Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensorless DPCC of PMLSM Using SOGI-PLL-Based High-Order SMO With Cogging Force Feedforward Compensation

In order to reduce the adverse effect of the large thrust ripple of permanent magnet linear synchronous motor (PMLSM) on mover position estimation accuracy and solve the sliding mode chattering problem of the traditional sensorless algorithm based on sliding mode observer (SMO), this article proposes a systematic method to solve above issues. First, the high-order SMO method is used to suppress harmonic generation from the source of sliding mode chattering. Second, an adaptive filter based on a second-order generalized integrator (SOGI) is constructed to filter out the harmonics. Then, the normalized quadrature phase-locked loop (PLL) method is used to realize the closed-loop extraction of position information. Finally, the deadbeat predictive current control (DPCC) with the current feedforward compensation strategy is proposed to further improve the dynamic performance and mover position estimation accuracy. The simulation model and experimental platform are built. The simulation analyses and test results show that the proposed method has a better performance than conventional techniques.
Individual costs and societal benefits: the privacy calculus of contact-tracing apps

Purpose During 2020, governments around the world introduced contact-tracing apps to fight the COVID-19 pandemic. In order for contact-tracing apps to be efficient tools in combatting pandemics, a significant proportion of the population has to install it. However, in many countries, the success of apps introduced during the COVID-19 pandemic has been limited due to lack of public support. This paper aims to better understand why consumers seem unwilling to install and use a contact-tracing app. Design/methodology/approach In this study, the authors test a number of determinants hypothesized to influence acceptance of contact-tracing apps based on the theory of privacy calculus (Dinev and Hart, 2006). Both perceived privacy concerns, as well as perceived hedonic, utilitarian and pro-social benefits are included. The hypotheses are tested through SEM analysis on a representative sample of 1,007 Swedish citizens. Findings The results indicate significant privacy concerns with using contact-tracing apps. However, this is to some extent offset by perceived hedonic and pro-social positive consequences of using the app. This study further shows that a general positive attitude towards innovation increases acceptance of the app. Originality/value The study contributes to research on consumer privacy, both in general in its application of the calculus model but also specifically in the context of contact-tracing apps. Moreover, as the results highlight which aspects that are important for consumers to accept and install an app of this kind, they also represent an important contribution to policymakers in countries around the world.
Advances in Knowledge of Wild Toxocariasis in Patagonia (Argentina): Toxocara canis

ABSTRACT: Seventeen specimens of carnivores of the families Felidae (Puma concolor and Leopardus geoffroyi) and Canidae (Lycalopex culpaeus) were collected in different localities of Nahuel Huapi National Park (Argentina) from August 2005 to May 2018. The specimens were processed by necropsy, and ascaridid parasites were obtained only from L. culpaeus. Morphological analysis indicates all the specimens belong to Toxocara canis, and specific identification was confirmed by PCR procedure. This study represents the first molecular identification of T. canis from Andean foxes in Argentina, and provides information on the spatial distribution of T. canis in wild environments in Patagonia.
Voicing Assimilations by French Speakers of German in Stop-Fricative Sequences

Voicing assimilations inside groups of obstruents occur in opposite directions in French and German, where they are respectively regressive and progressive. The aim of the study is to investigate (1) whether non native speakers (here French learners of German) are apt to acquire subtle L2 specificities like assimilation direction, although they are not aware of their very existence, or (2) whether their productions depend essentially upon other factors, in particular consonant place of articulation. To that purpose, a corpus made up of groups of obstruents (/t/ followed by /z/, /v/ or /f/) embedded into sentences has been recorded by 16 French learners of German (beginners and advanced speakers). The consonants are separated by a word or a syllable boundary. Results, derived from the analysis of consonant periodicity and duration, do not stand for an acquisition of progressive assimilation, even by advanced speakers, and do not show differences between the productions of advanced speakers and beginners. On the contrary the boundary type and the consonant place of articulation play an important role in the presence or absence of voicing inside obstruent groups. The role of phonetic, universal mechanisms against linguistic specific rules is discussed to interpret the data.
Learning from Each Other—A Bibliometric Review of Research on Information Disorders

Interest in research connected to information disorders has grown considerably in recent years. The phrase “information disorders” refers to three different notions: dis-, mis-, and malinformation. It is difficult to pin down this new and dynamic phenomenon of informational disruption and to assess its impact on society. Therefore, we conducted a bibliometric analysis on the complexity of information disorders using the Web of Science Core Collection database from 1975 to June 2021. We analyzed 8964 papers with the goal to have an overall picture of the topic, clarify the knowledge framework of research in this field, examine the development dynamics, identify future research directions and increase the understanding of the research on information disorders. The following are our main findings: the number of publications, authors, and journals has increased; research on information disorders has earned considerable attention in multiple academic fields; there are more and more works written in collaboration by scholars from different parts and cultures of the world. This paper makes important contributions to the literature, not only by providing researchers and practitioners with a coherent and perceptible intellectual basis to find answers, but also by bringing valuable insights for further investigation and future research directions.
Genotyping of foot‐and‐mouth disease viruses collected in Sudan between 2009 and 2018

Abstract Foot‐and‐mouth disease (FMD) is widely distributed in Sudan where outbreaks occur on an annual basis especially during the winter months (December‐February). This study aimed to increase our understanding of the epidemiological patterns of FMD in Sudan and connections to neighbouring countries by characterizing the genetic sequences of FMD viruses (FMDV) collected from samples collected in 10 Sudanese states over a 10‐year period (between 2009 and 2018). FMDV was detected in 91 of the 265 samples using an antigen‐detection ELISA. Three serotypes were detected: O (46.2%), A (34.0%), and SAT 2 (19.8%). Fifty‐two of these samples were submitted for sequence analyses, generating sequences that were characterized as belonging to O/EA‐3 (n = 17), A/AFRICA/G‐IV (n = 23) and SAT 2/VII/Alx‐12 (n = 12) viral lineages. Phylogenetic analyses provided evidence that FMDV lineages were maintained within Sudan, and also highlighted epidemiological connections to FMD outbreaks reported in neighbouring countries in East and North Africa (such as Ethiopia and Egypt). This study motivates continued FMD surveillance in Sudan to monitor the circulating viral lineages and broader initiatives to improve our understanding of the epidemiological risks in the region.
Leveraging Convolutional Neural Network and Transfer Learning for Cotton Plant and Leaf Disease Recognition

: Automatic Recognition of Diseased Cotton Plant and Leaves (ARDCPL) using Deep Learning (DL) carries a greater significance in agricultural research. The cotton plant and leaves are severely infected by a disease named Bacterial Blight-affected by bacterium, Xanthomonas axonopodis pv. Malvacearum and a new rolling leaf disease affected by an unorthodox leaf roll dwarf virus. Existing research in ARDCPL requires various complicated image preprocessing, feature extraction approaches and cannot ensure higher accuracy in their detection rates. This work suggests a Deep Convolutional Neural Network (CNN) based DCPLD-CNN model that achieves a higher accuracy by leveraging the DL models ability to extract features from images automatically. Due to the enormous success of numerous pre-trained architectures regarding several image classification task, this study also explores eight CNN based pre-trained architectures: DenseNet121, NasNetLarge, VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and Xception models by Fine-Tuning them using Transfer Learning (TL) to recognize diseased cotton plant and leaves. This study utilizes those pre-trained architectures by adding extra dense layers in the last layers of those models. Several Image Data Augmentation (IDA) methods were used to expand the training data to increase the model's generalization capability and reduce overfitting. The proposed DCPLD-CNN model achieves an accuracy of 98.77% in recognizing disease in cotton plant and leaves. The customized DenseNet121 model achieved the highest accuracy of 98.60% amongst all the pre-trained architectures. The proposed method's feasibility and practicality were exhibited by several simulated experimental results for this classification task.
EfficientSing: A Chinese Singing Voice Synthesis System Using Duration-Free Acoustic Model and HiFi-GAN Vocoder

In this paper, we present EfﬁcientSing, a Chinese singing voice synthesis (SVS) system based on a non-autoregressive duration-free acoustic model and HiFi-GAN neural vocoder. Different from many existing SVS methods, no auxiliary duration prediction module is needed in this work, since a newly proposed monotonic alignment modeling mechanism is adopted. More-over, we follow the non-autoregressive architecture of Efﬁ-cientTTS with some singing-speciﬁc adaption, making training and inference fully parallel and efﬁcient. HiFi-GAN vocoder is adopted to improve the voice quality of synthesized songs and inference efﬁciency. Both objective and subjective experimental results show that the proposed system can produce quite natu-ral and high-ﬁdelity songs and outperform the Tacotron-based baseline in terms of pronunciation, pitch and rhythm.
Comparative Phylogeography of Southern African Bird Species Suggests an Ephemeral Speciation Model

Southern Africa is remarkably rich in avian species diversity; however, the evolutionary and biogeographic mechanisms responsible for that diversity are, in general, poorly understood, and this is particularly true with respect to the many species that are endemic or near-endemic to the region. Here, we used mtDNA to assess genetic structure in three southern African bird species to determine whether each was genetically panmictic, or whether there was standing genetic variation upon which abiotic factors (e.g., climate, biome boundaries, geographic features) could have acted to drive lineage diversification. Haplotype diversity was partitioned into two (two species) or three (one species) distinct haplotype clusters that did not reflect biogeographic or biome partitioning; instead, haplotype clusters overlapped in central South Africa. Population and demographic analyses, along with ecological niche modeling and Bayesian Skyline Plots, indicated that each of the three species were likely isolated in refugia during Pleistocene climatic perturbations, with subsequent expansions from refugia resulting in present-day overlapping distributions. Collectively, our analyses suggest that an ephemeral speciation model is operating in southern Africa, driven by the dynamic climatic oscillations that characterize the region. At least some of the regional endemic bird species (e.g., White-eyes, Zosterops spp.) may be the result of sufficiently long periods in refugia as opposed to the distinct but ephemeral clusters recovered within our three focal species.
Developing a guiding model of educational leadership in higher education during the COVID-19 pandemic: A grounded theory study

This research aims to explore educational leadership during the COVID-19 pandemic from the perspectives of higher education students and to develop a guiding model of educational leadership for ‘new normal’ with the novel emerging components. This research is conducted using grounded theory method and social network analysis. The first study group includes 32 participants, second study group includes another 26 participants, and final group includes 12 participants. Participants in all groups are university students studying in a higher education institution in Turkey. Written documents, personal interviews and group discussion are used for data collection. Based on analysis, a guiding model is developed which illustrates the concept of educational leadership for the new normal, which is composed of “networking, enhancing educational practices, calmness & compassion, analytical & strategical thinking, and transparency”. Also, the social network analysis shows that “encouraging online communities, promoting social interaction, creating a safe and inclusive learning environment, providing learning resources, leading under pressure, emphasizing optimism, making data-driven decisions” are cornerstones in terms of educational leadership for the new normal. In addition to those substantially noted key concepts, some higher education students also seem to be in need of some other aspects of educational leadership such as inspiration for learning, open dialogue, risk planning and leveraging capacity of community. The participants also indicate that successful educational leadership is about understanding others’ perspectives, rather than sticking to leader’s perspective. © 2022, Ozgen Korkmaz. All rights reserved.
Improving RNN-T for Domain Scaling Using Semi-Supervised Training with Neural TTS

Recurrent neural network transducer (RNN-T) has shown to be comparable with conventional hybrid model for speech recognition. However, there is still a challenge in out-of-domain scenarios with context or words different from training data. In this paper, we explore the semi-supervised training which optimizes RNN-T jointly with neural text-to-speech (TTS) to better generalize to new domains using domain-speciﬁc text data. We apply the method to two tasks: one with out-of-domain context and the other with signiﬁcant out-of-vocabulary (OOV) words. The results show that the proposed method signiﬁcantly improves the recognition accuracy in both tasks, resulting in 61.4% and 53.8% relative word error rate (WER) reductions respectively, from a well-trained RNN-T with 65 thousand hours of training data. We do further study on the semi-supervised training methodology: 1) which modules of RNN-T model to be updated; 2) the impact of using different neural TTS models; 3) the performance of using text with different relevancy to target domain. Finally, we compare several RNN-T customization methods, and conclude that semi-supervised training with neural TTS is comparable and complementary with Internal Language Model Estimation (ILME) or biasing.
Combining current knowledge of Cypripedium calceolus with a new analysis of genetic variation in Italian populations to provide guidelines for conservation actions

The split between conservation science and real‐world application is an ongoing issue despite several calls for unification. Researchers are empowered to partially bridge the research‐implementation gap by making their findings more accessible. Cypripedium calceolus is the most recognizable orchid of the European flora, and is currently facing habitat change and fragmentation, in addition to threats from collectors and illegal traders. Although several studies have focused on the ecological and genetic features of the species, a comprehensive account of how such aspects can be translated into concrete conservation recommendations is still missing. In this study, we describe microsatellite genetic variation in 188 individuals from different Italian populations of C. calceolus. Our results indicate the need for immediate conservation action for the most isolated populations in the Central Apennines and north‐western Italy. Although our genetic findings are specific to the Italian populations, our aim is to review ecological and population genetic aspects in C. calceolus and their implications for conservation against the existing threats. Therefore, our detailed guidelines for translocation, habitat management and post‐translocation monitoring can be used to inform conservation strategies in threatened populations of C. calceolus across its range.
Benchmark Dataset for Short-Term Market Prediction of Limit Order Book in China Markets

Limit order books (LOBs) have generated big financial data for analysis and prediction from both academic community and industry practitioners. This article presents a benchmark LOB dataset from the Chinese stock market, covering a few thousand stocks for the period of June to September 2020. Experiment protocols are designed for model performance evaluation: at the end of every second, to forecast the upcoming volume-weighted average price change and volume over 12 horizons ranging from 1 second to 300 seconds. Results based on a linear regression model and deep learning models are compared. A practical short-term trading strategy framework based on the alpha signal generated is presented. The data and code are available on Github (github.com/HKGSAS). Key Findings ▪ There is a gap between benchmarking a high-frequency LOB dataset and model for researchers to objectively assess prediction performances, which this article serves to bridge. ▪ A more practically effective set of features is proposed to capture both LOB snapshots and periodic data. The prediction target is similarly too simplistic in the published literature—mid-price direction change for the next few events, which is not suitable for a practical trading strategy. The authors propose to predict the price change and volume magnitude over 12 short-term horizons. ▪ This article proposes comparing the performance of baseline linear regression and state-of-the-art deep learning models, based on both accuracy statistics and trading profits.
Knowledge Distillation Based Training of Universal ASR Source Models for Cross-Lingual Transfer

In this paper we introduce a novel knowledge distillation based framework for training universal source models. In our proposed approach for automatic speech recognition (ASR), multi-lingual source models are ﬁrst trained using multiple language-dependent resources before being used to initialize language speciﬁc target models in low resource settings. For the proposed source models to be effective in cross-lingual transfer to novel target languages, the training framework encourages the models to perform accurate universal phone classiﬁcation while ignoring any language-dependent characteristics present in the training data set. These two goals are achieved by applying knowledge distillation to improve the models’ universal phone classiﬁcation performance along with a shufﬂing mechanism that alleviates any language speciﬁc dependencies that might be learned. The beneﬁts of this proposed technique are demonstrated in several practical settings, where either large amounts or only limited quantities of unbalanced multilingual data resources are available for source model creation. Compared to a conventional knowledge transfer learning method, the proposed approaches achieve a relative WER reduction of 8-10% in streaming ASR settings for various low resource target languages.
Study on the Impact of Institutions on the Labor Productivity of Private Enterprises in Vietnam through the Spillover Effect from State-Owned Enterprises

The paper analyzes the impact of institutions on the labor productivity of small and medium-sized private enterprises through the spillover effect from state-owned enterprises (SOEs). The authors used data samples from three datasets: (i) The Annual Enterprise Survey conducted by the General Statistics Office of Vietnam (GSO) from 2010 to 2018; (ii) Institutional data (PCI) published by the Vietnam Chamber of Commerce and Industry (VCCI) from 2010 to 2018; (iii) GSO 2012 I-O balance sheet and a set of tabular data containing 666,221 observations at the enterprise and provincial levels in Vietnam from 2010 to 2018, including both listed and unlisted enterprises. The model’s experimental result shows that institutional improvement boosts labor productivity of domestic private enterprises through a horizontal and forward spillover channel from SOEs. Through the backward spillover channel from SOEs, how institutional improvement affects the labor productivity depends on the degree of backward spillover channel from SOEs.
An Agent for Competing with Humans in a Deceptive Game Based on Vocal Cues

In this work we present the development of an autonomous agent capable of competing with humans in a deception-based game. The agent predicts whether a given statement is true or false based on vocal cues. To this end, we develop a game for collecting a large scale and high quality labeled sound data-set in a controlled environment in English and Hebrew. We develop a model that can detect deception based on vocal statements from the participants of the experiment, and show that the model is more accurate than humans. We develop an agent that uses the developed deception model and interacts with humans within our deceptive environment. We show that our agent signiﬁcantly outperforms a simple agent that does not use the deception model; that is, it wins signiﬁcantly more games when played against human players. In addition, we use our model to detect whether a statement will be perceived as a lie or not by human subjects, based on its vocal cues.
The Mediating Role of Psychological Flexibility in Explaining Authenticity and Life Satisfaction with Alexithymia

Accepted: 12.03. 2021 Within the scope of this study, the relationships among alexithymia, psychological flexibility, authenticity and life satisfaction of individuals were investigated. In addition, the possible meditating role of psychological flexibility on the relationships among alexithymia, authenticity and life satisfaction were investigated. The research model is the relational screening model, which is one of the descriptive models that aims to examine the mediating role of psychological flexibility in the relationships among individuals' alexithymia, authenticity and life satisfaction. As the data collection tools, "Personal Information Form", "Toronto Alexithymia Scale (TAS-20)", "Authenticity Scale", "Psychological Flexibility Scale" and "Life Satisfaction Scale" were used. The sampling group of the study was composed of 702 adult individuals between the ages of 18 and 67Whilst analysing the data, SPSS 22 Package Program was used to highlight the relationships between variables (Pearson correlation coefficient) and mediation test. In the study, Bootstrap analysis and Sobel test were used to measure the statistical significance of the mediating effects of the model. According to the research findings, while there was a significant negative relationship between alexithymia and psychological flexibility variables and authenticity and life satisfaction variables, a positive significant relationship was found among psychological flexibility, authenticity and life satisfaction. According to the mediation test, psychological flexibility had a partial mediating role in the relationships among alexithymia, life satisfaction and authenticity. Alexithymia explained 52% of authenticity and 21% of life satisfaction through psychological flexibility. The present article discusses the abovementioned research results within the framework of the relevant literature, and suggestions are made accordingly.