Reviewerly/exHarmony huggingface dataset:
RESEARCH ACTIVITIES IN UKRAINIAN LANGUAGE AND LITERATURE LESSONS AS A MEANS OF SHAPING STUDENTS’ CORE COMPETENCIES

The article discloses and describes the formation of research skills in the lessons of the Ukrainian language and literature, highlights the essence of research activities, presents the main definitions and formulates its own vision for the definition of this concept. A group of principles has been identified that meet the requirements of research work and contribute to the formation of research skills of students in the process of studying the Ukrainian language and literature. Based on the analysis of psychological and pedagogical literature and the author’s own pedagogical experience, the main methods are determined that contribute to the formation of analytical abilities among students, the ability to compare, classify and generalize information. The stages of organization of research work of students at the institutions of general secondary education by means of consistent integration of science and educational process are disclosed. One of the effective forms of conducting classes in the Ukrainian language and literature in the process of forming research skills is defined as a lesson-study. Its structure was submitted, the main stages were described. It has been proved that the use of elements of problematic, search, research, heuristic methods makes the learning process more productive. The development of students’ research skills helps to achieve certain goals: to raise students’ interest in learning and to motivate them to achieve better results
Impulsing the Development of Students' Competency Related to Mathematical Thinking and Reasoning through Teaching Straight-Line Equations

The research was carried out to develop students' ability to think and reason mathematically by teaching straight-line equations in a plane. Accordingly, teaching activities were designed according to five learning stages, which were integrated with mathematical thinking levels according to Van Hiele's model. Simultaneously, the learners' mathematical thinking and reasoning competencies were assessed according to the competency requirements specified in the Mathematics General Education Program and the levels of Van Hiele's model, the above three aspects of knowledge, skills and attitudes. The experiment involved 84 students in class 10, 44 of whom were in the experimental group, and 40 were in the control group. The research results showed that students in the experimental group achieved higher mathematical thinking and reasoning skills. Specifically, the two groups had equivalent results for the level of visualization and analysis. However, at the informal deduction and formal deduction and rigor levels, the ranking results of the two groups had a clear difference. The study group observations and students' opinion surveys also revealed that learning stages were designed according to Van Hiele's model and thought-provoking measures and visual images and language contributed to students' interest in learning and positive thinking.
Evaluation Scheme of Focal Translation for Japanese Partially Amended Statutes

For updating the translations of Japanese statutes based on their amendments, we need to consider the translation "focality;" that is, we should only modify expressions that are relevant to the amendment and retain the others to avoid misconstruing its contents.In this paper, we introduce an evaluation metric and a corpus to improve focality evaluations.Our metric is called an Inclusive Score for DIfferential Translation: (ISDIT).ISDIT consists of two factors: (1) the n-gram recall of expressions unaffected by the amendment and (2) the n-gram precision of the output compared to the reference.This metric supersedes an existing one for focality by simultaneously calculating the translation quality of the changed expressions in addition to that of the unchanged expressions.We also newly compile a corpus for Japanese partially amendment translation that secures the focality of the post-amendment translations, while an existing evaluation corpus does not.With the metric and the corpus, we examine the performance of existing translation methods for Japanese partially amendment translations.
User-centred design thinking

Users’ experience and their opinions are getting important in the design process. This chapter focuses on user-centred design thinking in workplace design and management. The concept of user-centred design is originally developed in fields other than architecture, such as ergonomics, industrial design, and computer science. Recently, user involvement in workplace research/management is essential to satisfy users’ needs and requirements. The most successful strategy is the design that meets the needs and requirements of real people. User-centred design thinking in workplace design/management promises a model that satisfies employees and is capable of increasing productivity. This theory can be key to supporting users’ needs and contributing to meet their goals. This chapter aims to explain the terms of user-centred design (UCD), design thinking (DT), and user-centred design thinking (UCDT). In addition, this chapter will describe several UCDT processes. The last section discusses the contribution of UCDT to workplace research.
Animal Species Recognition System using Deep Learning

Animals watching is a common hobby but to identify their species requires the assistance of Animal books. To provide Animal watchers a handy tool to admire the beauty of Animals, we developed a deep learning platform to assist users in recognizing species of Animals endemic to using app named the Imagenet of Animals (IoA). Animal images were learned by a convolutional neural network (CNN) to localize prominent features in the images. First, we established and generated a bounded region of interest to the shapes and colors of the object granularities and subsequently balanced the distribution of Animals species. Then, a skip connection method was used to linearly combine the outputs of the previous and current layers to improve feature extraction. Finally, we applied the SoftMax function to obtain a probability distribution of Animals features. The learned parameters of Animals features were used to identify pictures uploaded by mobile users. The proposed CNN model with skip connections achieved higher accuracy of 99.00 % compared with the 93.98% from a CNN and 89.00% from the SVM for the training images. As for the test dataset, the average sensitivity, specificity, and accuracy were 93.79%, 96.11%, and 95.37%, respectively.
Artificial Intelligence and Children's Education: Review Study

Literature review is an important part within the content of any scientific study. It fulfills many purposes and objectives, including that it gives the researcher ideas about the most important variables that the previous studies have proven their importance or lack of importance in a specific field or ideological and cognitive direction. This study came with the aim of compiling the latest Arab and foreign theoretical studies, highlighting the process that researched the subject of artificial intelligence AI and the education of children, classifying, analyzing and commenting on their results in order to help students and researchers in the educational field to use it as a tool in their research. The current review study will help the researcher to see previous studies and to identify points of similarities and differences between the results of these studies, as well as giving an impression about the relationship between the studied variables, providing the researcher with information about the science that has been accomplished and which can be conducted and applied, and the necessary research topics. Further research and study are recommended in it to enrich the topics of artificial intelligence AI and the education of children, especially during the current period of transition to blended learning and online educational systems.
archer at SemEval-2021 Task 1: Contextualising Lexical Complexity

Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021.This paper presents the system created to assess single words lexical complexity, combining linguistic and psycholinguistic variables in a set of experiments involving random forest and XGboost regressors.Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word's in-context complexity.
Microsoft teams 365 and online learning: The student’s perception

This study aims to determine the effectiveness of online learning using Microsoft Teams 365. The entire population in this study was used as a research sample, namely all students of the Chemistry Education study program, Indonesian Christian University, Jakarta. Perception data was collected using a questionnaire with dimensions of understanding the material, appearance and accessibility of Microsoft Teams 365. From the results of data processing, it was found that more than 50% of students' perceptions of Microsoft Teams 365 were good in all indicators of understanding the material and appearance. On the use dimension with connectivity and accessibility indicators more than 50% of students' perceptions of the Microsoft 365 team are good, but on the quota-saving indicator 42% of students do not agree. Keywords: Microsoft teams, E-learning, Cemistry
Permainan Edukasi Operasi Hitung Matematika Berbasis Multimedia pada SD STRADA VAN LITH II

SD Strada Van Lith II is an elementary school that the early establishment of Strada societysince 1924, and has branches in central Jakarta, tangerang, bekasi and east jakata. SD Strada Van LithII is a branch of east jakarta, duren palm. The problems that occur are like using whiteboards, markers,guidebooks and others so that this study aims to provide a more enjoyable learning atmosphere in thegame world, especially games that are educative. The purpose of the study is to facilitate learning andproduce instructional media-based educational game in the form of software on mathematics subjectsthat can be used as an alternative media to play while learning. The research method used in thisresearch is Multimedia Development Life Cycle (MDLC). The results of this study are desktop-basedapplications, that this application can provide new solutions in terms of teaching and learning activitiesand provide positive feedback for SD Strada Van Lith II.
How it started/ how it’s going’: Aligning Teacher Educators’ designs, approaches and identities in our new online reality

E-learning scholars have long predicted the conversion of conventional education to online learning spaces. That conversion has now happened. Regardless of what occurs in a post-pandemic era, teaching and learning will never be the same. Where does that leave teacher educators (TEs) who grapple with how to best prepare future teachers for the new era? The aim of our research was to determine the impact of supporting a small group of Chilean TEs in re-designing their online instructional approaches by aligning them with contemporary learning theories and goals. In the Chilean context, prior to the pandemic, e-learning was still on the periphery, and transitions from teacher-directed approaches had only just begun. In this 10-month qualitative inquiry, we focused on the TEs experiences online as they adopted sociocultural-based, 21st century instructional designs, and implemented strategies intended to promote agency and engagement in their students. The TEs long-held teacher-centric identities and approaches sometimes interfered in this trajectory. Yet, their heightened critical awareness of the ineffectiveness of traditional teaching paradigms in online settings combined with their grounded efforts and perseverance, resulted in positive evidence of ‘real’change to their designs, practices and identities – changes many have been seeking in educational systems for some time.
Interactive education is a guarantee of education quality

Organization of modern lessons on the program of development of school education, radical reform of the education system in the educational process on the basis of the Law “On Education”, ways to use interactive methods in all educational institutions, the teacher's preparation for the lesson, the provision of methodological assistance to young teachers on the basis of the “teacher-student” system, the teacher's creativity, creative approach to the lesson.
Analysis of Data Mining Associations on Drug Sales at Pharmacies with APRIORI Techniques

The purpose of the research is to utilize artificial intelligence techniques in analyzing drug sales. Sources of data used are observations and interviews with shop owners. The method used as a solution is the Association method with the Apriori technique. By using the RapidMiner software, the test results are obtained using a minimum support of 25% and a minimum of 60% confidence as many as 5 rules with a predetermined itemset. The results of the study are expected to provide information and make it easier for related parties to find combinations of selling items. The results of this analysis can be used by pharmacies for marketing strategies and product promotion.
Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting

Cross-lingual text classification (CLTC) is a challenging task made even harder still due to the lack of labeled data in low-resource languages.In this paper, we propose zero-shot instance-weighting, a general model-agnostic zero-shot learning framework for improving CLTC by leveraging source instance weighting.It adds a module on top of pre-trained language models for similarity computation of instance weights, thus aligning each source instance to the target language.During training, the framework utilizes gradient descent that is weighted by instance weights to update parameters.We evaluate this framework over seven target languages on three fundamental tasks and show its effectiveness and extensibility, by improving on F1 score up to 4% in singlesource transfer and 8% in multi-source transfer.To the best of our knowledge, our method is the first to apply instance weighting in zeroshot CLTC.It is simple yet effective and easily extensible into multi-source transfer.
Texting and Chatting Styles of Grade 11 Students : A Case in the Philippines

This study analyzed the text and chat messages of the students and its influences in students' written output/essay.Specifically, it analyzed the text and chat messages of the students as to style, word, formation, spelling, capitalization and punctuation and the significant relationship between text and chat messages.Also, it determined the influences of text and chat to the students' written outputs.This is a descriptive and qualitative study that made use descriptive and correlational design.It was conducted at So. Domingo National Trade School having 200 respondents who were in Grade 11.Data were gathered from the text messages and chats between the respondents and the researchers.Students' written outputs like essays was also gathered.Findings revealed that onomatopoeic spelling, and omitted apostrophes were mostly used by the respondents in text messages, while acronyms and initialisms, omitted apostrophes and emoticons were mostly present in their written outputs.There was also a negative trend of relationship between textism and written outputs.Therefore, textism did not affect the writing performance of the students.
Siamese Object Tracking Algorithm Combining Residual Connection and Channel Attention Mechanism

Aiming at the problem that Siamese tracking algorithm is easy to track drift or loss in complex situations such as target deformation and similar object interference, a target tracking algorithm combining residual connection and channel attention mechanism is proposed.First, the shallow structure features and the deep semantic features extracted from the template branch network are effectively fused through residual connections to improve the model's representational ability.Second, the channel attention module is introduced to make the model adaptively weighted to different semantic target feature channels to improve the generalization ability of the model.Finally, a weight mask based on correlation response values is designed and proposed to increase the weight of similar semantic target loss values during offline training, so that the model is enhanced discrimination of similar semantic targets in end-to-end offline learning.The results from comparative experiments with mainstream tracking algorithms on standard tracking datasets OTB, Temple-Color128, VOT2016 and VOT2018 show that the algorithm is highly competitive in tracking accuracy and success rate, with superior real-time performance and reliability.
Paradigm Clustering with Weighted Edit Distance

This paper describes our system for the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering, which asks participants to group inflected forms together according their underlying lemma without the aid of annotated training data.We employ agglomerative clustering to group word forms together using a metric that combines an orthographic distance and a semantic distance from word embeddings.We experiment with two variations of an edit distance-based model for quantifying orthographic distance, but, due to time constraints, our systems do not outperform the baseline.However, we also show that, with more time, our results improve strongly.
Technology Acceptance Model pada Sistem Informasi Akademik berbasis Web

Academic information system is an important system in students learning’s activities because it’s used as connector between students and lecturers. In addition, academic information system also acts as a liaison between lecturers and other relevant external parties. Academic information system provides several services, both for students and lecturers. These facilities include the provision of information on study plans, grades, schedules, and courses. However, only a few facilities are frequently used so that the use of the academic information system is not optimal.The problem in this study is the need for an evaluation to determine the acceptance of the academic information system by users, so that the system can be improved and developed. This study aims to determine the acceptance of web-based academic information system by users at STMIK Sinar Nusantara Surakarta with the Technology Acceptance Model or TAM.
Neural Machine Translation with Synchronous Latent Phrase Structure

It is reported that grammatical information is useful for machine translation (MT) task.However, the annotation of grammatical information requires the highly human resources.Furthermore, it is not trivial to adapt grammatical information to MT since grammatical annotation usually adapts tokenization standards which might not be suitable to capture the relation of two languages, and the use of subword tokenization, e.g., Byte-Pair-Encoding, to alleviate out-of-vocabulary problem might not be compatible with those annotations.In this work, we propose two methods to explicitly incorporate grammatical information without supervising annotation; first, latent phrase structure is induced in an unsupervised fashion from a multi-head attention mechanism; second, the induced phrase structures in encoder and decoder are synchronized so that they are compatible with each other using constraints during training.We demonstrate that our approach produces better performance and explainability in two tasks, translation and alignment tasks without extra resources.Although we could not obtain the high quality phrase structure in constituency parsing when evaluated monolingually, we find that the induced phrase structures enhance the explainability of translation through the synchronization constraint.
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles

This paper describes Humor-BERT, a set of BERT (Devlin et al., 2019) Large based models that we used to solve the SemEval-2021 Task 7: Detecting and Rating Humor and Offense (Meaney et al., 2021).It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble approach to solve various sub-tasks that were part of the challenge.We also present a comparative analysis of various models we tried.Our method was ranked 4 th in Humor Controversy Detection, 8 th in Humor Detection, 19 th in Average Offense Score prediction and 40 th in Average Humor Score prediction globally.F1 score obtained for Humor classification was 0.9655 and for Controversy detection it was 0.6261.Our user name on the leader board is ThisIstheEnd and team name is End-Times.
macech at SemEval-2021 Task 5: Toxic Spans Detection

Toxic language is often present in online forums, especially when politics and other polarizing topics arise, and can lead to people becoming discouraged from joining or continuing conversations.In this paper, I use data consisting of comments with the indices of toxic text labelled to train an RNN to determine which parts of the comments make them toxic, which could aid online moderators.I compare results using both the original dataset and an augmented set, as well as GRU versus LSTM RNN models.
Mapping and Prediction of Community Health Level in The East Java Region Using ANN Backpropagation Method

Predicting the level of public health in an area is very important to know the development of public health in order to give consideration to local governments in making policies that can increase the level of health and health services for the community. So we need a system that is able to make predictions and describe the development of public health in the years to come. The data used in the study were the percentage of the population who had health complaints and outpatient treatment for a month, the percentage of households that had access to proper sanitation, the percentage of households that had access to decent drinking water sources, the percentage of poor people, the average length of schooling, infant mortality rate, number of health centers and life expectancy. Backpropagation neural network method is a method that is often used in forecasting or prediction. Where this method can identify activities based on past data. Past data will be studied by neural networks so that they have the ability to make decisions on data that has never been studied. This method is expected to provide a prediction of the level of health by studying data from previous years. The results of this study produced a predictive map of the level of public health in the East Java region in 2019, 2020 and 2021 based on the model obtained from the training results with an epoch of 100 and an MSE value of 0.0173254.
ACCUMULATION OF DATA PERTURBATION TECHNIQUES FOR PRIVACY PRESERVING DATA CLASSIFICATION

In this work propose different data perturbation techniques that deal with multi dimensional data perturbation and achieve high privacy guarantee and zero- loss of accuracy, various perturbation techniques are used for different classifiers. The main aim is to perturb the multi dimensional data to preserve multi dimensional information. There are three different data perturbation techniques are proposed and their performance are compared. The three data perturbation techniques are Additive Perturbation, Multiplicative Perturbation, Rotation Perturbation. The Proposed system shows that the multiplicative and rotation perturbation makes balance between the privacy and accuracy is done effectively. The performance over the perturbed dataset is greater than the original dataset. Using the classifiers C4.5 and SVM the privacy and accuracy of the proposed techniques are compared.