Reviewerly/exHarmony huggingface dataset:
A Multimodal Dataset to Create Manufacturing Digital Twins

This paper introduces a multimodal dataset created for research on digital twins in the manufacturing domain. Digital twins refer to the digital representations of physical world objects, and they require data to be accurately modeled. By incorporating various data modes, the digital twin representations in computational environments can become more complex and precise. To this end, we propose a dataset that consists of videos recorded inside a manufacturing laboratory, featuring different people performing assembly sequences in varying ways. In addition to the videos, we also incorporated facial capture, lateral capture, and top capture to analyze the pose of the subjects, position of hands and tools, and actions performed during product assembly. Our dataset was able to successfully label 3 different actions (hold, release, screw) for 4 different kinds of tools (ratchet, wrench, allen key, screwdriver), indicating when the subject starts and ends each action for each tool.
To predict first-year grades by including data on relationships with people inside and outside the university

In this study, in order to predict first-year credits, an important indicator for preventing students from dropping out of college, we created a model using three types of data: basic data before entering college, relationship data with non-students, and credit data, and conducted comparative verification for each combination of the three types of data. In the experiment, the accuracy of the model improved as the number of data was increased from basic data, relationship data, and unit data. In order to accurately predict students at high risk of dropping out, the number of credits for the spring semester of the first year should be available after the number of credits is available, but in order not to overlook high-risk students, we used a logistic regression model with an awareness of recall and showed that prediction is possible to some extent using basic data and relational data.
Cryptocurrencies Responses to the Covid-19 Waves

Pandemics are not new and have occurred at different stages in human history (Ferguson et al. 2020). However, their impacts on financial markets are different and sometimes divergent. The market's sensitivity to these crises can provoke unexpected responses and sometimes disclose the precariousness of a market considered a riskless or safe haven.&amp;nbsp;Covid-19 crises are still one of the most disturbing health crises of this decade. Starting in china, this pandemic spread rapidly to threaten the whole globe, which explains the global interest in studying its impact on economic and financial stability around the world, especially that, Goodell and Goutte (2020) highlights that this virus is inflicting unprecedented global destructive economic damage.The research focuses on cryptocurrency's market sensitivity to the pandemic framework. This market arouses the researcher's interest in their apparition. This interest arises with the emergence of Covid-19 since the end of 2019. Moreover, the analysis of the cryptocurrency market's sensitivity in a health crisis is a first since the emergence of this market whose could challenge its performance.&amp;nbsp;Empirically, the research adopts an econometric approach based on the DCC-EGarch model to analyze the dynamic relationship between the Covid-19 and the cryptocurrency market volume of transaction evolution. It presents, to the best of our knowledge, an unprecedented empirical investigation of the pandemic second wave's impact on the dynamic relationship between Covid-19 cases and cryptocurrencies transaction volume.&amp;nbsp;The remainder of the paper is as follows. We start with a literature review. We pass them to the data and the applied methodology. Finally, we describe the empirical results and conclude.
Introduction to the Sub-Interval Analysis. Part 2: Sub-Interval Images. Big Data

A sub-interval analysis (SI analysis or SI-analysis or S-I analysis or SIA or S-IA) is a new field or direction of applied mathematical research. It deals with properties of entire intervals and their sub-intervals without considering the insides of the sub-intervals.&amp;nbsp;That is each sub-interval can be represented by only three quantities: by the two coordinates and one weight. This can evidently ease and simplify some considerations and calculations. The S-IA can be developed both as a pure mathematical and mainly as an applied research, in particular in the field of economics. For example, reporting periods in accounting and audit can evidently be considered as some sub-intervals.&amp;nbsp;If this entire interval is a one-dimensional smooth schedule, then these sub-intervals can represent a one-dimensional stepped sub-interval image. If this entire interval is a two-dimensional smooth picture, then these sub-intervals can represent a two-dimensional stepped sub-interval image.&amp;nbsp;
Timbral Metrics for Analysis of Metal Production

This chapter presents an overview of timbre in metal production from a psychoacoustic and computational musicology perspective, particularly focusing on the use of acoustic feature extraction. Both performance and recording technology have undoubtedly influenced tone in metal productions, but how can the underlying acoustic feature sets shaped by this technology inform production and analysis? This chapter includes an applied feature set extraction of stems from My Dying Bride, and concludes with a speculative view of how such features could be used in the training of machine learning classifiers.
Introduction

This book offers state-of-the-art research on the design and evaluation of assistive translation tools and systems to facilitate cross-cultural and cross-lingual communications in health and medical settings. This book illustrates using case studies important principles of designing assistive health communication tools which are (1) detectability of errors to boost user confidence by health professionals; (2) adaptability or customizability for health and medical domains; (3) inclusivity of translation modalities (written, speech, sign language) to serve people with disabilities; and (4) equality of accessibility standards for localised multilingual websites of health contents. To summarize these key principles for promotion of accessible and reliable translation technology, we use the acronym I-D-E-A.
The Effect of Digital Literacy on the Risks of Children Dropping Out of School During the Covid-19 Pandemic

The Covid-19 pandemic has negative implications for all aspects of life, including aspects of education. It is feared that the transition to a distance learning system (PJJ) that adopts digital technology will reduce students' learning abilities (learning loss) and potentially increase the risk of dropping out of school. This study aims to study the effect of digital literacy on the risk of dropping out of school for children aged 7-18 years during the Covid-19 pandemic. This study used a quantitative approach that using secondary data of Susenas 2021, Podes 2021, and IP-TIK 2021 publications with 250,921 samples of unit analysis being individuals aged 7-18 years with school status in the academic year (2019/2020) at the elementary to high school levels. Data analysis was performed using descriptive methods and multilevel binary logistic regression. The results of the analysis show that digital literacy as a proxy for digital access (use of mobile phones, computers/laptops, and internet access) has a negative significant effect on the risk of dropping out children of school. Childrens that have higher the digital literacy will have the lower the risk of dropping out children of school. The significant control variables were sex, school level, employment status of the head of household, length of schooling of the head of household, household expenditure quintile, and classification of residence. While the contextual (regional) variable that has a significant effect on the risk of dropping out of school is the information and communication technology development index (IP-TIK)
The Effect of Student Engagement on Boarding School Students' Online Learning Achievement

Boarding school students must participate in online learning due to the COVID-19 pandemic. Student engagement is an important aspect that must be considered in online learning. This study aims to determine: (1) the level of student engagement of boarding school students, (2) to investigate the effect of simultaneous behavioral engagement, emotional engagement, and cognitive engagement on learning achievement, and (3) to determine the relationship between behavioral, emotional and cognitive engagement with learning achievement, (4) to measure the relative and effective contribution of behavioral, emotional and cognitive engagement on learning achievement. This research is quantitative research with an ex post facto type. 182 boarding school students participated in this study. The data collection instrument used is the engagement scale from Fredricks &amp; McCloskey. Data processing using descriptive and inferential statistics. The inferential test used is multiple linear regression and Pearson correlation. This study’s results indicate a significant influence between behavioral, emotional, and cognitive engagement on the learning achievement. The influence of these three variables on learning achievement is 46.7%. While the results of the correlation test showed that there is a significant relationship between the variables of behavioral, emotional, and cognitive engagement in learning achievement.
An Analysis of English for Young Learner Teachers’ Classroom Management

This current study was a case study that aimed to analyze EYL teachers' classroom management performance at Kolaka Regency, Southeast Sulawesi, Indonesia. Likewise, this study also explored the root problem behind its' limitation. As part of the methodology, five teachers were involved in the observation. All the instrument components were validated throughout the SPSS Program analysis. After the instrument was validated, the classroom interaction process was observed and recorded three times. In addition, the interview session was also gained to ensure the observation result. All the data were analyzed descriptively. The data analysis showed that the EYL teachers in Kolaka Regency had attempted effective planning, teaching technique, and student/teacher relationship simultaneously but not comprehensively. The study also found that the EYL teachers at Kolaka Regency knew the meaning of pedagogic competence as the upstream of classroom management and the effort to implement it. However, they may not be optimal due to their teaching qualities ability. Therefore, the study recommended that there must be much appreciation for EYL teachers. The study also suggested accomplishing a similar study in a larger context to support the EYL teacher's welfare policy.
A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset.
Software Projects Crest and Trough in Pakistan: A Management Spectrum

Software industry has become as popular factor in the progress triumph of every country. Software project management techniques are significantly applicable in the development cycle of software projects. Despite that software projects face many tarnished reputations of poor performance in terms of schedule, budget, quality, and many other management and controlling correlated factors. Influencing factors which makes the software project successful are under the prominence of extensive research for more than the 40 years. Still, most of the relevant research in this territory has focused on developing countries with little consideration of less advanced countries. The purpose of this paper is to explore the success and failure factors in the Pakistani software environment using Qualitative research analysis approach. For this purpose, almost thirty software houses were targeted from four provinces of Pakistan and collected perception-based data through questionnaires and interviews from related personals. We further elaborate and compare some critical factors from past literatures in this research. Finally, the outcomes and findings showed better management and development aspects for a healthy software project growth in the domain of Pakistan. The importance of this research is to cover both theoretical and practical dimensions of software project management. We have found that proper scheduling, appropriate estimation of cost and time, Agile methodology, RMMM (risk mitigation, monitoring, management) approach, team bonding, team or staff motivational and communication factors significantly affected on successful projects.
Remote Sensing Image Lossy Compression Based on JPEG with Controlled Visual Quality

Remote sensing image compression plays an important role in Earth observation applications. Lossy compression techniques are efficient means to reduce the size of images, although the distortions are unavoidable. Consequently, the image quality should be controlled carefully. This paper discusses this topic with its application to JPEG. Using visual quality metrics, we show that JPEG is able to provide compression performance not worse than many advanced coders for invisible distortions and/or if images have complex content. We show that mean square error (MSE) can be provided by a two-step approach and predicted with high accuracy using a simple neural network. Such a neural network employs QF as one of the inputs and several parameters characterizing image complexity as other inputs. Different sets of input parameters are analyzed, and we show that even four input parameters produce a quite accurate prediction of MSE and, respectively, PSNR, with errors less than 1.5 dB with low computation load. This is shown using images used in training and for verification and validation image sets. Then, it is easy to determine a needed QF to provide a desired quality.
Growth Effect of Migrant Remittances and Human Capital: Panel Evidence from Sub-Saharan African Countries

Purpose: The aim of this study is to examine the growth effect of migrant remittances and human capital in sub-Saharan Africa (SSA) economies.&#x0D; &#x0D; Theoretical Framework: The study adopts the Cobb-Douglas production function of the AK model to examine the growth effect of migrant remittances and human capital in sub-Saharan Africa (SSA) economies.&#x0D; &#x0D; Design/Methodology: Data from 27 SSA countries for the period 2000-2020 was obtained from the World Development Indicator, a publication of the World Bank. The study variables include, real per capita income, migrant remittances, human capital, domestic investment, trade openness and inflation. The panel fully modified ordinary least square (PFMOLS) technique was deployed to analyse the study model.&#x0D; Findings: The study’s outcome showed that migrant remittances positively influence and cause growth while human capital has not contributed to growth in the SSA region.&#x0D; &#x0D; Research, Practical &amp; Social Implication: From the study findings, it is recommended that regional government should encourage the continuous inflow of migrant remittances to their countries and make conscious efforts in developing the level of human capital in their country via constant training, reducing or eradicating the number of school drop-out at all levels (primary, secondary, and tertiary).&#x0D; &#x0D; Originality/Value: In this study, the issue of migrant remittances, human capital and growth is re-examined by (i) considering a panel of similar economies, (ii) estimating Cobb-Douglas production function for the effect of migrant remittances via technology and human capital, (iii) adopting a robust technique (PFMOLS) that relax the assumption of homogeneity among parameters across economies and the exogeneity of migrant remittances.
Concept-Based Language Instruction Effects for Second and Heritage Language Learners: The Case of Korean Speech Level

Abstract Concept-Based Language Instruction ( C-BLI ) has been shown to be an effective approach for teaching second language (L2) learners complex grammatical features. However, it is unclear whether C-BLI is equally effective for heritage language ( HL ) learners. 30 L2 and 27 HL learners of Korean completed a series of tasks designed in a pre-intervention-post-test model that focused on speech level through verbal morphology of honorific second-person address forms. The positive change seen in self-reported understanding of speech level from pre- to post-test in both groups was not significantly different, indicating equal self-reported gains in understanding, regardless of group. However, the qualitative analysis showed differences in the ways that L2 and HL learners verbalized their understanding and appropriateness judgments of speech level. The findings provide evidence that HL learners benefit equally in overall growth, although slightly differently in how they develop, from C-BLI .
Pose‐guided adversarial video prediction for image‐to‐video person re‐identification

Abstract The image‐to‐video (I2V) person re‐identification (Re‐ID) is a cross‐modality pedestrian retrieval task, whose crux is to reduce the large modality discrepancy between images and videos. To this end, this paper proposes to predict the following video frames from a single image. Thus, the I2V person Re‐ID can be transformed to video‐to‐video (V2V) Re‐ID. Considering that predicting video frames from a single image is an ill‐posed problem, this paper proposes two strategies to improve the quality of the predicted videos. First, a pose‐guided video prediction pipeline is proposed. The given single image and pedestrian pose are encoded via image encoder and pose encoder, respectively; then, the image feature and pose feature are concatenated as the input of the video decoder. The authors minimize the difference between the predicted video and true video, and simultaneously minimize the difference between the true pose and predicted pose. Second, the conditional adversarial training strategy is employed to generate high‐quality video frames. Specifically, the discriminator takes the source image as condition and distinguishes whether the input frames are fake or true following frames of the source image. Experimental results demonstrate that the pose‐guided adversarial video prediction can effectively improve accuracy of I2V Re‐ID.
Contents list

Permissions Request permissions Contents list J. Anal. At. Spectrom., 2023, 38, 1725 DOI: 10.1039/D3JA90036C This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content.
Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting

In the workflow of machine/deep learning, a common question that data scientists ask before training a model is whether to feed the model with the entire set of input dataset features or just a subset of them. In this scenario, the precise selection of features from the input data has a significant impact on the efficiency of model training and the final performance, especially in datasets with many variables. This decision is typically made before model training and in a static way (i.e., the feature selection is applied, and then the model is trained). This paper presents a new methodology based on metaheuristics to dynamically select features. This approach has been applied to train a deep neural network to solve two problems: univariate and multivariate time series forecasting. The performance of the proposed method has been compared to the random forest model (a well-known machine learning model with a highly efficient internal feature selection method). The proposed method outperformed the random forest model in terms of mean squared error, demonstrating the validity of the approach.
The Design and Implementation E-Scaffolding Enhance Learning (ESEL) Using Web-based Simulation on Moment of Inertia

In this study, E-Scaffolding Enhance Learning (ESEL) using Web-based simulation has been designed and implemented. The scaffolding approach in this research is a conceptual scaffolding of the moment of inertia. Web-based simulation consists of 2D and 3D simulation. The 3D simulation was developed using unity software and 2D simulation was developed using scratch and articulate storyline software. All simulation was published as an Html file, and finally inserted into the website. The validation of the product was carried out by experts such as a physics lecturer and a physics teacher. The implementation phase was carried out through collaborative distance learning between one Senior High School in Bandung and one school in West Nusa Tenggara. The validation results show that ESEL using Web-based simulation on moment of inertia is deserved with an average score of 92% in the very good category. Nevertheless, there are several improvements still need to be made in terms of the appearance of the Web-based 3D simulation especially when it is accessed by phone cell. The appearance of the Web-based 3D simulation is still not responsive for smartphones. But based on the questionnaire result of students, ESEL using Web-based simulation on moment of inertia has a value 71% which means that ESEL has a good category of validation and is interesting for the students. ESEL using Web-based simulation also improves studentsâ€™ activities in the distance learning of physics classroom.
OpenCitations at LIBER Annual Conference 2021: ‘How Can Open Infrastructures Support the Role of Research Libraries?’

For the second year, OpenCitations has taken part in the LIBER annual conference.