Reviewerly/exHarmony huggingface dataset:
The Implementation of Indonesian Language Learning for Dyslexic in Children at Elementary Schools in Bengkulu

This research is motivated by Indonesian language learning which requires each student to master four language skills, namely listening, speaking, reading and writing. In children with dyslexia or reading difficulties, the implementation efforts made by the 7th grade teacher of the Bengkulu City Special School 05 so that the child continues to master language skills despite having difficulty reading. This study aims to describe the methods used by teachers in Indonesian language learning so that dyslexic children can master reading skills. This type of research is descriptive qualitative research. Data collection techniques used were observation, interviews, and documentation. Meanwhile, the data analysis techniques used were data reduction, data presentation, and finally verification. The results of the study concluded that the implementation of learning in dyslexic children was preparation, implementation, and evaluation. The Indonesian language learning method used by the 7th grade teacher of SLB N 05 Bengkulu City in dyslexic children is the lecture method, the audiolingual method, the skimming method, and the picture and picture cooperative method.
Prospects and Challenges of Implementing Cloud Accounting in Bangladesh

The objectives of this study are to understand the meaning of cloud accounting, to investigate whether it is favorable for performance of the organization and what are the challenges if a country like Bangladesh wants to implement it.Primary data have been collected from 300 respondents selected from the field of accounting, such as accountants, accounting graduates of different universities, teachers and bankers.To measure the reliability and validity of the sample size and data, KMO and Bartlett's test have been adopted and the results proved to be reliable and valid for the study.Regression analysis has been done to find out the positive impact of cloud accounting on organizational performance and negative impact of cloud accounting on existing accounting system of the organization.The results of regression analysis supported our alternative hypotheses that cloud accounting can improve organizational performance, but it has also some negative impacts.Descriptive statistics have been used to find out the probable challenges that may be faced by organizations that want to implement it.This is a pioneering study because there is little research on this topic, thus it is expected to develop awareness about cloud accounting in field of accounting in Bangladesh.
Improving Convolutional Neural Network Expression via Difference Exponentially Linear Units

Abstract Convolutional Neural Network (CNN) has been applied to various tasks with great success. Adding an activation function is an important way to introduce nonlinearity into convolutional neural networks. The commonly used activation functions mostly use different forms of negative feedback for the negative input, however, some researchers have recently proposed positive feedback methods for the negative input such as Concatenated Rectified Linear Units (CReLU) and Linearly Scaled Hyperbolic Tangent (LiSHT) and achieved better performance. To explore this idea further more, we propose a new activation function called Difference Exponentially Linear Unit (DELU). The proposed DELU activation function can optionally provide positive and negative feedback for different values of negative inputs. Our experimental results on the commonly used datasets such as Fashion Mnist, CIFAR10, and Imagenet show that DELU outperforms other six activation functions, Leaky ReLU, ReLU, ELU, SELU, Swish, SERLU.
SPELLING ERROR ANALYSIS IN STUDENTS’ WRITING ENGLISH COMPOSITION

This research has objective to find out the kind of spelling errors written by the students and to determine the most dominant kind of spelling error written by the students of STIE AAS Surakarta in the academic year 2017/2018. This study used a descriptive qualitative approach. In this study, researchers revealed conclusions by collecting data. The object of the research were taken from 24 students of STIE AAS Surakarta, especially in the 2017/2018 academic year. In collecting data, the researcher used the documentation method. In analyzing errors, the steps were the identification of errors, classification of errors, description of errors, explaining errors, and tabulation of data. The data were tabulated to determine the frequency of errors found in the students’ writing composition. From the results of the study, it was found that there are some errors found in the results of student essays in the aspect of spelling as 50 data which consist of some errors such as in omission as 20 data or 40 %, in substitution as 14 data or 28 %, in insertion/addition as 10 data or 20 % and transposition as 6 data or 12 %. While the most dominant spelling error occurs in the aspect of omission as 20 data or 40 %.
The Effect of Clustering in Filter Method Results Applied in Medical Datasets

For a deeper and richer analytic processing of medical datasets, feature selection aims to eliminate redundant and irrelevant features from the data. While filter has been touted as one of the simplest methods for feature selection, its applications have generally failed to identify and deal with embedded similarities among features. In this research, a hybrid approach for feature selection based on combining the filter method with the hierarchical agglomerative clustering method is proposed to eliminate irrelevant and redundant features in four medical datasets. A formal evaluation of the proposed approach unveils major improvements in the classification accuracy when results are compared to those obtained via only the applications of the filter methods and/or more classical-based feature selection approaches.
An AI-Based Methodology for the Automatic Classification of a Multiclass Ebook Collection Using Information From the Tables of Contents

Book recommendation to support professors and students in the identification of relevant sources is of significant importance for both universities and digital libraries and, hence, motivates the development of a recommendation system.This paper aims at automatically classifying a multiclass corpus that was created from ebooks from the Springer collection, which is available through the Hellenic Academic Libraries' subscription, by utilizing an unsupervised neural network (NN) (self-organizing maps, SOM) and two deep neural network (DNN) architectures, namely, a long short-term memory (LSTM) and a convolutional neural network (CNN) combined with a LSTM(CNN+LSTM) under various configuration scenarios.The vector construction leverages information that was extracted from the table of contents (ToC) of each book using the TF-IDF weighting scheme (for the first case) and the Keras tokenizer (for the second).Extensive experiments were conducted using various configurations of preprocessing steps, NN set up and vector and vocabulary sizes to assess their impact on the classifier's performance.Furthermore, we show that majority voting is more suitable for selecting the dominant label for a specified node.The experimental analysis showed the feasibility of developing a recommendation system for supporting professors and students in the identification of related sources based on a detailed thematic description (e.g., abstract or table of contents of a book) rather than a few keywords.In the conducted experiments, the subsystem that utilized the DNN (LSTM) performed the best, with F1-scores of 67% for the 26 categories and 80% for the 5 general categories, whereas SOM realizes F1-scores of less than 5% in both cases.
Modeling and Validating Role-Based Authorization Policies for a Port Communication System with UML and OCL.

Modern sea or inland ports rely on digital communication and systems to boost rapid turnover of trade.Stakeholders like shippers, shipping lines, container terminals and port authorities collaborate and compete using their own legacy applications.Many sea ports operate Port Community Systems (PCS) to orchestrate processes between the players.These software systems are potential targets of security threats that may lead to payment fraud, espionage of competitors, smuggling, theft, export control violations, up to disasters involving dangerous goods possibly effecting public mains.In our approach we apply modeling to the field of information security.We combine and focus on Role-Based Access Control (RBAC) with constraints and Attribute-Based Access Control (ABAC) for finer grained authorization constraints.In a concrete case study we model authorization policies within port communities that partly utilize dedicated PCS.The purpose is to increase the integrity of exchanged data and thus reduce the risks of attacks or failures.We employ the UML-based Specification Environment (USE) and its OCL support to validate specified security properties for a typical container shipping scenario.
The Teacher-Student Chatroom Corpus

The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English.The lessons took place in an online chatroom and therefore involve more interactive, immediate and informal language than might be found in asynchronous exchanges such as email correspondence.The fact that the lessons were one-to-one means that the teacher was able to focus exclusively on the linguistic abilities and errors of the student, and to offer personalised exercises, scaffolding and correction.The TSCC contains more than one hundred lessons between two teachers and eight students, amounting to 13.5K conversational turns and 133K words: it is freely available for research use.We describe the corpus design, data collection procedure and annotations added to the text.We perform some preliminary descriptive analyses of the data and consider possible uses of the TSCC.
Classification of 4-bit S-Boxes for BOGI Permutation

Bad Output must go to Good Input (BOGI) is the primary design strategy of GIFT, a lightweight block cipher that was presented at CHES 2017.Because this strategy obviates the need to adhere to the required conditions of S-boxes when adopting bit-permutation, cryptographic designers have more S-box choices.In this paper, we classify all 4-bit S-boxes that support BOGI, called "BOGI-applicable S-boxes," and evaluate them in terms of the cryptographic strength and efficiency.First, we exhaustively show that only 2,413 Permutation-XOR-Equivalence (PXE) classes over 4-bit S-boxes are BOGI-applicable.After refining the PXE classes with respect to the differential uniformity (U) and linearity (L), we suggest 20 "Optimal BOGI-applicable" PXE classes that provide the best (U, L).Our security evaluations revealed that all optimal BOGI-applicable S-boxes fulfill the security properties considered by the designers of GIFT and that the differences between them exist in the other properties.Moreover, we explore the resistance of GIFT variants against differential and linear cryptanalysis by replacing the existing S-box with other optimal BOGI-applicable S-boxes.Based on the results, we identify the best attainable resistance with the bit-permutation of GIFT-64.Lastly, we suggest notable S-boxes that support competitive performance, jointly considering the cryptographic strength and efficiency for GIFT-64 and GIFT-128 structures, respectively.
FEATURES OF RESEARCH ACTIVITY OF STUDENTS WITH DIFFERENT LEVELS OF INTELLIGENCE DEVELOPMENT IN INSTITUTIONS OF GENERAL SECONDARY EDUCATION IN UKRAINE AND POLAND

The study pays special attention to the influence of cultural factors on the development of students' intelligence in the process of research learning. The study of the development of students 'intelligence used a cross-cultural method, which included a comparative analysis of the development of students' intelligence in Ukraine and Poland. The concept of intelligence development with pedagogically balanced and methodically motivated use of components of computer-oriented methodical system of research training of students of natural sciences and mathematics in secondary schools of Ukraine and Poland is offered. A retrospective analysis is performed and the classification of the concepts "intelligence", "thinking", "giftedness" in the context of the significant role of intellectually gifted students in the development of society is given. The data obtained in the process of experimental research were used to analyze the most relevant in the process of research training of students of natural sciences and mathematics cycle information resources COMSRL. The existence of correlations between the advantages in the attitude of students and teachers to the use of certain information resources and the levels of intellectual development of students has been studied. The need for the selection of information resources to increase creativity, motivation and level of intellectual development of students, which leads to increased efficiency of research training. The results were significant at the level of reliability р £ 0,001. Spirituality in the cultures of Ukraine and Poland is manifested in different ways, so the value living space of gifted students differ in structure. Indicators of giftedness of students living in different socio-cultural environments differ because they participate in different polysystemic processes.
Fast learning neural network based on texture for Arabic calligraphy identification

&lt;span id="docs-internal-guid-5c723154-7fff-a7b2-3582-b7c2920a9921"&gt;&lt;span&gt;Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. &lt;/span&gt;&lt;/span&gt;
A Correlation Between Students’ Intelligence Quotient (IQ) and Their Writing Ability for the Eighth Grade

This article was focused on the correlation between students’ intelligence quotient (IQ) and their writing ability. The purpose of the study was to find out whether there is any positive correlation between students’ intelligence quotient (IQ) and their writing ability. This study was quantitative research. The sample was 25 students. To collect the data, the researchers informed the students’ an instrument in multiple-choice tests as many as 15 questions for intelligence quotient (IQ) and an essay which is a form in the descriptive text for writing ability. Descriptive statistic and hypothesis testing were used to analyze the data in this research. Furthermore, the mean score of students’ intelligence quotient (IQ) was 78.00 and 66.24 was the mean score of students’ writing. Moreover, the Standard Deviation of intelligence quotient (IQ)was 3.464 and writing was 4.772. The present researcher found out the result of the correlation coefficient was .403 at p =.046 which was lower than 0.05 (as the standard significance level in SPSS) at two tails. This implied the first Ho (null hypothesis) was rejected and first Ha (alternative hypothesis) was accepted. It means that there was a correlation between students’ intelligence quotient (IQ) and their writing ability for the eighth graders of MTs Ihya’ Ulumuddin.&#x0D; Keywords: Intelligence Quotient (IQ), Writing Ability, and Intelligence
The Design and Implementation of an Educational Augmented Reality Application for Logical Circuit Design

The purpose of the study is to determine the effect of augmented reality applications on the learning of engineering students in their professional courses. The objective of the study is to ensure that augmented reality technologies, which are among the most important technologies of today, are used in educational settings. The other important objective of the study is to ensure that the augmented reality technologies used in professional training in countries such as the USA, Canada and the Netherlands are also used in our country and to provide a world-class education opportunity. Also, contributing to the economy of our country with the successful studies to be carried out in this regard is among the objectives of the study because of the increasing interest in augmented reality software throughout the world and the formation of an important economy related to the development of these kinds of software.In the study, an augmented reality application developed by the researcher was used in the learning process of a subject included in the faculty of engineering education curriculum. The study was performed using a convergent parallel mixed-methods design in which both qualitative and quantitative data were used simultaneously. The result of the study showed that there were no significant difference between the average attitude towards AR applications of the students participating in the study and the gender, class, and father education level; however, it is seen that there was a significant difference between maternal education status. It was also determined that the difference between the students’ average attitude towards AR applications before and after the study was significant.Similarly, it was observed that the difference between the AR attitude averages before and after the study and the AG attitude average 4 weeks after the study was also observed to be significant. It was also found that the students who participated in the study found the AR applications remarkable, entertaining, understandable and useful for the instructor. Moreover, they stated that using AR applications in lessons would contribute to fast and active learning and increase success.
API Vulnerabilities In Cloud Computing Platform: Attack And Detection

Nowadays most of cloud management software such as OpenStack and CloudStack provide an API to facilitate the communication and exchange of data between users, application, cloud components and infrastructure.Due to complexity of cloud management software implementation, the provided API has vulnerabilities which can be exploited by malicious party.Once exploited, it can cause security issue and disrupt the availability of services running on the cloud infrastructure.Hence; it is importance to address cloud API security by identifying potential threats, demonstrating how such threats could be exploited and how to detect such threat.This paper presents the topic of API Vulnerabilities in Cloud Computing Platform: Attack and Detection.We will discuss the vulnerabilities of the API in cloud management software.Based on these vulnerabilities, this paper will demonstrate how eavesdropping on cloud API authentication services and API exhaustion attack can be initiated.To address the threat due to the vulnerabilities of the API, we need to detect attack which exploits the vulnerabilities.Thus this paper also proposes and demonstrates methods to detect such attack effectively.Method to detect ongoing API exhaustion attack will be based on AD3 algorithm.From the experiments result, it shows that attacks on the cloud platform AP can be detected effectively.
Clothing Classification using Unsupervised Pre-Training

Deep Learning has changed the way computer vision tasks are being solved in recent times. Deep Learning based approaches have achieved outstanding results in computer vision tasks including image classification, segmentation, object detection. Most of this success has been achieved by training deep neural networks on labelled data. In general, the more labelled data is fed to a deep learning model, the more accurate the model will be. However, labelling is time consuming and sometimes even impossible. Fashion and e-commerce are domains where a large amount of unlabelled data is available. There is a huge need to leverage these data without labels. The aim of this paper is to explore and evaluate the possibility and effectiveness of using massive amount of unlabelled data to build deep learning models. We compare the performance of these models with the performance of models built with labelled data. Specifically, we compare fully supervised deep learning with two deep learning methods with unsupervised pre-training. Our pre-trainings are based on clustering of features called DeepCluster and rotation as a self-supervision task. The comparison is performed on the DeepFashion dataset. Our experimental results have shown that using unsupervised pre-training can attain comparable classification accuracy ($\sim$1-4 % difference) on image classification comparing to fully supervised models. Furthermore, we have shown that our models uses five times less labelled data during the fine-tuning phase and still achieves comparable accuracy ($\sim$3-4 % difference) comparing to fully supervised models. These results demonstrate the potential of using unsupervised pre-training approaches in achieving comparable results to fully supervised models.
Dampak Penggunaan Media Sosial Terhadap Perilaku Belajar Siswa

This research is based on the fact that there are still students who do not understand the use of social media properly so that it has an impact on student learning behavior at school. This has an effect on the unsatisfactory student learning outcomes at school because social media is not used for learning but is used more as a place of entertainment. Based on this, this study will reveal how the influence of the use of social media on learning behavior. The method used in this research is correlational quantitative data collection using a Likert scale. Then analyzed using the help of the SPSS 16.00 for windows program. The results of this study can be concluded that there is an effect of the use of social media on student learning behavior as evidenced by the results of testing the accepted hypothesis are Ha and Ho which is indicated by the value of t = 6.011, which is greater than t table = 2.001. Then the level of influence of the use of social media on student learning behavior is in the high category indicated by the tcount value of 6.011. Based on the results of this study, it can be seen that excessive use of social media has a profound effect on learning behavior at school.
Deep Learning Approaches for Object Detection

In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.