Reviewerly/exHarmony huggingface dataset:
Mispronunciation Detection in Articulation Points of Arabic Letters using Machine Learning

Mispronunciation detection in the articulation points of Arabic letters is a comparably less researched area. However, recently it is gaining popularity as the manual method of learning Al-Quran requires more time and manpower. In this technological era, most people, especially the younger generation, do not have time for the face-to-face learning method, where the listening, correction, and repetition of correct pronunciation take place in real-time. The system presented in this work has the capability to act as an alternative to support the manual learning process as well. Without denying the main role of Quran teachers, this system is not intended to replace the manual teaching method but to complement it and to ensure the art of reciting the Al-Quran is not lost and every person is able to somehow correct their tajweed. The mispronunciation detection system has been developed and tested to present the easiest way for Muslims to learn and recite Al-Quran with a better understanding of Tajweed rules. For feature extraction, RASTA PLP is used. The Hidden Markov Model (HMM) is used for training and recognition processes. Since the Arabic language is a very delicate language, mispronunciation detection is very challenging. However, promising results are achieved using the presented application. RASTA-PLP showed an 85%recognition rate. A real time application named Intelligent Quran Recitation Assistant (IQRA) showed 90% of the results, while a recorded application showed 98% of results. These results indicate that the conducted research is successfully implemented.
Operational Status and Improvement of Joint Curriculum-Focusing on the Hight Schools in Daegu

The purpose of this study is to provide the improvement methods of Joint curriculum through the integrated research analyzing the operation status and satisfaction of Joint curriculum. The results are as follows: Firstly, difficulty in accessing NICE, difficulty in instructing students for class management, frequent changes in paper forms, and differences of academic schedules between schools were suggested as the major difficulties for instructors in instructing and managing classes. Therefore, there is an urgent need for simplified approval process for NICE, tightened attendance system, and a standard form of document for more effective performance. Secondly, the burden of increased workloads has fallen on the teacher who is in charge of Joint curriculum. Thus, if 3 or 4 schools specializing in different fields collaborate together to operate a cooperative joint curriculum, schools might be able to reduce the administrative burdens. Third, opinions differ on the employment standards and the standard criteria of hiring methods is still absent. Therefore, for the high school credit system to be settled down, a flexible instructor recruitment methods and flexible class hours must be prepared. Finally, the result shows that students who took joint curriculum course felt high satisfaction and that the course helped out with their career education and guidance. Thus, actively promoting the high corelation between career and Joint curriculum may act as the key factor in activating Joint curriculum and constructing a firm foundation for the high school credit system.
Research on education information management platform based on big data

The development of big data in education breaks through the traditional teaching management mode, and gradually changes the existing teaching methods. Under the influence of technological innovation in the information age, the rational use of big data information for comprehensive perception, analysis and sharing, so as to provide an effective basis for the development of education. Therefore, on the basis of understanding the development of big data education information management, this paper analyzes how data mining algorithm based on Spark platform can provide users with effective information according to the design form of practical education platform, and compares the application performance of K-means clustering algorithm and collaborative filtering recommendation algorithm.
GitMonitor: a Software for Monitoring Project Schedules with Git Data

In software project management, a schedule is a critical component to monitor. Schedule monitoring often involves direct synchronization across stakeholders. However, with the use of Git as a version control system, there is a possibility of getting development velocity information from actual activities. With information available in the Git database, GitMonitor aims to extract relevant data used in schedule monitoring. The data will be extracted from feature branches where the development process is going on. A feature branch is typically tied with a task. Thus monitoring the feature branch can be used to track the status of a task, current development changes, and the person who did the changes. In this way, schedule monitoring can be done with more factual data from the actual development activities.
Quantum finite automaton algorithm for mineral and hydrocarbon exploration in Sentinel-1A SAR data

What is meant by quantum image processing? Prior to answering this question, the basis of quantum computing must be understood. Many academic institutions deal with mineral image processing as software that facilitates obtaining results without a perfect understanding of how the image processing algorithms are operating. In this view, if the same algorithm is applied in a certain area, it will deliver different results in different other areas. This is considered as uncertainty in applying the same algorithm. Many academic institutions are practicing this sort of uncertainty research and education, especially in the nations that are trying to occupy advanced world-educational ranks. This chapter demonstrates a novel algorithm of quantum automaton for automatic detection of hydrocarbon microseepage and the altered minerals such as ferrous iron, calcite, silt clay, and kaolinite. The main speculation used in this investigation is that there is a correlation between SAR image backscatter and mineral dielectric constants, in which every mineral has its own dielectric constant. In this understanding, these physical properties of different minerals can be determined and clustered using quantum automaton. Consequently, quantum automaton delivers an excellent promise in hydrocarbon microseepage exploration in the SAR-polarized VH image.
Knot Semantic Logic and Deep Structure of Statements

Abstract This article aims to analyze the deep structure of sentences by applying the knot semantic logic principle in the surface structure of the sentences. This article will help to judge some controversial statements that have legal and social effects. The knot semantic logic theory is a novel approach to explaining a literature’s symmetry structure. Chiasm, parallelism, and concentricity are all examples of symmetry structure. This new theory introduces a new method for detecting symmetry in words, sentences, verses, essays, chapters, and entire books. This analysis will use Knot semantic logic point of view. The methodology used in the analysis has two main procedures: (1) classification of the structure and (2) analyzing some examples from some official/formal statements and controversial statements using Paltridge’s classification of thematic progression and knot semantic logic. The result of this research is that the valid argument has surface and Deep Structure. Some fallacies statements such as presumption, generalization, distortion, and deletion have a deep structure that forms ring composition (Type I of knots semantic logic) and parallelizations (Type III of knots semantic logic). This research results generate a new idea about perspective truth, Original perspective domain, and Response perspective domain
A Comparative analysis of security issues &amp; vulnerabilities of leading Cloud Service Providers and in-house University Cloud platform for hosting E-Educational applications

Cloud technology provides several advantages to users, including quick and simple deployment methods, lower costs, uninterrupted resource availability, scalability and elasticity, and the capacity to rapidly adapt to the needs of IT industries. Students and teachers can benefit from the capabilities provided by this kind of design since it permits them to use the electronic environment in a dynamic and interactive learning, teaching, and evaluation process. Employee data is stored in the cloud by 48% of educational institutes, and student data is stored in the cloud by 30%. In 2020, the majority of educational institutes (60%) and account compromise (33%) were victims of phishing attempts. According to Gartner, more than 95% security faults in the cloud are due to the customers. He also discussed that security is an expensive investment that not everyone can make. Data security, specifically confidentiality, integrity, and availability, is one of the most critical issues for cloud providers. If no specific security measures are installed, more frequent cyber-attacks might cause severe material damage. The aim of this work is to deploy e-educational application on cloud for automation of University academic activities over leading cloud providers like GCP or AWS and using in house cloud data center. Also, using the real-time experimental setup, conduct a comparative analysis of security concerns and vulnerabilities. Security is critical to the successful migration and adoption of cloud technology in the educational sector, according to the research.
Cortical Functional Connectivity By Partial Directed Coherence In Relation To Creative Thinking: A Case Study

This paper investigates the underlying brain process in creative thinking. The investigation is about the directional brain connectivity for creative thinking between architectural students and nonarchitectural students. This study involved four students from the Faculty of Built Environment and School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor. The participants were required to complete two sketches of the creative thinking task. The creative thinking task is guided by the Torrance Test for Creative Thinking. While sketching, the brain signals of the participants were captured using electroencephalography. Then, partial directed coherence was applied to analyze the information pathway. The results show that there were differences in both outputs: thinking product (participants’ sketches) and thinking process (brain pathways). These initial findings show that it is feasible to use objective measures (thinking process) to assess creativity to augment the subjective assessment of creativity.
Construction of Visualization Model of Maternal Health Care Based on Domain Ontology

In order to effectively analyze and utilize a large number of maternal health care diagnosis and treatment guidelines, electronic medical records and clinical data, and promote the sharing and reuse of knowledge and experience in the field of maternal health care, the project uses ontology technology to carry out semi-automatic ontology construction through analysis and reference seven steps and knowledge extraction technology, The ontology knowledge base model of pregnancy and childbirth domain is constructed. In order to further show the domain ontology of pregnancy and childbirth, through the visual analysis of the domain ontology of pregnancy and childbirth, using data visualization technology and domain oriented strategy, this paper constructs the domain ontology visualization model of pregnancy and childbirth.
Position control with dynamic obstacle avoidance in omnidirectional mobile robots

The development of a position controller for omnidirectional mobile robots with dynamic obstacle avoidance is addressed in this work. Asymptotic stability is proven under Lyapunov theory, and the analysis of the fulfillment of the dynamic obstacle avoidance task is also outlined. Numerical simulations are presented to illustrate the capabilities of the proposed control law.
Emerging Technologies in Smart Classroom Education

Recently, emerging technologies gained widespread popularity in the education area. Especially in the current pandemic situation, they enable expanded access to education and improve the learning experience. Therefore, it is necessary to study innovative technologies and how to utilize them in the teaching-learning process. A survey about technology usage in classroom settings and a literature review of various technology-based teaching practices are the basis of an analysis of the application of emerging technologies and their impact on the education process. The opinions of Bulgarian teachers on the pedagogical use of information and communication technology (ICT) tools in schools are considered. Also, the studies about using technology-enhanced teaching in school education are examined. The goal is to assess and outline those emerging technologies that could be part of future smart school education. Findings from the survey concerning the application of emerging technologies in school education are also presented. The results obtained can inform teachers about innovative technology tools and their potential to make the teaching-learning process more engaging and smart.
A Study of Two-Way Short- and Long-Term Memory Network Intelligent Computing IoT Model-Assisted Home Education Attention Mechanism

This paper analyzes and collates the research on traditional homeschooling attention mechanism and homeschooling attention mechanism based on two-way short- and long-term memory network intelligent computing IoT model and finds the superiority of two-way short- and long-term memory network intelligent computing IoT model. The two-way short- and long-term memory network intelligent computing IoT model is improved and an improved deep neural network intelligent computing IoT is proposed, and the improved method is verified based on discrete signal homeschooling classification experiments, followed by focusing on the application research of the two-way short- and long-term memory network intelligent computing IoT model-assisted homeschooling attention mechanism. Learning based on neural network, human behavior recognition method combining spatiotemporal networks, a homeschooling method integrating bidirectional short- and long-term memory networks and attention mechanisms is designed. The visual attention mechanism is used to add weight information to the deep visual features extracted by the convolutional neural network, and a new feature sequence incorporating salient attention weights is output. This feature sequence is then decoded using an IndRNN independent recurrent neural network to finally classify and decide on the homeschooling category. Experiments on the UCF101 dataset demonstrate that the incorporation of the attention mechanism can improve the ability of the network to classify. The attention mechanism can help the intelligent computing IoT model discover key features, and the self-attention mechanism can effectively capture the internal features of homeschooling and optimize the feature vector. We propose the strategy of combining the self-attention mechanism with a bidirectional short- and long-term memory network to solve the family education classification problem and experimentally verify that the intelligent computing IoT model combined with the self-attention mechanism can more easily capture the interdependent features in family education, which can effectively solve the family education problem and further improve the family education classification accuracy.
Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K‐means clustering and labeled ensemble classifier algorithm

Abstract Breast cancer is a very dangerous disease that mainly affects women. It is a deadliest disease that highly affects the women's life. Therefore, it is necessary to predict and classify this deadly disease for early diagnosis. There exist numerous data mining techniques for early prediction and classification of this disease. The big data based analytical model provides the better solution for storing, manipulating, and analyzing a great number of mammographic images. In this article, a new improved fractional rough fuzzy K‐means clustering strategy is considered for disease prediction. Then, a new Tunicate Swarm Algorithm (TSA) is introduced to optimize the weight parameters. TSA is a bio‐inspired metaheuristic optimization approach. Finally, the labeled ensemble classifier (LEC) is utilized for classifying the stages of breast cancer as malignant and benign. Here, the data is randomly generated from breast cancer Wisconsin dataset (diagnosis) obtainable on UCI machine learning repository. The proposed strategy is compared with different existing strategies, like Logistic Regression Classifier, Random Forest Classifier. From the analysis, it is observed that the proposed big data based analytical model using LEC provides 99.3% accuracy that is very high when compared to the accuracy of existing approaches.
Evaluation of Interactive Modalities Using Preference Factor Analysis for Wheelchair Users

Losing the ability to move is one of the major issues that weaken the independent living ability of elderly/disabled people. To solve this issue, mobility assistive devices such as Intelligent Wheelchairs (IW) are being developed. In terms of the interactive modality, there are two main modality systems used in intelligent wheelchairs namely unimodal systems and multimodal systems. To justify the need for multimodal interactive capabilities over the unimodal interactive capabilities for an intelligent wheelchair, a proper human study should be conducted before developing the wheelchair. Studies that can be referenced in the existing literature have failed to compare the two interactive modalities using user preferential factors such as social factors, personal or individual factors, and technical factors. Technical performance analyses that have been conducted either excluded user perspective or excluded the interactive modality performance. Therefore, the purpose of this paper is to provide a detailed analysis of a study that would comprehensively analyze not only the personal, social or cultural factors, also technical factors that would affect the final preferential outcome of the interactive modality system for a prospective wheelchair. This analysis is conducted from the user's perspective and further validates the performance of the interactive modality in a wheelchair user context through technical performance analysis. Also, this paper analyses the user preferential factors different age groups and social groups prioritize in selecting an interactive modality.
Rhythm Quantization: Notes on the History of a Technocultural Practice

Abstract This chapter sketches a general history of rhythm quantization as a widespread practice in popular music culture. Quantization—a sound technology that automatically maps microrhythmic fluctuations onto the nearest beat available within a predefined metric grid—challenges traditional notions of musicking as an embodied activity that is grounded in the co-presence of human agents. At the same time, it encapsulates cultural and cognitive processes that are entirely human, fitting into a broader historical shift towards chronometric precision in Western music. Questions arising from this apparent contradiction are taken up in this chapter, which situates rhythm quantization as an emergent technocultural practice, examining its attendant technologies and requisite structures of music-theoretical knowledge, as well as its reception within the context of different musical genres.
Developing a System to Analyze Comments of Social Media and Identify Friends Category

Today users on the social platform are expressing their emotions, ideas, proposals and views. The opinion may articulate critical opinions in various ways and may include different polarities such as positive, negative or neutral and it is often a difficult challenge for people to appreciate the feeling of each opinion and the time it takes. The analysis of the feeling in each statement will resolve this issue. This paper presents a framework to analyze social media comments e.g. Facebook and identify a category of friends. First, some public profiles are selected and comments are retrieved from different posts of them. Secondly, those comments are stored as dataset and pre-processed for sentiment analysis. After that, the pre-processed data trained and tested in a sentiment analysis model developed by us. From the sentiment of data, we then identify friends. For example, a friend with a positive sentiment of comment can be considered as a good friend. The evaluation of the performance is measured. A decent accuracy is achieved by the system.
Massive open online courses as a resource of mixed learning (on the example of humanities)

The dominant direction in the development of online education, actively discussed by the pedagogical community, is training using massive open online courses (MOOCs). The key factor in the effectiveness of the implementation of MOOCs in the practice of educational organizations is primarily the availability of elite education. In modern conditions, which determine the priority of lifelong education (lifelong learning), it is particularly relevant to form schoolchildrens readiness for self-education, which can be facilitated by training on MOOC platforms. The broad discussion of MOOC implementation in educational practice is more concerned with university education, additional education and the system of advanced training. The use of MOOCs in school education in the publications of teachers-researchers is not considered fully enough and is mostly related to natural sciences. The paper analyzes the MOOC on the example of humanities and the features of the implementation of mixed learning models (inverted learning, autonomous group, individual trajectory) based on the use of online courses. This analysis suggests that there should be a need for more in-depth research in this area and the need to introduce elements of learning into school practice through massive open online courses. This will resolve the contradiction between the need for differentiation and individualization of training and the potential of existing unused massive open online courses.
Research progress and theoretical analysis of robot formation control

In recent years, with the continuous development of artificial intelligence technology, robots began to enter our life. UAV light display, rescue robot and home cleaning robot have brought great convenience to our life, but for more heavy and complex working environment and tasks, the problem-solving ability of single robot is far from enough, and the advantage of multiple robots is particularly obvious. The formation movement of multi-agent system requires multiple mobile robots to move to the target area in the form of formation at the same time, and always maintain a given formation in the process of movement. The group behavior of multi robot cooperation can be controlled by formation control. This paper introduces the research progress and some observation indexes of robot formation control at the present stage, focuses on different formation control methods, and finally looks forward to the development direction of multi robot system in the future. This paper has reference significance for the research of multi robot system.