Reviewerly/exHarmony huggingface dataset:
EFL UNIVERSITY STUDENTS EMPLOYED A CRITERIA OF HEDGES DURING THEIR SPEAKING CLASS WITHIN THE ACADEMIC SETTING

Communication becomes an essential aspect in language classroom because it bridges the meaning of a person’s ideas. Therefore, as foreign language learners, the students have to have a good understanding at how to interact effectively with other students. Regarding to this, the researcher considered it is crucial to investigate English Foreign Language (EFL) university students’ ways in communication during their speaking class which includes hedges. The objectives of the research was to find out the types of hedges employed by EFL university students during their speaking class within the academic setting. This study was conducted at Politeknik LP3I Makassar and it was focused on the students in the first semesters in subject Basic Speaking with the total participants were 23 students of one class. The method used was descriptive qualitative method. There were 3 recordings based on the students’ interactions in the classroom. The theory from Riekkinen and Svarova (2009) was used to analyze the data gain. After transcribing and analyzing the data, the results showed that the students in EFL speaking class utilized some types of hedges. The hedges used involves personal evaluation, expression of limitation, expression of hesitation, and the combine hedges. Besides, the students employ hedges for some reasons.
COLLABORATIVE FILTERING APPROACH: SKINCARE PRODUCT RECOMMENDATION USING SINGULAR VALUE DECOMPOSITION (SVD)

The recommendation system provides recommendations for something, be it goods, songs, or movies. The term system is not limited to a service system but concerns a model that can provide recommendations. With recent technological advances, many companies provide various skincare products because current generations are increasingly aware of self-care. With various choices, someone may experience confusion in determining the product they want to buy. Therefore, we need a system that can provide product recommendations run on any platform we use. The most common method for recommendation systems often comes with Collaborating Filtering (CF) where it relies on the past user and item dataset. The singular value decomposition (SVD) method uses a matrix factorization technique that predict the user's rating based on historical ratings. The measurement of the model's accuracy is the RMSE average of 1.01276, indicating that this value results from the best parameters. The results focus on showing skincare product recommendations to users sorted based on rating predictions.
Knowledge Management and Developing Human Resources in the Secretariat of the Central Library- Baghdad University

Knowledge management contribute to the overall private university libraries to develop libraries for the purpose of creating human and technological resources by investing research and development, as well as education and training for life.This study followed the methodology of the descriptive and historical pillars of knowledge management for the years 1990 to the present day in 2017, depending on statistical figures obtained by the researchers from the General Secretariat of the Central Library at the University of Baghdad, and the Human Resources Division, which specialized human resources training after 2004 (and before those years from 1982 until 2002, where the continuing education was committee of the fixed committees are working in cooperation with the Electronic and Computer Center at the University of Baghdad, and the training was for landlords it will be according to the library need of technical development or at the request of the employee)Using modern technology and techniques for the purpose of technically and administratively rehabilitation, comparing this phase with the previous stages to find out the results of the comparison and the statement of the importance of training and education, and how to address
PENGEMBANGAN WEBSITE PEMBELAJARAN MENGGUNAKAN MODEL GOLD STANDARD PROJECT BASED LEARNING

Currently, teachers are required not only to be able to master the content of the subjects being taught but also to be able to develop various digital-based learning media such as websites. Currently, the majority of teachers have been able to use learning websites, but there are still very few who are able to create and manage their own learning websites. Therefore, this study aims to determine whether the application of the Gold Standard Project Based Learning (GS-PjBL) in the form of training and mentoring is able to encourage teachers to be able to develop learning websites. The implementation of GS-PjBL consists of 7 stages, namely (1) Problems and challenges, (2) Study and development, (3) Testing, (4) Role enhancement, (5) Reflection, (6) Revision, (7) Product publication. . Based on the evaluation results obtained information that there is 1 group has been able to develop and complete a learning website, and the other group has been able to make menu settings on the theme, and install and choose a theme and complete the selection of hosting services. In addition, as an evaluation material, at the end of the activity, participants also filled out a satisfaction questionnaire with the ongoing GS-PjBL model of training and mentoring activities. Based on the response analysis, participants' satisfaction above 79% is in the good category. This shows that participants are satisfied with the activities they have received even though there are some shortcomings.
THE IMPLEMENTATION OF SOCIO-AFFECTIVE STRATEGIES IN IMPROVING INDONESIAN JUNIOR SECONDARY SCHOOL STUDENTS’ SPEAKING ABILITY

The study aimed at finding out: (1) whether the implementation of socio-affective strategies could improve Indonesian junior secondary school students’ English speaking ability; and (2) the students’ perceptions on the implementation of socio-affective strategies in teaching and learning English speaking skill. The study employed quantitative research method. Pre-experimental research design was used to assess the improvement of the students’ English speaking ability and questionnaire was used to find out the students’ perception on socio-affective strategies in English speaking classroom. Thirty three (33) seven grade students of Public Islamic Junior Secondary School (MTs. Negeri) Gowa Regency South Sulawesi Province were taken as samples of the study by using cluster random sampling technique. The pre-test and post-test results were collected from the students’ speaking assessment then analyzed by using IBM Statistic SPSS 20 Software. Questionnaire responses were classified into sub-themes and then analyzed using percentage. The study found: (1) the t-test value of English speaking ability was greater than t-table (16.476&gt;1.693). Therefore, the alternative hypothesis (H1) was accepted and null hypothesis (H0) was rejected. It can be concluded that there was significant improvement on students’ English speaking ability by implementing socio-affective strategies at the seventh grade students of MTs Negeri Gowa Regency; (2) the result of questionnaire analysis showed that the students had positive responses to the implementation of socio-affective strategies in speaking class with the range score of 130-142. In addition, the students believed that socio-affective strategies have benefit to their interest and behavior toward language learning, and improved their English speaking ability. &#x0D;
STUDENTS’ PERCEPTIONS ON THE TEACHER USING L1/L2 IN TEACHING EFL CLASS

This study aimed to find out students’ perceptions on the teacher using L1/L2 in teaching EFL Class. It employed a qualitative research which used case study. The participants of this study were three students with categories advance, intermediate, and low. The data were collected by using in-dept interview. The items for the interview were developed from the previous research Elmetwally (2012) which were found appropriate to be used. The findings revealed that the use of Indonesian language in the EFL Class are generally positive. The three participants stated that they prefered when learning with the teachers used of L1/L2 in teaching EFL Classroom e.g explaining the material, explaining difficult vocabulary, translating and presentation. In addition, the students’ perceptions has a different percentage of used L1/L2 in EFL Class. The participants with Advance category stated that she was needed 50% Indonesian explanations when explaining materials, giving examples, explaining formulas and theories. Then, she was needed 50% the full English explanations when the teacher greeting and opening the lessons, checking attendance, giving assignments, correcting students mistakes, closing the lessons, giving homework, and announcing grades. Meanwhile, the intermediate students’ stated that she was needed a full English explanations as much as 45% and needed to be used when explaining the materials, and the rest 55% was needed on the Indonesian explanations to clarify the materials that was explained using English earlier. Furthermore, the low students’ stated that she was needed as much as 80% for Indonesia explanations for explaining the material more deeply so that students become more understand. And the rest 20% was needed for English explanations in opening and closing the teaching activity, and giving an assignment so that the students would get used to it. Moreover, it can be concluded that the use of L1/L2 as a facilitator in their learning, as a tool to help them get a better understanding when learning English.
STUDENTS’ PERCEPTION TOWARD THE USE OF EDMODO IN EFL CLASSROOM

This research intended to investigate the students’ perception toward the use of Edmodo as the English quizzes medium in the EFL classroom. Qualitative research was used in this research. A case study design was applied in this research. Seven students who have used Edmodo as the English quizzes medium in one of the universities in Garut volunteered as the research participants. The data collected by using a semi-structured interview. Three participants were interviewed in a face-to-face setting, while other participants were interviewed by using WhatsApp call and WhatsApp voice notes. The data then analyzed by following Mile and Huberman’s theory that emerges from data that systematically gathered and analyzed through the research process. The steps of analyzing the data in this research were reducing the data, displaying the data, then drawing a conclusion. The result showed that students' experiences influence their perceptions. Two students who have used Edmodo for longer than the others are very understanding and can convey their opinions well. Furthermore, all participants experienced some bad things in using Edmodo, but the researcher did not found any related theory. However, the researcher did a cross-check in the Play Store in the comments section, and it was found that some Edmodo users comment on the same thing that related to this finding. Even though, all participants conveyed positive perceptions toward the use of Edmodo as the English quizzes medium.
Teacher’s Experience in Presenting Vocabulary to Young EFL Learners

This research aims to find out the techniques in presenting vocabulary to young EFL learners and to investigate difficulties and constraints as perceived by teachers in teaching vocabulary to young learners. A qualitative case study design will be used in this research. Data will be taken from teaching and learning activities by using observation and interview. The collected data will be classified, analyzed and concluded. This research is expected to be used as a reference for potential young learners’ teachers to teach in the future.
Use of Data Mining for Intelligent Evaluation of Imputation Methods

In real-world situations, researchers frequently face the difficulty of missing values (MV), i.e., values not observed in a data set.Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance.Most of the literature in this field deals with different imputation methods.However, few studies deal with a comparative evaluation of the different methods as to provide more appropriate guidelines for the selection of the method to be applied to impute data for specific situations.The objective of this work is to show a methodology for evaluating the performance of imputation methods by means of new metrics derived from data mining processes, using quality metrics of data mining models.We started from the complete dataset that was amputated with different amputation mechanisms to generate 63 datasets with MV; these were imputed using Median, k-NN, k-Means and Hot-Deck imputation methods.The performance of the imputation methods was evaluated using new metrics derived from quality metrics of the data mining processes, performed with the original full file and with the imputed files.This evaluation is not based on measuring the error when imputing (usual operation), but on considering the similarity of the values of the quality metrics of the data mining processes obtained with the original file and with the imputed files.The results show that -globally considered and according to the new proposed metric, the imputation methods that showed the best performance were k-NN and k-Means.An additional advantage of the proposed methodology is that it provides predictive data mining models that can be used a posteriori.
Japanese Communication Skill Analysis Through Saiwa Activities

Communication skills in students is one of the important assets to improving the soft skills of conveying messages to others. The things that support a student to convey messages well are the ability to understand information well, and the ability to convey the message back to other parties. This also applies when someone is studying a foreign language, especially Japanese. If we do not have good communication skills, the information will not be conveyed properly. This study aims to describe the Japanese language communication skills of students in the Japanese Language Education Study Program, Universitas Negeri Semarang. This communication capability is carried out in the form of Saiwa activities or retelling information or news that has been listened. The research methodology used is to use self-help activities or retelling the news that students have listened. To measure this ability is done by means of presentation tests and interview tests. Based on the results of the presentation capability assessment, it can be seen that The average value of the interview results for the samples "fair" category. The average value of the acquisition of the results retelling information on the entire sample is "more than enough" category.
Fair Evaluation of Graph Markov Neural Networks

We introduce a new dataset named WikiVitals which contains a large graph of 48k mutually referred Wikipedia articles classified into 32 categories and connected by 2.3M edges. Our aim is to rigorously evaluate the contributions of three distinct sources of information to the label prediction in a semi-supervised node classification setting, namely the content of the articles, their connections with each other and the correlations among their labels. We perform this evaluation using a Graph Markov Neural Network which provides a theoretically principled model for this task and we conduct a detailed evaluation of the contributions of each sources of information using a clear separation of model selection and model assessment. One interesting observation is that including the effect of label dependencies is more relevant for sparse train sets than it is for dense train sets.
Adapt a Generic Human-Centered AI Design Framework in Children's Context

Through systematically analyzing the literature on designing AI-based technologies, we extracted design implications and synthesized them into a generic human-centered design framework for AI technologies to better support human needs and mitigate their concerns. When adapting the framework to children's context, understanding their specific needs, behaviors, experiences, and social environments is needed. Therefore, we are working on projects to explore tailored design considerations for children, such as through investigating children's use of existing AI-based toys and learning technologies. By participating in the ACM CHI 2023 Workshop on "Child-Centred AI Design: Definition, Operation, and Considerations," we hope to learn more about how other researchers in this field approach designing child-centered AI technologies, exchange ideas on the research landscape of children and AI, and explore the possibility to develop a practical child-centered design framework of AI technologies for technology designers and developers.
Synthesis of Opacity-Enforcing Winning Strategies Against Colluded Opponent

This paper studies a language-based opacity enforcement in a two-player, zero-sum game on a graph. In this game, player 1 (P1) wins if it can achieve a secret temporal goal described by the language of a finite automaton, no matter what strategy the opponent player 2 (P2) selects. In addition, P1 aims to win while making its goal opaque to a passive observer with imperfect information. However, P2 colludes with the observer to reveal P1's secret whenever P2 cannot prevent P1 from achieving its goal, and therefore, opacity must be enforced against P2. We show that a winning and opacity-enforcing strategy for P1 can be computed by reducing the problem to solving a reachability game augmented with observer's belief states. Furthermore, if such a strategy does not exist, winning for P1 must entail the price of revealing his secret to the observer. We demonstrate our game-theoretic solution of opacity-enforcement control through a small illustrative example and in a robot motion planning problem.
Counterfactual Learning on Graphs: A Survey

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning. We divide existing methods into four categories based on problems studied. For each category, we provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. We point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, we compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a ``one-stop-shop'' for building a unified understanding of graph counterfactual learning categories and current resources. We also maintain a repository for papers and resources and will keep updating the repository https://github.com/TimeLovercc/Awesome-Graph-Causal-Learning.
Polarity based Sarcasm Detection using Semigraph

Sarcasm is an advanced linguistic expression often found on various online platforms. Sarcasm detection is challenging in natural language processing tasks that affect sentiment analysis. This article presents the inventive method of the semigraph, including semigraph construction and sarcasm detection processes. A variation of the semigraph is suggested in the pattern-relatedness of the text document. The proposed method is to obtain the sarcastic and non-sarcastic polarity scores of a document using a semigraph. The sarcastic polarity score represents the possibility that a document will become sarcastic. Sarcasm is detected based on the polarity scoring model. The performance of the proposed model enhances the existing prior art approach to sarcasm detection. In the Amazon product review, the model achieved the accuracy, recall, and f-measure of 0.87, 0.79, and 0.83, respectively.
Thematic context vector association based on event uncertainty for Twitter

Keyword extraction is a crucial process in text mining. The extraction of keywords with respective contextual events in Twitter data is a big challenge. The challenging issues are mainly because of the informality in the language used. The use of misspelled words, acronyms, and ambiguous terms causes informality. The extraction of keywords with informal language in current systems is pattern based or event based. In this paper, contextual keywords are extracted using thematic events with the help of data association. The thematic context for events is identified using the uncertainty principle in the proposed system. The thematic contexts are weighed with the help of vectors called thematic context vectors which signifies the event as certain or uncertain. The system is tested on the Twitter COVID-19 dataset and proves to be effective. The system extracts event-specific thematic context vectors from the test dataset and ranks them. The extracted thematic context vectors are used for the clustering of contextual thematic vectors which improves the silhouette coefficient by 0.5% than state of art methods namely TF and TF-IDF. The thematic context vector can be used in other applications like Cyberbullying, sarcasm detection, figurative language detection, etc.
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning

Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a reasoning paradigm where agents anticipate the learning steps of other agents to improve cooperation among themselves. As MARL uses gradient-based optimization, learning anticipation requires using Higher-Order Gradients (HOG), with so-called HOG methods. Existing HOG methods are based on policy parameter anticipation, i.e., agents anticipate the changes in policy parameters of other agents. Currently, however, these existing HOG methods have only been applied to differentiable games or games with small state spaces. In this work, we demonstrate that in the case of non-differentiable games with large state spaces, existing HOG methods do not perform well and are inefficient due to their inherent limitations related to policy parameter anticipation and multiple sampling stages. To overcome these problems, we propose Off-Policy Action Anticipation (OffPA2), a novel framework that approaches learning anticipation through action anticipation, i.e., agents anticipate the changes in actions of other agents, via off-policy sampling. We theoretically analyze our proposed OffPA2 and employ it to develop multiple HOG methods that are applicable to non-differentiable games with large state spaces. We conduct a large set of experiments and illustrate that our proposed HOG methods outperform the existing ones regarding efficiency and performance.