Sketch plays an important role in human nonverbal communication, which is a superior way to describe specific objects visually. Generating human free-hand sketches has become topical in computer graphics and vision, i...
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ISBN:
(纸本)9781450376259
Sketch plays an important role in human nonverbal communication, which is a superior way to describe specific objects visually. Generating human free-hand sketches has become topical in computer graphics and vision, inspired by various applications related to sketches such as sketch object recognition. Existing methods on sketch generation failed to utilize stroke sequence information of human free-hand sketches. Especially, a recent study proposed an end-to-end variational autoencoder (VAE) model called sketch-rnn which learned to sketch with human input. However, the performance of sketch-rnn is affected by the original input seriously hence decreased its robustness. In this paper, we proposed a sequence-to-sequence model called sketch-aae to generate multiple categories of human-like sketches of higher quality than sketch-rnn. We achieve this by introducing an adversarial autoencoder (AAE) model, which uses generative adversarial networks (GAN) to improve the robustness of VAE. To our best knowledge, for the first time, the AAE model is used to synthesize sketches. A VGGNet classification model is then formulated to prove the similarity between our generated sketches and human free-hand sketches. Extensive experiments both qualitatively and quantitatively demonstrate that the proposed model is superiority over the state-of-the-art for sketch generation and multi-class sketch classification.
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising. The recent trend in these areas calls for anomaly detection on...
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ISBN:
(纸本)9781450367837
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising. The recent trend in these areas calls for anomaly detection on time-evolving data with high-dimensional categorical features without labeled samples. Also, there is an increasing demand for identifying and monitoring irregular patterns at multiple resolutions. In this work, we propose a unified end-to-end approach to solve these challenges by combining the advantages of adversarial autoencoder and Recurrent Neural Network. The model learns data representations cross different scales with attention mechanisms, on which an enhanced two-resolution anomaly detector is developed for both instances and data blocks. Extensive experiments are performed over three types of datasets to demonstrate the efficacy of our method and its superiority over the state-of-art approaches.
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.
It is of great practical significance to accurately model and analyze abnormal events in time series. For example, the identification of anomaly patterns on infrastructure sensor curves helps locate equipment failures...
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ISBN:
(纸本)9781450384131
It is of great practical significance to accurately model and analyze abnormal events in time series. For example, the identification of anomaly patterns on infrastructure sensor curves helps locate equipment failures. In this paper, we propose an unsupervised anomaly detection approach for time series, which can comprehensively consider both point anomalies and subsequence anomalies. We innovatively introduce RNN into the architecture of adversarial autoencoder to better analyze anomaly events based on overall relationship of time series. In addition, we innovatively apply the Outlier Exposure technique for the performance optimization of anomaly detector. Meanwhile, a WGAN-based method is utilized to generate anomaly datasets through normal distribution learning. Finally, we apply the proposed method for fraud detection on a financial statement dataset and intrusion detection on a network traffic dataset. Experimental results demonstrates that our model can comprehensively consider different anomaly types in time series, and achieve promising detection performance overall. In the experiment of fraud detection, the LSTM integrated AAE model achieves an F1 score of 0.810, while the Outlier Exposure enhanced model achieves an F1 score of 0.894. This indicates that our method can improve the performance of current audit systems and facilitate discovering malicious behaviors.
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each ...
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ISBN:
(纸本)9781450379984
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this work, we propose a novel end-toend knowledge directed adversarial learning framework, which portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. Furthermore, the domain knowledge is incorporated in an unsupervised manner to guide the optimization and further boosts the performance. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art. We further prove the effectiveness of automatic domain knowledge incorporation in performance enhancement.
This work deals with taking an unsupervised approach to abstractive text summarization where a large set of sentences is converted into a concise summary highlighting the essential details. This is achieved with the u...
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ISBN:
(纸本)9781665473507
This work deals with taking an unsupervised approach to abstractive text summarization where a large set of sentences is converted into a concise summary highlighting the essential details. This is achieved with the use of an adversarial autoencoder model. The model encodes the input to a smaller latent vector and the decoder decodes this latent code to generate the higher dimensional output with some loss. Unlike variational autoencoders, AAE's use discriminators to learn using adversarial loss. K-Means clustering and language models are used to get the final summary. This model has been tested with different datasets like the Amazon, Rotten Tomatoes and Yelp reviews dataset to essentially do an opinion summarization task and this is finally evaluated using ROGUE-1, ROGUE-2,ROGUE-L and BLEU scores. The same task is also conducted on a dataset in Hindi. We obtain a ROGUE-1 score of around 24% for Amazon, Yelp and CNN/Daily Mail dataset and a score of 12% for Rotten Tomatoes while the score obtained for the Hindi news articles dataset is only 8%.
Automated fraud detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money ...
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ISBN:
(纸本)9783030415792;9783030415785
Automated fraud detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc., which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. In our work, we propose a semi-supervised learning detection model, FraudJudger, to analyze user behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from raw data with the help of adversarial autoencoder (AAE). Compared with other state-of-the-art fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data. Besides, we deploy FraudJudger on a real-world financial platform, and the experiment results show that our model can well generalize to other fraud detection contexts.
Detecting irregularity in an image or video is an important task in quality control or automatic visual inspection. This paper presents an image embedding technique for detecting an irregularity or abnormality in imag...
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ISBN:
(纸本)9789813292918;9789813292901
Detecting irregularity in an image or video is an important task in quality control or automatic visual inspection. This paper presents an image embedding technique for detecting an irregularity or abnormality in images. This can further be utilized in image screening application. In the proposed architecture, deep adversarial autoencoder is trained to extract the features from images. Using these features and skip-gram model, we develop the image2vec architecture to capture contextual probability in an image. Various score aggregation techniques are explored and its performance is reported. As a case study, we present a scenario of foreign body object detection in clinical-grade X-ray images. The proposed approach is found to correctly detect and localize abnormality in images.
While vast amounts of personal data are shared daily on public online platforms and used by companies and analysts to gain valuable insights, privacy concerns are also on the rise: Modern authorship attribution techni...
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ISBN:
(纸本)9781450390965
While vast amounts of personal data are shared daily on public online platforms and used by companies and analysts to gain valuable insights, privacy concerns are also on the rise: Modern authorship attribution techniques have proven effective at identifying individuals from their data, such as their writing style or behavior of picking and judging movies. It is hence crucial to develop data sanitization methods that allow sharing of users' data while protecting their privacy and preserving quality and content of the original data. In this paper, we tackle anonymization of textual data and propose an end-to-end differentially private variational autoencoder architecture. Unlike previous approaches that achieve differential privacy on a per-word level through individual perturbations, our solution works at an abstract level by perturbing the latent vectors that provide a global summary of the input texts. Decoding an obfuscated latent vector thus not only allows our model to produce coherent, high-quality output text that is human-readable, but also results in strong anonymization due to the diversity of the produced data. We evaluate our approach on IMDb movie and Yelp business reviews, confirming its anonymization capabilities and preservation of the semantics and utility of the original sentences.
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.
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