Reviewerly/exHarmony huggingface dataset:
BARRIERS AND MOTIVATION FOR LEARNING ENGLISH: A CASE STUDY

In countries where learning English as a Second Language (ESL) is made compulsory, yet it is actually a foreign language to many learners, problems occur among learners. Language is the reflection of culture itself as the language can shape the society. Problems in language learning may occur due to several barriers. Firstly, cultural barrier is one of the barriers that is difficult to resolve. The barriers may influence the motivations to learn. The aim of this study is to investigate cultural barriers and motivation issues that might influence the process of learning English. 69 respondents participated in this quantitative study. The instrument used is a survey and it has 3 parts; 1) demographic profile, 2) culture and language learning and 3) motivation and language learning. Findings reveal that cultural barriers and motivational issues do have influence on learners’ language learning. Findings of this study bear interesting implications towards the teaching and learning of English as second language. &#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0629/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Developing European Key Competences with Green Education

Building&amp;Learning Project is action-research focused on the environmental consequences of technology. The didactic design is structured on the classic program proposed for the scholar discipline Technology for the first grade of secondary school; it tries to answer to the requirements of the National Plan for a Digital School (PNSD). Our first aim is to overcome the diktat “teach how to use the media” and reach the capacity to “teach with the media”, to avoid the danger of a “technocentric illusion” and to get the purpose to develop the competences proposed by Europe and Italy. Our teacher-researcher prepared every lesson to help the students to: a) get the disciplinary aim; b) understand the process and the consequences that every technology brings with itself; c) apply the theory in a practice activity. Every lesson is divided in two phases: the theoretical part is based on the method of Cooperative Learning, while the second part is a practice activity where students build with LEGO Education Kit a model that forecasts the application of the theory. After the first period, we introduced also a Story-telling part because the students showed great difficulty in expressing the theory explained. &#x0D; Keywords: European key competences - Media Education – Cooperative Learning – Sustainability education – STEM
PENGARUH KECERDASAN EMOSI, LINGKUNGAN SEKOLAH TERHADAP MOTIVASI BELAJAR DAN DAMPAKNYA TERHADAP PRESTASI BELAJAR SISWA

&lt;p align="justify"&gt;&lt;em&gt;This research is to analyze the effect of emotional intelligence, school environment on learning motivation and its impact on student achievement. The research methodology used is quantitative research using survey methods that are intended to provide an explanation. Survey research is research that takes a sample from a population and uses a questionnaire as a primary collection tool. There are 3 variables used, they are independent variable, mediation variable and dependent variable. Emotional intelligence and school environment as independent variables, learning motivation as mediating variables and learning achievement as dependent variables. The study involved as many as 89 students as respondents, the data were analyzed with SmartPLS 3.0. The results showed that emotional intelligence had a positive and significant effect on learning motivation, emotional intelligence had a positive and significant effect on learning achievement, the school environment had a positive and significant effect on learning motivation, the school environment had a positive and significant effect on learning achievement and learning motivation had a positive and significant effect towards learning achievement.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p align="justify"&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p align="justify"&gt; &lt;/p&gt;
Teaching Aid For Diagnosing Motorcycle Damages Using Back Propagation Artificial Neural Network

The challenge of learning media in the world within the next 1 to 2 years is Bring Your Own Device. It forces the learning paradigm to think quickly to follow the development of technology that can optimally use it. In the Control Systems II course, there are some stereotypes that some of the material is mainly an Artificial Neural Network (ANN) was limited to theory and simulations and is difficult to be applied. Teaching aids are interpreted as teaching material that is used to help teachers in carrying out the teaching and learning activities in the classroom. The purposes of this study are: (1) to create teaching aid for ANN material to diagnose motorcycle damage in the Control System II Course (2) to define the accuracy of the application of the teaching aid for the material of ANN in the Control System II Course. The prototyping approach model is used to generally define the teaching aid product that will be developed. In detail, the development methods include (1) listen to the customer, (2) build or revise a mock-up, and (3) customer test drives mockup. Teaching aids products are built in the form of application for the diagnosis of motorcycle damages using the Back-Propagation ANN. This application can detect four types of motorcycle damages based on the sample sounds of motorcycles included. The application can recognize the type of damage from 100 new sound data outside its knowledge-base with a 60% accuracy level.
Machine Learning Application for Classification Prediction of Household’s Welfare Status

Various approaches have been attempted by the Government of Indonesia to eradicate poverty throughout the country, one of which is equitable distribution of social assistance for target households according to their classification of social welfare status. This research aims to re-evaluate the prior evaluation of five well-known machine learning techniques; Naïve Bayes, Random Forest, Support Vector Machines, K-Nearest Neighbor, and C4.5 Algorithm; on how well they predict the classifications of social welfare statuses. Afterwards, the best-performing one is implemented into an executable machine learning application that may predict the user’s social welfare status. Other objectives are to analyze the reliability of the chosen algorithm in predicting new data set, and generate a simple classification-prediction application. This research uses Python Programming Language, Scikit-Learn Library, Jupyter Notebook, and PyInstaller to perform all the methodology processes. The results shows that Random Forest Algorithm is the best machine learning technique for predicting household’s social welfare status with classification accuracy of 74.20% and the resulted application based on it could correctly predict 60.00% of user’s social welfare status out of 40 entries.
VR-Enabled Chatbot System Supporting Transformer Mass-Customization Services

Chatbot is a conversational question answering (Q&amp;A) system capable of natural language communication between a computer system and a person. The use of chatbots for 24-hour customer service provides quick responses that solve problems online. This approach is quickly becoming a convenient way for companies to enhance their customer services without location or knowledgeable staff limitations. This research proposes a system framework and develops a prototype virtual reality (VR) enabled transformer mass-customization consultation chatbot. The chatbot technique is a retrieval-based intelligent system. First, thousands of transformer specific frequently asked questions (FAQs) are collected as a Q&amp;A dataset for technical supports retrieval. More than 1.2 million engineering Wikipedia pages and engineering technical papers are used to train a word embedding model used for natural language processing and question-answer retrieval. The chatbot is integrated into a virtual reality (VR) immersive user interface (UI) environment enabling users to make transformer design changes while querying the system about specifications and standards while interacting with 3D models from the company’s knowledge base archive. The system provides two unique UIs for personal computer (PC) and a helmet-based immersive interface. The system supports real-time consultation of mass-customized transformers and their bills of materials (BOM) for design review, analysis and cost estimation.
AR Support System for Therapy in 3 to 8-Year-Old Children with Altered Fine Motor Skills

In this project, an augmented reality (AR) system was designed and developed to contribute to therapy in children from ages 3 to 8 who suffer from fine motor skills disorder or seek to improve their skills during their neurodevelopment.The system was designed in the Unity engine along with a Leap Motion sensor so that the kids' hands can interact with scenarios created in the platform.The construction of the system followed the respective phases that describe the cascade methodology for the development of software systems, including the study of requirements and use cases.Additionally, it is intended that the progress of the child is tracked when he/she is performing a set of activities to develop fine motor skills.The Feine Motonik "FeMo" built module, has an 88.9% usability according to the study carried out in this article, which contemplates the test phase with the objective users who describe the situation of disorders in fine motor skills.
An experimental analysis of a GP hyperheuristic approach for evolving low-cost heuristics for profile reductions

Researchers used graph-theory approaches to design the state-of-theart low-cost heuristics for profile reduction. This paper evolves and selects four low-cost heuristics for profile reduction using a genetic programming hyperheuristic approach. This paper evaluates the resulting heuristics for profile reduction from the genetic programming hyperheuristic approach in two application areas against the low-cost heuristics for solving the problem. The results obtained on a set of standard benchmark matrices taken from the SuiteSparse sparse matrix collection indicate that the resulting heuristics from the genetic programming hyperheuristic approach does not compare favorably with a high-quality heuristics for profile reduction.
Frequency-Multiplexed Rate-Adaptive Quantum Key Distribution with High-Dimensional Encoding

We offer a photon-efficient QKD protocol based on energy-time entangled biphoton frequency combs that supports multiplexing and rate adaptivity under various channel conditions.
Consistent batch normalization for weighted loss in imbalanced-data environment

In this study, classification problems based on feedforward neural networks in a data-imbalanced environment are considered. Learning from an imbalanced dataset is one of the most important practical problems in the field of machine learning. A weighted loss function (WLF) based on a cost-sensitive approach is a well-known and effective method for imbalanced datasets. A combination of WLF and batch normalization (BN) is considered in this study. BN is considered as a powerful standard technique in the recent developments in deep learning. A simple combination of both methods leads to a size-inconsistency problem due to a mismatch between the interpretations of the effective size of the dataset in both methods. A simple modification to BN, called weighted BN (WBN), is proposed to correct the size mismatch. The idea of WBN is simple and natural. The proposed method in a data-imbalanced environment is validated using numerical experiments.
Sequences of numbers via permutation polynomials over some finite rings

A polynomial can represent every function from a finite field to itself. The functions which are also permutations of the field give rise to permutation polynomials, which have potential applications in cryptology and coding theory. Permutation polynomials over finite rings are studied with respect to the sequences they generate. The sequences obtained through some permutation polynomials are tested for randomness by carrying out known statistical tests.
Penerapan Pembelajaran Autonomi dalam Meningkatan Keterampilan Membaca Text Bahasa Inggris

To describe how autonomous learning increases students’ English text reading skills. The research was conducted at Faculty of Economics, MH. Thamrin University, East Jakarta. The result of the research is as follows: (1) autonomous learning implementation at Faculty of Economics, MH. Thamrin University is generally good. The achievement of all indicators of autonomous learning is very high. (2) Students’ English text reading skills at Faculty of Economics, MH. Thamrin University is generally very good. The achievement of all indicators of reading skills strategies is very high. (3) Autonomous learning has been successfully implemented to increase the students’ English text reading skills at Faculty of Economics, MH. Thamrin University. Based on the research result, the students’ English text reading skills could be enhanced by deeply implementing the indicators of autonomous learning through 1). Developing self-awareness, 2). Developing effective learning strategies, 3). Developing thinking skills, 4). Developing organizational skills, 5). Developing cooperative skills, 6). Developing metacognitive skills, 7). Developing problem solving dan supporting others. It proves that autonomous learning can increase students’ English text reading skills.
Decryption speed up of ElGamal with composite modulus

Public key cryptosystems such as RSA, rebalanced RSA and ElGamal have the disadvantage of serious asymmetry between encryption and decryption speed. We reduced the CRT (Chinese Remainder Theorem) exponents maintaining full sized private exponent in ElGamal with composite modulus (CRT–ElGamal) for the fast decryption as in rebalanced RSA. In this case, unlike rebalanced RSA, decryption speed up can be obtained without losing of the fast encryption speed which is comparable to RSA with small public exponent. As a result, it is possible to propose the fast public key cryptosystem in which both encryption and decryption are fast, by reducing the asymmetry (i.e., fast encryption/slow decryption) in CRT–ElGamal encryption.
An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification

Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. (RNA-seq) Ribonucleic acid sequencing is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-Seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy.
THE EFFECTIVENESS OF CLASS DISCUSSION LEARNING METHOD BY USING CISCO WEBEX APPLICATION IN IMPROVING STUDENTS’ SPEAKING SKILLS AND LEARNING OUTCOMES AT THE ENGLISH LANGUAGE DEPARTMENT OF UNIVERSITY OF MUHAMMADIYAH TANGERANG

Abstract&#x0D; This study aims to determine the effectiveness of the use of class discussion learning models by using the Cisco Webex application in improving students’ speaking skills and learning outcomes at the University of Muhammadiyah Tangerang. The research method was used a quasi-experimental research method with the research samples were students who took the speaking subject in the English Study Program. The research design used pretest and posttest control group design. A different test was conducted to measure the differences in the effectiveness of using learning media of Cisco Webex application and using of other applications learning media based on learning outcomes. It can be concluded that the level of effectiveness of class discussion method using of Cisco Webex application learning media in the experiment class is higher than the control class using other applications learning media 82.25 &gt; 80.52.
Petri Type 2 Fuzzy Neural Networks (PT2FNN) for Identification and Control of Dynamic Systems—A New Structure and a Comparative Study

The techniques and theories used for identification and control of uncertain dynamic systems based on the universal approximators are diverse, but can mainly be divided into two categories, some are basic universal approximators as fuzzy systems (FS), neural networks (NN) and wavelet neural networks (WNN). And other hybrid approximators which are combinations between two basic approximators as fuzzy neural wavelet networks (FWNN) and petri fuzzy systems (PFS). These approximators can be used to estimate the dynamic of system in an aim to construct a controller, as we can use it directly to estimate the control law using specific learning algorithms. In this paper, a hybrid structure between type 2 fuzzy logic and petri networks (PT2FNN) is proposed as a new approximator to alleviate the problem of uncertainties with an optimal cost. By incorporating Petri layers, the number of rules is optimized. Moreover, the time consumed is reduced using a new inference type 2 method. The parameter update algorithms are derived based on the gradient method. This hybrid structure is tested for the identification and the control of uncertain dynamic plants. The simulation results showed that the proposed structure performs better compared with other approximators.
Media Pembelajaran Mobuya Untuk Pembelajaran Keragaman Budaya

The purpose of this research is to develop thematic learning media materials the beauty of cultural diversity through learning media development mine own country Mobuya in elementary school. Media development methods in this study is a ADDIE Model (analysis, design, development, implementation, and evaluation). Media developed consists of learning Mobuya media, material cultures in West Kalimantan, and the manual, as for the results of validation include validation of the material retrieved 92.50% with a very worthy category, validation of the syllabus obtained 92, 30% with a very worthy category, validation RPP gained 93, 33% by category is well worth the first learning media validation, retrieved 70, 83% by category are fit enough for the second validation obtained 74, 19% by category is pretty decent. The result of the question form teacher on learning Mobuya learning media on the 22nd of January 2019 obtained results of 88.75% question form the application of learning media use by category is very viable, next on 23 January 2019 results obtained from the implementation of learning by using learning media of 96, 25% by category is very worthy to be used on the learning process in schools thematic basis. Limited trials retrieved retrieved from sngket that results in a spread on the learning process gained 97, 53%. Field trials retrieved pretest results with an average of 54, 70 and post test obtained an average of 68, 4.
A Multicluster Approach to Selecting Initial Sets for Clustering of Categorical Data

Aim/Purpose: This article proposes a methodology for selecting the initial sets for clustering categorical data. The main idea is to combine all the different values of every single criterion or attribute, to form the first proposal of the so-called multiclusters, obtaining in this way the maximum number of clusters for the whole dataset. The multiclusters thus obtained, are themselves clustered in a second step, according to the desired final number of clusters. Background: Popular cluster methods for categorical data, such as the well-known K-Modes, usually select the initial sets by means of some random process. This fact introduces some randomness in the final results of the algorithms. We explore a different application of the clustering methodology for categorical data that overcomes the instability problems and ultimately provides a greater clustering efficiency. Methodology: For assessing the performance of the proposed algorithm and its comparison with K-Modes, we apply both of them to categorical databases where the response variable is known but not used in the analysis. In our examples, that response variable can be identified to the real clusters or classes to which the observations belong. With every data set, we perform a two-step analysis. In the first step we perform the clustering analysis on data where the response variable (the real clusters) has been omitted, and in the second step we use that omitted information to check the efficiency of the clustering algorithm (by comparing the real clusters to those given by the algorithm). Contribution: Simplicity, efficiency and stability are the main advantages of the multicluster method. Findings: The experimental results attained with real databases show that the multicluster algorithm has greater precision and a better grouping effect than the classical K-modes algorithm. Recommendations for Practitioners: The method can be useful for those researchers working with small and medium size datasets, allowing them to detect the underlying structure of the data in an intuitive and reasonable way. Recommendation for Researchers: The proposed algorithm is slower than K-Modes, since it devotes a lot of time to the calculation of the initial combinations of attributes. The reduction of the computing time is therefore an important research topic. Future Research: We are concerned with the scalability of the algorithm to large and complex data sets, as well as the application to mixed data sets with both quantitative and qualitative attributes.
Analisis Kebutuhan Bahasa Inggris Pramusaji di Hotel Berbintang 5 di Bali

This study aimed at analyzing the English language skills functions and expressions needed as a waiter/ss. This research was a qualitative descriptive. The method used in obtaining the required information were interviews and documentation, in which subjects in this research were Asst. F&amp;B Service Manager, Restaurant Manager, Supervisor, and Restaurant Staff in 5-Star Hotel in Bali. The results of this study indicated that (1) Speaking &amp; listening skills are the main poins that must be mastered by a waiter/ss, and the accuracy of grammar and fluency in communication also have an equally important role, (2) The expression of the language used adjusts to predetermined language functions based on the implementation of Sequence of Service (SOS) in restaurants. From the results of this study it may be necessary to further research on analyzing the needs of English Waiters in a broader scope.