Nevertheless, current spammer group detection methods ignore the overlapping between spammer groups. Moreover, hand-c...
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ISBN:
(纸本)9789819756056;9789819756063
Detecting spammer groups is important for maintaining the normal operation of e-commerce platforms. Nevertheless, current spammer group detection methods ignore the overlapping between spammer groups. Moreover, hand-crafted indicators-based methods lack universality. Aiming at these problems, we propose a deep reinforcement learning-based spammer group detection approach. First, we model the review dataset as a user-item bipartite graph that serves as the interaction environment of the agent, and utilize the BiNE model to get the initial user vector representations. Then, we model the generation of candidate groups in the bipartite graph as the Markov decision process and introduce the self-attention mechanism to aggregate node features. Meanwhile, we use a deep Q-network to obtain the overlapped candidate group sequences. Finally, we apply an adversarial autoencoder to detect spammer groups. Experiments on Yelp Miami, Yelp New York, YelpCHI and Amazon datasets reveal that our method performs better than three baselines.
Multi-view face generation from a single image is an essential and challenging problem. Most of the existing methods need to use paired images when training models. However, collecting and labeling large-scale paired ...
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Multi-view face generation from a single image is an essential and challenging problem. Most of the existing methods need to use paired images when training models. However, collecting and labeling large-scale paired face images could lead to high labor and time cost. In order to address this problem, multi-view face generation via unpaired images is proposed in this paper. To avoid using paired data, the encoder and discriminator are trained, so that the high-level abstract features of the identity and view of the input image are learned by the encoder, and then, these low-dimensional data are input into the generator, so that the realistic face image can be reconstructed by the training generator and discriminator. During testing, multiple one-hot vectors representing the view are imposed to the identity representation and the generator is employed to map them to high-dimensional data, respectively, which can generate multi-view images while preserving the identity features. Furthermore, to reduce the number of used labels, semi-supervised learning is used in the model. The experimental results show that our method can produce photo-realistic multi-view face images with a small number of view labels, and makes a useful exploration for the synthesis of face images via unpaired data and very few labels.
Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive. By combining the generative stability of a ...
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ISBN:
(纸本)9798350381641
Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive. By combining the generative stability of a beta-variational autoencoder (VAE) with the discriminative strengths of generative adversarial networks (GANs), we propose a novel model, beta-VAEGAN. We investigate methods for composing anomaly scores based on the discriminative and reconstructive capabilities of our model. Existing work focuses on linear combinations of these components to determine if data is anomalous. We advance existing work by training a kernelized support vector machine (SVM) on the respective error components to also consider nonlinear relationships. This improves anomaly detection performance, while allowing faster optimization. Lastly, we use the deviations from the Gaussian prior of beta-VAEGAN to form a novel anomaly score component. In comparison to state-of-the-art work, we improve the F-1 score during anomaly detection from 0.85 to 0.92 on the widely used MITBIH Arrhythmia Database.(1)
Deep learning algorithms (DLAs) have achieved better results than traditional methods in the field of multivariate geochemical anomaly recognition because of their strong ability to extract feature from nonlinear data...
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Deep learning algorithms (DLAs) have achieved better results than traditional methods in the field of multivariate geochemical anomaly recognition because of their strong ability to extract feature from nonlinear data. However, most of DLAs are black-box approaches because of the high nonlinearity characteristics of the hidden layer. In addition, the integration of domain knowledge into the DLAs to ensure physical consistency is a challenge for DLAs in geoscience. In this study, we adopted the adversarial autoencoder (AAE) algorithm for geochemical anomaly detection. The interpretability of the model is improved by visualizing features and integrating geological domain knowledge into the loss function of the AAE. The feature visualization method was used to display the changes of information in the model calculation process to further understand the inherent operation law and principle of the neural network. The penalty term was added to the optimized loss function, and the spatiotemporal and genetic relationships between felsic intrusions and mineralization were integrated into the AAE with the aim of improving the geological interpretability of the network. The added penalty item can guide the changes in the stage of data reconstruction and improve the understandability of the results of geologically constrained AAE. In addition, the effectiveness of injecting the concept of physical constraints into the AAE can be verified via feature visualization. A case study in the southern Jiangxi Province and its surrounding areas was performed to identify multivariate geochemical anomalies. The results obtained by the geologically constrained AAE demonstrated a strong spatial correlation with the outcrop of intrusions in the study area, and most of the known mineral deposits are located in or near the highly anomalous areas.
In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings without pri...
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In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings without prior knowledge of their characteristics. This method deals with the crucial problems related to the presence of speckle, the spatial correlation structures in SAR images, and the lack of annotated data to train a detection algorithm. Our proposed method aims to address these issues through a self-supervised learning algorithm. First, we propose to mitigate the SAR speckle through the deep learning SAR2SAR algorithm. We then develop an adversarial autoencoder (AAE) to reconstruct anomaly-free SAR images from despeckled data taking into account potential spatial correlation structures. Finally, a change detection processing step is applied between the input and the output to detect anomalies. Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.
Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. autoencoder neural networks learn to reconstruct norm...
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ISBN:
(纸本)9783030461508;9783030461492
Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalies, where the reconstruction error exceeds some threshold. Here we analyze a fundamental problem of this approach when the training set is contaminated with a small fraction of outliers. We find that continued training of autoencoders inevitably reduces the reconstruction error of outliers, and hence degrades the anomaly detection performance. In order to counteract this effect, an adversarial autoencoder architecture is adapted, which imposes a prior distribution on the latent representation, typically placing anomalies into low likelihood-regions. Utilizing the likelihood model, potential anomalies can be identified and rejected already during training, which results in an anomaly detector that is significantly more robust to the presence of outliers during training.
Historical document denoising is the most challenging step in the field of image processing and computer vision. In this paper, we propose a novel end-to-end adversarial autoencoder (AAE) to generate clean images and ...
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ISBN:
(纸本)9781728150543
Historical document denoising is the most challenging step in the field of image processing and computer vision. In this paper, we propose a novel end-to-end adversarial autoencoder (AAE) to generate clean images and to show how adversarial autoencoders can be used in historical document denoising. We used the adversarial autoencoder (AAE), which uses the generative adversarial networks (GAN) so as to suit the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior. The experiments results prove that our approach functions more positively than the cutting-edge approaches on synthetic and real world images at a lower computational cost as well.
Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behavio...
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Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed -size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies.
Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem because anomalies in graphs can manifest in three different ...
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Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem because anomalies in graphs can manifest in three different forms: anomalous nodes, anomalous edges, and anomalous subgraphs. It is crucial to detect all these anomaly types within a single framework to provide a unified solution to the graph anomaly detection task. The main objective of this study is to propose a model that is capable of detecting all static graph anomalies in a single architecture across various domains. In this paper, we introduce DeGAN (Decomposition -based unified Graph ANomaly detection), a novel framework for unified graph anomaly detection in static networks. DeGAN combines two deep learning concepts with graph decomposition to identify anomalous graph objects: a graph neural network and an adversarial autoencoder. DeGAN is featured with its capability to detect anomalies in a single process, and adopting graph decomposition has improved performance compared to the traditional adversarial learning approach. DeGAN is evaluated with six real -world datasets to demonstrate that our framework can work in multiple domains. Experimental results demonstrate that DeGAN is capable of detecting anomalous nodes, edges, and sub -graphs within a single model. Additionally, the effectiveness of the subcomponents of DeGAN has been demonstrated through experimentation. Even though DeGAN is proposed for plain graphs, it can be extended to attributed and dynamic graphs.
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