Miracle-dz/UniFAR-FTUs huggingface dataset:
Spoken Term Detection and Relevance Score Estimation Using Dot-Product of Pronunciation Embeddings

The paper describes a novel approach to Spoken Term Detection (STD) in large spoken archives using deep LSTM networks. The work is based on the previous approach of using Siamese neural networks for STD and naturally extends it to directly localize a spoken term and estimate its relevance score. The phoneme confusion network generated by a phoneme recognizer is processed by the deep LSTM network which projects each segment of the confusion network into an embedding space. The searched term is projected into the same embedding space using another deep LSTM network. The relevance score is then computed using a simple dot-product in the embedding space and calibrated using a sigmoid function to predict the probability of occurrence. The location of the searched term is then estimated from the sequence of output probabilities. The deep LSTM networks are trained in a self-supervised manner from paired recognition hypotheses on word and phoneme levels. The method is experimentally evaluated on MALACH data in English and Czech languages.
Evaluation of the effectiveness of transcranial electrostimulation in treatment of neuropsychiatric disorders

Objectives: Evaluation of the effectiveness the method of transcranial electrostimulation in treatment of neuropsychiatric disorders with the use of a patches by the company “Aganyan”. Materials and methods: The study was a double-blind, randomized, placebo-controlled study, participated 106 patients with neuropsychiatric disorders. All participants in were divided into tables according to gender, age and diagnosis. Each subject was given the “Aganyan” patches and a special brochure, in which the method of application was indicated in detail. The wearable patch includes a flexible substrate, a binder an adhesive layer, with an electrode foil attached to it. Patients applied one patch behind each ear. The patches were applied for eight hours every third day for three months. To assess the effectiveness of therapy in patients the following tests were used: The Montreal Cognitive Assessment Scale; MMSE Scale: Concise Mental Status Scale; diaries of observation of the patient’s condition to identify side effects; special brochures in which the subjects independently indicated the effects of the “Aganyan” patches. Tests were performed before and after the use of the “Aganyan” patches. Results: When using the patches of the “Aganyan” company, none of the participants in the study had any side effects; According to the results of the Montreal test according to the criterion of memory and the MMSE test, the effectiveness of the patch was noted in patients with all clinical diagnoses. The greatest positive dynamics was revealed according to the results of the Montreal test according to the criterion of memory in patients with migraine (30%), insomnia (31%), vascular dementia (32%), and according to the results of the MMSE test in patients with diagnoses: cerebrovascular disease: consequences of a cerebral infarction brain (31%), vascular dementia (56%). Conclusion: The patches of “Aganyan” company have proven its effectiveness through electrical stimulation with low-intensity current in patients in different age groups with different clinical diagnoses.
Racial and Ethnic Differences in the Antecedents of Cyberbullying Victimization in Early Adolescence: An Ecological Systems Framework

This study investigates multiple-level antecedents of cyberbullying victimization among early adolescents. Data from the Health Behavior in School-Aged Children, 2009 to 2010 cohort study in the United States were used. The sample included White, Black, Latino, and Asian adolescents, ages 10–14 (N = 8481). Bivariate analysis, logistic regression, and subgroup analysis were conducted. Among White adolescents, female sex, older age, and bullying victimization were positively associated with cyberbullying victimization, whereas parental awareness was negatively associated. Among Black adolescents, bullying victimization was positively associated with cyberbullying victimization, but parental employment was negatively associated. Among Latino adolescents, older age was positively related to cyberbullying victimization, whereas “other” family structures were negatively related. Among Asian adolescents, “other” family structure and bullying victimization were positively related to cyberbullying victimization, whereas parental awareness, parental employment, and “quite well off” family socioeconomic status were negatively related. Parental awareness moderated the association between parental employment and cyberbullying victimization.
Reliable Intensity Vector Selection for Multi-Source Direction-of-Arrival Estimation Using a Single Acoustic Vector Sensor

In the context of multi-source direction of arrival (DOA) estimation using a single acoustic vector sensor (AVS), the received signal is usually a mixture of noise, reverberation and source signals. The identiﬁcation of the time-frequency (TF) bins that are dominated by the source signals can signiﬁcantly improve the robustness of the DOA estimation. In this paper, a TF bin selection based DOA estimation pipeline is proposed. The proposed pipeline mainly involves three key steps: key frame identiﬁcation, TF bin selection and DOA extraction. We identify the key frames by frame-wisely examining the effective rank. Sub-sequently, the geometric medians of the selected key frames are extracted to alleviate the impact of extreme outliers. The simulation results show that the accuracy and the robustness of the proposed pipeline outperform the state-of-the-art (SOTA) tech-niques.
Analysis by Synthesis: Using an Expressive TTS Model as Feature Extractor for Paralinguistic Speech Classification

Modeling adequate features of speech prosody is one key factor to good performance in affective speech classiﬁcation. However, the distinction between the prosody that is induced by ‘how’ something is said (i.e., affective prosody) and the prosody that is induced by ‘what’ is being said (i.e., linguistic prosody) is neglected in state-of-the-art feature extraction systems. This results in high variability of the calculated feature values for different sentences that are spoken with the same affective intent, which might negatively impact the performance of the classiﬁ-cation. While this distinction between different prosody types is mostly neglected in affective speech recognition, it is explicitly modeled in expressive speech synthesis to create controlled prosodic variation. In this work, we use the expressive Text-To-Speech model Global Style Token Tacotron to extract features for a speech analysis task. We show that the learned prosodic representations outperform state-of-the-art feature extraction systems in the exemplary use case of Escalation Level Classiﬁcation.
Adversarial Voice Conversion Against Neural Spoofing Detectors

The naturalness and similarity of voice conversion have been signiﬁcantly improved in recent years with the development of deep-learning-based conversion models and neural vocoders. Accordingly, the task of detecting spooﬁng speech also attracts research attention. In the latest ASVspoof 2019 challenge, the best spooﬁng detection model can distinguish most artiﬁcial utterances from natural ones. Inspired by recent progress of adversarial example generation, this paper proposes an adversarial post-processing network (APN) which generates adversarial examples against a neural-network-based spooﬁng detector by white-box attack. The APN model post-processes the speech waveforms generated by a baseline voice conversion system. An adversarial loss derived from the spooﬁng detector together with two regularization losses are applied to optimize the parameters of APN. In our experiments, using the logical access (LA) dataset of ASVspoof 2019, results show that our proposed method can improve the adversarial ability of converted speech against the spooﬁng detectors based on light convolution neural networks (LCNNs) effectively without degrading its subjective quality.
The Architecture of a Real-Time Control System for Heating Energy Management in the Intelligent Building

Very often, constructors and designers of intelligent building and building automation systems have a choice: to create a compact system with a limited configuration and modifying the system’s behavior possibilities or provide a fully configurable solution at the expense of introducing a full SCADA system equipped with an additional knowledge database and inference system equipped with learning capabilities. In the presented work, we show that there is a third solution. Using a multilayer control system composed of programmable FPGAs, small PCs, and cloud computing resources, we can design and implement a fully configurable intelligent control system for the building’s heating. Our solution combines the compactness of the structure and the ease of installation and assembly.
“I worry about this patient EVERY day”: Geriatrics Clinicians’ Challenges in Caring for Unrepresented Older Adults

Unrepresented older adults are at risk for adverse outcomes, and clinicians who care for them may face ethical dilemmas and unique challenges when making person-centered care recommendations. However, little is known about their perspectives on clinical challenges in caring for this population. An online survey was used to assess issues around providing care for unrepresented patients. Ninety-two American Geriatrics Society members working with older adults in inpatient and/or outpatient settings completed the survey. Descriptive qualitative analysis of narrative survey responses identified five broad themes: (a) health risk characteristics of patients, (b) care decisions facing the team, (c) psychosocial considerations by the team, (d) patient outcomes, and (e) burden of the provider and/or health system. These findings demonstrate that geriatrics clinicians face challenges in working with unrepresented adults in both inpatient and outpatient settings. We interpret these results in light of existing literature and propose collaborative approaches that may improve outcomes.
Automatic Lip-Reading with Hierarchical Pyramidal Convolution and Self-Attention for Image Sequences with No Word Boundaries

In this paper, we propose a novel deep learning architecture for improving word-level lip-reading. We ﬁrst incorporate multi-scale processing into spatial feature extraction for lip-reading using hierarchical pyramidal convolution (HPConv) and self-attention. Speciﬁcally, HPConv is proposed to replace the conventional convolution features, leading to an improvement over the model’s ability to discover ﬁne-grained lip movements. Next to deal with ﬁxed-length image sequences representing words in a given database, a self-attention mechanism is proposed to integrate local information in all lip frames without assuming known word boundaries, so that our deep models automatically utilize key feature in relevant frames of a given word. Experiments on the Lip Reading in the Wild corpus show that our proposed architecture achieves an accuracy of 86 . 83% , yielding a relative error rate reduction of about 10% from that obtained with a state-of-the-art scheme of averaging frame scores for information fusion. A detailed analysis of the experimental results also conﬁrms that weights learned from self-attention tend to be zero at both sides of an image sequence and focus non-zero weights in the middle part of a given word.
Cross-Lingual Voice Conversion with Disentangled Universal Linguistic Representations

Intra-lingual voice conversion has achieved great progress recently in terms of naturalness and similarity. However, in cross-lingual voice conversion, there is still an urgent need to improve the quality of the converted speech, especially with nonparal-lel training data. Previous works usually use Phonetic Poste-riorgrams (PPGs) as the linguistic representations. In the case of cross-lingual voice conversion, the linguistic information is therefore represented as PPGs. It is well-known that PPGs may suffer from word dropping and mispronunciation, especially when the input speech is noisy. In addition, systems using PPGs can only convert the input into a known target language that is seen during training. This paper proposes an any-to-many voice conversion system based on disentangled universal linguistic representations (ULRs), which are extracted from a mix-lingual phoneme recognition system. Two methods are proposed to remove speaker information from ULRs. Experimental results show that the proposed method can effectively improve the converted speech objectively and subjectively. The system can also convert speech utterances naturally even if the language is not seen during training.
Driving Factors and Growth Potential of Provincial Carbon Productivity in China

Climate change has become a global concern, and the development of a green economy has attracted wide attention. Understanding the driving factors and growth potential of provincial-level carbon productivity is crucial for China’s green economic development in the new normal phase. In this study, the logarithmic mean Divisia index (LMDI) is adopted to systematically investigate the driving factors of provincial carbon productivity and explore the growth potential of provinces’ carbon productivity based on the clustering analysis. The results show that: (1) China’s provincial carbon productivity presents an increasing trend in 2001–2017, but the differences in carbon productivity among provinces are widening. (2) Economic activity and industrial structure are key to push up regional carbon productivity in China, while energy intensity is the main factor pulling it down. (3) The potential for carbon productivity improvement varies greatly among provinces in the four groups. Specifically, in groups 1 and 2, the developed provinces have little potential for improving carbon productivity, while the developing provinces in group 4 are just the opposite. These findings can enlighten policymakers that the development of a green economy should focus on optimizing and upgrading industrial structure and reducing energy intensity, and provincial heterogeneity must be considered when formulating green economic development policies.
Effect of Democracy and Land Language on Human Development Index

The factors that affect Human Development Index have been investigated extensively in previous researches. These studies consider education as an important factor and focus on the quality of education such as teachers’ skills, educational facilities and student conditions. Democracy has also been established as an important factor in HDI. In this study, we examined two variables, the democracy index and the land language index as fundamental variables of the Human Development Index (HDI). The land language index is a new variable created to signify the importance of the medium of instruction in higher education institutes, particularly in the Science, Technology, Engineering and Mathematics (STEM) fields. We performed a multiple-correlation analysis with the available data to find the correlation of these two indices on HDI. We found that together, the democracy index and land language index had a strong correlation to HDI. The results of this study can have a significant impact on social scientists and policymakers and should be considered for further research.
Deflate‐inflate: Exploiting hashing trick for bringing inference to the edge with scalable convolutional neural networks

With each passing year, the compelling need to bring deep learning computational models to the edge grows, as does the disparity in resource demand between these models and Internet of Things edge devices. This article employs an old trick from the book "deflate and inflate" to bridge this gap. The proposed system uses the hashing trick to deflate the model. A uniform hash function and a neighborhood function are used to inflate the model at runtime. The neighborhood function approximates the original parameter space better than the uniform hash function according to experimental results. Compared to existing techniques for distributing the VGG‐16 model over the Fog‐Edge platform, our deployment strategy has a 1.7 × ‐ 7.5 × speedup with only 1–4 devices due to decreased memory access and better resource utilization.
Semiparametric tests for equality of two independent variances

Abstract In this article, we propose new semiparametric and bootstrap tests based on the ratio of two variances under a density ratio model and compare them with existing tests for testing the equality of two variances from two independent populations. We showed that the ratio of two independent variances follows an asymptotic log-normal distribution. Also presented in this article are large sample asymptotic results of the semiparametric estimator of the ratio of the two sample variances. We also present a theoretical comparison of our semiparametric test on the ratio of two variances with the existing non parametric test using Pitman’s relative efficiency. Numerical comparisons of the performance of various tests are performed via simulation studies. We also use two numerical examples to illustrate the implementation of new tests.
Exotic and Emergent Citrus Viruses Relevant to the Mediterranean Region

Citrus production in the Mediterranean area is of considerable importance, in both cultural and economic terms, and the viability of the industry greatly depends on proper phytosanitary management. In this review, we focus on exotic and emerging dangerous citrus viruses that have still not been reported in the countries of the Mediterranean area, that are not yet regulated or that are restricted to certain small areas. We also discuss the contribution that old and new technologies may offer for valuable surveys aimed at promoting the adoption and sharing of better control measures and the production of pathogen-tested citrus trees and rootstocks.
Spatio-Temporal Variation of Reference Evapotranspiration and Its Climatic Drivers over the Tibetan Plateau during 1970–2018

Reference evapotranspiration (ET0) is a key component of hydrologic cycle and it is important for water resources management. Analysis of ET0 changes is particularly critical for understanding the impacts of climatic change on hydrology in ecologically fragile regions. In this study, using the Penman–Monteith method and the Mann–Kendall test, the variation characteristics of ET0 on the Tibetan Plateau (TP) from 1970 to 2018 was analyzed, and the dominant climatic factors controlling the change of ET0 was also explored. The result shows that in TP region: (1) there was an abrupt change in the trend of ET0 around 1997, and the ET0 declined at a rate of −25.9 mm/decade during 1970–1996 but increased by 31.1 mm/decade during 1997–2018; (2) ET0 is most sensitive to solar radiation, then relative humidity, wind speed and mean temperature; (3) the decrease of ET0 before 1997 was mainly due to the decline of wind speed and the increase of relative humidity, while the increase of ET0 after 1997 was mainly due to the decrease of relative humidity. The results of this study can provide data reference for the research of water balance on the TP.
A Hybrid Seq-2-Seq ASR Design for On-Device and Server Applications

This paper proposes and evaluates alternative speech recognition design strategies using the hybrid autoregressive transducer (HAT) model. The different strategies are designed with special attention to the choice of modeling units and to the integration of different types of external language models during ﬁrst-pass beam-search or second-pass re-scoring. These approaches are compared on a large-scale voice search task and the recognition quality over the head and tail of speech data is analyzed. Our experiments show decent improvements in WER over common speech phrases and signiﬁcant gains on uncommon ones compared to the state-of-the-art approaches.
Laboratory measurements with DAS: A fast and sensitive tool to obtain elastic properties at seismic frequencies

Forced-oscillation stress-strain laboratory measurements are increasingly employed to obtain elastic and viscoelastic properties of rocks at seismic frequencies. Yet these measurements are time-consuming and expensive, due in part to the use of metal or semiconductor strain gauges, which need to be glued to the sample. Such gauges are fragile, have relatively low sensitivity, and measure very local strain only so the measurements can be affected by a slight misalignment of the system assembly and local heterogeneity of the rock. The emergence of fiber-optic distributed acoustic sensing (DAS) technology provides an alternative means of measuring strain. Strain measurements with DAS involve winding an optical fiber around the sample multiple times and connecting it to a DAS recording unit. Pilot experiments performed using this setup on a range of rocks and materials show good agreement with strain gauge measurements. Advantages of DAS over strain gauges include much higher strain sensitivity (down to 10−11) and signal-to-noise ratio (and hence, shorter time required for measurements), larger dynamic range, ability to measure average (rather than local) strain in the sample, and robustness at elevated temperatures. Although the pilot experiments demonstrate the potential of DAS for rock physics measurements, further research and improvement of the proposed methodology are required to obtain independent estimates of Young's modulus and Poisson's ratio and to port the system into a pressure vessel to obtain rock properties under in-situ conditions.
Dynamic Clustering for Wafer Map Patterns Using Self-Supervised Learning on Convolutional Autoencoders

Defect pattern analysis in wafer bin maps (WBM) plays a significant role in the semiconductor manufacturing process because it helps identify problematic steps or equipment so that process engineers can take appropriate actions to improve the overall yield. Clustering algorithms have been widely used to detect different defect patterns. However, most clustering algorithms, such as K-means clustering and self-organizing map, are required to determine the number of clusters in advance. To resolve this issue, we propose a self-supervised learning-based dynamic WBM clustering method. The proposed model first uses pseudo-labeled data, of which, the labels are dynamically determined by the Dirichlet process mixture model (DPMM). Thereafter, it is fine-tuned using pseudo-labels in a self-supervised manner. Experimental results based on the WM-811K dataset indicate that the proposed model not only outperforms the benchmark models but also demonstrates robustness to hyperparameters. In addition, the defect patterns identified by our model are more accurately and distinctively localized than those identified by the benchmark models.
Accurate free‐water estimation in white matter from fast diffusion MRI acquisitions using the spherical means technique

To accurately estimate the partial volume fraction of free water in the white matter from diffusion MRI acquisitions not demanding strong sensitizing gradients and/or large collections of different b‐values. Data sets considered comprise ∼ 32‐64 gradients near b=1000s/mm2 plus ∼ 6 gradients near b=500s/mm2 .