Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer;identifying movies or music that a user will find interest, etc. However, the existing recomm...
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ISBN:
(纸本)9781728198293
Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer;identifying movies or music that a user will find interest, etc. However, the existing recommendation approaches mainly focus on capturing user-item interaction patterns for prediction, and ignore the user's side information such as visit frequency and duration. In this paper, we study the side information aided website recommendation problem that using the browsing history of a set of users and their side information to predict the websites that will be of interest to a certain user. We propose a novel recommendation approach called SI-VAE that incorporates side information with the variational autoencoders (VAEs) model for top-k recommendation. The proposed method takes both user-website interaction information and side information as input, and adopts an encoder/decoder model to generate user's interested websites from partial observations. The model of SI-VAE is implemented as a neural network, and trained with a multinomial likelihood objective function to form the ranking of user-website interaction probabilities. We conduct extensive experiments on two real-world datasets, which show that the proposed model outperforms the baselines in a number of performance metrics in website recommendation.
For time or safety critical scenarios when faulty predictions or decisions can have crucial consequences, such as in certain telecommunications scenarios, reliable prediction models and accurate data are of the essenc...
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ISBN:
(纸本)9781665462839
For time or safety critical scenarios when faulty predictions or decisions can have crucial consequences, such as in certain telecommunications scenarios, reliable prediction models and accurate data are of the essence. When modeling and predicting data in such scenarios, data with censored covariates remain an issue as ignoring them or imputing them with lack of precision may cause inaccurate or uncertain predictions. In this paper, we provide a fast, reliable variational autoencoder framework which can handle covariate censoring in high dimensional data. Our numerical experiments demonstrate that our framework compares favorably to alternative methods in terms of prediction accuracy for both the response and the covariates, while enabling estimation of the prediction uncertainties. We moreover demonstrate that the method is at least 8 times faster than the benchmark models used in this paper, and more robust to initial imputations and noise than existing models. The method can also be used directly for predicting unseen data, which is a challenge for some state-of-the-art methods.
Deep learning-based articulation-to-speech (ATS) systems designed for individuals with speech disorders have been extensively researched in recent years. However, conventional methods have faced challenges in represen...
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Deep learning-based articulation-to-speech (ATS) systems designed for individuals with speech disorders have been extensively researched in recent years. However, conventional methods have faced challenges in representing the transformation in latent space across speech and electromagnetic articulography (EMA) domains, resulting in low speech quality. In this paper, we propose a variational autoencoder (VAE)-based end-to-end ATS model called PARAN that efficiently produces high-fidelity speech from EMA signals. Our model adjusts a prior distribution of latent representations from EMA signals to match a posterior distribution derived from speech utilizing a normalizing flow mechanism. To further enhance the clarity and intelligibility of the synthesized speech, we incorporate an additional loss function aimed at predicting phonetic information from EMA signals. Experimental results demonstrate that our model outperforms previous methods in terms of speech quality and intelligibility.(1)
Recently, side-channel attacks based on deep learning (DLSCAs) have attracted much attention. Many works have improved the performance of DLSCAs by designing advanced neural network architectures and training strategi...
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ISBN:
(纸本)9798350358261;9798350358278
Recently, side-channel attacks based on deep learning (DLSCAs) have attracted much attention. Many works have improved the performance of DLSCAs by designing advanced neural network architectures and training strategies. There are few studies on leakage models for DLSCAs. Existing researches usually utilize the intermediate value Hamming weight (HW) and the intermediate value itself (ID) as leakage models. Training a classifier with good performance is challenging due to the many label classes in the ID leakage model. The HW leakage model can significantly reduce the number of labels, but it will cause samples imbalance. In this paper, we propose a new DLSCA leakage model, named Balanced Labels Compression (BLC). We consider dividing sensitive intermediate values with same lowest epsilon bits into same class to obtain balanced labels. Then, we train a classifier using the compressed BLC labels and profiling energy traces. At the attack phase, the probability distribution of BLC labels is extended to the probability distribution of sensitive intermediate values. We conduct extensive comparison experiments with HW, ID, and BLC leakage models under the two scenarios of sufficient and insufficient profiling energy traces. Further, we exploit VAE to improve attack performance when energy traces are insufficient. Experimental results show that VAE-based data augmentation can significantly reduce required energy traces.
Enhancing the commercial viability and efficacy of PV solar cell usage necessitates a comprehensive investigation into fault identification. This study introduces the development of a defect detection system tailored ...
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This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing are...
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ISBN:
(纸本)9781728141923
This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing area;for example, a male with a scarf, it is difficult to predict he has a beard in the covered area or not? In this paper, we propose a novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance. Given a trained standard variational autoencoder (VAE) for un-occluded face generation. To be specified, we search for the possible set of VAE coding vector for the current occluded input image, and the predicted coding should be robust to the missing area. The possible facial appearance set is then recovered through the decoder of VAE model. Experiments show that our method successfully predicts recovered results in large missing regions;these results are diverse, and all are reasonable to be consistent with the observable facial area, i.e., both the facial geometry and the personal characteristics are preserved.
One of the most promising architectures for generative models is the variational autoencoder (VAE). To reconstruct Batik patterns for this work, we used a deep convolutional VAE architecture. Reconstruction outcomes f...
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This study introduces a design-thinking-driven approach aimed at enhancing the accuracy and robustness of Human Activity Recognition (HAR) using sensor data from Internet of Things (IoT) devices. The proposed algorith...
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Motivated by increasingly exploding data traffic of online video services, the prediction of the popularity profile of video contents becomes very important for network traffic prediction, recommendation systems, and ...
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Anomalies in data often convey critical information that can be leveraged in a variety of applications. For the military engaged in combat, this can amount to identifying threats early and preserving a lethal edge ove...
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Anomalies in data often convey critical information that can be leveraged in a variety of applications. For the military engaged in combat, this can amount to identifying threats early and preserving a lethal edge over an adversary. In other more benign cases it can corrupt data integrity and lead to ineffective application of other data analysis techniques. To tackle the problem of anomaly detection, there are several common methods provided in statistics and machine learning literature, including variational autoencoders (VAEs). Using a VAE, we develop a novel objective function to improve its performance detecting anomalies. Additionally, we introduce a modeling pipeline that works in the fully unsupervised context, where one does not know the true proportion of anomalies present in the data. To construct this pipeline, we fit reconstruction errors using a Gaussian mixture model (GMM) and select the model whose characteristics best match our performance metrics. Using our approach, we observe an increase in anomalies detected against a standard objective function, and we measure an average improvement of 0.4021 in F1 scores. We show our findings using four labeled benchmark data sets and apply our conclusions on an open-source, unlabeled data set taken from ***.
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