Reviewerly/exHarmony huggingface dataset:
Increased Knowledge of Literacy of Latin Letters and Reading the Qur'an of Housewives in Barru Regency

Some of the partner problems that are the focus of PKM activities are among others; The existence of limitations in reading and writing Latin letters in this case IRT is illiterate. The second problem is the lack of knowledge of Reading the text of the Qur'an. The existence of this problem, the solution offered in this PKM activity is: increasing the knowledge of literacy of Latin letters of Housewives in Barru Regency. increase knowledge about reading the text of the Quranic scriptures, increase the ability to think in IRT in Barru Regency, increase knowledge in science and technology and art at IRT in Barru Regency, increase knowledge of religion at IRT in Siawung Village, Barru Regency, Increase knowledge about the importance of innovation in IRT in Barru Regency.&#x0D; The solution that occurs can be eliminated through IRT training and assistance activities in Barru Regency through activities to increase knowledge of Latin letter literacy and Quranic literacy, the focus of this activity is focused on Siawung Village, Barru Regency, Barru District, Barru Regency.&#x0D; The method used is the participatory method. This method is implemented by involving all members of partner groups, village governments, and the relevant local governments and several community leaders. It is hoped that they will be able to read and write the Quran, so that the IRT in Barru Regency can understand the reading of the Quran.&#x0D; The results of this PKM show that the increase in knowledge of reading Latin letters and the Quran in IRT in Barru Regency through PKM activities has given understanding to IRT in Barru Regency. IRT has been able to recognize letters, recognize syllables, recognize words, and recognize sentences. IRT has been able to write identities, so pkm activities carried out in Barru Regency have succeeded in increasing knowledge of literacy of Latin letters and the Quran.&#x0D; &#x0D;
The Politics of Sampling in the Age of Machine Learning

In this essay, Eduardo Navas considers the political implications of sampling as a cultural variable in relation to aesthetics and labor in juxtaposition with the emergence of machine learning and artificial intelligence. Navas reflects on how automated and self-training forms of production are reshaping the creative possibilities in music and culture. A key issue that he evaluates is how self-training algorithms are repositioning the creative process as a political trope part of meta-creativity.
Data-centric UML profile for agroecology applications: Agricultural autonomous robots monitoring case study

The conceptual design of information systems is mandatory in several application domains. The advent of the Internet of Things (IoT) technologies pushes conceptual design tools and methodologies to consider the complexity of IoT data, architectures, and communication networks. In agroecology applications, the usage of IoT is quite promising, but it raises several methodological and technical issues. These issues are related to the complexity and heterogeneity of data (social, economic, environmental, and agricultural) needed by agroecology practices. Motivated by the lack of a conceptual model for IoT data, in this work, we present a UML profile taking into account different kinds of data (e.g., sensors, stream, or transactional) and non-functional Requirements. We show how the UML profile integrates with classical UML diagrams to support the design of complex systems. Moreover, We prove the feasibility of our conceptual framework through a theoretical quality assessment and its implementation in the agroecology case study concerning the monitoring of autonomous agricultural robots.
USING WHATSAPP AS A LEARNING MEDIA IN TEACHING WRITING

This paper discusses about the use of WhatsApp application as a learning media in teaching writing at the eleventh grade students of SMAN 2 Bone academic year 2020/2021. The study aimed to find out the teacher implementation of using the WhatsApp application in teaching writing at the eleventh grade of SMAN 2 Bone. In addition, also to find students' perceptions in using the WhatsApp application in teaching writing at the eleventh grade of SMAN 2 Bone. This research includes qualitative research with descriptive qualitative. Sampling in this research using purposive sampling technique, where the sampling technique was to took one teacher and ten students. The data was collecting by observation and interview. This research researcher analyzed data by reduction, display and drawing conclusions/verification. The result of the research that the teacher implementation used of WhatsApp application in teaching writing was carried out by utilizing the features of the application, however the use of the WhatsApp application was not optimal because it was recommended for teacher to make learning variations to attract students’ interest in learning. According to students the used of this application can help in collecting assignments easily.
Polyglot, Label-Defined Dynamic Taint Analysis in TruffleTaint

Dynamic taint analysis assigns taint labels to sensitive data and tracks the propagation of such tainted data during program execution. This program analysis technique has been implemented in various analysis platforms targeting specific programming languages or program representations and has been applied to diverse fields such as software security and debugging. While some of these platforms support customization of their taint analysis, such customization is typically limited to certain analysis properties or to predefined options. This limitation can require analysis developers to modify the analysis platform in order to adapt other analysis properties or to implement new taint analysis applications.
Displaying Data

In the previous chapter, we created our data source, seeded the data, and created two functions to retrieve both a list of blog post summaries and a single, complete blog post. Now to make the front end of the site do something useful!
Development Student Worksheets (LKPD) Based on Contextual Class VII Private Junior High Schools Tiara Deli Serdang

This research aims to know (1) Student Worksheet Development (LKPD) of Contextual based social studies learning that is linked is suitable for use in grade VII students of SMP Swasta Tiara, (2) To find out the Student Worksheet Development (LKPD) contextual based social studies learning that is linked is effective for grade VII SMP Swasta Tiara.This research was conducted at Junior High School (SMP) Swasta Tiara at Deli Serdang.Research on the development model used is the Dick and Carey model.For sample research trials of individuals as much as 3 students, small groups of trials as much as 9 students and a sample test as many as 32 students.Trial results are obtained from the learning design expert 2 people, 2 people, media expert and 2 expert material.The results showed (1) teaching materials of Student Worksheet Development (LKPD) Contextual based social science learning is appropriate for use in social science subjects, (2) The use of teaching materials used in the learning process of social science with a count of tcount = ttable > 4.56 = 4.42, (3) Social Science Learning outcomes of class VII students with economic material using Student Worksheet Development (LKPD) teaching materials are higher than student learning outcomes using Ordinary Learning
Research on Talent Classification Based on Improved KNN Algorithm

With the rapid increase in the scale of colleges and universities, it is necessary to effectively classify the comprehensive quality of students, conduct stratified teaching for students, and apply KNN to the talent training in colleges and universities to provide support for the classified teaching of talent training in colleges and universities. However, the sample distribution of KNN algorithm is uneven or the sample size is too large, which affects the accuracy and efficiency of the algorithm. To solve this problem, the KNN algorithm is improved, the rough set upper and lower approximation concept is introduced to define the upper and lower approximation space of samples, and the KNN adaptive weighted classification decision function is proposed to improve the classification accuracy and efficiency. Experiments show that this method not only ensures the classification accuracy, but also improves the classification efficiency.
Psychological Characteristics of Primary School Children

The article is devoted to some methods of work through which it is possible to implement a personality-oriented approach to learning.
Deteksi Uang Palsu Rupiah dengan Menggunakan Metode Deteksi Tepi Laplacian of Gaussian (LoG) dan Algoritma K-Means Clustering

Abstract. Detection of Counterfeit Rupiah Using the Laplacian of Gaussian (LoG) Edge Detection Method and the K-Means Clustering Algorithm Counterfeit money is a severe problem that is increasing in every country. The reason is the ease of getting information on making counterfeit money and the development of technology such as color printers. This study used data from 20 images of authentic rupiah banknotes and 20 photos of fake rupiah banknotes. Data analysis in this study consisted of four stages: reading the image, converting the image to grayscale, image segmentation, and grouping image values. The dataset of real money images were taken with a cellphone camera, while counterfeit money images were obtained from the website. After the dataset retrieval process, the image conversion process was carried out into a grayscale image; then, the image segmentation process proceeded. The conclusion obtained from this study is that edge detection with Laplacian of Gaussian combined with the K-Means Clustering algorithm is quite effective in detecting an image to determine the picture as whether real money or counterfeit money.Keywords: Counterfeit Money, Laplacian of Gaussian, K-Means Clustering. Abstrak. Uang palsu adalah masalah serius yang semakin meningkat di setiap negara. Penyebabnya ialah kemudahan mendapatkan informasi cara pembuatan uang palsu serta perkembangan teknologi seperti printer warna. Penelitian ini menggunakan data 20 gambar uang kertas rupiah asli dan 20 gambar uang kertas rupiah palsu. Analisis data pada penelitian ini terdiri dari empat tahap, yaitu membaca gambar, mengubah gambar menjadi skala abu-abu, segmentasi gambar, dan pengelompokan nilai citra. Pengambilan dataset berupa uang asli dilakukan dengan kamera handphone dan gambar uang palsu didapatkan dari website. Setelah proses temu kembali dataset, dilakukan proses konversi citra menjadi citra grayscale, kemudian dilakukan proses segmentasi citra. Kesimpulan yang diperoleh dari penelitian ini adalah deteksi tepi dengan Laplacian of Gaussian yang dikombinasikan dengan algoritma K-Means Clustering cukup efektif mendeteksi suatu citra untuk menentukan gambar tersebut sebagai uang asli atau uang palsu.Kata Kunci: Uang Palsu, Laplacian of Gaussian, K-Means Clustering.
Discriminative Feature Learning Framework With Gradient Preference for Anomaly Detection

Unsupervised anomaly detection methods can detect product defects in industrial images by leveraging only normal samples during model training. Currently, the representation-based method, as a popular unsupervised anomaly detection method, has achieved impressive performance. Extracting valuable feature vectors that can remarkably improve the performance of anomaly detection is essential in unsupervised representation learning. To this end, we propose a novel discriminative feature learning framework with gradient preference for anomaly detection. Specifically, a gradient preference-based selector is designed to construct a representative feature repository, which alleviates the interference of the redundant feature vectors. To further make the feature vectors of normal samples adapt to the target distribution, a discriminative feature learning framework with center constraint is presented. By introducing our proposed framework, the distribution of normal samples becomes more compact. In the inference stage, the anomaly score calculated by the distance between feature vectors of test data and the normal cases is used to detect anomalies. Moreover, our method can be easily extended to anomaly localization. Extensive experiments on three popular industrial anomaly detection datasets demonstrate our proposed framework can achieve competitive results in both anomaly detection and localization. Additionally, the results on two medical anomaly detection datasets validate the generalization performance of our proposed method.
Predicting the fluctuations of the bitcoin using machine learning

Bitcoin is the most trending cryptocurrency which is used worldwide. Nowadays many general people or investors investing on bitcoin. But it becomes great challenge to analyze or predict the bitcoin price. Because of its fluctuations it is very hard to predict the price of the bitcoin. By this time machine learning came into picture with many models to analyze the behavior of bitcoin price by using time series data. These models will give better insights to the people who wants to invest on the bitcoin and they will able to understand about the volatility of bitcoin. We can use many machine learning models for prediction. But accuracy of the model is the deciding factor. We used ARIMA, LSTM and Facebook Prophet models and after the prediction is over, we have designed an ensemble model which merges the different models. And based upon the error rate we have decided the best model.
An Examination of the Experiences of Turkish ELLs about the Chatbot Apps to Learn English

In education, chatbots have been used as learning environments that provide feedback for students to repeat their knowledge. The aim of this study is to examine Turkish students’ experiences about the chatbot apps they use to learn English. The study was carried out with 21 ELLs using a chatbot app at different ages. In this study, data were collected through semi structured interviews and analyzed using content analysis. The data obtained from the participants were coded and digitized into themes. The research results show that although chatbots have some shortcomings, they are seen as a sincere friend who teaches English. Moreover, participants who stated that they were embarrassed to communicate with live people expressed that learning English with chatbot applications was more convenient for them.
Introvert Character In The Novel Titled Sin By Faradita (Carl Gustav Jung Theory)

This study aims to (1) describe the introverted personality of Raga Angkasa in the novel Sin by Faradita, (2) describe the factors that cause the introverted personality of Raga Angkasa in the novel Sin by Faradita. This study used descriptive qualitative method. The object studied in this study is the novel Sin. Data was obtained by collecting data with basic techniques, namely reading the whole novel and then determining written data, transferring it into notes, collecting library data sources and data from other sources, and reading library data sources. The theoretical reference used regarding introverted personality is Carl Gustav Jung's theory. The result of this research can be concluded that the personality of Raga Angkasa character includes thinker type, feeling type, sensation type and intuitive type. While the factors that cause introverted personality include: Activity, Socialiability, Risk Talking, Impulsiveness, Expressiveness, Reflectiveness, and Responsibility.Keywords: Introvert, Novel, Personality, Character.
Men’s Language and Masculine Identity Construction in Sexist Jokes

&lt;em&gt;The power imbalance between men and women in society is reflected in their language use and it may be reinforced in sexist jokes. This is a sociolinguistic study on men’s language in sexist jokes on the Internet. This study is aimed at investigating men’s linguistic features and the masculine identities represented by those features. This is a descriptive qualitative study with complementary quantitative analysis. The theories of Tannen’s men language (1990) and Kiesling’s masculine identities (2007) were used as references to analyze the data. The results show that men’s identity of competing for solidarity embodied in discourses of insults, teasing, and joking is the most dominant identity represented in sexist jokes. This is in line with the main intention of sexist humor to laugh at women’s inferiority. Meanwhile, men’s dominance realized in report talk becomes the second most prevalent identity. The identities of politeness oaf, indicated by direct command, and freedom, shown by swearing and taboo words, are not influential as both do not contribute to the making of humorous expressions. These imply that context and communication purposes motivate speakers to use gender-related features. Reflecting that most of the masculine identities and the language features in sexist jokes represent men’s superiority, this kind of humor can be a way to maintain gender asymmetry in society. Besides, although humor leads to laughter and is considered healthy, sexist humor is dangerous as the expressions subordinate women. Therefore, being critical when reading the jokes and trying to avoid such jokes in communication is suggested.&lt;/em&gt;
DEVELOPMENT OF STUDENT’S EMOTIONAL SELF-REGULATION SKILLS IN MUSIC-LEARNING THEORY SPECIALTIES DURING THE PROCESS OF THEIR PROFESSIONAL VOCAL EDUCATION

Тhe article illustrates issues of perspective Music-learning theory teachers’ professional education and analyzes issues of the emotional self-regulation development during their professional vocal education, investigates the specifics of student’s adaptation to the emotional conditions of the public performance. Development of the individual’s cognitive potential is closely related with its emotional aspect development. The sensual cognition underlies the mental cognition of reality. Emotions play considerable role in the educational motivation formation: it serves as an incentive and activating function which is becoming the stimulus for energy action. Development of emotional self-regulation is the important aspect in the professional education of perspective Music-learning theory teacher. However emotions often can prevent successful musical education and upbringing processes ongoing. Excessive excitement, anxiety do not allow to develop on full the potential of music-learning theory specialty students and prevents the successful presenting of their own creative activity product. Excessive emotional excitement can cause the impulsiveness of behavior, losing focus on what is going on, the contravention of analysis, learning of academic information and the process of perception thrust. Emotional self-regulation skills formation must be an important component in the professional education of perspective music-learning theory pedagogue. Among the structure of the musical-theory learning theory student’s self-regulation are the following four components: self-esteem, self-control, self-correction, self-encourage. Methodical demonstration of personal musical performing skills on public as a must for students of music-theory learning specialties during the process of their professional education contributes to forming of the emotional self-regulation skills and keeping of self-control in stressful conditions during the public performance. The newfound skills of emotional self-regulation which were achieved during the concert performance practice, presentations to the Examination Board, perspective pedagogue can successfully transmit to the situations of pedagogical issues solutions during teaching of music-theory learning in the musical school.
Blockchain-Based Data Management for Smart Transportation

Smart services for Intelligent Transportation Systems (ITS) are currently deployed over centralized system solutions. Conversely, the use of decentralized systems to support these applications enables the distribution of data, only to those entities that have the authorization to access them, while at the same time guaranteeing data sovereignty to the data creators. This approach not only allows sharing information without the intervention of a “trusted” data silo, but promotes data verifiability and accountability. We discuss a possible framework based on decentralized systems, with a focus on four requirements, namely, data integrity, confidentiality, access control, and persistence. We also describe a prototype implementation and related performance results, showing the viability of the chosen approach.
The Role of Digital Marketing in Promoting Ansoff Matrix Strategies: A Survey Study in Al-Alamiah Store in Mosul City

The topic of digital marketing is one of the contemporary topics.At the present time, a group of new and vital technologies have emerged that would contribute to the marketing of products, especially the marketing of goods in general, and the soft commodities in particular, as it has developed very much in the last two decades, especially in light of crises including the Covid-19 pandemic crisis.The current research aims to demonstrate the role of digital marketing in its dimensions in promoting strategies based on the Ansoff matrix, on the assumption that digital marketing contributes to strengthening them, and the strategies adopted on the Ansoff matrix are also affected by digital marketing.Al-Alamiah store was selected in the city of Mosul, and a sample was taken from its (50) working employees.The research reached a set of conclusions, the most important of which were: The searched store's interest in digital marketing, and the work to activate its activities across websites and electronic pages, in order to excel in promoting its strategies.The research also reached a set of recommendations, the most important of which were: Applying strategies based on the Ansoff matrix, being strategies that many international stores operate in.
A Discussion on the Scalability of Heuristic Approximators (Extended Abstract)

In this work, we examine a line of recent publications that propose to use deep neural networks to approximate the goal distances of states for heuristic search. We present a first step toward showing that this work suffers from inherent scalability limitations since --- under the assumption that P≠NP --- such approaches require network sizes that scale exponentially in the number of states to achieve the necessary (high) approximation accuracy.
Frontiers and Trends of Blended Learning Research in China Based on Visualization Analysis of CNKI Database

In this paper, CiteSpace is used to conduct metrological and visual analysis of the core journals of blended learning research (2003-2021) collected by the China National Knowledge Infrastructure (CNKI). The results show that the number of studies on blended learning in China is increasing year by year, which indicates that the research on blended learning continues to attract attention and that blended learning is not yet mature. The research hotspots include the basic theory research of blended learning, teaching design research, learning resources and platform construction, and application practice research. The research frontiers include analysis and application research based on a specific learning platform, application research of blended learning in a certain field, and instructional design research. The development trend is changing from basic theory research to teaching application practice from focusing on teachers' teaching to students' learning.