Reviewerly/exHarmony huggingface dataset:
Pendampingan Strategi Mengerjakan Soal TOEFL Bagi Mahasiswa Pendidikan Bahasa Inggris FKIP-UIR

The implementation of this community service activity aims to increase the understanding of FKIP students at the Islamic University of Riau in working on TOEFL questions. This is very much needed by students as early as possible so that they can prepare themselves to compete in the world of work later after they graduate from college. The facts on the ground show that there are many job opportunities for those who have English proficiency both orally and in writing. Therefore, the service team sees the need for educators to equip students with the ability to work on TOEFL questions, so that later they will understand how to do it and get the maximum score according to the needs of the world of work. This service is carried out in the English laboratory of FKIP UIR. The mentoring participants were 25 final semester students of the English Education Study Program FKIP UIR. With this assistance, it is hoped that they will be motivated to study TOEFL and be able to prepare themselves to compete for jobs. This service activity received a positive response from all activity participants. They said that such activities could be carried out on an ongoing basis so that they would continue to receive guidance in studying the TOEFL. Furthermore, participants are motivated to continue to improve their ability to work on TOEFL questions in order to get better job opportunities for the future.
Performance analysis of low dimensional word embeddings to support green computing

It has become increasingly important to pay attention how much energy we use to operate various Artificial Intelligence (AI) and Machine Learning (ML) systems. In order to implement environmentally responsible solutions we need to reconsider our used storage resources and computational power. Training a natural language model is a time and energy demanding process. In recent years the language models are becoming extremely large and the trend is growing. The building process of these models are consuming an extremely large amount of computational power hence these demands huge amounts of energy. In our research we trained and evaluated low dimensional word2vec embedding models and analyzed their performance on building transition based dependency parsers to show that low dimensional models are still competitive and in many use cases may be sufficient.
The Analysis Schematic Structure of Research Article from Journal Indexed by Sinta

This study analyzes the schematic structure of Sinta's indexed research articles. The object of this research is in the form of fifteen indexed research articles of Sinta ranked 1 and 2. The schematic structure of this research article is then analyzed using SLF theory because this study is a study of the SFL genre. The researcher integrates the concept of Huang &amp; Chen (2018). In conducting data analysis, this study adapted the interactive model of Miles, Huberman &amp; Saldana (2014). It was found in this study that there are two structures in the research article, namely the mandatory structure and the optional structure. There are three mandatory structures, namely Reporting Findings (RF), Interpreting Findings (IF), and Literature Compare (LC), while Accounting Findings (AF) is not a mandatory phase. The rest are optional, namely; Explanation of Context (CE), Order of Presentation (SP), Restatement Analysis Procedure (RAP), Summarizing Findings (SF), Evaluation of Findings (EF), Recommending Further Research (RFS), Providing Pedagogical Implications (PI), Providing Pedagogical Implications (ISA). The analysis yielded (RAP)^(SF)^(CE)^(SP)^[[RF.IF]n.LC]^(AF)^(EF)^(RFS)^(PI)^(ISA).
The Poetry Anthology Ar-Robi fii qalbi by Sofi Ghoniyah: A Sociology of Author Perspective

This study aims to determine the sociology of the author in the poetry anthology Ar-Robi fii Qalbi by Sofi Ghoniyah based on the perspective of Rene Wellek and Austin Warren. This research focuses on four aspects, namely (1) the social background of the author in the anthology of poetry Ar-Robi fii Qalbi by Sofi Ghoniyah based on the perspective of Rene Wellek and Austin Warren; (2) the author's social integration in the poetry anthology Ar-Robi fii Qalbi by Sofi Ghoniyah based on the perspective of Rene Wellek and Austin Warren; (3) author's economic sources in the poetry anthology Ar-Robi fii Qalbi by Sofi Ghoniyah based on the perspective of Rene Wellek and Austin Warren; (4) professionalism in the authorship of Sofi Ghoniyah's anthology of poetry Ar-Robi fii Qalbi based on the perspective of Rene Wellek and Austin Warren. This type of research is qualitative research. The data source of this research uses an anthology of poetry entitled "Ar-robi fii qalbi" by Sofi Ghoniyah. While the secondary data sources of this research were obtained from reading books or journals discussing the sociological theory of the authors Rene Wellek and Austin Warren. Data collection techniques in this study used interview techniques and note-taking techniques. The data validation technique used in this study is to increase persistence, triangulate data, and discuss with colleagues and experts. Meanwhile, for the data analysis technique, this research uses the Miles and Huberman method, namely data reduction, data presentation, and drawing conclusions. The researchers produced several data, namely (1) the author of the poetry anthology Ar-Robi fii qalbi is an educated author and has close interactions with her environment. (2) The author of the poetry anthology Ar-Robi fii qalbi has a high degree of integration. He mingles with the general public. (3) The main economic source of the author in Ar-Robi's poetry anthology is not through her work but in her educational activities. As a teacher, her work is often used as learning material for her students. (4) The author of the poetry anthology Ar-Robi fii qalbi has high professionalism in the authorship of her work.
The Impact of Digital Marketing on Sales Performance: The Case of Lebanese Pharmaceutical Companies

The marketing of goods and services has remained one of the sectors that need constant innovation throughout the world today. An important marketing strategy in recent times has been digital marketing, defined as the use of electronic media by marketers to advertise items or services on the market with the primary goal of attracting consumers and allowing them to connect with the brand via digital media. Data is collected and filtered to offer the most comprehensive and relevant output for the research. Consequently, in order to conduct this research, the researchers plan to collect data from at least 100 individuals using a variety of ways. The data from the early answers had already been collected by the time this chapter was finished. Facebook proved to be the most effective method of contacting respondents’ study's goal is to find out how digital marketing affects sales of Lebanese Pharmaceutical Companies. The author used the instance of Lebanese pharmaceutical companies to examine the features of digital marketing, digital marketing zones in marketing, and their impact on brand awareness. According to the research, social media zones have a beneficial influence on brand exposure. Brand visibility is increased by the use of social communities, social publishing, and a tiny portion of social commerce through social media. Despite the fact that each channel has its own unique features and fan base, there are significant similarities in the kind of people that utilize the various channels.
Contouring a User Centered Chatbot for Diabetes Mellitus

Diabetes Mellitus (DM) is a chronic disease.Its management requires continuous adherence to medical care, self-management, and monitoring, to mitigate the risks.During COVID, the difficulties faced in accessing health facilities, created a sense of disconnect between doctors and patients.A Chatbot for Diabetes Mellitus, DBOT, was designed using a set of questions grouped under components-Diabetes History, Medical History, Family History, Medical History (for women only), Eating/Exercising Habits, Tracking Health Status (Lab Test Results), Prescription Status, Knowledge about reducing Risks, Frequently Asked Questions (FAQ), and DBOT Usage.To design the DBOT, JAVA Coding, xml designing language and an Android Studio tool are used.The flow charts and coding sheets for the components were devised.The DBOT was shared with the diabetic patients for their utilization and through a questionnaire survey and the feedback from the respondents about user experience, were collected.The questionnaire data were analyzed using statistical techniques like Two Sample 't" Test, Chi square, One-Way ANOVA, Correlation, and Factor Analysis to determine comparisons, relations, and associations between the components of DBOT.The future steps include incorporating the proposal, recommendations and suggestions of the users and restructure the DBOT with enhanced effectiveness.
Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

The rapid use of Twitter social media in recent times has an impact on the faster dissemination of disinformation which is very dangerous to followers. Detection of disinformation is very important to do and can be done manually by conducting in-depth information analysis. But given the huge amount of information, this approach is less effective. Another, more effective approach is to use a machine learning-based approach. Several studies on hoax information detection based on machine learning have been carried out where some studies analyze the content of a tweet and some others analyze hashtags which are the context of a tweet. The feature usually used to analyze hashtag sentiment data is the property feature of the creator's account. The creator accounts of disinformation are called buzzer accounts. This research proposes account property feature expansion of buzzer accounts combined with the SVM classifier which in several previous similar studies has a very good performance to detect the buzzer hashtag. The experimental results show that expanding the proposed feature can increase SVM's performance in detecting hashtag buzzers by more than 24% compared to using the baseline feature, and the average F1 score obtained from the combination of methods is 84%.&#x0D;
Multilingual repertoires and identity in social media: Syrian refugees on Facebook

Aims and objectives/purpose/research questions: This study explores the linguistic resources used on Facebook (FB) by six resettled Syrian refugees in the United Kingdom. It investigates the multilingual varieties and scripts employed, their discursive functions and their role in the construction of identity/ies. Design/methodology/approach: The study employs descriptive linguistic and discourse-centred online ethnographic approaches. These are complemented with insights from interviews with the informants. Data and analysis: The data set consists of 2,036 FB posts. Multilingual posts were subjected to a two-stage analysis: the first identified the affordances of new technologies for Arabic/English bilinguals – describing the function of different languages and scripts. The second stage employed a thematic analysis to frame a subsequent investigation of the stances taken by posters to construct identities and boundaries between themselves and others. Findings/conclusions: The study reveals how FB supports the construction and negotiation of multidimensional, complex personae, particularly in countering mainstream/media representations. It is a platform on which posters are able to display bilingual creativity. Originality: This article demonstrates the affordances of social media (SM) for newly settled Syrian refugees engaging in transnational networks. It identifies, as yet, unreported bilingual creativity in varieties of Arabic and English, particularly the use of a previously unidentified script, Englarabic. It ties this analysis to the study of identity performance and negotiation, and reveals the contested status of those now living in a new nation. Significance/implications: The study reveals how Syrian refugees employ complex linguistic repertoires in computer-mediated communication (CMC). We argue that SM platforms afford a creative, ideological space for bilingual refugees, as they negotiate their multiple identities online following resettlement. The study identifies linguistic innovation, particularly in the use of code-switching and Englarabic. It has implications for all those researching multilingual CMC creativity and identity, as well as those involved in understanding, planning for, and experiencing refugee (re)settlement.
Meta-analisis Pengaruh Metode Gasing Pada Pembelajaran Matematika SD dalam Meningkatkan Hasil Belajar Siswa

The background of this research is the low learning outcomes in Mathematics. Because mathematics is considered a difficult subject to understand, and the method given only uses formulas. So that students mostly memorize formulas that make students bored so that learning becomes meaningless. This study aims to analyze the effect of using the gasing method on elementary mathematics learning in improving student learning outcomes. The method used in this study is a meta-analysis method. The research data was obtained by searching online journals through Google Scholar or Google Scholars with the search keywords "The Effect of the Gasing Method", "The Gasing Method", "Elementary Mathematics Learning" and "Mathematics Learning Outcomes". From the search results obtained as many as 22 articles but which are relevant to use only 10 articles. Based on the results of the analysis, the use of the gasing method in elementary mathematics learning can have a good effect in improving student learning outcomes. &#x0D;
Modified Prophet+Optuna Prediction Method for Sales Estimations

A prediction method for estimation of sales based on Prophet with a consideration of nonlinear events and conditions by a modified Optuna is proposed.Linear prediction does not work for a long-term sales prediction because purchasing actions are based on essentially nonlinear customers' behavior.One of nonlinear prediction methods is the well-known Prophet.It, however, is still difficult to adjust the nonlinear parameters in the Prophet.To adjust the parameters, the Optuna is widely used.It, however, is not good enough for parameter tuning by the Optuna.Therefore, the Optuna is modified with a short-term moving mean and standard deviation of the sales for final prediction.More than that, specific event such as typhoon event is to be considered in the sales prediction.Through experiments with a real sales data, it is found the sensitivity of the parameters the upper window, lower window, event dates, etc. for the final sales and the effect of the Optuna is 11.73%.Also, it is found that the effect of the consideration of Covid-19 is about 2.4% meanwhile the effect of the proposed modified Optuna is around 3 % improvement of the prediction accuracy (from 80 % to 83 %).
Analysis of Minimalist Syntactic Complexity in English Writing among Secondary Level Students

This research is an analysis of minimalist syntactic complexity in the syntactic structures of secondary-level students of government schools. It highlights the null constituents in the syntactic structures of students writing in annual examinations. It also finds the occurrences of null constituents in the written syntactic structures of the students. For this purpose, the researchers selected ten government high schools [male students] of district Mardan. In this research, a hundred samples of attempted answer sheets of English paper (class 9 and 10) were collected through a random sampling technique. The theoretical framework of the Noam Chomskys Minimalist program was used for this research. The syntactic structures of the attempted answer sheets of the students were analyzed through parsing (tree diagram) with the perspective of minimalism. The data were analyzed qualitatively and quantitatively; the null constituents are discussed qualitatively, and the occurrences of null constituents shown are quantitatively through tabulation.
Dark Web: The Hub of Crime

Abstract: Now days Internet plays a significant role in our daily life. It’s become an essential part of all daily lifestyle. Dark Web is like an untraceable secret layer of the Internet which basically used to store and access the sensitive and confidential data. But we can see the huge misuse of this platform for conducting the criminal and illegal activities in a hidden way. In this paper, we are going to discuss about the overview of dark web and many browsers those are used to access dark web. We also discuss about the methods used in Dark web for anonymity and confidentiality. Here some interesting facts are also discussed about dark web and the different types of crimes to create awareness about this type of activities and the preventive action for these activities
A Small Target Detection Method Based on the Improved FCN Model

Traditional object detection is mainly aimed at large objects in images. The main function achieved is to identify the shape, color, and trajectory of the target. However, in practice, small target objects in the image must be detected in addition to large targets. Common small target detection (STD) is mainly used in intelligent transportation, video surveillance, and other fields. Small targets have small size, few pixels, and low resolution and are easily blocked. Small object detection has emerged as a research problem and a hotspot in the field of object detection. This study proposes an improved FCN model based on the full convolutional neural network (FCN) and applies it to the STD. The following is the central concept of the proposed method. Small targets are prone to occlusion and deformation in the image data. The deformation here is mainly reflected in the larger shape obtained by shooting at different angles. Therefore, it is a challenge to fully and accurately obtain the characteristics of small targets. The traditional method based on multilayer feature fusion cannot achieve ideal results for STD. This study is based on FCN and introduces a spatial transformation network. The network can optimize the handling of problems such as partial occlusion or deformation of small objects. The use of a spatial transformation network can alleviate the problem of poor feature extraction caused by partial occlusion or deformation of small targets, improving final detection accuracy. The experimental results on public datasets show that the proposed method outperforms other deep learning algorithms (DLA) in the detection accuracy of small target objects. Furthermore, the model’s training time is reduced. This study’s research provides a good starting point for the detection, recognition, and tracking of some small objects.
Utilizing Edge Attention in Graph-Based Code Search

Code search refers to searching code snippets with specific functions in a large codebase according to natural language description.Classic code search approaches, using information retrieval technologies, fail to utilize code semantics and bring noisy and irrelevant keywords.During the last recent years, there has been an ample increase in the number of deep learning-based approaches, which embeds lexical semantics into unified vectors to achieve higher-level mapping between natural language queries and source code.However, these approaches are struggling with how to mine and utilize deep code semantics.In this work, we study how to leverage deeper source code semantics in graph-based source code search, given graph-based representation is a promising way of capturing program knowledge and has rich explainability.We propose a novel code search approach called EAGCS (Edge Attention-based Graph Code Search), which is composed of a novel code graph representation method called APDG (Advanced Program Dependence Graph) and a graph neural network called EAGGNN (Edge Attention-based GGNN) which can learn the latent code semantics of APDG.Experiment results demonstrate that our model outperforms the GGNN-based search model and DeepCS.Moreover, our comparison study shows that different edge enhancement strategies have different contributions to learning the code semantics.
Pengaruh Penggunaan Video Sebagai Media Pembelajaran Terhadap Hasil Belajar Tematik Kelas III di SDIT Fathona Palembang

The purpose of this study was to determine whether there is an effect of using video media on thematic learning outcomes in grade III elementary school and of course in the future it can be useful for various parties because the current curriculum is very demanding on the skills and abilities of educators because it improves the quality and quality of current education. This is the main focus of the government's attention. For this reason, using video media is one of the efforts to improve student learning outcomes whether later it is good for use in elementary schools and especially in grade III elementary schools. The research method used by the researcher is quantitative research with the help of SPSS 22. The results show that thematic learning outcomes in class III increase after using video learning media, which means that there is an effect of using video media on teaching and learning activities or thematic learning outcomes in class III, especially in SDIT Fathona Palembang and of course in the future can be applied.
How big is Big Data? A comprehensive survey of data production, storage, and streaming in science and industry

The contemporary surge in data production is fueled by diverse factors, with contributions from numerous stakeholders across various sectors. Comparing the volumes at play among different big data entities is challenging due to the scarcity of publicly available data. This survey aims to offer a comprehensive perspective on the orders of magnitude involved in yearly data generation by some public and private leading organizations, using an array of online sources for estimation. These estimates are based on meaningful, individual data production metrics and plausible per-unit sizes. The primary objective is to offer insights into the comparative scales of major big data players, their sources, and data production flows, rather than striving for precise measurements or incorporating the latest updates. The results are succinctly conveyed through a visual representation of the relative data generation volumes across these entities.
THE INFLUENCE OF ONLINE GAMES TO STUDENTS ENGLISH ACHIEVEMENTS IN SMA PGRI 4 PADANG

This study aims to analyze the influence of online games to students English achievement in SMA PGRI 4 Padang. This research is motivated by the existence of problems that make students unable to divide their study time well. This type of research is a descriptive study with a qualitative approach. This study used questionnaires, interviews and student grade documents as intruments. Researchers used a questionnaire as an instrument as many as 54 students filled out a questionnaire, and 10 students were interviewed as data in this study. From the results of the questionnaire, the researcher found 2 influence based on indicators of playing online games, namely time distortion and obsession with online world games with the proportion of students who have problems with time distortion (17%) and obsessed with online games (12%). Meanwhile, based on the results of the interview, the researchers found several problems including students who were not wise in dividing the time between the learning process in class and playing online games and the time frequency in playing online games which was quite long from 1 hour a day, and obsessed with online games because it can benefit them financially and to gain popularity. Finally, to support the data that has been analyzed, in the results of the data analysis, the researcher found that only 5 students had a fairly good progress on the English scores for mid-semester 1. So, it can be concluded that the results of this study online games can have a negative influence on students English learning achievement at SMA PGRI 4 Padang.