Reviewerly/exHarmony huggingface dataset:
A Proposed Vision of the Transformation of the Arab Universities into Smart Digital Universities

This research develops a proposed vision to transform Arab universities into smart digital universities. The descriptive research approach is used to achieve the research objectives. The research sample consists of 450 faculty members and 75 educational experts randomly selected by stratified random method. The questionnaire is adopted as a research instrument. The findings indicate that a proposed vision can be developed to transform Arab universities into smart digital universities by addressing several themes; the philosophical premises of the proposed vision, the features of the proposed vision “smart university administration, smart people, smart university environment, and knowledge network”, determining the requirements necessary to implement the proposed vision, setting the appropriate foundations for the proposed implementation and success in Arab universities, and demonstrating the potential challenges and threats that may stand in the way of implementing the proposed vision and methods to overcome them.
A Systematic Approach to the Formation of Design Competencies of a Future Teacher

Considers a systematic approach to the formation of design competencies of future primary school teachers. The study showed that the use of a systematic approach is an effective tool for the development of skills and abilities of future teachers. It allows you to use the project methodology, develop creative and communicative abilities of students, as well as increase their motivation for the learning process. The article may be useful for teachers at pedagogical universities and primary school teachers who want to effectively form the design competencies of their students and pupils.
FE-TAC: an effective document classification method combining feature extraction and feature selection

An effective classification method requires the most informative and relevant set of features. In this paper, we discuss an enhanced text classification method combining feature extraction (FE) and feature selection. First, we used the FE method to extract features from text data and then apply the feature selection method to select the most relevant features out of those extracted features. During feature selection, we introduce a new measure called term affinity to the class (TAC) to estimate the degree of retaining capability of the term as a member of the particular class. TAC is computed based on the combination of normalise document frequency and summing up the occurrence frequency of the term to the specific class. Experimental results on three existing datasets - BBC, Classic4, 20 Newsgroup, and our own dataset called 'Sangai' show that the proposed method outperforms the other competent methods in terms of accuracy.
New Applications of Graph Neural Networks in Cosmology

Upcoming cosmological surveys will provide unprecedented amount of data, which will require innovative statistical methods to maximize the scientific exploitation. Standard cosmological analyses based on abundances, two-point and higher-order statistics of cosmic tracers have been widely used to investigate the properties of the cosmic web and Large Scale Structure. However, these statistics can only exploit a subset of the entire information content available. Our goal is thus to implement new data analysis techniques based on machine learning to extract cosmological information through forward modelling, by directly exploiting the spatial coordinates and other observed properties of galaxies and galaxy clusters. Specifically, we investigated a new representation of large-scale structure data in the form of graphs. This data format can be directly fed to Graph Neural Networks, a recently proposed class of supervised Deep Learning algorithms. We tested the method on dark matter halo catalogues in different cosmologies, finding promising results. In particular, the method can discriminate among different dark energy models with high accuracy, through both binary classification ( $$99\%$$ -accuracy) and multi-class classification ( $$97\%$$ -accuracy). Moreover, it provides constraints on the value of $$w_0$$ , through regression, with high precision.
Characteristics of Networks Generated by Kernel Growing Neural Gas

This research aims to develop kernel GNG, a kernelized version of the growing neural gas (GNG) algorithm, and to investigate the features of the networks generated by the kernel GNG. The GNG is an unsupervised artificial neural network that can transform a dataset into an undirected graph, thereby extracting the features of the dataset as a graph. The GNG is widely used in vector quantization, clustering, and 3D graphics. Kernel methods are often used to map a dataset to feature space, with support vector machines being the most prominent application. This paper introduces the kernel GNG approach and explores the characteristics of the networks generated by kernel GNG. Five kernels, including Gaussian, Laplacian, Cauchy, inverse multiquadric, and log kernels, are used in this study. The results of this study show that the average degree and the average clustering coefficient decrease as the kernel parameter increases for Gaussian, Laplacian, Cauchy, and IMQ kernels. If we avoid more edges and a higher clustering coefficient (or more triangles), the kernel GNG with a larger value of the parameter will be more appropriate.
Research on the Development of Teaching and Learning Support Models for the Utilization of AI-based Learning Assistance Systems in Universitie

Since the emergence of the artificial intelligence platform called Chat GPT, interest in the use of AI in education has been further amplified. However, AI-based learning support systems that can be employed in university education are very limited. Therefore, this research aims to develop a Teaching-Learning support model for utilizing AI-based learning assistance systems in universities. The derived Teaching-Learning support model is structured around the processes of 'Analysis and Design (pre-class)', 'Implementation (during class)', and 'Assessment (post-class)', reflecting the stages of Teaching-Learning interaction. In the Analysis and Design phase, it is categorized into individual and student group diagnosis. In the Implementation phase, it is categorized into data-driven course design and problem-solving oriented teaching. Finally, the 'Assessment' phase presents evaluation of both learners and the course (program). The presented model underwent modifications and improvements through expert validation.
The Effects of Coal Mining on Land Use and Land Surface Temperature: A Case Study of Korba District, Chhattisgarh

Coal is one of the most important mineral which is used as a fuel for generating energy in India, with the increasing urbanization and industrialization, need for more fuel is increasing resulting in extraction of coal in more and more amount, therefore in order to fulfil the demand, mining activities has increased a lot with time, besides the extraction of coal, many other minerals are also being extracted for various purposes as well. Increase in such mining activities are leading to adverse change in air, water and land quality, changes in land use land cover pattern and variation in surface temperature, these continuous changes are therefore, im-portant to be studied in order to assess its impact on the environment for suture sustainability and better management of available resources. The current study has been done for the Korba district with special attention towards the mining areas as major area of the district after agricultural and cultivation land is occupied with mining area and related industries which is on spreading at higher pace since last many years, due to which variation in surface temperature and changes in land use pattern can be seen in its vicinity which is increasing with the time. According to the shifting patterns of land use and land cover, both urban regions and coal mine areas have grown by roughly 35 and 12%, respectively. In the central area of Korba district, the number of mines has risen, and the mining area has expanded over the last thirty years. Through a temporal analysis of the data, the study found that between 2000 and 2021, both the mining zones and adjacent urban areas witnessed increased temperatures. This temperature rise can be linked to the expansion of mining operations and the deforestation that accompanies such activities.
Smooth Composite-Space RRT: An Improved Motion Planner for Manipulators Under Incomplete Orientation Constraint

The incomplete orientation constraint (IOC), which is not necessary to require an end-effector motion constrained by all three spatial directions, exists widely in practical manipulation tasks. For example, when moving a container of liquid from one place to another, we can rotate the container horizontally or tilt it slightly if it is not full. In our previous work [1], a Composite-space RRT algorithm is presented to solve such a problem for spherical wrist manipulators that plans collision-free paths in the composite space. However, the planned path exhibits shaking or ripple like fluctuations, which will lead to shaking at the robot’s end-effector tool and bad performance of the task. In this paper, we propose a Smooth Composite-space RRT algorithm that combines the Composite-space RRT algorithm with B-spline interpolation method to improve the smoothness of the orientation path. And we demonstrate the effectiveness of our approach on the Willow Garage’s PR2 simulation platform in Robot Operating System (ROS) with two IOC cases.
A Study of the Application of Guided Discovery Method in English Grammar Teaching in Junior High School

Grammar is an important part of language. However, in actual grammar teaching, teachers directly impart grammar rules and students passively accept them. This has led to students’ disinterest in English grammar lessons and unsatisfactory grammar learning results. In order to promote students’ grammar learning, in recent years, some experts and scholars have begun to explore the application of guided discovery method in grammar classrooms. Guided discovery method is a teaching method that emphasizes guidance and focuses on discovery. Through setting up a situation, the teacher guides students to engage in the discovery activity of grammar rules by communicating with each other.This thesis uses questionnaire survey and interviews to analyze the application of guided discovery method in junior high school English grammar classrooms, as well as to analyze the positive effects of junior high school English teachers’ use of the guided discovery method on students’ grammar learning. Based on Bruner’s Discovery Learning Theory and Piaget’s Constructivism Learning Theory, this thesis takes Nanning High-tech Industrial Development Zone Minda Middle School as an example. It was found that under the guidance of guided discovery method, students’ interest and motivation in learning grammar have improved. Also, students’ autonomous learning abilities have been enhanced. Finally, students’ grammar learning strategies were cultivated through guided discovery method. Students can summarize grammar knowledge and solve grammar problems by reasoning and inducting. However, there are a small number of students who still do not have that strong learning ability cannot understand the tasks assigned by the teacher in class, so they do not learn grammar efficiently.
PENGARUH BERMAIN MONOPOLI HURUF TERHADAP KEMAMPUAN BERBICARA ANAK USIA 5-6 TAHUN DI TK TELKOM MAKASSAR

The aim of this research is to determine the impact of playing letter monopoly on the speaking abilities of children aged 5-6 years at Telkom Kindergarten Makassar. The approach in this research uses a quantitative approach with a Quasi Experimental Design research type. This study took 12 children as samples, with details of 6 children each in the experimental group and the control group. Wilcoxon Sign Rank test and descriptive statistical analysis were used to analyze the collected data. The results showed that the speaking ability of children in the experimental group who were treated with playing letter monopoly was better than the control group. According to data analysis, it shows that the experimental group experienced an average increase of 24.8, and the control group experienced an average increase of 14.5. According to the results of the Wilcoxon Sig test. (2-tailed) 0.027 &lt; 0.05 so H0 is rejected and H1 is accepted. Therefore, it was concluded that playing letter monopoly had a significant effect on the speaking ability of children aged 5-6 years at Telkom Kindergarten Makassar.
Quantum simulation of preferred tautomeric state prediction

Abstract Prediction of tautomers plays an essential role in computer-aided drug discovery. However, it remains a challenging task nowadays to accurately predict the canonical tautomeric form of a given drug-like molecule. Lack of extensive tautomer databases, most likely due to the difficulty in experimental studies, hampers the development of effective empirical methods for tautomer predictions. A more accurate estimation of the stable tautomeric form can be achieved by quantum chemistry calculations. Yet, the computational cost required prevents quantum chemistry calculation as a standard tool for tautomer prediction in computer-aided drug discovery. In this paper we propose a hybrid quantum chemistry-quantum computation workflow to efficiently predict the dominant tautomeric form. Specifically, we select active-space molecular orbitals based on quantum chemistry methods. Then we utilize efficient encoding methods to map the Hamiltonian onto quantum devices to reduce the qubit resources and circuit depth. Finally, variational quantum eigensolver (VQE) algorithms are employed for ground state estimation where hardware-efficient ansatz circuits are used. To demonstrate the applicability of our methodology, we perform experiments on two tautomeric systems: acetone and Edaravone, each having 52 and 150 spin-orbitals in the Slater Type Orbital - 3 Gaussian (STO-3G) basis set, respectively. Our numerical results show that their tautomeric state prediction agrees with the Coupled Cluster Singles and Doubles (CCSD) benchmarks. Moreover, the required quantum resources are efficient: in the example of Edaravone, we could achieve chemical accuracy with only eight qubits and 80 two-qubit gates.
Analyzing the changes of elementary pre-service teacher’s noticing occurred in the process of participating in the actual class and learning community

This study aims to get an implication on elementary pre-service teacher education by exploring how the noticing of pre-service teacher changes within a learning community. In this study, a learning community consisting of a elementary pre-service teacher and researchers was formed to plan and conduct actual classes and to reflect teaching strategies. Voice and video data on actual classes and learning community activities, reflective journals and post-interviews of the elementary pre-service teacher, field notes of the researcher, and students’ outcomes were collected. The characteristics of elementary pre-service teachers' noticing were analyzed in three stages: attending, interpreting, and deciding how to respond. The results are as follows. First, the pre-service teacher’s attending moved from the result of task to student’s statistical thinking. Second, the pre-service teacher’s interpreting changed from a lack of diversity and specificity of evidence to diversity and specificity. Third, pre-service teacher’s deciding changed from unproductive deciding to productive deciding.
Analisis Sentimen Review Hotel Menggunakan Metode Deep Learning BERT

The COVID-19 pandemic has resulted in declining tourism visits and hotel occupancy. Hoteliers must monitor visitor lifestyles to sustain their businesses. One way to achieve this is by understanding the sentiment of hotel visitors through review analysis, enabling better decision-making regarding service and business aspects in the hotel industry. This research applies the natural language processing deep learning model BERT to analyze positive and negative sentiments from hotel visitor reviews in Indonesia. The BERT model undergoes a pre-trained and fine-tuned process to produce accurate sentiment analysis. Evaluation results demonstrate that the fine-tuned SmallBERT model performs well, trained on a dataset of 515k hotel reviews for five epochs. The SmallBERT model achieves an accuracy of 91.40%, precision of 90.51%, recall of 90.51%, and an F1 score of 90.51% when evaluated with manually labelled datasets. Visualizations of the predominantly positive sentiment comparisons are conducted using Tableau.
The Implications of the Andy Warhol Foundation v. Goldsmith Ruling by the United States Supreme Court

The recent United States Supreme Court ruling in Andy Warhol Foundation v. Goldsmith (2023) has clarified the concept of fair use. The Supreme Court ruling can be summarized as follows: “In the United States Copyright Act, the first factor in determining fair use is the purpose and character of the use. Transformativeness is the degree to which the use has a different purpose and character from the original work. Transformativeness is evaluated by considering the two factors of ‘commercial character’ and ‘justification of use’. If transformativeness is found, it means that the first factor of fair use has been passed favorably. Additionally, for fair use to be recognized overall, especially in relation to the first factor of the purpose and character of use, it must be reviewed in the context of the specific use of the relevant case. In the case of specific use, if the purpose of the original work and the subsequent work are different, fair use may be established.” When compared to the Google ruling of the United States Supreme Court in 2021, there is a greater possibility that the justification of use related to compatibility, standards, and efficiency will be recognized in technical cases, but the justification of use of another's work is relatively less likely to be recognized in artistic cases where the individual expression of the artist is the main part. In this regard, it is also noteworthy that the distinction between ‘technical transformativeness’ and ‘artistic transformativenss’ may be regarded as established by recent US case laws.
Enhancing Students’ Listening Skill by Spotify Application

Listening cannot be separated from all activities, but in reality most of the teacher does not pay attention the student's ability to listen and that can affect students' ability in English especially when they want to talk to other people but don’t understand what is being said otherwise because they lack in listening. In the learning process, students may face difficulties one of them is difficulty in conveying speech. Many people say that listening is difficult, due to lack of focus or failure to focus. Therefore, practice concentration by listening more. The objective of the study is to find out spotify podcast application able to enhance the students’ listening skill. The writer apply pre-experimental method with one group pre-test and post-test design, and collected the data based on the test. The research findings showed that the students’ had poor score in pretest. After treatment, their listening ability in identify of listening comprehension increase significantly. The result of the research were the mean score obtained by the students’ through pre-test was 41.6 and post–test was 72.5 with the t-test value orientation is greater than t-table (16.8&gt;2.06). Listening through Spotify podcast in term of identify the listening comprehension 74,2% The result of calculating t-test of the indicators in the students’ t-test listening ability in listening through Spotify podcast application was greater than t-table 24.1&gt;2.06. It can be concluded that spotify podcast application can enhance the students’ ability in listening skill.
Inventory Management Using Blockchain and Big Data

Inventory management is a cutting-edge system for tracking and managing the different parts and components of a larger inventory structure. It includes a variety of features that can help make the process more efficient, including inventory tracking, automated ordering, tracking of customer orders, and more. Present work explores the potential benefits of leveraging these technologies for inventory management. It examines how blockchain and big data can be used to increase transparency, accuracy, and security of inventory management operations. Furthermore, these surveys the current applications of blockchain and big data in inventory management and how these technologies can be used to reduce costs, increase profit, enhance efficiency and improve customer satisfaction. Finally, let's go over potential issues with and solutions for big data and blockchain integration in inventory management. The results given here support the use of blockchain and big data technology as a valuable tool for inventory management.
A Study of Existing Approaches to Transaction Analysis in the Bitcoin Network

Today, the Bitcoin network faces a number of challenges, such as flawed user identification and fraudulent transaction methods that are used by criminals to conduct illegal activities. As a result, there is a growing need to improve existing tools for tracking transactions, as well as to develop new methods for identifying money in the Bitcoin network. The paper presents a study and systematization of the subject area problems, and also considers possible approaches to neutralize it. The purpose of this work is to analyze the existing methods of transaction verification of the Bitcoin network. Within the framework of the work, a system architecture is proposed, which includes a comprehensive approach to the process of transaction analysis. Each of the stages of this process is described: information gathering, aggregation, processing and analysis. An extended set of empirical rules (heuristics) for transaction analysis, which are used in existing clustering methods, is considered. The results of this work will provide an opportunity to improve the existing Bitcoin transaction verification methods and develop a new one with the possibility of increasing the efficiency of the process of identification of illegally obtained funds and their sources.