Reviewerly/exHarmony huggingface dataset:
DESKRIPSI PELAKSANAAN PEMBELAJARAN MATEMATIKA MENGGUNAKAN GOOGLE CLASSROOM OLEH GURU DI KELAS X SMA

The purpose of this study was to find out and describe how the implementation of mathematics learning using google classroom was carried out by the teacher in class X MIA SMAN 1 Jongkong. The subjects of this study came from class X MIA which consisted of 24 students. This research method is descriptive quantitative. Collecting data from interviews, questionnaires, learning outcomes tests by teachers with the material of linear equations and inequalities of one variable containing absolute values, as well as documentation. The research description shows that the implementation of mathematics learning using google classroom by the teacher gets a percentage of 59.37% and is in the sufficient category, the learning plan assessment by linking the TPACK (Technological Pedagogical and Content Knowledge) values gets a percentage of 57.14% and is in the medium category, the presence of students in the implementation of learning mathematics using google classroom gets a percentage of 100% and is in a good category, the percentage of student learning outcomes in learning mathematics using google classroom is 82.67% and is in the very good category, student responses get a percentage of 74.79% and are in the category high, the teacher's response gets a percentage of 71.25% and is in the high category.
Application of Sustainable Drainage System as a Model of Flood Control with Environmental Insight in Medan City

The area around the Universitas Sumatera Utara Campus, Medan city, is an area that is often hit by floods, especially when there is quite heavy rain with a fairly long duration. Based on initial observations, it can be said that this is due to the drainage channel being unable to accommodate the high rainwater discharge. To overcome this, the 2021 USU TALENTA Community Service Program offered a solution by creating a sustainable drainage system that is environmentally insight. The solution starts with the smallest thing that each household in the affected area can do, namely through making biopori holes. In addition to biopori, another solution taken is through the construction of infiltration wells and groundwater tanks. Conducted training and simulation activities on these three solutions of environmentally sustainable drainage systems. It is hoped that it will gradually reduce water runoff directly into the city drainage, but the water can be partially absorbed into the ground first, to reduce standing water which can eventually cause flooding.
Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines

Purpose: Effort Estimation is a process by which one can predict the development time and cost to develop a software process or product. Many approaches have been tried to predict this probabilistic process accurately, but no single technique has been consistently successful. There have been many studies on software effort estimation using Fuzzy or Machine Learning. For this reason, this study aims to combine Fuzzy and Machine Learning and get better results.Methods: Various methods and combinations have been carried out in previous research, this research tries to combine Fuzzy and Machine Learning methods, namely Logarithmic Fuzzy Preference Programming (LFPP) and Least Squares Support Vector Machines Machine (LSSVM). LFPP is used to recalculate the cost driver weights and generate Effort Adjustment Point (EAP). The EAP and Lines of Code values are then entered as input for LSSVM. The output results are then measured using the Mean Magnitude of Relative Error (MMRE) and Root-Mean-Square Error (RMSE). In this study, COCOMO and NASA datasets were used.Result: The results obtained are MMRE of 0.015019 and RMSE of 1.703092 on the COCOMO dataset, while on the NASA dataset the results of MMRE are 0.007324 and RMSE are 6.037986. Then 100% of the prediction results meet the 1% range of actual effort on the COCOMO dataset, while on the NASA dataset, the results show that 89,475 meet the 1% range of actual effort and 100% meet the 5% range of actual effort. The results of this study also show a better level of accuracy than using the COCOMO Intermediate method.Novelty: This study uses a combination of LFPP and LSSVM, which is an improvement from previous studies that used a combination of FAHP and LSSVM. The method used is also different where LFPP produces better output than FAHP and all data in the dataset is used for training and testing, whereas in previous research it only used a small part of the data.
Remote distribution of the quantum coherence resource through optical fiber

As one of the most remarkable features of quantum mechanics, quantum coherence is regarded as an invaluable quantum resource in quantum information. Using the optical fiber as one of the effective quantum channels, the remote distribution of quantum coherence through fiber is essential for potential practical applications. Here we propose the remote distribution scheme of the quantum coherence resource through an optical fiber. The two-mode squeezed state (TMSS) generated by the nondegenerate optical parametric amplifier is distributed through optical fibers, and the quantum coherence of the TMSS is analyzed by the relative entropy of the covariance matrix. We show that quantum coherence is robust against the distribution distance even if the entanglement disappears at short distribution distance due to the loss and excess noise in fiber channels. Our results provide a direct reference for the potential practical applications of quantum coherence.
A privacy-enhanced federated learning model training method

Federated learning is a new privacy protection framework for machine learning. The central server aggregates multiple participants to decentralized optimized parameters, then distributes the generated model to the client, and finally converges the global model. The model obtained by performance approaching centralized data training is trained under the condition that the data is not leave local. However, many studies have shown that this centralized federation system is vulnerable to confidentiality attacks by "honest but curious" attackers, using the gradient parameter information transmitted during federation model training to carry out reconstruction attacks or inference attacks, obtain the privacy data of participants or deduce some member information, which poses a severe challenge to the privacy protection of federated learning. In this paper, a hybrid defense strategy based on confusion self-encoder combined with localized differential privacy is proposed. On the one hand, the labels of the participants' local data are confused through the self-encoder network, so as to cut off the relationship between the gradient information and the original data. On the other hand, the localized differential privacy mechanism is used to disturb the transmitted gradient parameter information, and a model performance loss constraint mechanism is designed to reduce the impact of noise addition on the model performance. Experiments show that the hybrid defense strategy proposed in this paper can effectively resist reconstruction attacks and inference attacks in the process of federated learning model training, and achieve a better balance among computing overhead, model performance and privacy security.
Personality Prediction Based on Facebook Media Social Status Using the Method Naïve Bayes and KNN

Social media is now almost a part of life for people of almost all ages with diverse personalities. This study aims to predict a person's personality based on Facebook social media status using the Naïve Bayes algorithm and K-NN personalities based on the big five. The data taken from MyPersonality which is divided into 40% training data, and 60% test data produces an accuracy rate of 100%, 100% precision, 100% recall can be concluded that the prediction variables are (significant) with the Naive Bayes method. While predictions using the KNN method showed an average personality prediction value with an accuracy of 58.96% (Insignificant), precision 99.12% (Significant), and recall 2.34% (Insignificant). Based on research can be concluded that both Naïve Bayes and KNN methods can be used to predict personality based on Facebook status.
Podučavanje vizualizacije podataka i osnovnih vještina izrade karata na Liberal Arts Collegeu

Communicating data has become vital in an increasingly data-dependent society. Because data are often communicated graphically, many colleges and universities are offering courses that teach the principles of data visualisation. In this article, the author reflects on his experience of teaching such a course at a liberal arts college. The course introduced students to the tidyverse suite of packages for the programming language R, including the data visualisation package ggplot2, which implements Wilkinson’s (2005) ‘grammar of graphics’. The packages sf and tmap were used to extend the capabilities of the tidyverse packages for analysing geospatial data and producing thematic maps. This article provides insights into effective teaching strategies, including intended learning outcomes, the core syllabus, pedagogy and assessment. Sample tasks and visualisations are also presented to demonstrate how essential cartographic skills were imparted to students.
Proposed Marketing Strategies for Increase Sales by Increasing Purchase Intention for Vokraf Classes

Abstract : Industry 4.0 may result in the loss of 35% of jobs over the next five years due to the greater utilisation of digitization, as stated by Monovatra Predy Rezky (2019). It is possible that this number will approach 75% within the next ten years. The replacement of human labour by automated technologies and ever-more complicated algorithms is happening at an alarming rate. Because of this, the production process will become more efficient, and there will be a lower need for people in the distribution phase. The transition of industries requires the teaching and retraining of human resources in order to be successful. The technologies of Industry 4.0 require a specific set of skills and areas of knowledge from its users. Vokraf solves these issues by offering lessons in creative sectors to solve individual issues, give possibilities for potential students, and improve online education systems. The purchase intention of Vokraf has been hampered by poor marketing and promotional efforts. It’s possible that the platform is not reaching its intended audience, which may discourage course enrollment. The objective of this research is to determine the factors that influence purchase intent for this company and to recommend marketing strategies that will increase sales of Vokraf class. This study will include both quantitative and qualitative research techniques. As part of the quantitative techniques, a questionnaire will be sent to the respondent who could end up being a customer, the results of this survey questionnaire as supporting data for the author for the study, which was preceded by literature data and internal and external analysis. The result of this research Vokraf purchasing intention factors. Brand Awareness, Advertising, and Sales Promotion strongly affect purchasing intention. Vokraf’s online educational platform must leverage its knowledge to deliver courses that satisfy its target audience’s demands and interests for brand promotion. Vokraf’s online educational platform must meet its target audience’s needs for brand promotion. Vokraf may adjust its social media strategy to its brand, market, and advertising goals. Showcase Vokraf’s engaging personalised lessons. Vokraf may offer many payment and pricing tiers for different consumer groups and budgets to lessen customer financial strain for sales promotion. Vokraf sells personalised education via social media. Cost and payment options allow Vokraf to enrol more students. As internet infrastructure improved, Vokraf increased their online advertising reach. Affordability may diminish market competition. Specialists and customised training help Vokraf compete with other platform.
High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus

Currently, a significant portion of published research on online hate speech relies on existing textual corpora. However, when examining a specific context, there is a lack of preexisting datasets that include the particularities associated with various conditions (e.g., geographic and cultural). This issue is evident in the case of online anti-immigrant speech in Mexico, where available data to study this emergent and often overlooked phenomenon are scarce. In light of this situation, we propose a novel methodology wherein three domain experts annotate a certain number of texts related to the subject. We establish a precise control mechanism based on these annotations to evaluate non-expert annotators. The evaluation of the contributors is implemented in a custom annotation platform, enabling us to conduct a controlled crowdsourcing campaign and assess the reliability of the obtained data. Our results demonstrate that a combination of crowdsourced and expert data leads to iterative improvements, not only in the accuracy achieved by various machine learning classification models (reaching 0.8828) but also in the model’s adaptation to the specific characteristics of hate speech in the Mexican Twittersphere context. In addition to these methodological innovations, the most significant contribution of our work is the creation of the first online Mexican anti-immigrant training corpus for machine-learning-based detection tasks.
Affordance-based approach towards understanding user preconceptions of XR-based technical instructions

&lt;p&gt;The TIER model is a designer tool for designing technical instructions for extended reality (XR) environments. This article reports a pilot study towards validating the model with user preconceptions towards XR-based technical instructions. Understanding preconceptions is important in user-centered design. The pilot test entails the presentation of instructions for a LEGO construction task via video and VR. The results indicate interest and willingness to test XR-based solution, but reluctance for regular use. The TIER model showed how the model supported the process of recognizing preconceptions and operated as a tool for an organized design process of XR-based technical instructions. &lt;/p&gt;
Information technologies in the education of contemporary China: reality and opportunities

With the rapid development of modern information technologies, their integration into all areas of education is accelerating, which contributes to the penetration of innovations into the education space. The author emphasizes that the design of many educational technologies was focused on the goals of education and included a sufficient number of educational concepts and elements. However, if teachers blindly adhere to the traditional concept of “technology is only a tool”, then this creates a sense of superiority of their own status and a narrow understanding of the value of technology.&#x0D; The author relies in his research on the latest achievements in the field of not only high technologies that are used in education, but also on psychological and social research on the impact of the latter both on education and on a person. The author proposes to perceive technologies in the context of a new culture, and shows possible ways of their more adequate use. Working with artificial technological objects is not only the acceptance of an artificial object, but also the adoption of a technical culture, which indicates a new cultural relationship between people and technology, and vice versa. On the basis of analyzing the phenomenon and essence of contemporary information technology, the author of this article tries to find out the problems existing in the application of contemporary information technology in education through a questionnaire survey, and reflects on the influence of technology on teachers and students in the education system.&#x0D; The answers of the survey participants made it possible to present a picture of the use of information technologies in education as a holistic and complex process. The analysis of the results obtained, as well as everyday practical training in the skills of using high technologies, enabled the author to establish that the influence of information technologies on education is complex, this is a new stage in the development of education as a whole, which allows us to characterize the process of their use as a holistic system. The influence of technology has completely changed all structures and all participants in education. The results of the survey also made it possible to identify a number of problems that indicate shortcomings in the modern space of education, the didactic nature of learning in the sense of conveying and receiving information in new conditions (we are talking not only about digitalization as a process, but also about changes aimed at transforming teaching methodology).
INVESTIGATING STUDENTS’ SPEAKING FEARS IN MAKING ENGLISH VIDEO PROJECT

Speaking skills are one of the most productive language skills. Where the speakers will produce an utterance through the means of speech. In the implementation of learning, sometimes there are still many students who feel afraid to speak English, even though speaking ability is very important to create good two-way communication. This study aims to investigate the speaking fears that students experience when making English video projects. The method used in this research is descriptive-qualitative. The participants in this study were 35 high school students who learned English through an English video project. The data were obtained through direct observation and interviews with several students regarding the fears they experienced when making an English video project. Data analysis through data reduction, data display, and conclusion. The results of the study show that there are still many students who feel afraid when speaking English and making English videos. The factors that influence this fear include background knowledge, messy grammar, embarrassment, and a lack of confidence, as well as feelings of pressure when practicing conversations in English through making English videos.
Predicting Learning: Understanding the Role of Executive Functions in Children's Belief Revision Using Bayesian Models

There are many processes that support scientific reasoning. For example, one recent finding is that when children are prompted to engage in predicting an event’s outcome, learning is boosted as compared to learners who are prompted to evaluate it post hoc. One possible explanation for this “prediction boost” is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for individuals with still-developing executive functions. In this paper, we combine multiple analytic approaches to investigate the potential role of executive functions in elementary school-aged children’s science learning. We present an experiment that investigates children’s science learning during a water displacement task where a “prediction boost” had previously been observed – children either made an explicit prediction or evaluated an event post hoc (i.e., postdiction). In this paper we consider the relation of executive function measures and learning, which were collected following the main experiment. Via mixed effects regression models, we find that stronger executive function skills, (i.e., stronger inhibition and switching scores), were associated with higher accuracy in Postdiction but not in the Prediction Condition. Using a theory-based Bayesian model, we fit children’s individual performance on the learning task (capturing “belief flexibility”), comparing this “flexibility” to the other measures to understand the relationship between belief revision, executive function, and prediction. Children in the Prediction Condition showed near ceiling “belief flexibility” scores, and significantly higher than children in the Postdiction Condition. We also found a significant correlation between children’s executive function measures (as given by inhibition and switching in the posttest) to our “belief flexibility” parameter, but only for children in the Postdiction Condition. These results indicate that when children are providing responses post hoc, they may require stronger metacognitive capacities to navigate the learning task. Additionally they suggest that, especially for individuals who might otherwise struggle due to still-developing executive functions, the prediction boost is explained by increased flexibility in the belief revision process.
CODE-SWITCHING AND CODE-MIXING IN ELT

The study aimed to analyze Code-switching and Code-mixing between English and Indonesian language used by the lecturer in teaching the subject Communicative English Grammar for the second semester students of English education department of STAI DDI Pinrang.The method applied was qualitative descriptive. The data source was the utterances of the lecturer in teaching Communicative English Grammar. The data were collected by recording what the lecturer uttered in teaching-learning process and interviewing the lecturer to gain information about the functions of switching and mixing his code in teaching-learning process. Then, the collected data were classified and analyzed based on the types of code-switching and code-mixing and the functions based on the theory of Hoffman. The results showed that:1)The types of code-switching used by the lecturer of Communicative English Grammar in teaching and learning process were: inter-sentential, intra-sentential, and emblamatic/tag switching, and establishing continuity with the previous speaker. while for the types of code-mixing were: intra-sentential code mixing, intra-lexical code mixing, and involving a change of pronunciation; 2) The Functions or reasons of using both code-switching and code-mixing by the lecturer of Communicative English Grammar were: Repetition used for clarification, Intention of clarifying the speech content for students, and interjection. The findings implied that both code-switching and code-mixing were used by the communicative English grammar lecturer in English teaching-learning process, so it showed that both code-switching and code-mixing are necessary to facilitate ELT particularly in complicated subjets like grammar. In other words, the lecturers should consider the usage of both of them to meet the objectives of ELT. Keywords: Code-Switching, Code-Mixing, English Language Teaching
Magnetic Omnidirectional Wheel for Ferromagnetic Surface Cleaning Robots

Robots for cleaning/inspecting surfaces ferromagnetic are used to guarantee safety and speed. The robot’s versatility is by using magnetic fields to obtain adhesion on a ferromagnetic surface. However, there are limitations to the magnetic force and maneuverability of the robot. This article aims to present the development of a magnetic mecanum omni wheel, using additive manufacturing to generate extra magnetic force to assist robots in adhering to ferromagnetic surfaces with high maneuverability. For this, we realized studies about the magnetic arrangement, topological optimization, structural analyses, and practical tests to determine the magnetic force until obtaining the final wheel concept.