Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Speech Demodulation-Based Features for Replay Detection

Replay attack presents a great threat to Automatic Speaker Ver-iﬁcation (ASV) system. The speech can be modeled as amplitude and frequency modulated (AM-FM) signals. In this paper, we explore speech demodulation-based features using Hilbert transform (HT) and Teager Energy Operator (TEO) for replay detection. In particular, we propose features, namely, HT-based Instantaneous Amplitude (IA) and Instantaneous Frequency (IF) Cosine Coefﬁcients (i.e., HT-IACC and HT-IFCC) and Energy Separation Algorithm (ESA)-based features (i.e., ESA-IACC and ESA-IFCC). For adapting instantaneous energy w.r. t given sampling frequency, ESA requires 3 samples whereas HT requires relatively large number of samples and thus, ESA gives high time resolution.The experiments were performed on ASV spoof 2017 Challenge database for replay spoof speech detection (SSD). The experimental results shows that ESA-based features gave lower EER. In addition, linearly-spaced Gabor ﬁlterbank gave lower EER than Butterworth ﬁl-terbank. To explore possible complementary information using amplitude and frequency, we have used score-level fusion of IA and IF. With HT-based feature set, the score-level fusion gave EER of 5.24 % (dev) and 10.03 % (eval), whereas ESA-based feature set reduced the EER to 2.01 % (dev) and 9.64 % (eval).
Unsupervised Temporal Segmentation of Human Action Using Community Detection

Temporal segmentation of complex human action videos into action primitives plays a pivotal role in building models for human action understanding. Studies in the past have introduced unsupervised frameworks for deriving a known number of motion primitives from action videos. Our work focuses towards answering a question: Given a set of videos with humans performing an activity, can the action primitives be derived from them without specifying any prior knowledge about the count for the constituting sub-actions categories? To this end, we present a novel community detection-based human action segmentation algorithm. Our work marks the existence of community structures in human action videos where the consecutive frames around the key poses group together to form communities similar to social networks. We test our proposed technique over the stitched Weizmann dataset and MHADI01-s motion capture dataset and our technique outperforms the state-of-the-art techniques of complex action segmentation without the count of actions being pre-specified.
Review of Sympathetic Blocks: Anatomy, Sonoanatomy, Evidence, and Techniques

Abstract The autonomic nervous system is composed of the sympathetic and parasympathetic nervous systems. The sympathetic nervous system is implicated in situations involving emergent action by the body and additionally plays a role in mediating pain states and pathologies in the body. Painful conditions thought to have a sympathetically mediated component may respond to blockade of the corresponding sympathetic fibers. The paravertebral sympathetic chain has been targeted for various painful conditions. Although initially injected using landmark-based techniques, fluoroscopy and more recently ultrasound imaging have allowed greater visualization and facilitated injections of these structures. In addition to treating painful conditions, sympathetic blockade has been used to improve perfusion, treat angina, and even suppress posttraumatic stress disorder symptoms. This review explores the anatomy, sonoanatomy, and evidence supporting these injections and focuses on ultrasound-guided/assisted technique for the performance of these blocks.
On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation

This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by augmenting an additional alignment head to the multi-head source-to-target attention component. This is used to compute sharper attention weights. We describe how to use the alignment head to achieve competitive performance. To study the effect of adding the alignment head, we simulate a dictionary-guided translation task, where the user wants to guide translation using pre-defined dictionary entries. Using the proposed approach, we achieve up to 3.8% BLEU improvement when using the dictionary, in comparison to 2.4% BLEU in the baseline case. We also propose alignment pruning to speed up decoding in alignment-based neural machine translation (ANMT), which speeds up translation by a factor of 1.8 without loss in translation performance. We carry out experiments on the shared WMT 2016 English→Romanian news task and the BOLT Chinese→English discussion forum task.
Dithered Quantization for Frequency-Domain Speech and Audio Coding

A common issue in coding speech and audio in the frequency domain, which appears with decreasing bitrate, is that quantization levels become increasingly sparse. With low accuracy, high-frequency components are typically quantized to zero, which leads to a mufﬂed output signal and musical noise. Bandwidth extension and noise-ﬁlling methods attempt to treat the problem by inserting noise of similar energy as the original signal, at the cost of low signal to noise ratio. Dithering methods however provide an alternative approach, where both accuracy and energy are retained. We propose a hybrid coding approach where low-energy samples are quantized using dithering, instead of the conventional uniform quantizer. For dithering, we apply 1bit quantization in a randomized sub-space. We further show that the output energy can be adjusted to the desired level using a scaling parameter. Objective measurements and listening tests demonstrate the advantages of the proposed methods.
Unsupervised Domain Adaptation Based on Source-guided Discrepancy

Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different and labels in the target domain are unavailable. An important question in unsupervised domain adaptation is how to measure the difference between the source and target domains. Existing discrepancy measures for unsupervised domain adaptation either require high computation costs or have no theoretical guarantee. To mitigate these problems, this paper proposes a novel discrepancy measure called source-guided discrepancy (S-disc), which exploits labels in the source domain unlike the existing ones. As a consequence, S-disc can be computed efficiently with a finitesample convergence guarantee. In addition, it is shown that S-disc can provide a tighter generalization error bound than the one based on an existing discrepancy measure. Finally, experimental results demonstrate the advantages of S-disc over the existing discrepancy measures.
The Impact of Egg Nutrient Composition and Its Consumption on Cholesterol Homeostasis

Nutrient deficiencies and excess are involved in many aspects of human health. As a source of essential nutrients, eggs have been used worldwide to support the nutritional needs of human societies. On the other hand, eggs also contain a significant amount of cholesterol, a lipid molecule that has been associated with the development of cardiovascular diseases. Whether the increase of egg consumption will lead to elevated cholesterol absorption and disruption of cholesterol homeostasis has been a concern of debate for a while. Cholesterol homeostasis is regulated through its dietary intake, endogenous biosynthesis, utilization, and excretion. Recently, some research interests have been paid to the effects of egg consumption on cholesterol homeostasis through the intestinal cholesterol absorption. Nutrient components in eggs such as phospholipids may contribute to this process. The goals of this review are to summarize the recent progress in this area and to discuss some potential benefits of egg consumption.
Mesenchymal Stromal/Stem Cells in Regenerative Medicine and Tissue Engineering

As a result of over five decades of investigation, mesenchymal stromal/stem cells (MSCs) have emerged as a versatile and frequently utilized cell source in the fields of regenerative medicine and tissue engineering. In this review, we summarize the history of MSC research from the initial discovery of their multipotency to the more recent recognition of their perivascular identity in vivo and their extraordinary capacity for immunomodulation and angiogenic signaling. As well, we discuss long-standing questions regarding their developmental origins and their capacity for differentiation toward a range of cell lineages. We also highlight important considerations and potential risks involved with their isolation, ex vivo expansion, and clinical use. Overall, this review aims to serve as an overview of the breadth of research that has demonstrated the utility of MSCs in a wide range of clinical contexts and continues to unravel the mechanisms by which these cells exert their therapeutic effects.
A Retina-Inspired Encoder: An Innovative Step on Image Coding Using Leaky Integrate-and-Fire Neurons

This paper aims to build an image coding system based on a model of the mammalian retina. The retina is the light-sensitive layer of tissue located on the inner coat of the eye and it is responsible for vision. Inspired by the way the retina handles and compresses visual information and based on previous studies we aim to build and analytically study a retinal-inspired image quantizer, based on the Leaky Integrate-and-Fire (LIF) model, a neural model approximating the behavior of the ganglion cells of the Ganglionic retinal layer that is responsible for visual data compression. In order to have a more concrete view of the encoder's behavior, in our experiments, we make use of the spatiotemporal decomposition layers provided by extensive studies on a previous retinal layer, the Outer Plexiform Layer (OPL). The decomposition layers produced by the OPL, are being encoded using our LIF image encoder and then, they are reconstructed to observe the encoder's efficiency.
Action Recognition Based on Discriminative Embedding of Actions Using Siamese Networks

Actions can be recognized effectively when the various atomic attributes forming the action are identified and combined in the form of a representation. In this paper, a low-dimensional representation is extracted from a pool of attributes learned in a universal Gaussian mixture model using factor analysis. However, such a representation cannot adequately discriminate between actions with similar attributes. Hence, we propose to classify such actions by leveraging the corresponding class labels. We train a Siamese deep neural network with a contrastive loss on the low-dimensional representation. We show that Siamese networks allow effective discrimination even between similar actions. The efficacy of the proposed approach is demonstrated on two benchmark action datasets, HMDB51 and MPII Cooking Activities. On both the datasets, the proposed method improves the state-of-the-art performance considerably.
Low-Light Color Image Super-Resolution Using RGB/NIR Sensor

We propose a method for super-resolution (SR) of low-resolution (LR) color images taken in low-light scenes. Our method is based on multi-frame SR technique, which fuses multiple LR images taken at different camera positions to synthesize a high-resolution color image. Previous methods have implicitly assumed that LR images could be captured with less noise and blur. However, heavy noise and motion blur will be imposed on images taken in low-light scene. They make it difficult to super-resolve low-light images with high quality. To overcome this problems, we utilize a single sensor that captures red, green, blue (RGB) and near-infrared (NIR) information. Since NIR images taken using an NIR flash unit can be captured with less noise, they contribute to effective reduction of image artifacts in super-resolving LR images. Using multiple RGB/NIR raw images, we jointly perform deblurring, denoising and SR of LR images. Experiments using real raw data demonstrate the effectiveness of our method.
Improving Mongolian Phrase Break Prediction by Using Syllable and Morphological Embeddings with BiLSTM Model

In the speech synthesis systems, the phrase break (PB) prediction is the ﬁrst and most important step. Recently, the state-of-the-art PB prediction systems mainly rely on word embeddings. However this method is not fully applicable to Mongolian language, because its word embeddings are inadequate trained, owing to the lack of resources. In this paper, we introduce a bidirectional Long Short Term Memory (BiLSTM) model which combined word embeddings with syllable and morphological embedding representations to provide richer and multi-view information which leverages the agglutinative property. Experimental results show the proposed method outperforms compared systems which only used the word embeddings. In addition, further analysis shows that it is quite robust to the Out-of-Vocabulary (OOV) problem owe to the reﬁned word embedding. The proposed method achieves the state-of-the-art performance in the Mongolian PB prediction.
Whispered Speech to Neutral Speech Conversion Using Bidirectional LSTMs

We propose a bidirectional long short-term memory (BLSTM) based whispered speech to neutral speech conversion system that employs the STRAIGHT speech synthesizer. We use a BLSTM to map the spectral features of whispered speech to those of neutral speech. Three other BLSTMs are employed to predict the pitch, periodicity levels and the voiced/unvoiced phoneme decisions from the spectral features of whispered speech. We use objective measures to quantify the quality of the predicted spectral features and excitation parameters, using data recorded from six subjects, in a four fold setup. We find that the temporal smoothness of the spectral features predicted using the proposed BLSTM based system is statistically more compared to that predicted using deep neural network based baseline schemes. We also observe that while the performance of the proposed system is comparable to the baseline scheme for pitch prediction, it is superior in terms of classifying voicing decisions and predicting periodicity levels. From subjective evaluation via listening test, we find that the proposed method is chosen as the best performing scheme 26.61% (absolute) more often than the best baseline scheme. This reveals that the proposed method yields a more natural sounding neutral speech from whispered speech.
Classification of Correction Turns in Multilingual Dialogue Corpus

This paper presents a multiclass classification of correction dialog turns using machine learning. The classes are determined by the type of the introduced recognition errors while performing WOz trials and creating the multilingual corpus. Three datasets were obtained using different sets of acoustic-prosodic features on the multilingual dialogue corpus. The classification experiments were done using different machine learning paradigms: Decision Trees, Support Vector Machines and Deep Learning. After careful experiments setup and optimization on the hyper-parameter space, the obtained classification results were analyzed and compared in the terms of accuracy, precision, recall and F1 score. The achieved results are comparable with those obtained in similar experiments on different tasks and speech databases.
3D Leaf Tracking for Plant Growth Monitoring

This article presents a 3D approach in plant growth monitoring and deals with the tracking of leaves of sunflower plants. Our aim is to compute time-series of individual leaf area, under water stress and control conditions. These data will then be used by biologists to study the drought resistance of various sunflower species. Our method to track the leaves in 3D has been evaluated on a set of 132 point clouds obtained via classical structure-from-motion techniques and multi-view stereo software. These 3D acquisitions have been performed on 12 sunflower plants (6 water-stressed, 6 well-watered) during a period of one month (11 measurement dates per sunflower plant). This method gives promising results for both conditions (water-stressed and well-watered), for different species and is able to follow the growth of the plants, as well as to detect new leaf emergence and leaf decay.
A Cross-Layer Based Network for Faster Image Generation

Owing to the great success of generative adversarial networks (GANs), unsupervised learning based image generation is popular currently. This paper presents a cross-layer architecture for the generators in GANs, which passes inputs to every subsequent layer and encourages the flow of information and gradients throughout the network. While traditional networks with $L$ layers have $L$ connections, the proposed network with cross-layer architecture has $2L-1$ direct connections. For each layer, the network input and the output of its previous layer are used as inputs. Extensive experiments demonstrate that our network can generate images at a higher speed without introducing extra parameters by comparing with two state-of-the-art GANs, namely deep convolutional GAN and Wasserstein GAN-GP, on two datasets: Fashion-MNIST and CelebA.
Speech Emotion Recognition by Combining Amplitude and Phase Information Using Convolutional Neural Network

Previous studies of speech emotion recognition utilize convolutional neural network (CNN) directly on amplitude spectro-gram to extract features. CNN combines with bidirectional long short term memory (BLSTM) has become the state-of-the-art model. However, phase information has been ignored in this model. The importance of phase information in speech processing ﬁeld is gathering attention. In this paper, we propose feature extraction of amplitude spectrogram and phase information using CNN for speech emotion recognition. The modiﬁed group delay cepstral coefﬁcient (MGDCC) and relative phase are used as phase information. Firstly, we analyze the inﬂu-ence of phase information on speech emotion recognition. Then we design a CNN-based feature representation using amplitude and phase information. Finally, experiments were conducted on EmoDB to validate the effectiveness of phase information. Integrating amplitude spectrogram with phase information, the relative emotion error recognition rates are reduced by over 33% in comparison with using only amplitude-based feature.
Angular Softmax for Short-Duration Text-independent Speaker Verification

Recently, researchers propose to build deep learning based end-to-end speaker veriﬁcation (SV) systems and achieve competitive results compared with the standard i -vector approach. In addition to deep learning architectures, optimization metric such as softmax loss or triplet loss, is important for extracting speaker embeddings which are discriminative and generalizable to unseen speakers. In this paper, angular softmax (A-softmax) loss is introduced to improve speaker embedding quality. It is investigated in two SV frameworks: a CNN based end-to-end SV framework and an i -vector SV framework where deep discriminant analysis is used for channel compensation. Experimental results on a short-duration text-independent speaker veriﬁcation dataset generated from SRE reveal that A-softmax achieves signiﬁcant performance improvement compared with other metrics in both frameworks.
Siamese Recurrent Auto-Encoder Representation for Query-by-Example Spoken Term Detection

With the explosive development of human-computer speech interaction, spoken term detection is widely required and has attracted increasing interest. In this paper, we propose a weak supervised approach using Siamese recurrent auto-encoder (RAE) to represent speech segments for query-by-example spoken term detection (QbyE-STD). The proposed approach exploits word pairs that contain different instances of the same/different word content as input to train the Siamese RAE. The encoder last hidden state vector of Siamese RAE is used as the feature for QbyE-STD, which is a fixed dimensional embedding feature containing mostly semantic content related information. The advantages of the proposed approach are: 1) extracting more compact feature with fixed dimension while keeping the semantic information for STD; 2) the extracted feature can describe the sequential phonetic structure of similar sounds to degree, which can be applied for zero-resource QbyE-STD. Evaluations on real scene Chinese speech interaction data and TIMIT confirm the effectiveness and efficiency of the proposed approach compared to the conventional ones.
Mitigating Confirmation Bias on Twitter by Recommending Opposing Views

In this work, we propose a content-based recommendation approach to increase exposure to opposing beliefs and opinions. Our aim is to help provide users with more diverse viewpoints on issues, which are discussed in partisan groups from different perspectives. Since due to the backfire effect, people's original beliefs tend to strengthen when challenged with counter evidence, we need to expose them to opposing viewpoints at the right time. The preliminary work presented here describes our first step into this direction. As illustrative showcase, we take the political debate on Twitter around the presidency of Donald Trump.
Multicomponent 2-D AM-FM Modeling of Speech Spectrograms

In contrast to 1-D short-time analysis of speech, 2-D modeling of spectrograms provides a characterization of speech attributes directly in the joint time-frequency plane. Building on existing 2-D models to analyze a spectrogram patch, we propose a multicomponent 2-D AM-FM representation for spectrogram decomposition. The components of the proposed representation comprise a DC, a fundamental frequency carrier and its harmonics, and a spectrotemporal envelope, all in 2-D. The number of harmonics required is patch-dependent. The estimation of the AM and FM is done using the Riesz transform, and the component weights are estimated using a least-squares approach. The proposed representation provides an improvement over existing state-of-the-art approaches, for both male and female speakers. This is quantified using reconstruction SNR and perceptual evaluation of speech quality (PESQ) metric. Further, we perform an overlap-add on the DC component, pooling all the patches and obtain a time-frequency (t-f) a periodicity map for the speech signal. We verify its effectiveness in improving speech synthesis quality by using it in an existing state-of-the art vocoder.
Bayesian Patchworks: An Approach to Case-Based Reasoning

Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficient computationally aided diagnostic tool that thinks in the same way might be helpful in locating key past cases of interest that could assist with diagnosis. This article develops a novel mathematical model to mimic the type of logical thinking that physicians use when considering past cases. The proposed model can also provide physicians with explanations that would be similar to the way they would naturally reason about cases. The proposed method is designed to yield predictive accuracy, computational efficiency, and insight into medical data; the key element is the insight into medical data, in some sense we are automating a complicated process that physicians might perform manually. We finally implemented the result of this work on two publicly available healthcare datasets, for heart disease prediction and breast cancer prediction.