Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and Feasibility of Automatic Speech Recognition for Modeling Student Collaborative Discourse in Classrooms

Automatic speech recognition (ASR) has considerable potential to model aspects of classroom discourse with the goals of automated assessment, feedback, and instructional support. However, modeling student talk is besieged by numerous challenges including a lack of data for child speech, low signal to noise ratio, speech disfluencies, and multiparty chatter. This raises the question as to whether contemporary ASR systems, which are benchmarked on adult speech in idealized conditions, can be used to transcribe child speech in classroom settings. To address this question, we collected a dataset of 32 audio recordings of 30 middle-school students engaged in small group work (dyads, triads and tetrads) in authentic classroom settings. The audio was sampled, segmented, and transcribed by humans as well as three ASR engines (Google, Rev.ai, IBM Watson). Whereas all three ASRs had high word error rates, these mainly consisted of deletion errors. Further, Google successfully transcribed a greater proportion of utterances than the other two, but with more word substitutions; insertions were low across the board. ASR accuracy was robust to different speakers and recording idiosyncrasies evidenced by <5% of variance in error rates attributable to the student and recording session. We found that ASR errors had a larger negative effect on downstream natural language processing tasks at the word, phrase, and semantic levels rather than at the discourse level. Our findings indicate that ASR can be used to extract meaningful information from noisy classroom speech and might be more suitable for applications that require higher precision but are tolerant of lower recall.
The Impact of Teacher Education on English Teachers’ Views about Using Mother Tongues: A Teachers’ Perspective

After decades of persistent dominance of monolingual approaches in language teaching, we are now witnessing a shift to pluralist pedagogical practices that recognize learners’ mother tongues (MTs) as a valuable resource. This paper examines data from 44 questionnaire respondents and 4 interviewees to investigate teacher perspectives on using learners’ MTs in the classroom and the extent to which teacher education shaped their beliefs. The results suggest that while most of the participants stressed the importance of maximizing target language (TL) use, some of them also recognized the value of employing MTs for specific purposes, such as anchoring new learning, providing grammar explanations and task instructions, decreasing student and teacher anxiety, sustaining motivation, and supporting learner identity. Most participants agreed that their teacher education program exerted some influence on their beliefs and practices, but their personal experiences as learners and teachers were also named as influential sources. The most notable change in views related to an increased use of the TL, which contradicts recent findings relative to the value of using learners’ existing resources. The paper concludes by stressing the need to examine the curricula and objectives of teacher education programs in the light of the current research on multilingualism in education.
Investigating the effect of Automated Feedback on learning behavior in MOOCs for programming

The challenge of learning programming in a MOOC is twofold: acquiring programming skills and learning online, independently. Automated testing and feedback systems, often offered in programming courses, may scaffold MOOC learners by providing immediate feedback and unlimited re-submissions of code assignments. However, research still lacks empirical evidence of their effect on learning behavior of MOOC learners, with diverse backgrounds and goals. Addressing this gap, we investigated the connections between the use of automated feedback system and learning behavior measures, relevant for MOOCs: engagement, persistence and performance. Further, two subjective measures of success are examined: sense of learning and intention fulfilment. In an experimental design, we analyzed data of active learners in a Python programming MOOC (N=4652), comparing an experimental group provided with automated feedback with a control group that did not. In examining the effect of automated feedback, prior knowledge of programming and Python was considered. Empirical evidence was found for the relation between automated feedback usage and a higher engagement and better performance, as well as higher attendance in "active watchers" and "high-per-formed completers" clusters, obtained by cluster analysis. Learners reports on their experience with the automated feedback system supported these findings. Regarding the subjective measures of success, however, no difference was found between groups. Our study and the offered future research may contribute to the considerations regarding the integration of automated feedback in MOOCs for programming.
Towards Automated Generation and Evaluation of Questions in Educational Domains

Students learn more from doing activities and practicing their skills on assessments, yet it can be challenging and time consuming to generate such practice opportunities. In this work, we present a pipeline for generating and evaluating questions from text-based learning materials in an introductory data science course. The pipeline includes applying a T5 question generation model and a concept hierarchy extraction model on the text content, then scoring the generated questions based on their relevance to the extracted key concepts. We further classified the generated questions as either useful to learning or not with two different approaches: automated labeling by a trained GPT-3 model and manual review by expert human judges. Our results showed that the generated questions were rated favorably by all three evaluation methods. We conclude with a discussion of the strengths and weaknesses of the generated questions and outline the next steps towards refining the pipeline and promoting NLP research in educational domains.
Investigating Temporal Dynamics Underlying Successful Collaborative Problem Solving Behaviors with Multilevel Vector Autoregression

In collaborative problem solving (CPS), people’s actions are interactive, interdependent, and temporal. However, it is unclear how actions temporally relate to each other and what are the temporal similarities and differences between successful vs. unsuccessful CPS processes. As such, we apply a temporal analysis approach, Multilevel Vector Autore-gression (mlVAR) to investigate CPS processes. Our data were collected from college students who collaborated in tri-ads via a video-conferencing tool (Zoom) to collaborately engage a physics learning game. Video recordings of their verbal interactions were transcribed, coded using a validated CPS framework, and organized into sequences of 10-second windows. Then, mlVAR was applied to the successful vs. unsuccessful CPS sequences to build temporal models for each. A comparison of the models together with a qualitative analysis of the transcripts revealed six temporal relationships common to both, six unique to successful level attempts, and another eight unique to unsuccessful level attempts only. Generally, for successful outcomes, people were likely to answer clarification questions with reasons and to ask for suggestions according to the current game situation, while for unsuccessful CPS level attempts, people were more likely to struggle with unclear instructions and to respond to inappropriate ideas. Overall, our results suggest that ml-VAR is an effective approach for temporal analyses of CPS processes by identifying relationships that go beyond a coding and counting approach.
Actual Fictions

This Element sheds new light on the ubiquitous yet complex notion of mimesis. By systematically comparing the social dynamics of the Dutch population at a given time with the social dynamics of characters in Dutch literary fiction published in the same period, it aims to pinpoint the ways and the extent to which literary fiction either mirrors or shapes the societal context from which it emerged. While close-reading-based scholarship on this topic has been limited to qualitative interpretations of allegedly exemplary works, the present study uses the data-driven tools of social network analysis to systematically determine the imitative elements of the social dynamics of characters within larger-scale, representative collections of books of literary fiction. This Element also has a video abstract: www.cambridge. org/smeets
A CloudSat Perspective on the Cloud Climatology and Its Association with Aerosol Perturbations in the Vertical over Eastern China

Many efforts have been taken to investigate aerosol–cloud interactions from space, but only a few studies have examined the response of vertical cloud structure to aerosol perturbations. Three-dimensional cloud climatologies of eight different cloud types identiﬁed from the CloudSat level-2 cloud product during the warm season (May–September) in 2008–10 over eastern China were ﬁrst generated and analyzed. Using visibility as a proxy for cloud condensation nuclei, in combination with satellite-observed radar reﬂectivity, normalized contoured frequency by altitude diagrams of the differences in cloud radar reﬂectivity Z proﬁles under pollutedand clean conditionswere constructed. For shallow cumulus clouds (shallow Cu) Z tends to be inhibited, and it is enhanced in the upper layers for deep cumulus (deep Cu), nimbostratus (Ns), and deep convective clouds (DCC) under polluted conditions. Overall, analyses of the modiﬁed center of gravity (MCOG) and cloud-top height (CTH) also point to a similar aerosol effect, except for the nonsigniﬁcant changes in MCOGs and CTHs in deep Cu. The impacts of environmental factors such as lower-tropospheric stability andverticalvelocityarealso discussedforthesetypes of clouds.Althoughconsistent aerosol-induced elevations in MCOGs and CTHs for Ns and DCC clouds are observed, the effect of meteorology cannot be completely ruled out, which merits further analysis.
Periods and harmonic analysis on spherical varieties

Given a spherical variety X for a group G over a non-archimedean local field k, the Plancherel decomposition for L^2(X) should be related to "distinguished" Arthur parameters into a dual group closely related to that defined by Gaitsgory and Nadler. Motivated by this, we develop, under some assumptions on the spherical variety, a Plancherel formula for L^2(X) up to discrete (modulo center) spectra of its "boundary degenerations", certain G-varieties with more symmetries which model X at infinity. Along the way, we discuss the asymptotic theory of subrepresentations of C^{infty}(X), and establish conjectures of Ichino-Ikeda and Lapid-Mao. We finally discuss global analogues of our local conjectures, concerning the period integrals of automorphic forms over spherical subgroups.
Coronavirus Pandemic Secondary bacterial infections of the respiratory tract in COVID-19 patients

Introduction: Secondary Bacterial Infections (SBIs) of the respiratory system are one of the biggest medical concerns in patients undergoing hospitalization with a diagnosis of COVID-19. This study aims to provide relevant data for the initiation of appropriate empirical treatment after examining the etiology and antimicrobial resistance of SBIs in COVID-19 patients under care in the Intensive Care Units (ICUs) in the largest pandemic hospital of our country. Methodology: Between March 16, 2020 and December 31, 2021, 56,993 COVID patients were hospitalized, of which 7684 were admitted to ICUs. A total of 1513 patients diagnosed with SBIs have been included in this study. During the course of the study, demographic data, clinical course, etiology and antimicrobial resistance data of all patients were collected. Results: The most common causative agents of SBIs were inferred as Acinetobacter baumanii (35.1%), Staphylococcus aureus (15.2%), Klebsiella pneumoniae (12.3%) and Pseudomonas aeruginosa (10.4%). The isolation rates of carbapenem-resistant and colistin-resistant A. baumannii , K. pneumoniae and P. aeruginosa were 83.7%; 42.7%, 79.2%, and 5.6%, 42.7%, 1.7%, respectively. Acinetobacter pittii clustering was seen in one of the ICUs in the hospital. Multidrug resistant 92 (5.4%) Corynebacterium striatum isolates were also found as a causative agent with increasing frequency during the study period. Conclusions: SBI of the respiratory system is one of the major complications in patients hospitalized with COVID-19. The antimicrobial resistance rates of the isolated bacteria are generally high, which indicates that more accurate use of antibacterial agents is necessary for SBIs in patients hospitalized with COVID-19 diagnosis.
THE IMPLEMENTATION OF E-PORTFOLIOS TO SUPPORT SELF-DIRECTED ENGLISH LEARNING FOR SECONDARY STUDENTS

Nghiên cứu này triển khai việc sử dụng hồ sơ học tập điện tử để hỗ trợ tính tự chủ trong trong học tập tiếng Anh cho học sinh lớp 11 của một trường trung học phổ thông ở Việt Nam trong học kỳ 2 của năm học 2021-2022. Nghiên cứu hành động này sử dụng công cụ nghiên cứu là phỏng vấn và bảng câu hỏi. Một nhóm học sinh gồm có 35 người được mời tham gia dự án kéo dài trong 8 tuần, trong đó học sinh tạo hồ sơ học tập điện tử cá nhân trên nền tảng Wordpress phục vụ cho việc học tiếng Anh. Các dữ liệu thu được từ những cuộc phỏng vấn đã cho thấy những người tham gia không chỉ thay đổi thái độ đối với việc học tập tiếng Anh mà còn cải thiện được các kỹ năng học tập tự chủ như đặt mục tiêu, đánh giá, giải quyết các vấn đề. Kết quả trong bảng hỏi cũng cho thấy những phản hồi tích cực của học sinh đối với việc sử dụng hồ sơ học tập điện tử như là một công cụ để thúc đẩy tính tự chủ trong học tập tiếng Anh. Tuy nhiên, học sinh cũng gặp phải một số khó khăn như là thiếu thời gian thực hiện hồ sơ học tập điện tử một cách thành thạo và thiếu các kỹ năng công nghệ. Do đó, một số đề xuất và kiến nghị được đưa ra cho các nghiên cứu tiếp theo.
SV40 miR-S1 and Cellular miR-1266 Sequester Each Other from Their Targets, Enhancing Telomerase Activity and Viral Replication

Virus-encoded microRNAs (miRNAs) target viral and host mRNAs to repress protein production from viral and host genes, and regulate viral persistence, cell transformation, and evasion of the immune system. The present study demonstrated that simian virus 40 (SV40)-encoded miRNA miR-S1 targets a cellular miRNA miR-1266 to derepress their respective target proteins, namely, T antigens (Tags) and telomerase reverse transcriptase (TERT). An in silico search for cellular miRNAs to interact with viral miR-S1 yielded nine potential miRNAs, five of which, including miR-1266, were found to interact with miR-S1 in dual-luciferase tests employing reporter plasmids containing the miRNA sequences with miR-S1. Intracellular bindings of miR-1266 to miR-S1 were also verified by the pull-down assay. These miRNAs were recruited into the Ago2-associated RNA-induced silencing complex. Intracellular coexpression of miR-S1 with miR-1266 abrogated the downregulation of TERT and decrease in telomerase activity induced by miR-1266. These effects of miR-S1 were also observed in miR-1266-expressing A549 cells infected with SV40. Moreover, the infected cells contained more Tag, replicated more viral DNA, and released more viral particles than control A549 cells infected with SV40, indicating that miR-S1-induced Tag downregulation was antagonized by miR-1266. Collectively, the present results revealed an interplay of viral and cellular miRNAs to sequester each other from their respective targets. This is a novel mechanism for viruses to manipulate the expression of viral and cellular proteins, contributing to not only viral lytic and latent replication but also cell transformation observed in viral infectious diseases including oncogenesis.
Sparse Index Tracking Portfolio with Sector Neutrality

As a popular passive investment strategy, a sparse index tracking strategy has advantages over a full index replication strategy because of higher liquidity and lower transaction costs. Sparsity and nonnegativity constraints are usually assumed in the construction of portfolios in sparse index tracking. However, none of the existing studies considered sector risk exposure of the portfolios that prices of stocks in one sector may fall at the same time due to sudden changes in policy or unexpected events that may affect the whole sector. Therefore, sector neutrality appeals to be critical when building a sparse index tracking portfolio if not using full replication. The statistical approach to sparse index tracking is a constrained variable selection problem. However, the constrained variable selection procedure using Lasso fails to produce a sparse portfolio under sector neutrality constraints. In this paper, we propose a high-dimensional constrained variable selection method using TLP for building index tracking portfolios under sparsity, sector neutrality and nonnegativity constraints. Selection consistency is established for the proposed method, and the asymptotic distribution is obtained for the sparse portfolio weights estimator. We also develop an efficient iteration algorithm for the weight estimation. We examine the performance of the proposed methodology through simulations and an application to the CSI 300 index of China. The results demonstrate the validity and advantages of our methodology.
Epidemiological Study on Salmonella Prevalence in Sow Herds Using Direct and Indirect Detection Methods

In piglet production, the beginning of pork production, Salmonella prevalence requires greater attention as having an impact on the subsequent production steps. The aim of this study was to investigate Salmonella prevalence in three sow herds with attached piglet rearing units. Salmonella prevalence was investigated either directly by boot swabs and feces or indirectly by serum samples taken during gilt integration, the peripartal period, and piglet rearing. Boot swabs and feces were analyzed by real-time PCR and subsequent microbiology. Results indicated that high biosecurity measures in sow husbandry do not necessarily result in a low Salmonella prevalence. Furthermore, the sow herds’ Salmonella prevalence should not be used to infer the situation in the associated piglet rearing. The proportion of positive boot swabs was 10.5, 3.6, and 21.3% for sows (gilts and peripartal) with an inverse situation in piglet rearing with 50.0, 63.3, and 5.8% positive swabs for farms A, B, and C, respectively. Boot swabs are suitable as a direct sampling method to gain an overview of Salmonella prevalence in both sows and piglets. Indirect serum antibody testing can be useful, although it should be evaluated considering age-dependent levels of antibody titres.
Analysis of Kenya’s Atmospheric Moisture Sources and Sinks

Achievement of the United Nations Sustainable Development Goals (SDGs) is contingent on understanding the potential interactions among human and natural systems. In Kenya, the goal of conserving and expanding forest cover to achieve SDG 15 “Life on Land” may be related to other SDGs because it plays a role in regulating some aspects of Kenyan precipitation. We present a 40-yr analysis of the sources of precipitation in Kenya and the fate of the evaporation that arises from within Kenya. Using MERRA-2 climate reanalysis and the Water Accounting Model 2 layers, we examine the annual and seasonal changes in moisture sources and sinks. We find that most of Kenya’s precipitation originates as oceanic evaporation but that 10% of its precipitation originates as evaporation within Kenya. This internal recycling is concentrated in the mountainous and forested Kenyan highlands, with some locations recycling more than 15% of evaporation to Kenyan precipitation. We also find that 75% of Kenyan evaporation falls as precipitation elsewhere over land, including 10% in Kenya, 25% in the Democratic Republic of the Congo, and around 5% falling in Tanzania and Uganda. Further, we find a positive relationship between increasing rates of moisture recycling and fractional forest cover within Kenya. By beginning to understand both the seasonal and biophysical interactions taking place, we may begin to understand the types of leverage points that exist for integrated atmospheric water cycle management. These findings have broader implications for disentangling environmental management and conservation and have relevance for large-scale discussions about sustainable development.
Using CloudSat data to look at the cloud and precipitation structure in the Arctic

Clouds and precipitation are fundamental determinates of the Arctic climate. Despite the presence of clouds and storm systems, very few precipitations reach the ground. In fact, polar precipitation patterns are characterized by frequent sublimation of the water particles during their descent. This discrepancy between cloud coverage and storm systems and the amount of precipitation on the ground is not explained because relatively little is known about these features due partly to the lack of available data. Recently, CloudSat, a polar-orbiting satellite, was launched in order to provide unique information about the cloud and precipitation structure of the Arctic, which cannot be measured using current weather instruments. The data is used in the context of the Storm Studies in the Arctic, a field experiment that started in Fall 2007 in the Canadian Arctic, aims at describing the extreme weather occurring over Baffin Island and Iqaluit. The present study focuses on the Southern Baffin Island using data from CloudSat for the area covered by the Storm Studies in the Arctic field experiment. We analyzed 37 passes of CloudSat over Iqaluit and examined cloud altitude and type, environmental conditions like temperature and relative humidity, as well as the precipitation type, size, and rate. Our analysis shows that the cloud structure is characterized by formations that are mostly stratiform, composed of one to two cloud-layers at low- and mid-level, and mostly thin individual clouds. A low cloud base and a large cloud thickness seem to be requirements for precipitation over the region. Although these results do not give satisfying answers regarding what triggers sublimation , they provide general information about the macrostructure of the clouds. This is a first step in understanding why in the Arctic very little precipitation reaches the ground. We believe further information about cloud microstructure can be garnered using data from CloudSat. Our data therefore provides recommendation for the Storm Studies in the Arctic experiment that started after this study was conducted.
Adsorption behaviors and mechanisms of Al-Fe dual-decorated biochar adsorbent for phosphate removal from rural wastewater

Abstract The discharge of phosphorus from rural wastewater causes eutrophication, so phosphorus removal is regarded as an important part of water treatment. In this study, corn biochar (BC) was prepared at pyrolysis temperature of 450 °C, and then Al-Fe dual-decorated biochar (AFBC) was obtained through a two-step modification method (AlCl3 immersion before FeCl3 immersion) on the base of BC. The characterization results showed that Fe and Al were decorated on corn biochar in the form of FeOOH, Fe2O3, and AlOOH. Compared with Al/Fe mono-decorated biochar (ABC/FBC), AFBC exhibited superior phosphate removal performance in a wide pH range (pH 4.0–11.0). The adsorption behavior of AFBC was a spontaneous exothermic process, and conformed to the Langmuir and pseudo-second-order kinetic models. The adsorption mechanism was dominated by the electrostatic attraction and coordination between metal (hydrogen) oxide on biochar surface and phosphate. In the dynamic adsorption-desorption experiment, AFBC could reduce total phosphorus (TP) from 5.54 mg/L to 0.5 mg/L within 40 BV of actual rural wastewater. In addition, using phosphate-loaded AFBC as a soil conditioner promoted the growth of ryegrass, and it also provided an effective method to achieve bio-waste resource utilization and phosphorus reuse. Graphical Abstract