Miracle-dz/UniFAR-FTUs huggingface dataset:
Keyword identification framework for speech communication on construction sites

Worksite communication is a key to boosting teamwork and improving worker performance on the construction worksite. Communication among workers on the construction site mostly consists of speech communication. However, construction sites are typically noisy due to construction tasks like drilling and operation of heavy equipment. Meanwhile, workers on construction sites typically represent a range of different ethnic and linguistic backgrounds and have different speaking accents. This can make it difficult for the listener to understand the speaker clearly, leading to miscommunication and errors in decision making on the construction site. Technological advancements in recent years can be leveraged to mitigate this problem. In this paper, a keyword identification framework is developed for speech communication on the construction site. For this framework, 12 hours of raw audio data containing 18 crane signalman speech commands (referred to as “keywords”) are collected. The crane signalman uses specific keywords to communicate with the crane operator and guide the crane operator in the crane operations. The 2-second audio clips (this being the approximate duration of each keyword) are extracted from the raw audio dataset, and construction site noise is added. Moreover, mel-frequency cepstral coefficients are extracted from the waveform audio dataset. The extracted mel-frequency cepstral coefficients, in turn, are used to train the 1-dimensional convolutional neural network. After training, the model is found to achieve a training accuracy of 97.3%, a validation accuracy of 96.1%, and a testing accuracy of 93.8%. The model is further deployed for real-time identification of keywords in speech, with the model achieving an accuracy of 95.3%. In light of these findings, it can be concluded that the developed framework is suitable for real-time application in noisy construction sites for identifying specific keywords in speech.
COVID-19 Learning Loss and Recovery: Panel Data Evidence from India

We use a near-representative household panel survey of ∼19,000 primary-school-aged children in rural Tamil Nadu to study the extent of ‘learning loss’ after COVID-19 school closures, the pace of recovery in the months after schools reopened, and the role of a flagship compensatory intervention introduced by the state government. Students tested in December 2021, after 18 months of school closures, displayed severe deficits in learning of about 0.7 standard deviations (σ) in math and 0. 34σ in language compared to identically-aged students in the same villages in 2019. Using multiple rounds of in-person testing, we find that two-thirds of this deficit was made up in the 6 months after school reopening. Using value-added models, we attribute ∼24% of the cohort-level recovery to a government-run after-school remediation program which improved test scores for attendees by 0. 17σ in math and 0. 09σ in Tamil after 3-4 months. Further, while learning loss was regressive, the recovery was progressive, likely reflecting (in part) the greater take up of the remediation program by more socioeconomically disadvantaged students. These positive results from a state-wide program delivered at scale by the government may provide a useful template for both recovery from COVID-19 learning losses, and bridging learning gaps more generally in low-and-middle-income countries.
Intangibles and Participation in Global Value Chains in the EU: Evidence from the GLOBALINTO Input-Output Intangibles Database

The scope of this paper is to provide empirical evidence regarding intangible inputs, global value chains (GVCs) participation and their linkage with exports in the EU and the UK, utilizing data from WIOD and the newly constructed GLOBALINTO Input-Output Intangibles Database for the period 2000e2014. GVC participation metrics are calculated based on a production-based decomposition framework and include backward and forward participation indices. Intangible inputs follow a breakdown by origin into domestic and imported intangible inputs. Our empirical results suggest that GVC participation (both backward and forward) is a significant driver for exports and highlight the importance of intangibles’ origin in the exporting activities of the EU economies, especially in the case of the non-Euro
Priming of Solanum Tuberosum in vitro plants with PVY-specific interfering RNAs activates anti-viral resistance

Helper component-proteinase (HC-Pro) is a multifunctional suppressor protein synthesized by potato virus Y (PVY), which is able to neutralize the protective mechanisms of RNA interference (RNA-i) in potato plants, thereby causing systemic infection of the host plant and significant damage to the tuber material. This article demonstrates how one of the main functions of HC-Pro, the capture and retention of short interfering RNAs, 21-23 nt in size (siRNA) on the surface of protein subunits, can be used to create virus-resistant potato plants in vitro. The proposed method is based on the selective dissociation of the HC-Pro/siRNA complex from PVY-infected plants and siRNA-priming of healthy plants. Purified preparations of PVY-specific siRNAs were obtained using column gel filtration followed by immunoprecipitation and phenol-chloroform extraction. Injections of siRNA into the leaf plate of virus-free potato microplants, cultivars bred at Amanzholov University, led to a decrease in the accumulation of viral particles in the cytoplasm of plants inoculated with the wild PVY strain and the formation of resistance to PVY for the entire growing season.
Real Environment-Aware Multisource Data-Associated Cold Chain Logistics Scheduling: A Multiple Population-Based Multiobjective Ant Colony System Approach

Cold chain logistics (CCL) scheduling is important for smart cities as it directly affects the service quality and operating profits of logistics companies. However, traditional CCL models seldom reflect the real transportation environment, making the solutions hardly applicable to the real CCL scenes. Hence, this paper attempts to establish a multisource data-associated CCL model oriented to the real transportation environment. This environment is considered by employing the real-captured driving duration and distance between any two places. Three scheduling objectives (namely, quality losses, personnel and vehicle costs, and transportation costs) are taken into account. To efficiently solve the proposed multisource data-associated multiobjective CCL model, a multiple population-based multiobjective ant colony system (MPMOACS) approach is proposed. Based on the multiple populations for multiple objectives framework, the MPMOACS approach can optimize multiple objectives sufficiently, and thus obtain promising solutions distributed along the entire Pareto front. To further enhance the performance of the MPMOACS, a ranking-based local search strategy is also designed. Experiments are conducted on not only the existing benchmark instances but also a real environment-aware multisource dataset that is built based on real-captured transportation data of Guangzhou and Shenzhen, China. Compared with six state-of-the-art and very recent well-performing multiobjective optimization approaches, the proposed MPMOACS approach exhibits the overall best performance.
Digital Comic Media on Smartphones to Improve Communication Skills

Communication skills in English are essential in global relations. The media get relevant and needed information. In practice, improving communication skills in English requires learning media as a tool. This study aims to develop digital comic media and their influence on improving communication skills. This research used a method with the gradual development of ADDIE: analysis, design, development, implementation, and evaluation. After being validated by media experts and material experts, the development results show that digital comic meets the criteria as media. In addition, the results of testing the application of digital comic media in learning English found that the communication skills were better by the increase in the ability to guess, arrange arguments, formulate definitions, and generalize. Applying digital comic media to improve communication skills can be a reference in learning English.
An Empirical Study of ClassPoint Tool Application in Enhancing EFL Students' Online Learning Satisfaction

Keeping in view the basic necessity of the time, integrating Information and Communication Technology (ICT) in teaching-learning practices has become an integral part of higher education. In relation to English as a foreign language (EFL) learning, several forms of interactive platform have been applied to enhance students’ engagement, interest and learning satisfaction. Likewise, this quasi-experimental study was designed to investigate the role of ClassPoint tool integrated activities in strengthening e-learning satisfaction of undergraduate female EFL learners in Saudi Arabia. The intervention, i.e., the ClassPoint tool-integrated instruction for the experimental group, lasted for around a month, whereas the students from the controlled group received non-ClassPoint integrated instructions. Subsequently, the data from both groups was analyzed statistically via a t-test. Based on the analysis, the use of ClassPoint tool activities showed a significant increase in the e-learning satisfaction enrichment of EFL learners compared to the non-ClassPoint traditional instruction. Therefore, the study suggests using the ClassPoint tool at all educational levels in all modes of learning to keep learners stay engaged, motivated, and satisfied with their learning.
Paradoxes in Film Ratings

I selected a simple random sample of 100 movies from the Movie and Video Guide (1996), by Leonard Maltin. My intent was to obtain some basic information on the population of roughly 19,000 movies through a small sample. In exploring the data, I discovered that it exhibited two paradoxes about a threevariable relationship: (1) A non-transitivity paradox for positive correlation, and (2) Simpson’s paradox. Giving concrete examples of these two paradoxes in an introductory course gives to students a sense of the nuances involved in describing associations in observational studies.
Challenges remain in Building ASR for Spontaneous Preschool Children Speech in Naturalistic Educational Environments

Monitoring child development in terms of speech/language skills has a long-term impact on their overall growth. As student diversity continues to expand in US classrooms, there is a growing need to benchmark social-communication engagement, both from a teacher-student perspective, as well as student-student content. Given various challenges with direct observation, deploying speech technology will assist in extracting meaningful information for teachers. These will help teachers to identify and respond to students in need, immediately impacting their early learning and interest. This study takes a deep dive into exploring various hybrid ASR solutions for low-resource spon-taneous preschool (3-5yrs) children (with & without developmental delays) speech, being involved in various activities, and interacting with teachers and peers in naturalistic classrooms. Various out-of-domain corpora over a wide and limited age range, both scripted and spontaneous were considered. Acoustic models based on factorized TDNNs infused with Attention, and both N-gram and RNN language models were considered. Results indicate that young children have significantly different/developing articulation skills as compared to older children. Out-of-domain transcripts of interactions between young children and adults however enhance language model performance. Overall transcription of such data, including various non-linguistic markers, poses additional challenges.
Metodologia Gage R&R: uma abordagem de estimação intervalar via método delta multivariado

A qualidade dos mais variados processos de produção está diretamente associada à análise estatística de dados coletados dos mesmos. Portanto, para tentar minimizar a probabilidade de fracassos ou de falhas, métodos estatísticos são utilizados. Um dos estudos mais tradicionais para avaliar um sistema de medição é denominado como Gage R&R (MSA, 2010; MONTGOMERY; RUNGER, 1993), em que a variação total do sistema é decomposta em, principalmente, duas partes: Repetibilidade e Reprodutibilidade. Logo, o objetivo deste trabalho é realizar o estudo de Gage R&R, via método da Análise de Variância (ANOVA), para estimar os quadrados médios associados aos fatores aleatórios operador e peça, e, em seguida, através do Método dos Momentos, obter os estimadores para as componentes de variância associadas a Repetibilidade e a Reprodutibilidade. Posteriormente, estima-se o índice R&R (%) e obtêm-se um intervalo de confiança assintótico, que permite classificar o sistema de medição quanto a sua adequação, de acordo com um quadro de referência utilizado por acadêmicos e profissionais atuantes nas indústrias. Palavras-chave: Intervalo de Confiança. Operador. Peça. Qualidade. Repetibilidade. Reprodutibilidade
Technology Opportunity Recognition Algorithm and Decision Assistance for Non-Drug Antidepressant Field in China

The aim of this study is to propose a framework for non-pharmaceutical antidepressant technology opportunity identification in China, based on patent analysis and assisted decision making. Semantic mining technology was used to divide technology life cycle, classify technology, identify key technology associated opportunities, and identify core technology functions of 532 non-drug antidepressant patent data in China from 1995 to 2021. Combined with co-occurrence and multi-dimensional scale analysis, strategic coordinate maps and technology decision assistance maps were drawn. The development of non-drug antidepressant technology in China was divided into embryonic stage, formative stage, and development stage. Six technical categories, 30 high-frequency technical associated opportunities, and low-frequency and high-frequency core technical opportunities for non-drug antidepressants were summarized. Based on four technical fields and four functional clusters of strategic coordinate graphs, the technical opportunity decision auxiliary atlas was constructed. By comparison, this map is more intuitive than the technology-efficacy matrix, when presenting technology efficiency mapping. This study provides a set of systematic algorithms for opportunity identification and decision assistance based on patent data, in addition to reference for strategic decision making of non-drug antidepressant technology subjects in China.
Genome-Wide Identification of SMXL Gene Family in Soybean and Expression Analysis of GmSMXLs under Shade Stress

SMXL6,7,8 are important target proteins in strigolactone (SL) signal pathway, which negatively regulate the reception and response of SL signal, and play an important role in regulating plant branching. However, there is a relative lack of research on soybean SMXL gene family. In this study, 31 soybean SMXL genes were identified by phylogenetic analysis and divided into three groups. Based on the analysis of GmSMXL gene’s structure and motif composition, it was found that the GmSMXL members in the same group were similar. The results of cis-element analysis showed that GmSMXL genes may regulate the growth and development of soybean by responding to hormones and environment. Based on the tissue specificity analysis and GR24 treatment, the results showed that four GmSMXLs in G1 group were predominantly expressed in stems, axillary buds and leaves and involved in SL signal pathway. Finally, under shading stress, the expression of four genes in G1 group was slightly different in different varieties, which may be the reason for the difference in branching ability of different varieties under shading stress. We have systematically studied the SMXL gene family in soybean, which may lay a foundation for the study of the function of GmSMXL gene in the future.
A comprehensive analysis of G-protein-signaling modulator 2 as a prognostic and diagnostic marker for pan-cancer

Background: G-protein signaling modulator 2 (GPSM2) maintains cell polarization and regulates the cell cycle. Recent studies have shown that it is highly expressed in various tumors, but its pan-cancer analysis has not been reported. Methods: First, we analyzed the differential GPSM2 expression in normal and cancer tissues by the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Human Protein Atlas databases and investigated its expression effect on the survival of cancer patients by gene expression profiling interactive analysis 2 (GEPIA2). Second, we analyzed the GPSM2 phosphorylation level using the clinical proteomic tumor analysis consortium dataset. In addition, we investigated GPSM2 gene mutations in human tumor specimens and the impact of gene mutations on patient survival. Finally, we analyzed the relationship between GPSM2 expression and cellular immune infiltration through the TIMER 2.0 database. Meanwhile, the possible signaling pathway of the gene was analyzed by the Gene Ontology (GO)| Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to explore its potential mechanism. Results: GPSM2 is overexpressed in most cancers, which leads to reduced overall survival (OS) and disease-free survival in patients. The results of phosphorylation analysis suggest that tumor development involves a complex GPSM2 phosphorylation process. We identified GPSM2 mutation loci with the highest frequency of mutations in uterine corpus endometrial carcinoma (UCEC), and this mutation increased progression-free survival and overall survival in uterine corpus endometrial carcinoma patients. Finally, we found that the role of GPSM2 in tumors may be associated with cellular immune infiltration. Gene Ontology|KEGG pathway analysis showed that the enrichment pathways were mainly “mitotic nuclear division,” “chromosome segregation,” and “spindle. ” Conclusions: Our pan-cancer analysis provides a comprehensive overview of the oncogenic roles and potential mechanisms of GPSM2 in multiple human cancers.
FFM: A Frame Filtering Mechanism To Accelerate Inference Speed For Conformer In Speech Recognition

This paper proposes a frame filtering mechanism (FFM) to accelerate inference speed for speech recognition. The FFM consists of three parts: one frame invalid indicator distinguishing whether the frame is invalid or not, one filtering strategy removing invalid frames, and one extractor attention block recalling useful information from filtered frames. The feature sequence will become shorter after FFM block. As a result, the inference is accelerated. Compared to other downsampling approaches on LibriSpeech, our method can achieve best WER with lowest RTF. Experiments on Aishell-1 show that our approach reduces the sequence length by up to 73% and achieves 21.1%–34.5% relative RTF reduction with relative WER increasing no more than 5.8%.
Almost sure exponential convergence to consensus of random gossip algorithms

In this paper, we provide a proof of almost sure exponential convergence to consensus for a general class of ergodic edge selection processes. The proof is based on the multiplicative ergodic theorem of Oseledec and also applies to continuous time gossip algorithms. An example of exponential convergence in a non ergodic case is also discussed. Copyright © 2012 John Wiley & Sons, Ltd.
Adversarial and Sequential Training for Cross-lingual Prosody Transfer TTS

This study presents a method for improving the performance of the text-to-speech (TTS) model by using three global speech-style representations: language, speaker, and prosody. Synthesizing different languages and prosody in the speaker’s voice regardless of their own language and prosody is possible. To construct the embedding of each representation conditioned in the TTS model such that it is independent of the other representations, we propose an adversarial training method for the general architecture of TTS models. Furthermore, we introduce a sequential training method that includes rehearsal-based continual learning to train complex and small amounts of data without forgetting previously learned information. The experimental results show that the proposed method can generate good-quality speech and yield high similarity for speakers and prosody, even for representations that the speaker in the dataset does not contain.
High Step-Up Quadratic Impedance Source DC-DC Converter Based on Coupled Inductor

In this article, a new topology of quadratic impedance source dc-dc converters is proposed. In this topology, coupled inductor and auxiliary elements, such as diodes and capacitors, are used to increase the output voltage. The proposed converter can provide high-voltage gain, low-voltage sensitivity to duty cycle variations, continuous input current, the low-voltage stress on the switch, diodes, and capacitors, high efficiency, and common ground. First, the proposed converter is analyzed, and the components are designed. Moreover, loss analysis is given to prove its effectiveness. Then, the proposed converter is compared to other conventional converters to verify the claims. Finally, a 200-W laboratory sample of the proposed converter is implemented to reconfirm the given theories. Based on the experimental results, the proposed converter’s efficiency is equal to 95.3%, and the switch voltage stress is reduced about 30% compared to the output voltage.
Metamaterial models of curved spacetime

The electromagnetic properties of vacuous curved spacetime and of a certain fictitious material in flat spacetime are noncovariantly equivalent. This fictitious medium - known as the Tamm medium - is generally bianisotropic and nonhomogeneous. The Tamm medium offers opportunities for exploring the electromagnetic properties of certain curved-spacetime scenarios that may be impractical to explore by direct methods. The realization of various Tamm mediums as homogenized composite mediums was investigated. The approach taken involved the homogenization of relatively simple component materials, with the inverse Bruggeman formalism exploited to estimate appropriate constitutive parameters, shape parameters, and volume fractions for the component materials. Typically, Tamm mediums are highly anisotropic in regions corresponding to relatively large spacetime curvature (e.g., in the vicinity of spacetime singularities). In principle, at least, such high degrees of anisotropy may be achieved by homogenizing component particles that are highly elongated. The nonhomogeneous nature of Tamm mediums can be accommodated by adopting a piecewise homogeneous approach, which is valid for appropriate wavelength regimes.
On the Statistical Analysis of Ordinal Data When Extravariation is Present

Threshold models can be useful for analysing ordered categorical data, like ratings. Such models provide a link between the ordinal scale of measurement and a linear scale on which treatments are supposed to act. In this paper a simple agricultural plot experiment is considered with two sources of variation, namely between-plot variation and within-plot variation. It is shown that for a threshold model with two sources of variation maximum likelihood estimates can be obtained by iterative weighted least squares