Reviewerly/exHarmony huggingface dataset:
Implementation of the Problem Base Learning Model combined with E-STEM Based Student Worksheets on Learning Outcomes and Self Efficacy on Environmental Pollution Materials

The learning process has not emphasized the development of skills; this is because in learning students tend to memorize concepts so that learning outcomes and self-efficacy are not trained optimally. The formulation of the problem in this study is: is there a difference in learning outcomes and student self-efficacy between the use of the Problem Base Learning model combined with the E-STEM-based Student Worksheet with the Problem Base Learning model on environmental pollution material at MAN Banda Aceh? This research is quantitative with an experimental research approach. The research design used was pretest-posttest control group design. The population of this study was all students of class X MAN 3 Banda Aceh, totaling 84. The sampling technique used a total sampling of 84 students. The instrument used to measure learning outcomes is multiple choice questions using 30 questions and self-efficacy is measured using a questionnaire. Data analysis of learning outcomes and self-efficacy of students using independent sample t-test at a significant level of 0.05. The results showed that there were differences in student learning outcomes and self-efficacy after applying the Problem Base Learning model combined with the E-STEM-based Student Worksheet on environmental pollution materials at MAN Banda Aceh
A new framework for Arabic recitation using speech recognition and the Jaro Winkler algorithm

Automated recitation plays an important role in improving self-learning. It is based on Speech/Text recognition. The research in Arabic speech recognition is very limited. The few existing applications are only based on the Holy Qur’an. This article proposed a new system (Samee’a - ) to facilitate memorizing any kind of text such that poems, speeches and the Holy Qur’an. Samee’a system is based on Google Cloud Speech Recognition API to convert the Arabic speech to text and Jaro Winkler Distance algorithm to determine the similarity between the original and converted texts. The system has been tested using 70 collected files ranging between 12 to 400 words and some chapters from the Holy Qur’an. The average similarity achieved 83.33% for the 70 files and 69% for the selected chapters of the Holy Qur’an. These results were enhanced to 91.33 % and 95.66% after applying preprocessing operations on the text files and the Holly Qur’an respectively. To validate the obtained results, two comparison studies were performed. The Jaro Winker distance was successfully compared to the cosine and the Euclidean distance. In addition, the proposed system outperformed the related work with an improvement of the similarity reaching 5% when using section 30 of the Holy Qur’an. Finally, the user experience testing was carried out by 10 users of different ages (between 5 and 50-year-old) using small texts and some small chapters of the Holy Qur’an. The proposed system proved its efficiency.
A Chaotic-Map-Based Password-Authenticated Key Exchange Protocol for Telecare Medicine Information Systems

Telecare medicine information systems (TMISs) provide e-health services such that patients can access medical resources conveniently and doctors can prescribe treatments rapidly. Authentication is an essential security requirement in TMISs. In particular, the growth of password-based remote patient authenticated key exchange combining extended chaotic maps has enhanced the level of secure communications for TMISs. Recently, Lee suggested an improved random-number-based password-authenticated key exchange (PAKE) using extended chaotic maps and synchronized-clock-based PAKE using extended chaotic maps on Guo and Zhang and Xiao et al.’s PAKE. Unfortunately, we found that the nonce-based scheme of Lee is insecure against known session-specific temporary information and server spoofing attacks. To cope with the aforementioned defects, this study aims to provide a new secure PAKE based on extended chaotic maps with more security functionalities for TMISs. Additionally, we show that the proposed scheme for TMISs provides high security along with low communication cost, computational cost, and a variety of security features.
A Hybrid Movie Recommender System and Rating Prediction Model

In the current era, a rapid increase in data volume produces redundant information on the internet. This predicts the appropriate items for users a great challenge in information systems. As a result, recommender systems have emerged in this decade to resolve such problems. Various e-commerce platforms such as Amazon and Netflix prefer using some decent systems to recommend their items to users. In literature, multiple methods such as matrix factorization and collaborative filtering exist and have been implemented for a long time, however recent studies show that some other approaches, especially using artificial neural networks, have promising improvements in this area of research. In this research, we propose a new hybrid recommender system that results in better performance. In the proposed system, the users are divided into two main categories, namely average users, and non-average users. Then, various machine learning and deep learning methods are applied within these categories to achieve better results. Some methods such as decision trees, support vector regression, and random forest are applied to the average users. On the other side, matrix factorization, collaborative filtering, and some deep learning methods are implemented for non-average users. This approach achieves better compared to the traditional methods.
Real Time Mobile Ad Investigator: An Effective and Novel Approach for Mobile Click Fraud Detection

Today, mobile advertising is considered as the most effective medium to convey promotional messages to customers because of the excessive usage of mobile phones and tablets all around the world. However, this ecosystem has severely been affected by fraudulent activities due to a large sum of money circulated in the advertising industry. The term ad fraud is referred to as any kind of fraudulent activities that are executed by fraudulent users either a human or an automated script. The combat between researchers and fraudulent users never ends because more smarter strategies are being used by the fraudsters to bypass the significant number of detection and prevention solutions. The Real Time Mobile Ad Investigator-RTMAI is proposed as a software solution to address this problem where a novel supervised learning algorithm based on the hidden Markov model along with a rule engine have been proposed to classify fraudulent impressions in real time. Furthermore, RTMAI proposed a solution to address the class imbalance problem which is generic to most of the classification datasets. The experimental results show the significance of the proposed approach to classify the fraud or non-fraud clicks/events, impressions and even user sessions more confidently in real time.
Digital Transformation Of The Didactic Space Of Professional Training Of Teachers

The article substantiates the relevance of socio-cultural transformations in determining the strategy of education digitalization.The need for a digital educational space for professional training of teachers on the basis of digital transformation of the didactic process is revealed.Using the results of theoretical research, the factors and patterns of the digital educational process are identified: requirements of the digital economy to teachers, the didactic potential of modern digital technologies, the need for the pedagogical management of network generation Z socialization and instrumental approaches.The leading ideas of the approaches made it possible to present the features and main provisions of the subject-oriented digital educational environment for professional training of teachers and formulate its main components.Considering the content of the didactic space in the professional training of teachers, it is advisable to correlate it with methods, tools and digital technologies that create new opportunities for the educational process and a wide range of educational tasks.The organizational, methodological and substantive aspects of digital transformation of the didactic space are discussed.The methodological tools for professional training of teachers based on flexible transformation depending on professional and personal interests, as well as the saturation of the digital educational environment, are described.Digital transformation involves qualitative changes in the content and organization of the educational process, methods, forms, technologies, the role of the teacher in accordance with the conditions, capabilities, resources of the digital environment for self-realization and personal and professional development of educational subjects.
I Keep Counting: An Experiment in Human/AI Co-creative Songwriting

Musical co-creativity aims at making humans and computers collaborate to compose music. As an MIR team in computational musicology, we experimented with co-creativity when writing our entry to the “AI Song Contest 2020”. Artificial intelligence was used to generate the song’s structure, harmony, lyrics, and hook melody independently and as a basis for human composition. It was a challenge from both the creative and the technical point of view: in a very short time-frame, the team had to adapt its own simple models, or experiment with existing ones, to a related yet still unfamiliar task, music generation through AI. The song we propose is called “I Keep Counting”. We openly detail the process of songwriting, arrangement, and production. This experience raised many questions on the relationship between creativity and machine, both in music analysis and generation, and on the role AI could play to assist a composer in their work. We experimented with <em>AI as automation</em>, mechanizing some parts of the composition, and especially <em>AI as suggestion</em> to foster the composer’s creativity, thanks to surprising lyrics, uncommon successions of sections and unexpected chord progressions. Working with this material was thus a stimulus for human creativity.
The STEM Methodology and Graph Theory: Some Practical Examples

In this paper, we highlight that Graph Theory is certainly well suited to an applications approach. One of the basic problems that this theory solves is finding the shortest path between two points. For this purpose, we propose two real-world problems aimed at STEM undergraduate students to be solved by using shortest path algorithms from Graph Theory after previous modeling.
Using virtual reality to enhances efforts for introduce cultural heritage

At present, cultural heritage is in a dangerous condition and it is all influenced by several factors such as natural disasters and man-made disasters. For example, in Indonesia in March 2018 there was a destruction of the Dayak Indigenous Sites whose cases were damaged due to human factors, not to mention talking about the destruction of damaged cultural sites such as in several countries such as Iraq, Syria, Afghanistan &amp; Indonesia. The use of technology-based Virtual Reality (VR) in maintaining cultural heritage is very important because the identity that is in cultural heritage is the foundation of evidence of human life in the past. Threats in the form of human behavior and natural disasters have an impact of erasing historical and cultural traces which could threaten a nation in relation to the main national interests of a country. Maximizing the benefits of technological advancements by using Virtual Reality as an alternative to preserve cultural heritage &amp; cultural exploration, this utilization can lower the cost of study of cultural exploration such as exploration of cultural heritage locations that are far especially in transport costs.
Fraseologismi con la componente somatica cuore nella lingua croata e italiana. Approccio contrastivo

The article provides a cognitive analysis of phraseological units with the heart component in comparative terms. The purpose of the analysis is to show the similarities and differences in expressing emotions (positive, neutral and negative) between two languages originating from different linguistic groups: the Croatian language (South Slavic group) and the Italian language (from the group of the Romance languages). Phraseological units are analysed on the basis of three criteria: identical in both languages; partially adequate in both languages; idiomatic for one language. The research presents the motivation of phraseologisms and aims to prove that the heart is related to human emotional life and is a container for feelings.
A Progressive Single-Image Dehazing Network With Feedback Mechanism

In the past decade, deep learning methods, especially convolutional neural networks, have received much attention in applications of single-image dehazing. However, the haze in hazy images cannot be distinctly separated because it is complicatedly mixed with the background components. If we roughly remove the haze, the background tone of global atmospheric light may also be destroyed. To resolve the above problem and reconstruct clearer and higher-quality dehazing images, we introduced our progressive feedback progressive network (PFBN) in recurrent structure ties with a feedback mechanism. The feedback mechanism is implemented by stacking feedback blocks that contain feedback connections among iterations. At the input layer of each feedback block, its hidden state in the last iteration is delivered by a feedback connection to the present block as part of the input. The last hidden state, also referred to as high-level information, is fused with low-level information output by the previous block to generate effective feature representation. Moreover, we proposed an enhancement self-ensemble strategy to decrease the random error of the network to reconstruct clearer dehazing images. Finally, we designed a series of extensive experiments to verify the outstanding performance of our method.
The Implementation of The Flipped Classroom for Early Grade Students in Elementary School

The educational paradigm in the 21st-century coronavirus pandemic requires a deeper research study. This study aims to improve the mathematics learning outcomes of grade 1 students using the flipped classroom model and to find out their responses to the use of this model. The subjects of this study were 7 students who were grade 1 student. The research design used classroom action research consisting of planning, implementing, observing, and reflecting. Data were analyzed descriptively with qualitative and quantitative approaches. The results showed that the pre-cycle average value was 59, and an increase in cycle I became 77 and then 90 in cycle II. The minimum criteria are &gt; 70 so that the cycle is stopped in the second cycle. Student responses in using this model, in general, can be said to be positive. Where most stated that the application of this model made it easier to learn mathematical material, increased self-confidence, learning flexibility, responsibility, active and free to choose learning styles. Although this model was not found to increase learning independence optimally, it can still be used to accommodate independent learning activities at home and train the learning independence of low-grade student.
Rethinking Negative Sampling for Handling Missing Entity Annotations

Negative sampling is highly effective in handling missing annotations for named entity recognition (NER).One of our contributions is an analysis on how it makes sense through introducing two insightful concepts: missampling and uncertainty.Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling.Based on the sparsity of named entities, we also theoretically derive a lower bound for the probability of zero missampling rate, which is only relevant to sentence length.The other contribution is an adaptive and weighted sampling distribution that further improves negative sampling via our former analysis.Experiments on synthetic datasets and well-annotated datasets (e.g., CoNLL-2003) show that our proposed approach benefits negative sampling in terms of F1 score and loss convergence.Besides, models with improved negative sampling have achieved new state-of-the-art results on realworld datasets (e.g., EC).
A Functional Analysis of Mathematics Learning Support Platform - Including Utilization of AI -

In this study, the functions of on-line platforms are analyzed to provide implications for 'building a mathematics learning support system using artificial intelligence technology'.A total of 21 platforms developed in the United States, New Zealand, China, Japan, and Korea were analyzed.The researcher selected domestic and foreign mathematics learning support platforms that are easy to access and utilize AI as the target of analysis.For detailed analysis, the researcher directly accessed and checked the various functions of each platform.Based on the results of the primary analysis, the functions and characteristics of each platform were arranged in a table, and the secondary criteria for analysis were established using this table.The results were entered in the set analysis criteria table, and the representative cases that served as the basis for the analysis were saved in the document as a picture file.Finally, the researcher reviewed the analysis results, and synthesized and organized the user interfaces and functions of each platform using the PMI technique.As a result of the study, the functions to be installed in the mathematics learning support platform using AI in the future, improvements and considerations for platform development, and points to be considered when developing a platform utilizing AI were derived.
SAR Images Target Detection Based on YOLOv5

Synthetic aperture radar (SAR) images target detection is widely used in military reconnaissance and civil surveillance because of its strong anti-jamming ability. However, the traditional SAR images target detection technology can not be applied to all detection conditions since the feature extraction relies too much on the imaging configuration. With the help of more thorough data analysis, deep learning is undoubtedly the best way to solve this problem, meanwhile it has better generalization capability and generality. In this paper, the author proposed to use latest YOLOv5 as the target detection algorithm network, which has good detection effects for small objects in large scenes; The author also carried out data enhancement, migration learning and other improved optimization methods, comparing with other excellent networks under the same conditions to verify the superiority and flexibility of the method in detection accuracy and speed. The final mean average precision (mAP) reaches 0.994.
Full Content-based Web Page Classification Methods by using Deep Neural Networks

The quality of the web page classification process has a huge impact on information retrieval systems. In this paper, we proposed to combine the results of text and image data classifiers to get an accurate representation of the web pages. To get and analyse the data we created the complicated classifier system with data miner, text classifier, and aggregator. The process of image and text data classification has been achieved by the deep learning models. In order to represent the common view onto the web pages, we proposed three aggregation techniques that combine the data from the classifiers.
Robust Text Classifier for Classification of Spam E-Mail Documents with Feature Selection Technique

E-mails are an effective medium for sending information in various modes like text, audio, video, etc. from one person to another.Spam e-mail is a junk e-mail that unnecessary wastage memory space, wasting time to delete and maintain e-mails in the mailbox.The contribution of this research work is to develop a robust and computational efficient classifier that classifies the spam e-mail and ham e-mail documents.This paper analyzes and validates the spam e-mails documents using different data mining-based classification techniques.The most importance of this research work is to select the best classifier with reduce feature subset of datasets that achieve better accuracy compared to other existing classifiers.We have collected six types of Enron datasets and prepared the last seven Enron datasets that combine all these six Enron datasets.Then, filtering the datasets with the help of the WEKA data mining tool.In the first step, we perform preprocessing the datasets and remove all the irrelevant words from the datasets.We have used different classifiers like Nave Bayes, J48, Random Forest, Random Tree, and Adaboosting to analyze and classify ham and spam e-mails documents.We also compare the performance of the classifier in terms of accuracy where Random Forest gives better accuracy with all seven Enron datasets.Finally, we have used the SymmetricalUncert feature selection technique to make the optimized dataset with a reduced feature subset.The suggested Random Forest classifier gives 98.73% of accuracy with reduced features of Enron datasets.
Mass Communications: From Classics to the Virtual Models

The article is devoted to the widespread use of mass communications and their virtual models, features and limitations, as well as the comparison of virtual and classical models. To study virtual communications, it is not enough to describe the corresponding processes within the framework of classical models, as well as the communication elements included in them. In order to fully describe modern communication processes, it is proposed to supplement the classical models with such elements as feedback, perception and self-presentation of the user, as well as the virtual communication space.
ANALYSIS OF CLASSROOM INTERACTION USING FLANDER INTERACTION ANALYSIS CATEGORIES SYSTEM (FIACS) IN ENGLISH CLASS-UNKRISWINA SUMBA

Classroom interaction relates to teachers’ teaching style in determining the interaction occurs in the classroom. Teachers’ teaching style like teacher-centered will make students more passive in the classroom since the teacher talks all the time. The purpose of this research aimed to find out the percentage of the teachers’ and students’ talking time during classroom interaction and teacher-students characteristics during classroom interaction in English class at Unkriswina Sumba using FIACS.This research was a qualitative research design. The subject of this research was English teacher in English class who had been teaching more than five years and four years. The result found that teacher talk ratio (TT) was high that is 82,51%. Then, indirect teacher talk ratio (ITT) was 51,57.Moreover, direct teacher talk ratio (DTT) was 30,94% while students talk ratio/percentage of students talk (PT) was 13,00%. Then, silence or confusion rate (SC) was 4,48%. Last, indirect and direct ratio (ID) was 166, 67%. The result showed that indirect talk was dominant than direct talk in the conversation class while the most dominant characteristic in conversation class was content cross that is 85.It reflected that most of the teaching-learning time was devoted to questions, lectures and praises. The result of this research enriched the knowledge of the students, lectures, and other language learners that in the classroom, both the lectures and students should have the willingness to participate in the interaction. Keyword: Classroom Interaction, FIACS System, Teacher’s talk, Student’s talk,