Reviewerly/exHarmony huggingface dataset:
Implementasi Metode Time Series Dalam Forecasting Penggunaan Satusehat

Since the Covid-19 pandemic occurred in Indonesia, the government has made new innovations in the field of technology to help the government and society monitor the spread of Covid-19 in Indonesia. This innovation is the PeduliLindungi application, which has tracking and other features that people always use. The influence of PeduliLindungi has become an obligation for the Indonesian people, but since the endemic, the PeduliLindungi application has changed to the SatuSehat application which functions to integrate Indonesian people's health data. The change in the application from PeduliLindungi to SatuSehat reduced the level of application usage, so this research aims to determine the enthusiasm of the public in using the SatuSehat application as a health platform provided by the government in Indonesia. The research was carried out by predicting the number of users of the SatuSehat application based on user data from October 2022 to February 2023. The forecasted data determined the number of users in the next seven months. The research used the Time Series method with a Single Moving Average (SMA) and Single Exponential Smoothing (SES). The research results are in the form of forecasting calculations used to determine the level of enthusiasm of the community in accepting and using the SatuSehat application. After calculations, it was discovered that the research results showed that SatuSehat application users had decreased from March 2023 – September 2023. Error Forecasting showed that the accurate method for forecasting calculations for SatuSehat application users was the SMA method, which obtained MAD calculation results of 1,895,828,857 and MAPE 8.66%.
Bilingual Lexical and Conceptual Memory

Because the majority of the world’s population speaks more than one language, in this chapter we consider that a “bilingual mental lexicon” can be viewed as the default, which may be a more accurate way of exploring lexical and conceptual memory. We transition to examine how concepts and words are represented in the mind, with a particular emphasis on the dynamic, developmental nature of word-to-concept mapping and the distributed, overlapping characteristics of semantic representations. Against this background, we review several theoretical models of word processing – including word recognition, production, and translation. The first set of models includes the Word Association Model and the Concept Mediation Model. We then discuss another set of models that offer a more detailed account of the conceptual system by emphasizing the degree of overlap that exists between conceptual representations in the two languages. In the last section of the chapter, we review two localist-connectionist models: the BIA/BIA+/BIA-d and the Multilink Models.
New Approach for the Probabilistic Assessment of Organic Matter in the Source Rocks of the Bazhenov Formation for Estimation of Shale Hydrocarbons Resources

Abstract Maps of organic matter distributions are an essential part of the basin modeling process for evaluation of conventional and unconventional hydrocarbon resources. This approach has become widespread for both assessment of the initial oil and gas generation potential and final assessment of the resource base. An important step in this method is pyrolysis of the core material, which is carried out in order to assess the geochemical parameters of source rocks. The data can be used to create maps of the distributions of geochemical parameters, such as the organic carbon content, the hydrogen index, the oxygen index, S1,S2,S3, TMax. The accepted assumption regarding the distribution of these parameters determines the entire modeling result and the final generation volumes. It is difficult to overestimate the impact of this process on the result of assessing the prospects for both traditional and shale hydrocarbons. However, a lot of uncertainty is associated with interpolation of parameters analyzed in wells into the interwell space because different interpolation methods give fundamentally different results. Deterministic interpolation methods are limited because of their nature and thus cannot be used for probabilistic estimation, so they are not considered in this work. There are two interpolation methods that can perform this process in a multivariate form: the geostatistical approach (SGS, GRFS) and the new Amazonas method, which integrates elements of artificial intellect. The stochastic approach to modeling has the undeniable advantages of estimating probabilities, and it includes uncertainties, which demonstrates the general idea more clearly. In most geology-related fields, the deterministic approach is gradually fading into the background, giving way to stochastic methods. This article considers a comparison of the SGS and Amazonas methods, their prerequisites and limitations, advantages and disadvantages using the example of a database of geochemical measurements of the Bazhenov source rock data. The results of modeling by both methods are shown in this work along with the issues of statistical stability of the results. The geostatistical approach in interpolation has been used for decades. It has a simple and understandable operating principle, but there are several fundamental limitations. These limitations are associated with the assumption of stationarity of the random variable under study. The proposed Amazonas method also offers a simple and understandable way to calculate the quantity being analyzed at each point in space. However, the latter does not have the limitations of the geostatistical approach. Besides, this method does not require the condition of stationarity for the quantity being analyzed and is also capable, depending on the settings, of reproducing both smooth and abrupt transitions.
Sentiment Analysis of Twitter Data of Hepsiburada E-commerce Site Customers with Natural Language Processing

Social media usage has significantly increased, allowing people to freely express their wishes and complaints through these platforms. Consequently, this has caused a significant increase in data, providing information about users, companies, services, and products. However, making sense of this data with human effort is impossible, necessitating various methods. Sentiment analysis is one such method that helps us understand customers’ thoughts on products and companies. In this study, the emotions of e-commerce site users were analyzed using Turkish Twitter data. Text mining techniques were applied to Twitter data, which were analyzed and classified as positive and negative. It was found that 66.9% of the tweets were negative, and 33.1% were positive. The classification results were evaluated using precision, Recall, and F1 criteria. As a result of the evaluation, the sensitivity criterion for negative comments was 94%, and the precision criterion for positive comments showed the highest value with 86%. When looking at the F1 score, 85% for negative comments and 69% for positive comments were calculated. The accuracy rate of the model was found to be 79%.
Ontology-Based Food Recommender System for Nutrition in School-Age

Nutrition plays an important role in the body and child development. Therefore, it is very important for parent to understand the nutritional needs of children to grow healthy and smart. If nutritional intake is not met, malnutrition can occur in children it interferes whit their growth and development process. The food recommendation system in this study is based on knowledge modeling. The focus of the research is to develop a recommendation system using ontology with Semantic Web Rule Language (SWRL) and form a knowledge base according to the guidelines proposed by Recommended Nutrient Intakes (RNI). Additionally, an Artificial Intelligence (AI) telegram chatbot named NutritionChildreBot was developed for this purpose. The recommended food menu is following the nutritional needs of children aged 7-9 years. The acquired knowledge base will be managed to provide information to users. The results of this research evaluation are in the form of recommendations for selecting foods that meet children’s nutritional needs based on information obtained from reliable sources.Based on this value, the calculation of precision, memory, and F_Score obtained is 97,9% of the accuracy of the results recommended by the system
Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique

This research project aims to improve IT support efficiency at Indonesian company XYZ by using AI-based IT support ticket classification integration. This method involved collecting over 1,000 support tickets from the company's IT ticketing system, GLPI, and pre-processing the data to ensure the quality and relevance of the data for analysis. Claims data is enriched with relevant features, including textual information and categorical attributes such as urgency, impact, and requirement expertise. To improve the ticket preference matrix, AI-based language models, especially OpenAI's GPT-3, are used. These templates help to reclassify and improve the work of IT support teams. In addition, the ticket data is used to train the Random Forest classifier, allowing automatic classification of tickets based on their specific characteristics. The performance of the ticket classification system is evaluated using a variety of metrics, and the results are compared with alternative methods to assess effectiveness. of the Random Forest algorithm. This evaluation demonstrates the system's ability to correctly classify and prioritize incoming tickets. The successful implementation of this project at Company XYZ is a model for other organizations looking to optimize their IT support through AI-driven approaches. By providing simplified ticket classification and admission ticket reclassification based on AI algorithms, this research helps leverage AI technologies to improve IT support processes. Ultimately, the proposed solution benefits both support providers and users by improving efficiency, response times, and overall customer satisfaction.
An Image Steganographic Scheme Based on Edge Detection and Least Significant Bit Substitution

&lt;p&gt;This work presents a steganographic scheme based on Laplacian-of-Gaussian (LoG) edge detection and least significant bit (LSB) substitution. The cover image is first divided into continuous and non-overlapping 4&amp;times;4-pixel blocks. The pixel at the top left corner of each block (first pixel) is defined as the reference pixel. After the LoG edge detection, the blocks are classified as edge or non-edge blocks, and this information is embedded in the reference pixels. Each non-reference pixel is then embedded with 5 bits and 4 bits of cipher text if it belongs to an edge block or non-edge block, respectively. Compared to the method of Tseng and Leng, Bai et al., and Ghosal et al., proposed method increased the capacity by 39.6%, 7.3%, and 42.7%, respectively, in the &amp;ldquo;Lena&amp;rdquo; cover image. To test the generalizability of our method, an embedding capacity and image quality test were conducted using 10,000 512 &amp;times; 512 sized greyscale images from the BOSSBase dataset. Compared to the aforementioned previous methods, our method improved the capacity by 33.9%, 2.7%, and 36.1%, respectively, while maintaining an acceptable stego-image quality. Finally, proposed method can resist the detection of RS, pixel difference histogram analysis and second order SPAM features.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
FIVE KINDS OF IMMORTALITY

The author develops the idea that ancient Greek philosophy begins with attempts of the first theorists, especially Plato, to prove the immortality of the soul. For them, this meant, above all things, justifying that a person cannot escape moral responsibility or punishment for his wrongdoings. The author compares this kind of immortality, or this theory of immortality, to the ancient Greek concept of earthly immortality of the name. If a Greek had not achieved his glory in the creative realm of freedom (in politics, war, sports, knowledge, and arts), he did not get beyond the limits of his bodily existence. The first kind of immortality is also discussed in terms of Pla to’s moral pessimism, while the second kind of immortality is discussed in terms of moral optimism. Both kinds are considered in terms of the philosophical response to the question of a good and happy life. The third kind of immortality is defined by the author as Faustian immortality in the Modern Age. The concept of this kind of immortality is explained in terms of the Faus- tian individual, that is, based on J. W. Goethe’s idea of self-realization of personality through the activities of their native community. This kind of immortality is contrasted with the concept of escapist immortality, described by Russian artists, especially A. S. Pushkin and M. A. Bulgakov, as the greatest achievement of a modern creative person in the conditions of social unfreedom. Lastly, the concept of medial immortality is defined as a postmodern idea that the desired uniqueness of a personality is affirmed by its social acknowledgment via contemporary digital communication means, that is, medial means. The author’s main text contains important references to the Additions placed after the main text. To understand the dialogic nature of the research, Additions should be read alongside the main text, rather than after it.
Phase retrieval for single-frame interferogram with an irregular-shaped aperture based on deep learning

This paper proposes a high-precision phase retrieval method based on deep learning to extract the Zernike coefficients from a single-frame interferogram with an irregular-shaped aperture. Once the Zernike coefficients are obtained, the phase distribution can be retrieved directly using the Zernike polynomials. For many apertures, the root mean square (RMS) of the residual wavefront between the true and estimated wavefronts reached the order of 10-3 λ. Simulations were conducted under different noise conditions, indicating that the proposed method has high measurement accuracy and robustness. Experiments demonstrated that the accuracy achieved by this method was comparable to that of commercial phase-shifting interferometers. We believe that this method is useful for measuring optical surfaces with irregular apertures.
THE MAIN DIRECTIONS OF IMPROVING THE USE OF THE SUPER SERVICE “ADMISSION TO THE UNIVERSITY ONLINE” IN THE WORK OF THE ADMISSIONS COMMITTE OF KAZAN STATE AGRARIAN UNIVERSITY

Digital transformation is increasingly affecting various areas of higher education. Digital services and tools significantly change the principles of interaction between the admissions committee and the applicant, the teacher and the student, the higher education institution and the regulatory authorities. The process of introducing the super service “Admission to university online”, which began in 2019, was supposed to change the mechanism for submitting applications to universities. It was assumed that applicants would be able to upload the necessary documents to the service and enroll in the selected educational institution without the need for a personal visit. In the future, the super service was supposed to replace traditional forms of filing documents. However, by 2022, the submission of paper documents directly to the university is still the main and most popular mechanism for interaction between the applicant and the admissions committee. This work is a continuation of a study conducted since 2020 at Kazan State Agrarian University, the purpose of which is to identify the main obstacles to the effective use of the superservis “Admission to university online”. The paper presents and compares with last year’s results the data of a newly conducted survey of applicants who visited the university in person. The identified main reasons for refusing to use the digital service are the same, but the share of applicants who used this mechanism for submitting documents to the university is growing. The result of the study was proposals for the university’s admissions committees to more effectively implement digital tools and obtain the necessary competencies when interacting with applicants through superswervis. The proposed activities include the use of educational computer classes to access the super service, online counseling of applicants by university teachers, a chatbot for automatic answers to the most popular questions and the holding of various competitions and quizzes to raise awareness of the possibilities of the super service in social networks and in career guidance work. The authors' proposals should help the admissions committee to increase the level of customer focus and improve the image component of Kazan State Agrarian University.
Bildungdigital21

A new direction for the German digital education strategy? The German government has organized on the 22nd of February 2021 an online-event regarding a future strategy on digital education.
The Effectiveness of Online Speaking Tasks (OST) on English Communication Ability of Thai EFL Undergraduates

The purpose of this study was to investigate the effectiveness of online speaking tasks (OST) on the English communication ability of Thai EFL undergraduates. The study further examined students’ perspectives toward online oral presentation tasks. A questionnaire translated into the participants’ native language was utilized and distributed to collect data during the pandemic. The data were analyzed and interpreted using descriptive statistics, t-tests, and exploratory factor analysis (EFA). Four hundred and eighty-two students from Prince of Songkla University and Walailak University participated in this study. The study reveals that the use of technology is key to improving students’ English communication skills. Discourse analysis further showed that oral presentation tasks helped students improve their English communication skills. Pronunciation activity was the most difficult task students faced. It is strongly recommended that online learning through technology could be incorporated to complement the in-person learning environment.
Effects of Teacher and Student Relationships to Enhance the Learning Process

Teacher-student relationships are the keys to the success of both teachers and students. As part of classroom management, such relationships are the most important factors in determining whether teachers work as successfully. Library research deign is used in this research, to fill the gap the researchers reviewed (11) reliable research articles and (3) academic books. In this study, research population has not chosen because it has done based on library research method. The result obtained in this research by the library research method as a survey is as follow, which are the answer to the research questions. To have good and successful teaching the teachers should have good relationships with students in learning process. Research shows that, strategies teachers use, makes students feel engaged and stimulated to participate in the learning process. The study shows that, student’s positive relationship with their teachers positively impacts their interest and motivation in school which contributes to the enhancement of the learning process. Thus, researchers have found that, if teachers have good rapport with their students it effects on students learning process positively.
Unveiling unmasked faces: A novel model for improved mask detection using haar cascade technique

In response to the urgent need to enforce mask-wearing compliance during the COVID-19 pandemic, this "Face Mask Detection" project introduces a robust model for identifying individuals not wearing face masks in videos. Leveraging computer vision's Haar Cascade technique, the project achieves rapid face detection within video streams, facilitating accurate mask usage assessment. This initiative holds paramount importance due to the pivotal role of masks in curbing virus spread. The model finds practical applications in monitoring mask adherence in public settings, pinpointing potential COVID-19 hotspots through data analysis, and bolstering safety via integration into surveillance systems. By effectively addressing the intricate challenge of precise mask detection, this project makes significant contributions to public health endeavors and the mitigation of COVID-19 hazards. The proposed algorithm showcases remarkable performance across various metrics. With an impressive detection rate of 98.4%, it surpasses established methods such as CNN (91.26%), PCA+SVM (93.4%), and Adaboost (96.1%), signifying its potential to revolutionize mask detection technology.
Ethnopsychological Profile of a Chinese Students of Music Education as a Factor of Optimizing Academic Interaction

Russian university education is currently very popular among Chinese citizens, who often experience various adaptation problems with academic programs for joint education of Russian and Chinese students. In such cases, effective academic process and pedagogical interaction depend on the correct understanding of the ethnopsychological characteristics of foreign students. This article reviews scientific publications on the ethnopsychological profile of Chinese students, as well as introduces relevant pedagogical strategies of academic and pedagogical interaction in teaching Chinese students that major in musical studies in Russian institutions of higher education. The author conducted a survey of Chinese students and Russian faculty members at the Herzen Russian State Pedagogical University. The resulting principles of pedagogical interaction with Chinese students can facilitate the academic process in a Russian music conservatory.
Penggunaan Media Pembelajaran Yang Efektif Secara Daring pada Mata Pelajaran Rangkaian Listrik

In the present time, online-based learning has become an integral part of modern education, thanks to the advancements in information and communication technology that cannot be denied. However, there are several challenges that have emerged, particularly in the past two years. The global COVID pandemic has necessitated the implementation of online learning. In designing this form of learning, there are several factors that need to be considered. This article will discuss the effectiveness of online learning.
The Application of Augmented Reality Platform for Chemistry Learning

In this project, a new learning platform was developed. This project uses the technology of Augmented Reality (AR) to develop a graphic animation platform for learning purposes. By applying virtual objects with animations for learning, it helps to engage users and increase their learning efficiency. Besides, the AR platform combines hardware (i.e.s smartphone, headset, and target marker) and software (i.e., Unity, Vuforia, and C#) for the operation. The AR application allow the interaction of the virtual objects by grabbing target marker toward each other or touching the virtual button in real environment. This can increase the immersion and interaction of the user when learning chemistry. In additional, the “Chemical Learn” experiments was designed to study the effectiveness of the AR platform compared to traditional platform as well as to conduct user satisfaction surveys. The overall results showed that the AR learning platform can improved learning efficiency of users in chemistry compared to traditional learning methods. Moreover, users who participate in the survey are generally satisfied with the AR. It is believed that with the rising trend of technology, it is only a matter of time before people become familiar with the AR platform.