Miracle-dz/UniFAR-FTUs huggingface dataset:
Subtitle Translation as Markup Translation

Automatic subtitle translation is an important technology to make video content available across language barriers. Subti-tle translation complicates the normal translation problem by adding the challenge of how to format the system output into subtitles. We propose a simple technique that treats subtitle translation as standard sentence translation plus alignment driven markup transfer, which enables us to reliably maintain timing and formatting information from the source subtitles. We also introduce two metrics to measure the quality of subtitle boundaries: a Timed BLEU that penalizes mistimed tokens with respect to a reference subtitle sequence, and a measure of how much Timed BLEU is lost due to suboptimal subtitle boundary placement. In experiments on TED and YouTube subtitles, we show that we are able to achieve much better translation quality than a baseline that translates each subtitle independently, while coming very close to optimal subtitle boundary placement.
Investigating Process Algebra Models to Represent Structured Requirements for Time-sensitive CPS

Cyber-Physical Systems (CPS) contain complex compu-tational components that control physical entities. The design of these components must take into account the real-time and concurrent nature of these systems. Formulating requirements that describe CPS behaviors precisely, ruling out misunderstandings, is a crucial yet difﬁcult endeavor. To increase trust in the requirements, formal methods can be used to check relevant properties of the requirements. We investigate a process algebra to capture real-time behaviors and concurrency in CPS requirements in order to automate their analysis. We use a structured natural language to ﬁrst express CPS requirements: this takes into account current practice, indeed requirements should be easily writable as well as graspable by stakeholders with various points of view and ease communication among them. At the same time, requirements analysis using simulation or formal validation is possible by taking advantage of the requirements structure. We discuss translation from the structured requirements into the process algebra to automate the overall process. Our approach is implemented and is illustrated by an example issued from CPS4EU project 1 .
Genomic chronicle of SARS-CoV-2: a mutational analysis with over 1 million genome sequences

Use of information technologies to analyse big data on SARS-CoV-2 genome provides an insight for tracking variations and examining the evolution of the virus. Nevertheless, storing, processing, alignment and analyses of these numerous genomes are still a challenge. In this study, over 1 million SARS-CoV-2 genomes have been analysed to show distribution and relationship of variations that could enlighten development and evolution of the virus. In all genomes analysed in this study, a total of over 215M SNVs have been detected and average number of SNV per isolate was found to be 21.83. Single nucleotide variant (SNV) average is observed to reach 31.25 just in March 2021. The average variation number of isolates is increasing and compromising with total case numbers around the world. Remarkably, cytosine deamination, which is one of the most important biochemical processes in the evolutionary development of coronaviruses, accounts for 46% of all SNVs seen in SARS-CoV-2 genomes within 16 months. This study is one of the most comprehensive SARS-CoV-2 genomic analysis study in terms of number of genomes analysed in an academic publication so far, and reported results could be useful in monitoring the development of SARS-CoV-2.
Effects of Plant Covers and Mulching on the Biometric Parameters, Yield and Nutritional Value of Tomatillos (Physalis ixocarpa Brot. Ex Hornem.)

The aim of this study, which involved a field experiment conducted in north-eastern Poland, is to evaluate the phytometric parameters, yield, and biological value of fruit of three tomatillo cultivars grown in soil mulched with black PE (polyethylene) film and covered with PE film and non-woven PP (polypropylene) fabric. A two-factor field experiment was conducted in 2015 and 2017–2018 in the Agricultural Experiment Station owned by the University of Warmia and Mazury in Olsztyn. In the control treatment, tomatillo plants were grown without protective covers or mulch. Tomatillo fruits were harvested gradually, upon physiological maturity, which was determined based on changes in the color of the peel and calyx, and the aroma. The growth and development of tomatillos were evaluated based on plant measurements performed in the field. The chemical composition of tomatillo fruits was analyzed at the stage of full fruiting. During the three-year study, the weight, vertical diameter, and horizonal diameter of fruit were highest in cv. ‘Rio Grande Verde’ and lowest in cv. ‘Purple’. The combined use of mulch and plant covers accelerated fruit ripening. The total and marketable yields of tomatillo fruit were highest in cv. ‘Rio Grande Verde’ and lowest in cv. ‘Purple’. The fruit of cv. ‘Purple’ had the highest content of dry matter, total sugars, extract, β-carotene, and total polyphenols.
INTERSPEECH 2021 Acoustic Echo Cancellation Challenge

The INTERSPEECH 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication. Many recent AEC studies report good performance on synthetic datasets where the training and testing data may come from the same underlying distribution. However, AEC performance often degrades signiﬁcantly on real recordings. Also, most of the conventional objective metrics such as echo return loss enhancement and perceptual evaluation of speech quality do not correlate well with subjective speech quality tests in the presence of background noise and reverberation found in real-istic environments. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 5,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We also open source an online subjective test framework and provide an online objective metric service for researchers to quickly test their results. The winners of this challenge are selected based on the average Mean Opinion Score achieved across all different single talk and double talk scenarios.
THE RELATIONSHIP BETWEEN ECONOMIC GROWTH AND ENVIRONMENTAL POLLUTION IN TURKEY

This study examines the relationship between economic growth and environmental pollution in Turkey. The research uses annual time series data from 1970 to 2017. Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were used to test the stationarity of the series. In this the Autoregressive Distributed Lag (ARDL) model is used as an estimation technique. Furthermore, the classical additive decomposition method was used to forecast the pollution. The results indicate that economic growth has a positive significant effect on environmental pollution in the short-run and positive but insignificant effect in the long-run. When the long-run and short-run elasticities were compared it is found that the long-run elasticity is greater than the short-run elasticity which challenges the validity of the Environmental Kuznets Curve (EKC) hypothesis and provides evidence against its existence in Turkey. The paper suggests that robust and effective environmental policies should be strictly implemented and closely monitored to reduce the environmental pollution and to ensure the preservation of resources for future generations.
A Compact Strain Sensor Based on Microstructural Fabry-Perot Fiber Cavity

Fabry-Perot air chamber was constructed at the melting point (splicing location) of two single-mode fibers by glycerin assisted self-expansion method. The morphology of the Fabry-Perot air chamber was fabricated and optimized by modulating the splicing parameters (drawing process, discharging location, time and intensity) and the fibers’ end-face (plane or arc). The in-line or reflected Fabry-Perot cavities have been applied to determine the tensile strain in the range of 0-1.2 N. The train sensing performance of the spherical shaped FP cavity has been experimentally demonstrated with the best sensitivity of 3.628 nm/N, corresponding to the resolution of ~0.005 N. The proposed FP fiber sensor has the advantages of low cost, fast fabrication and easy-integration with the common fiber system.
Robust End-to-End Speaker Diarization with Conformer and Additive Margin Penalty

Traditionally, a speaker diarization system has multiple components to extract and cluster speaker embeddings. However, end-to-end diarization is more desirable as it facilitates optimizing one model in contrast to multiple components in a traditional set up. Moreover, end-to-end diarization systems are capable of handling overlapped speech. Recently proposed self-attentive end-to-end diarization model with encoder-decoder based at-tractors (EEND-EDA) is capable of processing speech from an unknown number of speakers, and has reported comparable performances to traditional systems. In this work, we aim to improve the EEND-EDA model. First, we increase the robustness of the model by incorporating an additive margin penalty for minimizing the intra-class variance. Second, we propose to replace the Transformer encoders with Conformer encoders to capture local information. Third, we propose to use convolutional subsampling and upsampling instead of manual subsampling only. Our proposed improvements report 21.6% relative reduction in DER on the evaluation full set of the track 2 of the DIHARD III challenge.
Developmental antecedents of young adults’ solidarity during the Covid‐19 pandemic: The role of sympathy, social trust, and peer exclusion from early to late adolescence

Abstract This study explored characteristics of young adults’ solidarity during the Covid‐19 pandemic by identifying three different profiles, characterized by low (23%), average (54%), and high solidarity (23%). Based on longitudinal Swiss panel data (N T1 = 797, M age T1 = 12.15 years, 51% female; 28% migration background representing diverse ethnicities; N T2 = 707, M age T2 = 15.33 years; N T3 = 596, M age T3 = 18.31 years), the study combined person‐ and variable‐centered approaches to examine whether sympathy, social trust, and peer exclusion at earlier phases in development predicted membership in pandemic‐related solidarity profiles (N T4 = 300, M age T4 = 20.33 years). All developmental predictors were significantly associated with the likelihood of expressing solidarity during the pandemic as young adults.
Water Retrieval Embedded Attention Network With Multiscale Receptive Fields for Hyperspectral Image Refined Classification

In hyperspectral image classification, deep learning (DL) based on abundant training samples has demonstrated its significance in classification performance. However, due to the limitation of available samples and the imbalance/similarity of classes in small-sized datasets, data-driven DL algorithms can hardly extract representative and effective features for interclass classification, and the subtle diagnostic spectral features for intraclass classification are easily covered or lost in the iterative feature extraction (FE). The restricted FE of interclass/intraclass results in the accuracy reduction and performance limitation of refined classification. To mitigate these issues, an attention network with multiscale receptive fields (MRFs) is proposed, embedding an inversion subnet for relative water content retrieval (RWCR). In classification, the three critical parts in the proposed network, namely, MRFs, embedded subnet, and multiple-attention mechanism, are responsible for multiscale feature merging, relative water content (RWC) feature enhancement, and paying attention to bands, channels, and multiscale features, respectively. The ablation studies on small-sized datasets show the accuracy improvements of interclass and intraclass in refined classification, which verifies the effectiveness of critical parts for extracting representative features and taking RWC features as the diagnostic biochemical signature from unbalanced and similar classes. The comparison results with typical DL models demonstrate the superiority of the proposed network. Moreover, the competitive advantage of the proposed network is demonstrated in comparison with traditional and state-of-the-art HSI classification methods.
Voice Privacy Through x-Vector and CycleGAN-Based Anonymization

With the rise in usage of voice assistants and spoken language interfaces, important concerns regarding voice data privacy have been prompted. In an attempt to reduce the threat of attacks on voice data, in this paper, we propose a speaker anonymization system based on CycleGAN. This method mod-iﬁes the speaker’s gender and accent information from the original speech signal. The proposed method gives a more natural-sounding anonymized voice in addition to a de-identiﬁed speaker. We have chosen baseline-1 of The Voice Privacy Challenge-2020 as our baseline system. Training of Cycle-GAN, ASR, and ASV experiments are performed on the subset of Librispeech corpus. In this paper, the double anonymization technique is also explored in which the CycleGAN-based anonymization technique is adopted on top of the baseline system. Experimental results show that combining the proposed method with the x-vector and neural source-ﬁlter (NSF) model-based method (baseline system) gives up to 5 . 61% relative improvement in EER of original-anonymized, enroll-trial pairs. However, it gives up to 19 . 30% relative improvement in EER for anonymized-anonymized enroll-trial pairs. We observed that along with the good speaker de-identiﬁcation, the anonymized utterances have adequate speech intelligibility and naturalness.
An Ultra-Low-Cost Soft Error Protection Scheme Based on the Selection of Critical Variables

The exponentially increasing occurrence of soft errors makes the optimization of reliability, performance, hardware area, and power consumption one of the main concerns in modern embedded processors. Since the design cost of hardware techniques aimed at improving the reliability of microprocessors is quite expensive for resource-constrained embedded systems, software-level fault tolerance mechanisms have been proposed as an attractive solution for soft error threats. However, many software-level redundancy-based schemes are accompanied by considerable performance overhead, which is not acceptable for many embedded applications. In this work, we have introduced an ultra-low-cost soft error protection scheme for embedded applications, which works based on source-code analysis and identifying critical variables. After identification, these vital variables are adequately protected by placing runtime checks at critical points of execution. Our experimental results based on several applications demonstrate that the proposed scheme can mitigate the failure rate by 47% with negligible performance degradation.
Design of an Uninterrupted Power Supply with Li-Ion Battery Pack: A Proposal for a Cost-Efficient Design with High Protection Features

While decreasing their cost, lithium-ion batteries began to enter a vast domain for energy storage field, including solar systems and electric vehicles, due to their high energy density compared to other types. Besides, li-ion batteries require a safe and secure ground to reach the best performance and decrease the explosion risk. The safe operation of the battery is based on the main protection features and balancing the cells. This study offers a battery BMS design that protects li-ion batteries from overcharging, over-discharging and overheating. It is also offering passive cell balancing, an uninterrupted power source to load, and monitoring data. The used controller is Arduino mega 2560, which manages all the hardware and software protection features. Software features that include 1) variable charging speed according to the batteries charging status, 2) measuring the batteries state of health and state of charge, 3) controlling the uninterrupted driver, 4) regulating the charge and discharge voltage, and 5) measure and display all readings.
Improving the interfacial property of carbon fibre/epoxy resin composites by grafting amine‐capped cross‐linked poly‐itaconic acid

To improve the interfacial properties of carbon fibre‐reinforced polymer composites, a surface treatment was used to cap cross‐linked poly‐itaconic acid onto carbon fibres via in‐situ polymerization after itaconic acid grafting. The chemical composition of the modified carbon fiber (CF) surface was characterized by X‐ray photoelectron spectral and Fourier‐transform infrared spectroscopy. Scanning electron microscopy and atomic force microscopy images showed that the poly‐itaconic acid protective sheath was uniformly capped onto the CF surface and that the surface roughness was obviously enhanced. Chemical bonds also played a key role in the interfacial enhancement. The results showed that the interfacial shear strength of the composites with poly‐itaconic acid on the carbon fibres (72.2 MPa) was significantly increased by 89.5% compared with that of the composites with pristine CF (38.1 MPa). Moreover, the poly‐itaconic acid sheath promoted a slight increase in mono‐fibre tensile strength. In addition, the interfacial mechanisms were also discussed. Meanwhile, the mechanical property of the functionalized CF/epoxy resin composites was also significantly improved.
Voice Quality in Verbal Irony: Electroglottographic Analyses of Ironic Utterances in Standard Austrian German

When using verbal irony in interpersonal communication, paraverbal cues can reduce the risk of misunderstandings. Besides fundamental frequency, intensity and duration, speakers could use voice quality parameters to disambiguate between ironic and literal utterances. How these paraverbal cues are used to mark irony appears to be language- and/or culture-specific. Since the role of voice quality in ironic utterances has not yet been investigated in Austrian German, the present study addresses this issue. In addition to the acoustic signal, the vocal fold vibration is recorded via electroglottography (EGG). The detailed analysis of the EGG data as well as the acoustic data, provides insight into voice quality characteristics of ironic and literal realisations of short utterances. The analyses reveal that, in Standard Austrian German, some differences in voice quality exist between ironic and literal realisations of utterances: When being ironic, speakers’ voices tend to be breathier, creakier or rougher. Differences are more pronounced in the older age group and in male speakers.
High-Fidelity Parallel WaveGAN with Multi-Band Harmonic-Plus-Noise Model

This paper proposes a multi-band harmonic-plus-noise (HN) Parallel WaveGAN (PWG) vocoder. To generate a high-ﬁdelity speech signal, it is important to well-reﬂect the harmonic-noise characteristics of the speech waveform in the time-frequency domain. However, it is difﬁcult for the conventional PWG model to accurately match this condition, as its single generator inefﬁciently represents the complicated nature of harmonic-noise structures. In the proposed method, the HN WaveNet models are employed to overcome this limitation, which enable the separate generation of the harmonic and noise components of speech signals from the pitch-dependent sine wave and Gaussian noise sources, respectively. Then, the energy ratios between harmonic and noise components in multiple frequency bands (i.e., subband harmonicities) are predicted by an additional harmonicity estimator. Weighted by the estimated harmonicities, the gain of harmonic and noise components in each subband is adjusted, and ﬁnally mixed together to compose the full-band speech signal. Subjective evaluation results showed that the proposed method signiﬁcantly improved the perceptual quality of the synthesized speech.
MPV Reduction of Furfural to Furfuryl Alcohol on Mg, Zr, Ti, Zr–Ti, and Mg–Ti Solids: Influence of Acid–Base Properties

The Meerwein–Ponndorf–Verley (MPV) reaction is an environmentally-friendly process consisting of the reduction of a carbonyl compound through hydrogen transfer from a secondary alcohol. This work deals with MPV reduction of furfural to furfuryl alcohol on different ZrOx, MgOx, TiOx, and Mg–Ti, as well as Zr–Ti mixed systems. The solids were synthesized through the sol–gel process and subsequently calcined at 200 °C. Characterization was performed using a wide range of techniques: ICP-MS, N2 adsorption-desorption isotherms, EDX, TGA-DTA, XRD, XPS, TEM, TPD of pre-adsorbed pyridine (acidity) and CO2 (basicity), DRIFT of adsorbed pyridine, and methylbutynol (MBOH) test reaction. ZrOx showed the highest conversion and selectivity values, which was attributed to the existence of acid–base pair sites (as evidenced by the MBOH test reaction), whereas the introduction of titanium resulted in the drop of both conversion and selectivity probably due to the increase in Brönsted-type acidity. As for MgOx, it had a predominantly basic character that led to the production of the condensation product of one molecule of furfural and one molecule of acetone, and thus resulted in a lower selectivity to furfuryl alcohol. The TiOx solid was found to be mainly acidic and exhibited both Lewis and Brönsted acid sites. The presence of the latter could account for the lower selectivity to furfuryl alcohol. All in all, these results seemed to suggest that the MPV reaction is favored on Lewis acid sites and especially on acid–base pair sites. The process was accelerated under microwave irradiation.
Affect Recognition Through Scalogram and Multi-Resolution Cochleagram Features

An approach to the categorization of voice samples according to emotions expressed by the speaker is proposed which uses Multi-Resolution Cochleagram (MRCG) and scalogram features in a novel way. Audio recordings from the EmoDB, EMOVO and Savee Data-sets are employed in training and testing of predictive models consisting of different sets of speech features. This study systematically evaluates the performance of the feature sets most commonly used in computational par-alinguistic tasks (i.e. emobase , eGeMAPS and ComParE ) in addition to MRCG-and scalogram-derived features and their fusion, across ﬁve different classiﬁers. The datasets used in this evaluation include speech in three different languages (German, Italian and English). MRCG features outperform the feature sets most commonly used in computational paralinguistic tasks, including emobase , eGeMAPS and ComParE , for the EmoDB (unweighted average recall, UAR = 59 . 15% ) and SAVEE (UAR = 36 . 12% ) datasets, while eGeMAPS provides the best overall UAR (33.84%) for the EMOVO dataset. A support vector machine (SVM) classiﬁer yields the best UAR for EmoDB (80.05%) through fusion of emobase , eGeMAPS , ComParE and MRCG, and for EMOVO (40.31%), through fusion of emobase , eGeMAPS and ComParE . For SAVEE, random forests provide the best result (46.55%) using the ComParE feature set.