Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons. The method was trained and tested on 26 retrospectively analysed datasets from 20 real patients. The proposed network outperforms the baseline methods in terms of all evaluation criteria.
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System

In this paper we present the results of an investigation of the importance of verbs in a deep learning QA system trained on SQuAD dataset. We show that main verbs in questions carry little influence on the decisions made by the system - in over 90% of researched cases swapping verbs for their antonyms did not change system decision. We track this phenomenon down to the insides of the net, analyzing the mechanism of self-attention and values contained in hidden layers of RNN. Finally, we recognize the characteristics of the SQuAD dataset as the source of the problem. Our work refers to the recently popular topic of adversarial examples in NLP, combined with investigating deep net structure.
Multimodal Name Recognition in Live TV Subtitling

In this paper, we present a method of combining a visual text reader with a system of automatic speech recognition to suppress errors when encountering out-of-vocabulary words – speciﬁcally names. The visual text reader outputs detected words that are mapped into a large list of names via the Leven-shtein distance. The detected names are inserted into the class-based language model on the ﬂy which improves recognition results. To demonstrate the effect on the real speech recognition task we use data from sports TV broadcasting where a lot of names are present in both the audio and video streams. We superseded manual vocabulary editing in live TV subtitling through respeaking by an automated online process. Further, we show that automatically adding the names to the recognition vocabulary online and with forgetting lowers the WER relatively by 39 % in comparison with the case when names of all sports-men are added to the vocabulary beforehand and by 15 % when only the relevant names are added beforehand.
Graph-Based Early-Fusion for Flood Detection

Flooding is one of the most harmful natural disasters, as it poses danger to both buildings and human lives. Therefore, it is fundamental to monitor these disasters to define prevention strategies and help authorities in damage control. With the wide use of portable devices (e.g., smartphones), there is an increase of the documentation and communication of flood events in social media. However, the use of these data in monitoring systems is not straightforward and depends on the creation of effective recognition strategies. In this paper, we propose a fusion-based recognition system for detecting flooding events in images extracted from social media. We propose two new graph-based early-fusion methods, which consider multiple descriptions and modalities to generate an effective image representation. Our results demonstrate that the proposed methods yield better results than a traditional early-fusion method and a specialized deep neural network fusion solution.
BUT OpenSAT 2017 Speech Recognition System

The paper describes BUT Automatic Speech Recognition (ASR) systems for two domains in OpenSAT evaluations: Low Resourced Languages and Public Safety Communications. The ﬁrst was challenging due to lack of training data, therefore multilingual approaches for BLSTM training were employed and recently published Residual Memory Networks requiring less training data were used. Combination of both approaches led to superior performance. The second domain was challenging due to recording in extreme conditions: speciﬁc channel, speaker under stress, high levels of noise. A data augmentation process was very important to get reasonably good performance.
Ionic Polypeptide Polymers with Unusual β-Sheet Stability.

Incorporating charged amino acid side chains in polypeptide polymer backbones to improve solubility usually leads to reduced secondary structuring. Here we show that highly water soluble (>15 mg.mL-1) β-sheets can be obtained via nucleotide monophosphate grafting onto simple poly(γ-propargyl- L-glutamate) backbone. This synthetic methodology has been applied to the synthesis of thymidine-based nucleopolypeptides presenting stable β-sheet conformation in aqueous solutions with pH values comprised between 4 and 8. These polymeric analogues of nucleoproteins exhibited selective interaction with simple DNA sequences displaying adenine.
A Deep Identity Representation for Noise Robust Spoofing Detection

The issue of the spooﬁng attacks which may affect automatic speaker veriﬁcation systems (ASVs) has recently received an increased attention, so that a number of countermeasures have been developed for detecting high technology attacks such as speech synthesis and voice conversion. However, the performance of anti-spooﬁng systems degrades signiﬁcantly in noisy conditions. To address this issue, we propose a deep learning framework to extract spooﬁng identity vectors, as well as the use of soft missing-data masks. The proposed feature extraction employs a convolutional neural network (CNN) plus a recurrent neural network (RNN) in order to provide a single deep feature vector per utterance. Thus, the CNN is treated as a convolutional feature extractor that operates at the frame level. On top of the CNN outputs, the RNN is employed to obtain a single spooﬁng identity representation of the whole utterance. Experimental evaluation is carried out on both a clean and a noisy version of the ASVSpoof2015 corpus. The experimental results show that our proposals clearly outperforms other methods recently proposed such as the popular CQCC+GMM system or other similar deep feature systems for both seen and unseen noisy conditions.
Language-Dependent Melody Embeddings

The paper explores the perspectives of applying the distributional approach to prosodic typology of languages. The method discussed here is an adaptation of the distributional semantics approach, as suggested by Mikolov, to melodic features of speech. The paper contains a detailed description of the new method, as well as a comparison of ﬁve European languages (English, Czech, German, Russian, and Finnish) in terms of melody embeddings. The total amount of speech data was over 500 hours. The experimental results show that melody embed-dings are language dependent. The proposed melody embedding model has shown reasonable results in language comparison.
Salient Target Detection Based on the Combination of Super-Pixel and Statistical Saliency Feature Analysis for Remote Sensing Images

The saliency analysis has become the important tool to detect the salient targets. However, due to complex target features and abundant background information interference, the traditional models are weak in salient target detection of remote sensing images. In this paper, a novel model based on the combination of super-pixel and statistical saliency feature analysis is proposed. The proposed model consists of three main steps. First, the statistical saliency feature map based on histogram statistical saliency analysis in the Lab color space is introduced. Then, information saliency feature map is obtained based on the combination of super-pixel segmentation and information entropy, and the statistical saliency feature map and the information saliency feature map are fused and enhanced to generate the final saliency map. Finally, the complete and accurate salient targets and regions of interest (ROIs) are obtained based on the improved Otsu segmentation method. Experimental evaluations show that the proposed model outperforms the state-of-the-art salient detection models.
Association between Anaemia in Children 6 to 23 Months Old and Child, Mother, Household and Feeding Indicators

In Low and Lower-Middle-Income countries, the prevalence of anaemia in infancy remains high. In early childhood anaemia cause irreversible cognitive deficits and represents a higher risk of child mortality. The consequences of anaemia in infancy are a major barrier to overcome poverty traps. The aim of this study was to analyse, based on a multi-level approach, different factors associated with anaemia in children 6–23 months old based on recent available Standard Demographic Health Surveys (S-DHS). We identified 52 S-DHS that had complete information in all covariates of interest in our analysis between 2005 and 2015. We performed traditional logistic regressions and multilevel logistic regression analyses to study the association between haemoglobin concentrations and household, child, maternal, socio-demographic variables. In our sample, 70% of the 6–23 months-old children were anaemic. Child anaemia was strongly associated with maternal anaemia, household wealth, maternal education and low birth weight. Children fed with fortified foods, potatoes and other tubers had significantly lower rates of anaemia. Improving overall household living conditions, increasing maternal education, delaying childbearing and introducing iron rich foods at six months of age may reduce the likelihood of anaemia in toddlerhood.
Exploring Spatio-Temporal Representations by Integrating Attention-based Bidirectional-LSTM-RNNs and FCNs for Speech Emotion Recognition

Automatic emotion recognition from speech, which is an important and challenging task in the ﬁeld of affective computing, heavily relies on the effectiveness of the speech features for classiﬁcation. Previous approaches to emotion recognition have mostly focused on the extraction of carefully hand-crafted features. How to model spatio-temporal dynamics for speech emotion recognition effectively is still under active investigation. In this paper, we propose a method to tackle the problem of emotional relevant feature extraction from speech by leveraging Attention-based Bidirectional Long Short-Term Memory Recurrent Neural Networks with fully convolutional networks in order to automatically learn the best spatio-temporal representations of speech signals. The learned high-level features are then fed into a deep neural network (DNN) to predict the ﬁnal emotion. The experimental results on the Chinese Natural Audio-Visual Emotion Database (CHEAVD) and the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpora show that our method provides more accurate predictions compared with other existing emotion recognition algorithms.
Fully Convolutional Neural Networks for Tissue Histopathology Image Classification and Segmentation

Analyzing tissue histopathology image is a new research area of the computer vision. It is getting more and more attention recently. Many pathologists and researchers have gained remarkable achievements in this area. However, most of previous works focused on improving the accuracy rather than the efficiency. In this paper, we propose a novel classification and segmentation framework based on fully convolutional neural networks, which can largely speed up large histopathology image analysis without damaging the accuracy too much. The method is validated through experiments on the Hematoxylin and Eosin (H&E) images of stomach tissues collected from real world. The experimental results show that the proposed method achieves comparable accuracy in both classification and segmentation tasks, along with a 16 times faster speed than the tested state-of-the-art methods.
Weighting Time-Frequency Representation of Speech Using Auditory Saliency for Automatic Speech Recognition

This paper proposes a new method for weighting twodimensional (2D) time-frequency (T-F) representation of speech using auditory saliency for noise-robust automatic speech recognition (ASR). Auditory saliency is estimated via 2D auditory saliency maps which model the mechanism for allocating human auditory attention. These maps are used to weight T-F representation of speech, namely the 2D magnitude spectrum or spectrogram, prior to features extraction for ASR. Experiments on Aurora-4 corpus demonstrate the effectiveness of the proposed method for noise-robust ASR. In multi-stream ASR, relative word error rate (WER) reduction of up to 5.3% and 4.0% are observed when comparing the multi-stream system using the proposed method with the baseline single-stream system not using T-F representation weighting and that using conventional spectral masking noise-robust technique, respectively. Combining the multi-stream system using the proposed method and the single-stream system using the conventional spectral masking technique reduces further the WER.
Filter Sampling and Combination CNN (FSC-CNN): A Compact CNN Model for Small-footprint ASR Acoustic Modeling Using Raw Waveforms

Learning an ASR acoustic model directly from raw waveforms using CNNs has proved to be effective, where convolutional layers with learnable ﬁlters are able to automatically extract useful features. However, these ﬁlters, with independent parameters, can be highly redundant resulting in inefﬁcient systems. In this paper, we propose a novel method to generate CNN ﬁlter parameters by ﬁrst sampling from a low-dimensional parameter space and then using a trainable scalar vector to perform a linear combination. This ﬁlter sampling and combination method (denoted as FSC) not only naturally enforces parameter sharing in the low-dimensional sampling space, but also adds to the learning capacity of ﬁlters. The FSC-CNN model has a sig-niﬁcantly smaller number of parameters and is more efﬁcient compared to conventional CNN models, which makes it feasible for small-footprint ASR. Experimental results on the WSJ LVCSR task show that FSC-CNNs are able to achieve a WER of 3.67 with a standard decoder set-up with only 1.19M nonlinear-layer parameters (better than a strong baseline CNN model with 3.2x more parameters). It also outperforms a CNN model with a similar number of parameters by a relative improvement of 10.26%.
5G Massive MIMO Architectures: Self-Backhauled Small Cells Versus Direct Access

In this paper, we focus on one of the key technologies for the fifth-generation wireless communication networks, massive multiple-input-multiple-output (mMIMO), by investigating two of its most relevant architectures: 1) to provide in-band backhaul for the ultra-dense network (UDN) of self-backhauled small cells (SCs), and 2) to provide direct access (DA) to user equipments (UEs). Through comprehensive 3GPP-based system-level simulations and analytical formulations, we show the end-to-end UE rates achievable with these two architectures. Differently from the existing works, we provide results for two strategies of self-backhauled SC deployments, namely random and ad-hoc, where in the latter SCs are purposely positioned close to UEs to achieve line-of-sight (LoS) access links. We also evaluate the optimal backhaul and access time resource partition due to the in-band self-backhauling (s-BH) operations. Our results show that the ad-hoc deployment of self-backhauled SCs closer to the UEs with optimal resource partition and with directive antenna patterns, provides rate improvements for cell-edge UEs that amount to ${30\%}$ and tenfold gain, as compared to mMIMO DA architecture with pilot reuse 3 and reuse 1, respectively. On the other hand, mMIMO s-BH underperforms mMIMO DA above the median value of the UE rates when the effect of pilot contamination is less severe, and the LoS probability of the DA links improves.
Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN

The emerging edge computing paradigm has given rise to a new promising mobile network architecture, which can address a number of challenges that the operators are facing while trying to support growing end user’s needs by shifting the computation from the base station to the edge cloud computing facilities. With such powerfully computational power, traditional unpractical resource allocation algorithms could be feasible. However, even with near optimal algorithms, the allocation result could still be far from optimal due to the inaccurate modeling of interference among sensor nodes. Such a dilemma calls for a measurement data-driven resource allocation to improve the total capacity. Meanwhile, the measurement process of inter-nodes’ interference could be tedious, time-consuming and have low accuracy, which further compromise the benefits brought by the edge computing paradigm. To this end, we propose a measurement-based estimation solution to obtain the interference efficiently and intelligently by dynamically controlling the measurement and estimation through an accuracy-driven model. Basically, the measurement cost is reduced through the link similarity model and the channel derivation model. Compared to the exhausting measurement method, it can significantly reduce the time cost to the linear order of the network size with guaranteed accuracy through measurement scheduling and the accuracy control process, which could also balance the tradeoff between accuracy and measurement overhead. Extensive experiments based on real data traces are conducted to show the efficiency of the proposed solutions.
Improved Accented Speech Recognition Using Accent Embeddings and Multi-task Learning

One of the major remaining challenges in modern automatic speech recognition (ASR) systems for English is to be able to handle speech from users with a diverse set of accents. ASR systems that are trained on speech from multiple English accents still underperform when confronted with a new speech accent. In this work, we explore how to use accent embeddings and multi-task learning to improve speech recognition for accented speech. We propose a multi-task architecture that jointly learns an accent classiﬁer and a multi-accent acoustic model. We also consider augmenting the speech input with accent information in the form of embeddings extracted by a separate network. These techniques together give signiﬁcant relative performance improvements of 15% and 10% over a multi-accent baseline system on test sets containing seen and unseen accents, respectively.
Unbiasing Semantic Segmentation For Robot Perception using Synthetic Data Feature Transfer

Robot perception systems need to perform reliable image segmentation in real-time on noisy, raw perception data. State-of-the-art segmentation approaches use large CNN models and carefully constructed datasets; however, these models focus on accuracy at the cost of real-time inference. Furthermore, the standard semantic segmentation datasets are not large enough for training CNNs without augmentation and are not representative of noisy, uncurated robot perception data. We propose improving the performance of real-time segmentation frameworks on robot perception data by transferring features learned from synthetic segmentation data. We show that pretraining real-time segmentation architectures with synthetic segmentation data instead of ImageNet improves fine-tuning performance by reducing the bias learned in pretraining and closing the \textit{transfer gap} as a result. Our experiments show that our real-time robot perception models pretrained on synthetic data outperform those pretrained on ImageNet for every scale of fine-tuning data examined. Moreover, the degree to which synthetic pretraining outperforms ImageNet pretraining increases as the availability of robot data decreases, making our approach attractive for robotics domains where dataset collection is hard and/or expensive.
Confidence Analysis for Breast Mass Image Classification

Computer-aided diagnosis (CAD) has great potential in providing real benefits to doctors and patients. Recent studies have, however, found lack of trust in CAD by radiologists in clinical diagnostic decision making. One of the main reasons is the lack of an appropriate confidence measure. This paper presents the first-ever study of classification confidence in the context of breast mass classification. We evaluated 11 state-of-the-art classification algorithms on breast mass image data using their confidence of classification metric, in addition to other standard evaluation metrics including accuracy and area under the curve (ROC). Experimental results show that although most classifiers produced very similar results with less than 2% difference in terms of accuracy and ROC, their performances are significantly different in terms of confidence levels. We suggest that the confidence measure should be used in conjunction with the existing performance metrics such as accuracy and ROC.
BUT System for Low Resource Indian Language ASR

This paper describes the BUT ‘Jilebi’ team’s speech recognition systems created for the 2018 low resource speech recognition challenge for Indian languages. We investigate modiﬁcations of multilingual time-delay neural network (TDNN) architectures with transfer learning and compare them to bi-directional residual memory networks (BRMN) and bi-directional LSTM. Our best submission based on system combination achieved word error rates of 13.92% (Tamil), 14.71% (Telugu) and 14.06% (Gujarati). We present the details of submitted systems and also the post-evaluation analysis done for lexicon discovery using unsupervised word segmentation.
Classification of Pulmonary Emphysema in CT Images Based on Multi-Scale Deep Convolutional Neural Networks

In this work, we aim at classifying emphysema in computed tomography (CT) images of lungs. Most previous works are limited to extracting low-level features or mid-level features without enough high-level information. Moreover, these approaches do not take the characteristics (scales) of different emphysema into account, which are crucial for feature extraction. In contrast to previous works, we propose a novel deep learning method based on multiscale deep convolutional neural networks. There are three contributions for this paper. First, we propose to use a base residual network with 20 layers to extract more high-level information. To the best of our knowledge, this is the first deep learning method for classification of emphysema. Second, we incorporate multi-scale information into our deep neural networks so as to take full consideration of the characteristics of different emphysema. Finally, we established a high-quality emphysema dataset which contains 91 high-resolution computed tomography (HRCT) volumes, annotated manually by two experienced radiologists and checked by one experienced chest radiologist. A 92.68% classification accuracy is achieved on this dataset. The results show that (1) the multi-scale method is highly effective in comparison to the single scale setting; (2) the proposed approach is superior to the state-of-the-art techniques.
Intra-Frame Context-Based Octree Coding for Point-Cloud Geometry

3D and free-viewpoint video has been slowly adopting a solid representation, such as using meshes and point-clouds. Among other characteristics, meshes provide direct surface representation, while point-cloud processing requires less computation. Points in the cloud are minimally represented by their geometry (3D position) and color. A common point-cloud geometry compression method is the octree representation, which acts on individual frames and can be further compressed by entropy encoding. This paper presents a lossless intra-frame compression method for point-cloud geometry, which uses the octree structure to provide better contexts for entropy coding. Results show that the proposed solution offers state-of-the-art performance, with an average rate reduction of 29% compared to the octree representation.