Reviewerly/exHarmony huggingface dataset:
Distributed Support Vector Ordinal Regression over Networks

Ordinal regression methods are widely used to predict the ordered labels of data, among which support vector ordinal regression (SVOR) methods are popular because of their good generalization. In many realistic circumstances, data are collected by a distributed network. In order to protect privacy or due to some practical constraints, data cannot be transmitted to a center for processing. However, as far as we know, existing SVOR methods are all centralized. In the above situations, centralized methods are inapplicable, and distributed methods are more suitable choices. In this paper, we propose a distributed SVOR (dSVOR) algorithm. First, we formulate a constrained optimization problem for SVOR in distributed circumstances. Since there are some difficulties in solving the problem with classical methods, we used the random approximation method and the hinge loss function to transform the problem into a convex optimization problem with constraints. Then, we propose subgradient-based algorithm dSVOR to solve it. To illustrate the effectiveness, we theoretically analyze the consensus and convergence of the proposed method, and conduct experiments on both synthetic data and a real-world example. The experimental results show that the proposed dSVOR could achieve close performance to that of the corresponding centralized method, which needs all the data to be collected together.
MORTALITY PREDICTION OF SURGICAL INTENSIVE CARE UNIT PATIENTS USING DEEP LEARNING-BASED SURVIVAL MODELS

Mortality prediction in surgical intensive care units (SICUs) is considered to be among the most critical steps in enforcing efficient treatment policies. This study aims to evaluate the performance of various deep learning models in predicting the mortality of patients admitted to SICUs. The survival of 2,225 adult patients admitted to SICUs was modeled using five salient deep learning-based survival models, namely, Cox-CC, Cox-Time, DeepSurv, DeepHit, and N-MTLR. The data were extracted from the Medical Information Mart for Intensive Care II (MIMIC-II) database. The performance of the models was compared using the time-dependent concordance index (Ctd-index) and integrated Brier score (IBS). From among the five models, DeepSurv achieved the most accurate prediction, while Cox-Time demonstrated the least optimal predictive ability. For DeepSurv, Cox-CC, DeepHit, N-MTLR, and Cox-Time, the mean Ctd -index was 0.773, 0.767, 0.765, 0.732, and 0.659, and the mean IBS was 0.181, 0.192, 0.195, 0.212, and 0.225, respectively. DeepSurv, Cox-CC, and DeepHit yielded comparable performance. Deep learning models are free from the stringent assumptions inherent in standard survival models. Hence, these models are considered flexible alternatives to the standard approaches in scalable, real-world survival problems.
Extracting Terminological Concept Systems from Natural Language Text

Abstract Terminology denotes a language resource that structures domain-specific knowledge by means of conceptual grouping of terms and their interrelations. Such structured domain knowledge is vital to various specialised communication settings, from corporate language to crisis communication. However, manually curating a terminology is both labour- and time-intensive. Approaches to automatically extract terminology have focused on detecting domain-specific single- and multi-word terms without taking terminological relations into consideration, while knowledge extraction has specialised on named entities and their relations. We present the Text2TCS method to extract single- and multi-word terms, group them by synonymy, and interrelate these groupings by means of a pre-specified relation typology to generate a Terminological Concept System (TCS) from domain-specific text in multiple languages. To this end, the method relies on pre-trained neural language models.
Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

This study discusses the application of the Fuzzy Time Series Markov Chain method which was developed by determining the length of the interval using the distribution method. In the fuzzy forecasting method, the determination of the length of the interval is an important thing that will affect the accuracy of the forecasting results. The development of this forecasting model aims to get better forecasting accuracy results. In this study, general inflation data for the city of Bandung is used for the period January 2016 – June 2021. The data is divided into two groups, namely in sample data and out sample data with a ratio of 90: 10. In the data processing process, the Python programming language is used. Based on the accuracy test using the MAPE method, it can be concluded that this method provides better forecasting results with a MAPE value of 1.16%.
Editing a Scholarly Journal

Editing a scholarly journal can be a lot of fun and can help your career in many ways.But it is not something to be undertaken lightly and has the potential to be a nightmare if you do not plan it properly.If you do it well, it can only enhance your reputation, but if you make a hash of it, it will expose your faults.An obvious advantage of editing a journal is that it can provide a great opportunity to grow your network, and help you become established in your field.It can be very interesting work, as it should give you exposure to the publishing industry; a wide coverage of scientific literature, including the latest findings and techniques; and an understanding of the vagaries of the review process.
Development of Moodle-Based E-Learning on the Website for German Reading Skills A2

This study examines the use of Moodle-Based E-Learning on the website for german reading skills A2. The purpose of this study is to define and explain how Moodle-based e-learning can be used in german reading skills A2 because of the low achievement of students in reading skills and not optimal use of the internet in supporting learning success. LMS is one way to develop online learning or e-learning, which is a web-based learning management application that facilitates learners to learn properly in a virtual classroom, a class in a virtual world. The LMS used in this study is a Moodle-based LMS. An analytical descriptive strategy was used as a research method. Therefore, the use of moodle- based e-learning is very suitable for use in learning German, given that currently the world of education cannot be separated from technology and internet networks, besides that there are many educational institutions that have been facilitated by infrastructure in utilizing existing technology. It is also proven that learning with the Moodle as LMS application is more effective than conventional learning in improving students' reading skills. From some of the results and research findings, several suggestions related to this research were put forward, the point of which is that it is time for a change in mindset in responding to the internet as one of the factors that support learning success.
Study of Plasma Electrolysis in Water: Alternative Technologies for Hydrogen Production

Plasma electrolysis has been studied as an alternative method for generating hydrogen. A plasma reactor is used in this electrolysis to produce glowing plasma and the reactive interface fluid layer has an asymmetrical spiral-rod electrodes configuration. Plasma electrolysis was carried out in 2 positions at the surface of the solution and at a depth of 5 mm. On the surface, it is divided into 4 regions of I-V characteristics, namely the conventional electrolysis area, the plasma formation region, the gas formation area that covers the anode, and the glowing plasma region. While at a depth of 5 mm, the formation of plasma with a voltage of 500 V takes 120 seconds to heat the solution with a conventional electrolysis mechanism around the anode. Glowing plasma at a depth of 5 mm resembles the shape of a ball with a larger diameter as the addition of stress and the addition of the concentration of NaCl solution. The mechanism for the formation of hydrogen molecules occurs in the reactive interfacial fluid layer. The plasma ball will increase with a greater voltage. The enlargement of the glowing plasma with increasing operating voltage shows that voltage is a very important factor in the generation of plasma in water. The diameter of the glowing plasma sphere produced for electrolysis at the surface of the solution and at a depth of 5 mm in the solution always increases with increasing voltage. Furthermore, the greater the molarity of the NaCl solution in water, the larger the glowing plasmasphere. The production of hydrogen increases as the voltage increases, this is indicated by the increase in spectrum intensity Hδ and H2. HIGHLIGHTS A unique coronavirus epidemic that has swept throughout the globe is examined and analysed COVID-19 outbreaks in various places are analysed using machine learning models, which are visualised using charts, tables, graphs and predictions depending on the available data For prediction models, the time series forecasting package (PROPHET) is utilised as part of machine learning GRAPHICAL ABSTRACT
Need Analysis of Student Worksheets Based on the Reading to Learn Approach in Teaching Reading Narrative Texts

The 2018 Program for International Student Assessment survey released in 2019 showed that the reading literacy HOTS skills of Indonesian students was low where Indonesia was ranked 74th out of 79 countries. .To improve students HOTS reading literacy the Ministry of Research, Technology and Higher Education developed a Literacy Improvement Program at the National Junior High School level in 2021 using The Reading to Learn Approach. In this context, the objective of this research were to develop Students Worksheet Based on The Reading To Learn Approach to improve Students' Higher Order Thinking Skills on the Competence of Reading Narrative Texts. This research is part of research and development namely stage of needs analysis. The approach used was a qualitative approach. The study was conducted in MTsN 2 Medan. This study focused on the Needs analysis stage of teachers and students regarding the worksheet based on the reading to learn approach that is going to developed. The instrument to collect the data was interview The findings shows that most of the respondents expect the worksheet of Narrative Text should be developed based on their needs. They felt very hard to read the texts in English because the materials were irrellevant and not interesting. The students also confessed their current worksheet of Narrative Text was inappropriate for them and the process of learning was boring. Therefore, some of the aspects of learning also needed to be concerned such as learning style, learning technique, the way of doing the task, the length and topic of the texts, the situation of the class, the activities in the worksheet, and the difficulties of the students, in order to make the students easier to learn English.
A Educational Understanding of the Art Experience Process of College Students in Class －Focusing on the Experience of ‘Self-expression Visualization’

The purpose of this study was to examine the implications for educational meaning and application through the art experience of ‘self-expression visualization’ of college students. The subjects of the study were five female students who took &lt;Child Creativity Development&gt;. &lt;Child Creativity Development&gt; has the meaning of liberal arts education in that students think various ways and lead classes through the convergence of interdisciplinary studies. As for the research method, qualitative research was conducted through interviews and focus group interviews. As a result of the study, first, students continued to interact with both ignorance and knowledge, second, they acquired integrated knowledge regarding reason and sensibility. Third, they improved and supported themselves. Finally, they showed understanding and creativity in life. The educational implication of ‘self-expression visualization’ is that students were able to find their own identity and learn how to communicate and express with others. It also helped students understand ordinary creativity and apply it to their learning and life.The art experience of ‘self-expression visualization’ for college students is meaningful in that students sublimated many things into the art of learning beyond the realm of formal instruction. They overcame their fear and difficulties in learning, found experiences and possibilities to think comprehensively, brought about positive changes concerning the distrustful and negative perspectives they held about themselves, and enabled creative learning through continuous thinking and effort. Thus, we can see that they were learning how to express and visualize themselves in regards to the connection between themselves as subjects and life itself through their inner changes.
A Study on the Teaching Method of Self-introduction for Employment Based on the Analysis of Self-introduction Items

This study aims to propose a teaching method of the “self-introduction” by presenting student error cases in order to find practical guidance measures for the corporate self-introduction for fourth year university students. The intention of the questioner in the items is analyzed, focusing on the common items in the corporate self-introduction.In this study, in order to analyze the intention of the items presented in the corporate self-introduction, the items commonly presented were selected for the self-introductions of a total of 40 corporate cases as experienced by fourth year university students. After selecting the common items, the intentions of the person who presented the question contained therein were analyzed, and the items of the self-introductions were classified and categorized accordingly. Furthermore, the intentions of the person who presented the question contained in the self-introduction items were revealed by type.In addition, the teaching method of the self-introduction was prepared for each item so that practical teaching of the corporate self-introduction could be provided to university students by presenting examples of students who made errors by misunderstanding the item intention.
Quantifying the Impact of Cognitive Biases in Question-Answering Systems

Crowdsourcing can identify high-quality solutions to problems; however, individual decisions are constrained by cognitive biases. We investigate some of these biases in an experimental model of a question-answering system. In both natural and controlled experiments, we observe a strong position bias in favor of answers appearing earlier in a list of choices. This effect is enhanced by three cognitive factors: the attention an answer receives, its perceived popularity, and cognitive load, measured by the number of choices a user has to process. While separately weak, these effects synergistically amplify position bias and decouple user choices of best answers from their intrinsic quality. We end our paper by discussing the novel ways we can apply these findings to substantially improve how high-quality answers are found in question-answering systems.
A Natural Language Processing Model Based on Pre-trained Deep Learning Models

Natural language processing (NLP) is a field of artificial intelligence (AI) whose primary purpose is to give computers the ability to understand written and spoken language in a human-like manner, mainly by creating computers that can read and respond to information in a human-like manner and then generate their text or speech as a response.However, the process of natural language processing may encounter the problem of inefficiency, so how to make the process of natural language processing more efficient is a direction that is currently being studied.This paper aims to derive a set of programmable rules from helping NLP describe human language by combining computational linguistics and statistics, machine learning and deep learning models.In this way, when text and speech data are combined, computers can understand human language in the form of text or audio data, such as the intent and emotion of the speaker or author.This paper introduces various types of deep learning systems used in NLP analysis and research and describes a pre-training-based approach to natural language processing.The final findings can improve operational efficiency, increase employee productivity, and streamline mission-critical business operations.
DCNR: A Transfer Learning Approach for Detecting and Classifying Non-FunctionalRequirements from User Reviews

Abstract With the increasing popularity of the web and mobile devices, mobile applications (Apps) are now used for a wide range of purposes. As mobile experiences are becoming more user-centered, system design is entirely dependent on who is using the app. For the success of the application, developers need to be aware of the users’ concerns and expectations of the application. Existing research has demonstrated that user evaluations often raise issues with an app’s usability, dependability, performance, or aesthetic appeal. These issues should be seen as Non-Functional Requirements (NFRs) in order to develop an application that meets users’ needs in all aspects. Since user reviews are often short, unstructured, and written in informal language, it might be hard to label them according to the standards of NFRs. In this context, our paper proposes a DCNR (Detection and Classification of Non-functional Requirements) approach that fine-tunes transformer language models (BERT, RoBERTa) to perform the classification tasks on our labeled UR-NFR dataset. In this approach, user reviews that contain requirements are identified automatically and further classified into seven different categories of NFRs (Usability, Look and Feel, Reliability, Portability, Performance, Maintainability, and Functional Suitability). Comparing the experimental results using existing machine learning techniques (Naive Bayes, BoW, TF-IDF), we found that our DCNR outperforms the existing approach. Among the two transformer-based language models incorporated by DCNR method, RoBERTa achieved better results than BERT in binary requirement and non-requirement classification, as well as in the multiclass classification of NFRs. In the binary classification and the multiclass classification of NFRs, the RoBERTa model has attained f1 scores of up to 88.2% and 70.7%, respectively. Overall, it can be concluded from the findings that transfer learning has a promising effect on improving the performance of our proposed approach.
“Thank You So Much, Father Jean Blanc!” “Mr. Yoshihiro Hayashi and Mrs. Tamako Hayashi, Thank You Very Much!” (Tcheonzamun 513th-528th)

The French missionary Dallet (1874) in his book wrote about ‘The thousand character essay’. (It is called as Tcheonzamun in Korea). Dallet (1874) explained that the ancient Chinese country, Tch’in, the people used the book to their children in order to instruct Chinese characters. Park et al. (2021a) and Park et al. (2021b) translated several Tcheonzamun poems. This time, the present researcher studied a poem of Tcheonzamun (Tcheonzamun 513th-528th), and the researcher tried to translate the poem. The title of this study is ‘Thank you so much, Mr Hayashi and Father Jean Blanc! ’ The present researcher tried to translate a part of ‘The thousand character essay (Tcheonzamun)’. The range of the poem was (Tcheonzamun 513th-528th). And the translating method was through the meaning of Chinese character for the Tcheonzamun poem. &lt;Number in Tcheonzamun Chinese characters (Pronunciation of Korean language in English alphabet)&gt; 517-520 車(Tcha) 駕(Ga) 肥(Bi) 輕(Gyeong) This is the second line. Please see your neighbor! Let’s talk about the livestock as an easy example! If the bridle (駕) were light (輕), it will be good for the cow or the horse when the livestock pulls the cargo. And if the livestock (車) were well fed and became fat (肥), it will be favorable for the difficult work. 525-528 勒(Leug) 碑(Bi) 刻(Gag) 銘(Myeong) This is the fourth line. As the result of your kind behaviors as shown on the second line, your neighbors will do you the following things. They write (銘) your works openly (刻). And they speak well of you (勒), and they express such a favorable feeling about you (碑)!
Work Engagement and Teaching Performance of Public-school Physics Teachers in the New Normal

Aims: This study correlated the extent of work engagement and level of teaching performance in the new normal of public school Physics and determined the challenges encountered in teaching Physics.&#x0D; Study Design: This quantitative study utilized a descriptive and correlational research design.&#x0D; Place and Duration of Study: This study was conducted among Physics teachers in the large division in Central Philippines during the school year 2021-2022.&#x0D; Methodology: Standardized questionnaire, validated and reliability-tested researcher-made checklist, and RPMS results were used to collect data needed for the study. Descriptive analysis using mean and standard deviation were used for the extent of work engagement and level of teacher performance while frequency count and percentage distribution were used for the challenges in teaching Physics. Spearman rank correlation determined the significant relationship between work engagement and teaching performance. &#x0D; Results: Overall, the extent of the work engagement of the Physics teachers is very often (M=4.88; SD=0.99). Meanwhile, the overall level of teaching performance is very satisfactory (M=4.46; SD=0.26). &#x0D; Moreover, there is a significant relationship between work engagement and teaching performance [ρ(62)=0.331, p=0.008]. Topping the challenges in teaching Physics are the voluminous paperwork and extended working hours (64 or 75%), diversity of learners (45.3%), and adaptability to educational trends (39.1%).&#x0D; Conclusion: This study discovered a significant relationship between work engagement and teaching performance. Hence, when the work engagement of teachers is greater, their teaching performance level also escalates. Vigor, dedication, and absorption are all contributory to work engagement, which influences teaching performance. Age, as well as years of teaching experience, matters a lot in work engagement. The longer one stays in the teaching profession, the more he or she becomes adept at work. Also, the educational qualification or preparation of a teacher is another factor influencing work engagement as seen clearly in the difference in the scores between Physics majors and non-Physics majors.
Instaread to Enhance Reading Comprehension and Improve Vocabulary Learning among Form 4 ESL Learners

This research reports the use of Instaread to enhance reading comprehension and to improve vocabulary learning among Form 4 ESL learners.It aimed to find out the effectiveness of technological tools as one of the reading comprehension strategies and to encourage learners to learn vocabulary among upper secondary school students especially during Covid -19 pandemic.Data was collected using a questionnaire, reading and vocabulary tests.The findings show that learners have positive feedback on the use of this intervention.They show active participation towards the lesson and it can be concluded that these reading comprehension successfully help the students to learn a language.