Reviewerly/exHarmony huggingface dataset:
The Use of Animation Video for Vocabulary Mastery

This study discussed an important aspect in learning English, namely the vocabulary. The teachers must have an easy, practical and interesting way to teach the vocabulary. Animated videos are considered as the right medium and very useful for teaching. This research focused on the effective use of animated cartoon videos and increasing the use of animated videos in improving the students’ learning mastery at grade IIV of Mayang 01 Junior High School. The purpose of the study was to determine the effectiveness and significance of the implementation or not using the technique itself. This research was quantitative. The method used in this research was by using test to collect instruments in collecting data. From the results of the research, it can be concluded that the students really like learning by using animated videos or cartoons. The respondents also are very positive about this media and students became more active in learning English. In conclusion, the use of video can be applied to all students.
Master class by winners and laureates of national competitions on pedagogical mastery as a form of translating experience in development of soft skills in students – future pedagogues

Introduction. Many of future professionals often neglect such important skills as communication, time management or teamwork. Employers are not only looking for employees with a standard set of technical skills, but also want their employees to have necessary “flexible skills”, i.e. soft skills. This is because such skills particularly enable one to realise effective and rational use of technical skills and knowledge. The aim of the study is to provide an analysis of the views of winners and laureates of national pedagogical excellence contests relative to the master class as a form of development of future teachers’ soft skills. Materials and methods. The questioning involved 88 teachers, laureates and winners of pedagogical mastery competitions. The respondents were presented with an authorly questionnaire “Master class as a form of translating experience” comprising 11 questions related to their experience of pedagogical work. The methods of mathematical statistics, such as measure of central tendency and variability; correlation analysis, were used. Survey results. The majority of the respondents (67%) consider the master class as an effective form of transferring knowledge and skills, sharing learning and teaching experience. At the same time, according to almost half of the pedagogues, the master class as a form of transferring experience in development of soft skills is fundamentally different from the traditional lesson. The correlation analysis showed that the extent of teachers’ motivation for using the innovator teachers’ experience in the former’s work does not depend on the age and pedagogical record, but depends on their awareness (r = 0.338; p &lt; 0.05). According to the teachers, the most important abilities characterising the pedagogical mastery of a modern teacher are subject competence and learner-centeredness. The most important skills for the respondents are those forming subject-centered relations (“how I understand myself and others”; “how I act”; “how I manage myself”) which set the format of teacher-student cooperation. Conclusion. The master class can be viewed as a form enabling: projecting unique situations where students learn to think independently, work in a team; productive transfer of best pedagogical practices, etc. Among the main functions of master classes are: mentoring as an efficient form of transferring experience to young pedagogues; reflexion on own professional mastery, setting a trajectory of individual self-education and self-perfection programme.
PEMEROLEHAN KOSAKATA PADA ANAK USIA DINI DI PAUD AL-HADID MELALUI PERMAINAN, NYANYIAN, DAN BERCERITA

This research aims to describe the types of vocabulary that appear inthe language of children aged 4 –5 years old at PAUD Al-Hadidthrough games, singing and telling stories as well as viewed frompsycholinguistic behaviorism. This research uses psycholinguisticbehavior theory. The research method used is descriptive with aqualitative form the data source of this research is the language ofchildren aged 4-5 years old which are obtained from playing, singingand telling stories. The data collection techniques used in this researchare tapping techniques, listening techniques, recording techniques, andnote taking techniques, drawing techniques. The results of this studyconclude that the aspect of linguistic behavior that can be observeddirectly and the relationship between (stimulus) and reaction(response) to effective language behavior is making reactions tostimuli. Behavior according to the psychology of behaviorism onlyexamines real observable events, namely human behavior. The typesof vocabulary that appear through playing, singing and telling storiesin children aged 4 –5 years old, there are six types of vocabulary thatappear, namely the vocabulary of substantive, adjective, names, partof body, numbers, verb and self-change (self, clues).
Multi-graph Convolutional Feature Transfer for Cross-domain Recommendation

Cross-domain recommendation(CDR) is an effective method to alleviate the data sparsity problem in the recommendation system. How to learn common and domain-specific feature embeddings of users and items is a challenge especially where the two domains do not completely share users. Based on the superiority of graph structure feature learning, we propose a method of multi-graph convolutional feature transfer for cross-domain recommendation (MGCDR). For the common feature learning, MGCDR applies the feature similarity method to construct the residual network with the overlapping users, and then pre-training the feature embedding of the overlapping users. For the specific feature learning, MGCDR transfers the pre-trained embedding to initialize the spcific-domain graph, and obtain specific features through domain-specific users information propagation. Moreover, the attention mechanism is adopted to adaptively fuse the feature embedding. MGCDR is a dual-target recommendation model that fully exploits cross-domain mutual enhancement and joint training. Experiments on two pairs of real-world cross-domain datasets show the effectiveness of MGCDR.
Statistical inference for normal mixtures with unknown number of components

Statistical inference for normal mixture models with unknown number of components has long been challenging due to the issues of nonidentifiability, degenerated Fisher matrix, and boundary parameters. In this paper, a penalized likelihood estimation procedure is proposed for mixtures of normals with unknown number of components to achieve both the order selection consistency and the root-n convergence rate for the component parameters estimators. We show that the proposed new estimator could avoid being trapped in certain degenerated regions of the nonidentifiable subset of the parameter space for over-fitted normal mixture models so that a regular asymptotic quadratic Taylor expansion of the mixture log-likelihood could be derived. With a suitable penalty function on mixing proportions, the new estimator is proved to be consistent on the order selection, and have an asymptotic normal distribution. Our derived sparsity conditions also reveal some surprising but interesting differences among some commonly used penalty functions and explain why the performance of some popularly used penalty functions, such as Lasso and SCAD, provide unsatisfactory results in the order selection. Extensive simulations and a real data analysis are conducted to demonstrate the effectiveness of the newly proposed estimator.
The Impact of Visualizing Design Gradients for Human Designers

Mixed-initiative Procedural Content Generation (PCG) refers to tools or systems in which a human designer works with an algorithm to produce game content. This area of research remains relatively under-explored, with the majority of mixed-initiative PCG level design systems using a common set of search-based PCG algorithms. In this paper, we introduce a mixed-initiative tool employing Exhaustive PCG (EPCG) for puzzle level design to further explore mixed-initiative PCG. We run an online human subject study in which individuals use the tool with an EPCG component turned on or off. Our analysis of the results demonstrates that, although a majority of users did not prefer the tool, it made the level design process significantly easier, and that the tool impacted the subjects' design process. This paper describes the study results and draws lessons for mixed-initiative PCG tool design.
Implementation of the K-Nearest Neighbor Algorithm for the Classification of Student Thesis Subjects

Students who have studied for a considerable amount of time and will complete a lecture process must complete the necessary final steps. One of them is writing a thesis, a requirement for all students who wish to graduate from college. Each student's choice of topic or specialization will be enhanced if it not only corresponds to their interests but also to their skills. K-Nearest Neighbor is one of the classification techniques used. K-Nearest Neighbor (KNN) operates by determining the shortest distance between the data to be evaluated and the K-Nearest (neighbor) from the training data. K-Nearest Neighbor is utilized to classify new objects based on the learning data closest to the new object. Therefore, KNN is ideally suited for classifying data to predict student thesis topics. This research concludes that optimizing the k value using k-fold cross-validation yields an accuracy rate of 79.37% using k-fold cross-validation = 2 and the K-5 value. Based on the K-Nearest Neighbor Algorithm classification results, 45 students are interested in computational theory thesis (RPL) topics, 32 students are interested in artificial intelligence (AI) thesis topics, and 21 students are interested in software development topics.
Brain Depletion Recognition Through Iot Sensors Empowered with Computational Intelligence

The world has been inspired by the development of Internet of Things enabling applications in recent years, which have brought outstanding solutions to a number of problems. This fast-growing industry is led by wireless sensor networks, radio frequency identification, and smart mobile technologies. Smart medical devices and wearables, for example, play an important part in the Internet of Things, as they may collect a variety of longitudinal patient-generated health data while also presenting preliminary diagnosis options. As part of their efforts to support patients utilizing IoT-based solutions, experts exploit the capabilities of machine learning algorithms to provide effective solutions in bleeding detection. This chapter outlines a smart IoT-based system for human brain haemorrhage diagnostics that employs deep learning algorithms to lower fatality rates and provide accurate treatment suggestions. To classify the pictures from the computed tomography scans for the intracranial dataset, the Nave Bayes Classification and Recurrent Neural Network were utilized. The classification results for the Naive Bayes classification and Recurrent neural network are superior to prior techniques such as support vector machines, knn, and kmediods. According to the findings, the recurrent neural network outperforms at classifying intracranial imagery. The output of the classification tool includes information on the type of brain haemorrhage, which aids in the validation of an expert’s diagnosis and is utilised as a learning tool for trainee radiologists to eliminate errors in current systems.
Islamic Education Values in the Implementation of ‘Gernasbaku’ in Muslim Families

The low literacy rate in Indonesia has attracted the attention of social elements, from the roots to the top of the leadership. The government is trying to improve literacy by launching the Gernas Baku program (National Movement for Parents to Read Books) to get children to like reading from an early age. This study aims to identify the value of education in the Gernas Baku program implemented by Muslim families of female lecturers at the Faculty of Islamic Studies, UMY. This study uses a qualitative approach with data collection techniques through interviews and documentation. The results showed that: 1) Gernas Baku program aims to support the initiative and active role of families in growing children's interest in reading through habituation at home; 2) Successful reading habits are supported by facilities provided by the family such as a reading corner or library at home, and children are invited to various places related to literacy; 3) Achievements from early childhood female lecturers of FAI UMY aged 2-4 years who have the characteristics of being honest, caring socially, being curious, appreciating achievements and liking to read; 4) Supporting factors for the Gernas Baku program are the cooperation of both parents, the interest and awareness of parents in reading assistance so that children can read and review the contents of the books they read. While the inhibiting factor is the lack of consistency of parents in providing assistance.
BARRIERS TO AUGMENTED REALITY APPLICATION IN CONSTRUCTION: AN EXPLORATORY STUDY IN MALAYSIA

Malaysian construction industry is always labelled as ancient industry because of resistance to change or adopt new technology in their working process. The construction industry in Malaysia is slow in adopting new technology compared to other industries. Therefore, it has a bad reputation for producing good quality products, and complete within time frame and cost. These problems arose because of failures to effectively communicate among parties in the construction project due to the complexity of the construction project. In the era of digitalisation, the utilisation of new technology could help the construction industry overcome the issues of processing complex information. The usage of Augmented Reality (AR) is one of the approaches. AR technology has gained much attention in the construction industry on how it potentially helps solve common problems in construction. AR integrates digital information with a real-time user environment to resolve communication issues in complex projects. Besides that, AR can minimise 2D drawing by digitally visualising the end product of a construction project. However, despite all the benefits AR can offer, the implementation of AR in Malaysian construction is still lagging. Therefore, the aim of this study is to determine the factors hindering the application of AR in Malaysian construction industry. In this study, a questionnaire survey was conducted to obtain the possible hindering factors in implementing AR among Malaysian construction companies. Relative Importance Indices (RII) were utilised to analyse the data. In this study, the questionnaires were distributed via email to 260 potential respondents and only 105 responded, which represents 40% of the response rate. From the analysis, it was revealed that the main factors that could hinder the AR implementation are: lack of knowledge about AR, lack of training on AR application, higher cost in investing AR technology and infrastructure, and absence of push from government and clients. In conclusion, to enable the progressive use of the technology, it is important to address the actual factors hindering the implementation of AR in Malaysian construction. At the same time, every party in the construction industry should play a role in promoting the use of AR in their construction project.
Secure and Efficient Implementation of Electronic Money

Abstract During the last years, central banks have discussed possible use of central bank digital currencies (CBDC) – electronic cash. Besides the financial and economic factors also the security and scalability of technical implementation of CBDC have been studied. Blockchain technology provides high level of security independent of the technical infrastructure and enables central banks to outsource most of the CBDC operations to the private sector while still having full control over the total amount of CBDC in circulation. Scalability of blockchain solutions depends on the possibility of decomposing (sharding) the blockchain. Electronic money and payments can be represented and organized in several ways, including accounts, bills/coins, and also unspent transaction outputs (UTXOs). We show how the representation of money and payments influences the existence of secure and decomposable blockchain implementations of electronic money. We show that the bill money scheme can be securely and efficiently implemented as sharded blockchains.
HAVING EXPERIENCE IN DEVELOPING THE EDUCATIONAL PROGRAM “GEODESY AND LAND MANAGEMENT”(SPECIALIZATION “LAND MANAGEMENT AND CADASTRE”) AT THE YURI FEDKOVYCH NATIONAL UNIVERSITY OF CHERNIVTSI

Substantiation of the educational program that provides training of highly competitive professionals who are able to actively operate in a market economy and social partnership; development of scientific priorities and innovation component. Improving the published educational and professional program within thecyclical process of development and implementation of educational and professional programs in accordance with the methodology of student-centered learning.In the process of development and further implementation of the educational program applied definition, generalization, identification, analysis, synthesis, induction and deduction, idealization, analogy, selective and continuous observations, control measurements, inventory, systematization, expertise, technological research, method of hypotheses, experiment and modeling.The structure and features of the standard of higher education approved by the Ministry of Education and Science of Ukraine at the first (bachelor's) level of knowledge 19 –Architecture and Construction, specialty 193 "Geodesy and Land Management" are analyzed. The current level of information and methodological support for the development of educational programs has been clarified. The peculiarities and essence of each of the stages of development of the educational program inaccordance with the methodology of student-centered learning are considered.The author's educational program that ensures the achievement of additional learning outcomes is proposed and measures to achieve them are justified. The spheres of use and potential users of the educational-professional program are presented. The educational and professional program is published on the official website, and the features of its components –in this publication in order to share experiences, receive recommendations, comments, suggestions on its content, components, specifics of implementation and optimization.The article is review and informative
Credit Card Fraud Detection Using Machine Learning Model

The most frequent issue in the modern world is detection of credit card fraud. The rise of e-commerce platforms and digital transactions is to blame for this. In most cases, credit card fraud occurs when the card is stolen and used for any unauthorised activity, and even when the scammer utilises the card’s information for his own gain. We have a range of credit card issues in the modern world. The credit card theft detection technology was introduced to identify fraudulent actions. The proposed work aim is to build and create a novel fraud detection algorithm for Streaming Transaction Data, with the goal of analysing customers’ historical transaction details and extracting behavioural patterns. The dataset for Cardholders transaction is created and classifying each transaction for genuine and fraud. Then, ML classifier, aggregate the transactions performed by cardholders from various categories in order to derive the behavioural patterns of the various groupings. Later, Random Forest Classifier (RFC) is trained over dataset and accuracy is evaluated for the classifier which resulted above 75.
Perbandingan Metode Dempster-Shafer dan Certainty Factor Untuk Mendiagnosa Jenis Gangguan Bipolar Berbasis Website

Bipolar is a mental illness that have very drastic emotional transformation. Bipolar disorders can interfere sufferer's activity if it has not been detected early. In general, the bipolar disorders sufferers difficult to control their circumstances or moods and it requires the proper treatment of the expert. An expert system to diagnose the kind of bipolar disorders built on this study using comparative methods of Depmster-Shafer and certainty factor based on website. The main expectation of this expert system application is to make it easier for the expert to diagnose bipolar disorders as well as to speed up the consultation process. In addition, this study aims to compare the two methods to determine the best method. The accuracy of this study is 93% of the certainty factor method while for the Dempster-Shafer method is 80% which can be summarized that certainty factor method is better than Dempster-Shafer in diagnose bipolar disorders because it has a higher degree of accuracy.
AroundArrow: Off-Screen POI visualization for handheld Augmented Reality in vertically dense regions

Augmented Reality (AR) applications often need to represent the user's points of interest (POIs) on the handheld display. Because of the limited field of view, certain POIs remain off-screen. In order to make the user aware of the off-screen POIs, researchers have proposed many visualization techniques. However, those techniques are not suitable for visualizing the vertically spread off-screen POIs. Moreover, visualizing many off-screen POIs (for a dense environment) on the device screen may create visual clutter. Addressing these issues, in this paper, we present AroundArrow, a technique to visualize off-screen POIs using 3D arrows. We use a decluttering technique that uses a system-independent recommendation system to prioritize POIs on directional basis. We evaluated this technique by comparing it with the state of the art techniques, namely Halo3d and Arrow2D. We found that AroundArrow performs significantly better than these techniques for distinguishing the distribution of off-screen POIs in terms of verticality. For the decluttering system, an empirical study was performed to establish an ideal range of a parameter, namely Cluttering Threshold (CT).
Parameter estimation of Gaussian mixture models (GMM) with expectation maximization (EM) algorithm

Bayesian Network (BN) is a model that applies Bayes principle with assumption that input variables can be interdependent. BN is described as a graph consisting of nodes and arcs. Node shows variables while arc shows relationship between nodes. Combined probability distribution between nodes in BN is built using gaussian mixture models (GMM) which is a type of density model consisting of components of Gaussian functions. There are 3 of mixture models, probability mixture model, parametric mixture model and continuous mixture. GMM parameters can be estimated using expectation maximization (EM) algorithm. EM algorithm is an iterative method that involves expectation (E-step) and maximization (M-step) and is often used to find estimated value of Maximum Likelihood (ML) of parameters in a probabilistic model, where the model also depends on unknown latent variables. E-step is calculating the expectation value of the log-likelihood function, while M-step maximizes the expected value of the log-likelihood function. Advantage of EM algorithm is that it can solve mixed function parameter estimation problems as well as parameters from incomplete data. EM algorithm can solve the log-likelihood function problem which is difficult to solve by simple analysis by assuming the existence of a value for an additional but hidden parameter.