Reviewerly/exHarmony huggingface dataset:
Effect of Repetition Method on Teaching English Process in Classroom

Currently, there are various kinds of foreign language teaching techniques that are used by the teachers in the classroom. One of these techniques is the repetition technique in which the teacher will repeat the previous lesson in order to improve the student's memory. The goal of this research is to get the findings regarding the differences before and after the students to get repetition lessons in the class. The method used in this study is a quantitative method in which the data will be collected for this research. First, the researcher gave tests to the students before the repetition method applied in the class every lesson. After that, the students will get another test but the repetition method applied before the test is conducted. It was found that there were differences between the students who were given a repetition method and students who had not been given a repetition method. Based on data obtained in the field, it is found out that the students who get English lessons with the repetition method get better grades than students who do not.
Fast Intra Coding Algorithm for Depth Map with End-to-End Edge Detection Network

Compared with traditional High Efficiency Video Coding (HEVC), 3D-HEVC introduces multi-view coding and depth map coding, which leads to significant increase in coding complexity. In this paper, we propose a low complexity intra coding algorithm for depth map based on end-to-end edge detection network. Firstly, we use Holistically Nested Edge Detection (HED) network to determine the edge location of the depth map. Secondly, we use Ostu method to divide the output of the HED into foreground region and background region. Finally, the CU size and the candidate list of intra mode are determined according to the region of coding tree unit (CTU). Experimental results demonstrate that the proposed algorithm can reduce the encoding time by 39.56% on average under negligible degradation of coding performance.
On the Significance of Questioning in Learning Activity

The article analyzes and compares modern educational technologies: the so-called ‘leading dialogue’ and the technology for generating student questions in collective learning activity. We discuss the role of spontaneous and provoked meaningful questions in collective learning activity. Using the experimental school course ‘New Biology’ (for 6th—9th grades) as an example, we provide a description of a teacher’s work aimed at promoting questioning in students: the questions are considered as a starting and finishing point in the collective search activity. Also, the article discusses the data of a comparative analysis of questions by students from different grades presented with the same problem. It considers the role of modeling in the emergence of meaningful questions and outlines the conditions in which meaningful questions appear, as well we the effects of systematic questioning in classrooms.
The functionality of entrepreneurial passion and entrepreneurial bricolage on micro-entrepreneur’s wellbeing

This study investigates the relationships between entrepreneurial passion, entrepreneurial bricolage, and subjective wellbeing. A total of 253 usable data were collected from the micro-entrepreneurs in Bangladesh and data were analyzed by SEMPLS3.0 employing structure equation modelling. The results indicate that subjective wellbeing is significantly predicted by entrepreneurial passion and bricolage. Bricolage also found to play a mediating role between passion and wellbeing. The results of the study validate that passionate entrepreneurs who embrace bricolage will achieve wellbeing through their ventures. The paper makes contribution to the knowledge domain by bridging the concept of subjective wellbeing with entrepreneurial passion and bricolage.
RETRACTED: Seismic analysis and optimization of building structure based on embedded system

The structure's dynamic model is an important factor for analyzing different environmental factors of construction activities. A simple non-linear model to learn the behavior of the building during an earthquake. Structure analysis is an important part of the evaluation and seismic design. When the earthquake law that has passed through the static analysis of the lateral load of the structure's weight began more than 100 years ago. Due to the nonlinear response of the dynamics and the structure, the advanced analytical procedure has been carried out over time. At least two data sets of the Differential Evolution (DE) and Particle Swarm Optimization (PSO) is in a non-linear dynamic model and the building based embedded system. It is estimated by using the parameter optimization algorithm of the building model. Model of performance and optimization algorithm performance: because there is no a lot of structural modeling expertise, this paper focuses on two aspects. The results, in these algorithms, algorithm Differential Evolution (DE) is, in most cases, it indicates that it is better than the corresponding. As in the model, the result is, it tells me that it is going well in the predicted scenario.
Speeding up Deep Learning Training by Sharing Weights and Then Unsharing

It has been widely observed that increasing deep learning model sizes often leads to significant performance improvements on a variety of natural language processing and computer vision tasks. In the meantime, however, computational costs and training time would dramatically increase when models get larger. In this paper, we propose a simple approach to speed up training for a particular kind of deep networks which contain repeated structures, such as the transformer module. In our method, we first train such a deep network with the weights shared across all the repeated layers till some point. We then stop weight sharing and continue training until convergence. The untying point is automatically determined by monitoring gradient statistics. Our adaptive untying criterion is obtained from a theoretic analysis over deep linear networks. Empirical results show that our method is able to reduce the training time of BERT by 50%.
Detect-S: an mHealth application to assist health professionals to identify suicide risk in hospitalized patients

Suicide is a serious public health problem that affects the whole world. This study describes development of the prototype for an mHealth application (app) intended to assist healthcare professionals to identify suicide risk in hospitalized patients and reports on testing of the app by some of these professionals, conducted to confirm its functionality.This is applied exploratory research into use of Information Technology within the healthcare field, based on application prototyping for mobile devices. The research was conducted at the Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) from 2017 to 2019. Six healthcare professionals, one data scientist, and three undergraduate students in Biomedical Informatics took part in the study. All research participants signed the free and informed consent form.The main findings show that the development team created a prototype named Detect-S, which became a cross-platform application (iOS and Android) offering 16 functions.It can be concluded that Detect-S has the potential to be a positive technological instrument that can be tested in a hospital setting to assist healthcare professionals to identify and manage patients with at risk of suicide.
Webpage visual feature extraction and similarity algorithm

Nowadays, the Internet is developing rapidly, and the types of websites are more abundant than in previous decades. However, people face more significant network security risks, and the losses caused by phishing websites are even more severe. Phishing websites imitate the interface of real websites. For better identifying phishing websites, this paper proposes a visual feature extraction method and a visual similarity algorithm. First, the visual feature extraction method improves the VIPS algorithm to extract the logo block and calculate the visual block's signature as the image feature of the visual block by perceptual hash technology. Then, the visual similarity algorithm uses the visual blocks' coordinates and thresholds to find a one-to-one correspondence. This method assigns weights according to the tree structure and logo. The Hamming distance of the visual features is measured to calculate the similarity of the visual blocks. It integrates the similarity of the visual blocks similarity and visual block weight to get the web pages' visual similarity. Finally, we use multiple pairs of phishing webpages and legitimate webpages to verify the feasibility of the algorithm and achieve excellent results.
Data Analysis and Application Platform for Generators-Grids-Loads Full-View Synchronized Measurement System

With the advent of the power electronic era of power system, the mechanism characteristics, analysis methods and operation control mode of power system have changed greatly, which puts forward diversified requirements for the types of measurement objects and device coverage of the monitoring system. Therefore, for a panoramic synchronous measurement system covering the power supply, power grid and load side of the power system, this paper proposes a master station structure based on multi-channel and multi database parallel. The transmission mode of different data in different communication scenarios and the data processing and access mode of three kinds of databases running side by side are designed. The problems of real-time application and storage of data under different time scale intervals and different protocols are solved. Secondly, combined with the actual measurement and application requirements of the system, the program logic architecture of the system application platform is built. On this platform, three kinds of applications are developed, including the disturbance preliminary judgment of load side (220 V), the frequency rendering which can realize the real-time estimation and display of national frequency, and the data analysis and visualization for the characteristics of frequency and harmonic data. It shows that the system application platform can meet the monitoring requirements, and also verifies the feasibility of the main station platform architecture.
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding

Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample similarities in the embedding space from an unlabeled dataset. Traditional UDML methods usually use the triplet loss or pairwise loss which requires the mining of positive and negative samples w.r.t. anchor data points. This is, however, challenging in an unsupervised setting as the label information is not available. In this paper, we propose a new UDML method that overcomes that challenge. In particular, we propose to use a deep clustering loss to learn centroids, i.e., pseudo labels, that represent semantic classes. During learning, these centroids are also used to reconstruct the input samples. It hence ensures the representativeness of centroids - each centroid represents visually similar samples. Therefore, the centroids give information about positive (visually similar) and negative (visually dissimilar) samples. Based on pseudo labels, we propose a novel unsupervised metric loss which enforces the positive concentration and negative separation of samples in the embedding space. Experimental results on benchmarking datasets show that the proposed approach outperforms other UDML methods.
Adaptive Search Algorithm Method of Whole-pixel Deformation for Ancient Building Painted Beams

Digital image correlation method (DIC) can realize non-contact displacement measurement of member by spraying uniform speckle on member surface and taking pictures before and after deformation. Considering the problems of poor recognition effect and low calculation efficiency in deformation measurement of traditional integral pixel algorithm for ancient building painted beams caused by painted pattern uneven gray scale, an Improve-Adaptive Rood Pattern Search (IARPS) method is proposed to calculate the entire pixel displacement. Firstly, the search radius of the first search point is estimated, and an exhaustive search is performed within the radius. Then, Adaptive Rood Pattern Search was introduced to search the whole pixel displacement through small diamond search. By using simulated speckle pattern displacement and the bending test of common regular painted pattern decorative wood beam, IARPS is compared with the coarse-fine search method commonly used in DIC. Through comparative analysis, the results show that IARPS method can effectively overcome the imprecision of local computation in the coarse-fine search method, and the calculation efficiency can be improved by 71.6%, which provides a new method to apply DIC to non-contact deformation measurement of ancient painted beams. Keywords: ancient painted beams, digital image correlation method, local exhaustively search
Linking organizational culture with financial performance: A literature review

Organizational culture and firm performance are some of the most researched topics in management because of their importance to organizations. Many studies have explored the relationship between organizational culture and firm performance. The aim of the study is to link organizational culture and firm financial performance and this link was explained by conducting a literature review. The paper investigated the linking organizational culture and financial performance and found that there are different types of organizational cultures and all of them affect the performance of organizations.
The Use of Personal Protective Equipment for Medical Recorders and Health Information During Covid-19 Pandemic in Indonesia

The 2019 Coronavirus (Covid-19) outbreak's existence requires health service facilities in Indonesia to focus on the principles of patient safety and the principles of staff safety.The purpose of this study was to determine the use of personal protective equipment for medical recorders and health information in the Covid-19 pandemic in Indonesia.This type of research is descriptive quantitative research.The sample of this study was 65 health service facilities.Data collection techniques using questionnaires and interviews.Data analysis techniques using descriptive statistics.The analysis was carried out to determine the distribution of the use of types of personal protective equipment for medical recorders and health information in the Covid-19 pandemic.The results showed that the use of personal protective equipment for medical recorders and health information in the Covid-19 pandemic included the use of 100% masks, with 59 types of surgical masks (90.8%) while cloth masks (9.2%), gloves (72.3) and before and after activities using a hand sanitizer (100%).In comparison, soap with running water was only (46.4%).Other personal protective equipment used by medical recorders and health information in Indonesia includes hazmat suits (27.7%), face shields (26.2%), eye protection (goggles) (21.5%).It can be concluded that the application of personal protective equipment for medical recorders and health information in Indonesia is not entirely following the PORMIKI Circular Letter No.HM.01.01 / 002 / III / 2020 so it is necessary to educate and socialize the importance of personal protective equipment, especially in the use of gloves. Keywords-personal protective
Dual Conditional Cross Entropy Scores and LASER Similarity Scores for the WMT20 Parallel Corpus Filtering Shared Task

This paper describes our submission to the WMT20 Parallel Corpus Filtering and Alignment for Low-Resource Conditions Shared Task. This year’s corpora are noisy Khmer-English and Pashto-English, with 58.3 million and 11.6 million words respectively (English token count). Our submission focuses on filtering Pashto-English, building on previously successful methods to produce two sets of scores: LASER_LM, a combination of the LASER similarity scores provided in the shared task and perplexity scores from language models, and DCCEF_DUP, dual conditional cross entropy scores combined with a duplication penalty. We improve slightly on the LASER similarity score and find that the provided clean data can successfully be supplemented with a subsampled set of the noisy data, effectively increasing the training data for the models used for dual conditional cross entropy scoring.
Effective Formal Verification for Galois-field Arithmetic Circuits with Multiple-Valued Characteristics

This study presents a new formal verification method for Galois-field (GF) arithmetic circuits with the characteristics of more-than two. The proposed method formally verifies the correctness of circuit functionality (i.e., the inputoutput relations given as GF-polynomials) by checking the equivalence between a specification and a gate-level netlist. In the proposed method, we represent a netlist as simultaneous algebraic equations and solve them based on a new polynomial reduction method efficiently applicable to arithmetic over extension fields F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pm</sub> , where the characteristic p is larger than two. Using reverse topological term order to derive the Gröbner basis, our method can complete the verification even when a target circuit includes bugs. Our experimental results show that the proposed method can efficiently verify practical F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pm</sub> arithmetic circuits, including those used in modern cryptography.
SISTEM KLASIFIKASI ARSIP DI BIRO AKADEMIK DAN KEMAHASISWAAN UNIVERSITAS NEGERI PADANG

This paper contains a discussion of how to use the archive classification system at the Bureau of Academic and Student Affairs, State University of Padang. The use of this archive classification system aims to assist employees at the Bureau of Academic and Student Affairs, State University of Padang in using the archive classification system according to their respective work units. The research method used in writing this paper is a qualitative descriptive research method. Data collection techniques were carried out by means of observation and interviews. The subject of data collection through observation at the Bureau of Academic and Student Affairs, State University of Padang. Observations are made by looking at how employees understand the classification of records. Furthermore, interviews were conducted with employees at the Bureau of Academic and Student Affairs, Padang State University. The author conducted a question and answer session with employees directly regarding the use of archive classification codes, the obstacles faced in using classification codes, and solutions to these problems. Based on these results, it can be concluded that the archive classification system in the Bureau of Academic and Student Affairs at the State University of Padang is actually used to assist employees in providing proper and correct archive classification codes. In the manual for the archive classification code at the Bureau of Academic and Student Affairs, State University of Padang, it can be concluded that the paper on the archive classification system at the Bureau of Academic and Student Affairs, State University of Padang is: (1) The archive classification system at the Bureau of Academic and Student Affairs, State University of Padang uses the subject system /problem (2) The archive classification system according to the Ministry of Research, Technology and Higher Education Regulation Number 23 of 2018 concerning Archive Classification, Archive Retention Schedule, and Security Classification System and Dynamic Archive Access in the academic and student fields has 6 archive classification codes, namely AK (academic) , TM (Student Admission), KR (Curriculum), EP (Educational Evaluation), Scholarship (BW), Student Affairs (KM).
Text Mining with R: A Tidy Approach, by Julia Silge and David Robinson. Sebastopol, CA: O’Reilly Media, 2017. ISBN 978-1-491-98165-8. XI + 184 pages.

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The impact of pre-processing algorithms in facial expression recognition

This paper proposes several pre-processing algorithms to improve facial expression recognition based on Convolutional Neural Networks (CNNs) models. The proposed CNN model was trained on the Extended Cohn-Kanade dataset (CK+) after applying the pre-processing stages and achieved competitive results (93.90% recognition accuracy) despite its simple and light architecture. Using this CNN model, a study on the impact of each pre-processing algorithm when extracting facial features is presented. In the end, it is understood that pre-processing algorithms help CNNs to extract the most relevant features for each facial expression more effectively, reducing the overfitting and increasing the recognition accuracy. Attention maps before and after the pre-processing step are shown in order to visualize its impact when the proposed CNN model makes a prediction.
Current aspects regarding “smart homes”/ ambient assisted living (AAL) including rehabilitation specific devices, for people with disabilities/ special needs

Introduction: The population life expectancy has increased (“over half the EU’s population predicted to be over-65 by 2070”), according to recent prevalence studies, being a result of the advancement of technology and medical science. This aging population has implications for society because there is increased number of older people requiring better quality of life. Materials and Methods: AAL represents the systems that may support completely the living area of a person and has the potential to facilitate the elderly to live longer and more safety in their family environments, allowing them to continue their current activities, facilitating participation in more activities at home and in the community and improving the cost-effectiveness, the quality of health and social services. A practical use of technology is the introduction of home networks, which involve notions such as: "smart homes ", “tele-health / tele-care” and even, possibly, “tele-medicine” to allow people with serious illnesses / conditions / and special needs to maintain an appropriate quality of life (QOL) at home. Discussions and Conclusions: AAL can contribute to an increased autonomy, self-confidence and mobility in people whose activity is limited to home environment, such as "the oldest olds” and/ or those with severe neuro-/ loco-motors disabilities, and so to reduce the risk of institutionalization, enhance security, prevent social isolation, thus allowing “older adults to age in place”. An important role in achieving this goal is representing by working in a multidisciplinary team (experts in the field of health - rehabilitation, gerontology -, social experts, technical/ informatics experts, engineering and robotics experts). Keywords: ambient assistive living, quality of life, special needs, tele-medicine, rehabilitation,