In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete ...
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ISBN:
(纸本)9781728103068
In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train a sequence-to-sequence model that directly maps the source language speech to the target languages discrete representation. Our proposed method can directly generate target speech without any auxiliary or pre-training steps with a source or target transcription. To the best of our knowledge, this is the first work that performed pure speech-to-speech translation between untranscribed unknown languages.
Light field digital images are novel image modalities for capturing a sampled representation of the plenoptic function. A large amount of data is typically associated to a single sample of a scene, and data compressio...
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ISBN:
(纸本)9781728144962
Light field digital images are novel image modalities for capturing a sampled representation of the plenoptic function. A large amount of data is typically associated to a single sample of a scene, and data compression tools are required in order to develop systems and applications for light field communications. This paper presents the study of the performance of a convolutional neural network autoencoder as a tool for digital light field image compression. Testing conditions and a framework for the experimental evaluation are proposed for this study. Different encoders and coding conditions are taken into consideration, obtained results are reported and critically discussed.
Future Connected and Automated Vehicles (CAVs), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequis...
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ISBN:
(纸本)9781728136165
Future Connected and Automated Vehicles (CAVs), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres will have access to suitably anonymized CAV mobility information. Safe and secure operations will then be contingent on early detection of anomalies. In this paper, a novel unsupervised learning model based on deep autoencoder is proposed to detect the self-reported location anomaly in CAVs, using vehicle locations and the Received Signal Strength Indicator (RSSI) as features. Quantitative experiments on simulation datasets show that the proposed approach is effective and robust in detecting self-reported location anomalies.
Botnets are the powerful and effective way of performing malicious activities over the internet. Over the years, it has evolved into many forms. Earlier bots used static IP to communicate with their command and contro...
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ISBN:
(纸本)9781538670019
Botnets are the powerful and effective way of performing malicious activities over the internet. Over the years, it has evolved into many forms. Earlier bots used static IP to communicate with their command and control server. This method stopped working as soon as that specific IP was identified and blocked. These days, domain fluxing botnets are mostly in practice. The idea is, using Dynamically Generation Algorithm (DGA) to generate domains and use it to connect with C&C server. Numerous researches have been done to detect DGA botnets. These includes deriving features based on alphanumeric distribution of DGA domains and performing classification on it. Other studies include network logs analysis, time series analysis etc. Most of these domain classification works rely upon the features developed and may not work well if the botmaster decides to generate domain with completely new features. We are concerned with developing algorithm that is resilient to feature change that also work well for domain generated by completely new algorithm that was not seen before. We generated 16 bit representation of domains using autoencoder and classified it as benign or DGA generated using supervised learning(with neural net and SVM). To make it work with previously unseen algorithm, we tweaked our method with mean activation of 16-bit domain representation. This helped improve classification accuracy for completely new set of domain generation algorithm by up to 16%.
Cancer arises from the accumulation of particular somatic genomic variants known as drivers. New sequencing technologies allow the identification of hundreds of variants in a tumor sample. These variations should be c...
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ISBN:
(纸本)9783030216429;9783030216412
Cancer arises from the accumulation of particular somatic genomic variants known as drivers. New sequencing technologies allow the identification of hundreds of variants in a tumor sample. These variations should be classified as driver or passenger (i.e. benign), but functional studies could be time and cost demanding. Therefore, in the bioinformatics field, machine learning methods are widely applied to distinguish drivers from passengers. Recent projects, such as the AACR GENIE, provide an unprecedented amount of cancer data that could be exploited for the training process of machine learning algorithms. However, the majority of these variants are not yet classified. The development and application of approaches able to assimilate unlabeled data are needed in order to fully benefit from the available omics-resources. We collected and annotated a dataset of known 976 driver and over 84,000 passengers from different databases and we investigated whether unclassified variants from GENIE could be employed in the classification process. We characterized each variant by 94 features from multiple omics resources. We therefore trained different autoencoder architectures with more than 80000 GENIE variants. autoencoder is a type of neural network able to learn a new features representation of the input data in an unsupervised manner. The trained autoencoders are then used to obtain new representations of the labeled dataset, with a reduced number of meta-features with the aim to reduce redundancy and extract the relevant information. The new representations are in turn exploited to train and test different machine learning techniques, such as Random Forest, Support Vector Machine, Ridge Logistic Regression, One Class SVM. Final results, however, does not show a significant increase in classification ability when meta-features are used.
Deep learning (DL) approaches have been applied in different sectors of medical imaging applications, i.e. classification, segmentation and detection tasks and shown superior performance. The DL based generative metho...
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ISBN:
(纸本)9781728114163
Deep learning (DL) approaches have been applied in different sectors of medical imaging applications, i.e. classification, segmentation and detection tasks and shown superior performance. The DL based generative methods are used for image denoising, enhancement and restoration task. In case of image analysis, image denoising is one of the most crucial preprocessing steps. Recently, there are various DL approaches are applied in image denoising problems and achieved state-of-the-art performance. In this work, we apply recurrent residual U-Net (R2U-Net) based autoencoder model for medical image denoising which is applied for digital pathology, dermoscopy, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images denoising tasks. The performance of R2U-Net based auto-encoder model is also evaluated for Transfer domain (TD) between MRI and CT scan images. The experiments have conducted on different publicly available medical image datasets and shows promising denoising results which can be applied in different medical imaging applications.
This paper introduces an autoencoder structure to transfer the eye makeup from an arbitrary reference image to a source image realistically and faithfully using both synthetic paired data and unpaired data in a semi-s...
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ISBN:
(纸本)9781728150239
This paper introduces an autoencoder structure to transfer the eye makeup from an arbitrary reference image to a source image realistically and faithfully using both synthetic paired data and unpaired data in a semi-supervised way. Different from the image domain transfer problem, our framework only needs one domain entity and follows an "encoding-swap-decoding" process. Makeup transfer is achieved by decoding the base representation from a source image and makeup representation from a reference image. Moreover, our method allows users to control the makeup degree by tuning makeup weight. To the best of our knowledge, there is no public large makeup dataset to evaluate data-driven approaches. We have collected a dataset of non-makeup images and with-makeup images of various eye makeup styles. Experiments demonstrate the effectiveness of our method with the state-of-the-art methods both qualitatively and quantitatively.
Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing network, shortly Lmser, was a further development of the traditional auto-encoder (AE) by folding the architecture with respect to the centr...
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ISBN:
(数字)9783030362041
ISBN:
(纸本)9783030362041;9783030362034
Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing network, shortly Lmser, was a further development of the traditional auto-encoder (AE) by folding the architecture with respect to the central coding layer and thus leading to the features of Duality in Connection Weight (DCW) and Duality in Paired Neurons (DPN), as well as jointly supervised and unsupervised learning which is called Duality in Supervision Paradigm (DSP). However, its advantages were only demonstrated in a one-hidden-layer implementation due to the lack of computing resources and big data at that time. In this paper, we revisit Lmser from the perspective of deep learning, develop Lmser network based on multiple fully-connected layers, and confirm several Lmser functions with experiments on image recognition, reconstruction, association recall, and so on. Experiments demonstrate that Lmser indeed works as indicated in the original paper, and it has promising performance in various applications.
In this paper, a novel framework for global diagnosis of autism spectrum disorder (ASD) using task-based functional MRI data is presented. A speech fMRI experiment is held to obtain local features related to the funct...
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ISBN:
(纸本)9781728123141
In this paper, a novel framework for global diagnosis of autism spectrum disorder (ASD) using task-based functional MRI data is presented. A speech fMRI experiment is held to obtain local features related to the functional activity of the brain. This study proposes both global diagnosis and local diagnosis by analyzing brain brainnetome atlas (BNT) which will lead to the first step of providing personalized medicine. The diagnosis pipeline consists of four steps on functional MRI volumes. The experimental results show that the global classification accuracy of our framework is about 75.8% and is much higher than other alternatives. Finally, comprehensive brain maps are provided for different individuals to indicate the degree of susceptibility of each brain area for autism, moving towards the idea of personalized medicine.
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