Reviewerly/exHarmony huggingface dataset:
AI REVOLUTION: EMPOWERING THE FUTURE WITH ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) is at the forefront of this transition, which is being brought about by the AI Revolution, which is responsible for reshaping the world as we know it. The application of artificial intelligence (AI) is becoming increasingly widespread across a variety of industries, including healthcare, finance, transportation, and education by name. The potential advantages of artificial intelligence are enormous, and it is essential to have a solid understanding of the effects that it will have on society. This study intends to investigate the empowering effects that artificial intelligence will have on the future, with a particular emphasis on its uses, problems, and limitations
Quantum Simulator as a Training Tool

n the field of quantum computing, the properties of quantum superposition offer a promising potential for solving complex problems in a few minutes, while classical computers would take years to solve the same problem. Therefore, many institutions have introduced courses related to quantum mechanics and quantum computing into their programs. However, there is currently no known web-based tool or PC application dedicated to the study of quantum computing. Quantum computers are not available for educational purposes. In the early stages of learning, students may benefit from a way to practice quantum computing with a visual tool. Quantum computing is usually expressed by a quantum circuit with its sequences of quantum gates. The article talks about the creation of an educational simulator for the study of quantum circuits with various gates, functions and transformation rules. This quantum simulator will help students learn the principles of quantum circuitry in their undergraduate and graduate courses. This tool will offer functions that allow you to build a quantum circuit, add and remove quantum gates, apply transformation rules, and observe the operation of a quantum circuit with its unitary matrix. By practicing with the quantum circuit simulator, students can strengthen their theoretical knowledge base about quantum computing and linear algebraic functions
The Study of Factors and Barriers That Negatively Affect the Development of the Creative Potential of the Individual

The concept of negative creativity and negative factors affecting creativity is still poorly understood, which indicates its scientific and practical significance. The purpose of the study is to analyze the factors and barriers that block or negatively affect the manifestation of creativity of teachers. The theoretical significance lies in the fact that the phenomenon of negative creativity was analyzed and external and internal factors that negatively affect the development of personal creativity were identified. The main methods are the analysis and generalization of scientific papers on the topic of research, questionnaires, and interviews among teachers. As a result of practical research, barriers to the development of the creative potential of teachers were found, such as a psycho-emotional state, an insufficient number of educational, methodological, and technological,
Online Nutritional Education Using Wordwall Game to Improve Knowledge Among Overweight and Obese Children in Palembang

The increasing prevalence of overweight and obesity in school children can be prevented by providing nutrition education to improve their nutrition knowledge. PowerPoint media and online games have improved learning outcomes and increased interest and motivation during online education. This study aimed to analyze the effect of online nutritional education using PowerPoint media, with and without Wordwall Game, on nutritional knowledge among overweight and obese elementary school-aged children in Palembang, from March 6th to 15th, 2023. It followed a quasi-experimental design, using a nonequivalent control group model, and measured pre and post-intervention data. The study utilized a purposive sampling technique, resulting in a total of 108 overweight and obese students aged 10-12, with 54 subjects in each group. Nutritional knowledge data were obtained through structured questionnaires previously tested for validity and reliability. The results of the independent t-test showed a significant difference in the mean scores of nutrition knowledge, with better results observed in the experimental group (p= ≤0.001). This indicates that providing online nutrition education through Wordwall Games and PowerPoint media is more effective in improving six aspects of nutritional knowledge in elementary school children than those who only use PowerPoint slides.
Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model

In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM) algorithm, which has high accuracy and efficiency. To forecast stock price time series, the model used is the LightGBM ensemble. At the same time, they were optimizing the determination of hyperparameters using Grid Search Cross Validation (GSCV). This study will also compare the LGBM algorithm with other algorithms to see which model is optimal in forecasting price stock data. In this study, the test used the RMSE metric by comparing the original data (testing data) with the predicted results. The experimental results show that the LightGBM model can compete with and outperform boosting-based forecasting models like XGBoost, AdaBoost, and CatBoost. In comparing forecasting models, the same dataset is used so that the results are accurate, and the comparisons are equivalent. In future research, paying attention to the data during pre-processing is necessary because it has many outliers. In addition, it is necessary to include exogenous variables and external variables, which are determined to involve many parties.
Development of an Algorithm for Recognition of the Kazakh-Latin Alphabet in the Image

The work is devoted to the study of the alphabet recognition process in the image. The goal is to implement a neural network based on the architecture of convolutional neural networks for recognition of the Kazakh Latin alphabet neural network in practice is considered. A convolutional neural network was formed, which was trained to distinguish handwritten English letters and numbers, since specific symbols and graphic features are a distinctive feature for the Kazakh-Latin alphabet. A neural network model has been constructed for preprocessing a data set. Based on the model, a test data set is synthesized, which is used to evaluate and control the quality of the model during training, and a test data set intended for final evaluation of the model's performance after the training process is completed. The general formulation of an effective algorithm capable of automatically recognizing the symbols of the Kazakh-Latin alphabet in images and presenting them in text format is presented. A new approach based on the use of deep convolutional neural networks is proposed.
Enhancing reservoir computing for secure digital image encryption using finance model forecasting

New research is changing the face of financial forecasting by combining reservoir computing with digital image encryption at a time when data security is of the utmost importance. This groundbreaking study combines digital image encryption with reservoir computing to suggest a novel method for financial forecasting. This creative method uses a reservoir network to encrypt digital photos securely, increasing their resistance to attacks and demonstrating the power of reservoir computing, a well-known machine learning concept. This approach significantly improves financial time series data forecasting accuracy and reliability using hyper-clusteratic models. When reservoir computing and hyper-chaotic models are tightly integrated, outcome is improved financial decision-making. Empirical tests have validated the technology's effectiveness and efficiency, showcasing its potential practical applications in financial forecasting and image encryption. The study examines numerical simulations in a dynamic reservoir framework that demonstrate encryption and decryption powers of reservoir computing, demonstrating its ability to comprehend input signals and generate answers that are desired. Critical phases include assessing the approach's effectiveness using metrics for encryption quality, attack resilience, and computing efficiency. Preparing picture representations for processing is also crucial. It is necessary to train the readout layer to translate reservoir states to encrypted picture pixels differently.
The Effectiveness of Brainswriting Technique In Teaching Writing Short Story Toward The Student

Tujuan dari penelitian ini adalah untuk mengetahui apakah teknik menulis Brains efektif dalam pengajaran menulis cerita pendek. Ada dua hipotesis dalam penelitian ini; hipotesis kerja (Ha) dan hipotesis nol (Ho). Populasi dalam Penelitian ini adalah siswa kelas VII SMPN Satu Atap Repuk Sintung. Penelitian ini menggunakan jenis penelitian quasi eksperimen dan desain penelitian yang digunakan adalah non-equivalent control group design. Dalam penelitian ini, kelompok eksperimen diajarkan menggunakan penulisan terbimbing dan kelompok kontrol diajarkan menggunakan metode konvensional. Data diperoleh dengan melakukan pre-test dan post-test. Pada pre-test, nilai rata-rata kelompok eksperimen adalah 67,93 dan kelompok kontrol adalah 68,23. Hasil post-test kelompok eksperimen sebesar 81,89, sedangkan kelompok kontrol sebesar 74,21. Hasil uji t juga menunjukkan terdapat perbedaan yang signifikan antara kelompok eksperimen dan kelompok kontrol. Nilai t untuk α = 5% dan n = 77 adalah 2,34 dan t tabel untuk t (0,05)(77) adalah 1,985. Hasil perhitungan membuktikan bahwa t-nilai lebih besar dari t-tabel. Disimpulkan bahwa teknik Brainwriting efektif digunakan dalam pengajaran menulis cerita pendek. Penggunaan rangkaian pertanyaan dan outline dapat membantu siswa dalam mengungkapkan gagasannya. The research aims to investigate whether Brains writing technique effective in teaching writing short story. There are two hypotheses in this research; working hypothesis (Ha) and null hypothesis (Ho). The population in this Research was Seventh grade students of SMPN Satu atap Repuk Sintung. The research applied a quasi-experimental study and the research design was a non-equivalent control group design. In this research, the experimental group was taught using guided writing and the control group was taught using conventional method. The data were gained by administering a pre-test and a post-test. In the pre-test, the mean score of the experimental group was 67.93 and the control group was 68.23. The result of post-test of the experimental group was 81.89, while the control group was 74.21. The result of the t-test also showed that there was a significant difference between the experimental group and the control group. t-value for α = 5% and n = 77 was 2.34 and t-table for t (0.05)(77) was 1.985. The calculation proved that t-value was higher than t-table. It is concluded that Brainswriting technique is effective to be used in teaching writing short story. The use of sequence of questions and outline can help the students to express their ideas.
Author Profiling from Short Romanized Urdu Messages: A Preliminary Investigation using Transfer Learning Models

Author profiling, a crucial task in natural language processing, involves identifying various attributes of an author, such as gender and age, from text. This study examines how transfer learning models in the context of author profiling from Roman Urdu text. We conduct experiments employing prominent models such as ELECTRA , BERT, RoBERTa, XLNet, Distil Bert, Distil RoBERTa,. Our analysis reveals superior performance in gender prediction using BERT, attaining an accuracy of 0.74698, precision of 0.7505, recall of 0.7462, and F1 score of 0.7456. On the other hand, RoBERTa demonstrates remarkable proficiency in age prediction with an accuracy of 0.8221, precision of 0.8215, recall of 0.8221, and F1 score of 0.8215. These findings showcase the effectiveness of transfer learning models in author profiling tasks offer insightful analysis for further research and applications in this domain.
Energy Trading in P2P Network by using Blockchain in Smart Grid

In recent years, the dramatic increase in demand of electricity utilization has introduced various environmental and sustainability concerns for Conventional Energy Grid based on fossil fuels, which driving the global energy manufacturers to adapt new efficient and resilient energy systems. The conventaional Grid is based on centralized system suffer from power losss, theft cases and cannot adopt new power systems properly. Presently the focus of the scientist and researchers is on the Clean Energy resources and Smart Grid, which has potential to address the contemporary problems found in conventional energy system. On the other side , Smart is a new technology, which allows the integration of clean energy resources such as wind, solar, geothermal, hydro, biomass, and tidal energy systems, ensuring energy availability at all times. The quintessential purpose of renewable technologies is to contrive a sustained community. Nevertheless, it has become a challenging concern for Smart Grid to integrate and coordinate enormous consignment of expanding users and connections. Consequently, blockchain has emerged as a resilient technology in various industries offering data integrity, security, building trust between parties, data immutability, and secure communication. These feature of blockchain making it an auspicious technology for decentralized Smart Grid and Renewable Energy criterion. In this research work, a blockchain based energy trading modal using Peer-to-Peer transactions management is presented. The main goal is to investigate an energy-trading model based on crypto wallets as a digital currency using smart grid and renewable energies. Moreover, contemporary issues of industrial and commercial communities adopting renewable Energy system will be address.
K-Nearest Neighbor Classifier for Classifying user Reviews on Social Media Networks

Gigantic content generated on social media networking sites have made the online users enabled to communicate their opinions and sentiments about products and other entities like political events etc. Opinion mining applications aim to provide facilities to user and companies for know about products in which they are interested. In this work, opinion mining system for comparative reviews is developed using supervised machine learning approach. For this purpose, K-nearest neighbor classifier is trained on a publicly available dataset. Effectiveness of the system is validated by comparing its performance with other classifiers.
Algebra Based Encryption and Decryption Algorithms

In this study an encryption/decryption algorithm is proposed and developed. The developed algorithm is based on the idea that any given plain text can be encrypted like a block cipher with a combination of three encryption keys k1, K2, ...KN that use any value between N = 1, 2, 3, n, . Then the cipher values can be used to make the blocks of alphabets containing only A, B, C, D, E, F, G, H, I, J, each block is separated by a space. The steps of algorithm could also be reversible for decryption of the cipher text. A MATLAB code is written to implement the algorithm and tested different input messages.Secret message consists of websitelink and bank account details. The specialty of the algorithm is that it can be ﬂexibly used to encrypt and decrypt the secret messages containing not only English alphabets but also those messages containing the numbers, punctuations, elementary mathematics operations and the special characters. The performance of the algorithm is evaluated in terms of computational time, memory usage. From the analysis it is found that the proposed algorithm is faster in terms of execution time as compared to the modern algorithm which makes the algorithm computationallysecure. The proposed research particularly contributes as the addition of knowledge in the ﬁeld of cryptography and generally to the information security; consequently can be beneﬁcial to the society.3
Urdu Sentiment Analysis: Future Extraction, Taxonomy, and Challenges

By the newly gained attention from several research areas for the field of opinion mining, work in Sentiment Analysis (SA) has also been increased. Sentiment analysis is actually a natural language processing (NLP) method which is implemented to decide whether the data is negative, positive or neutral. This analysis can also utilized to provide most appropriate countermeasures for various issues that are connected with particular fields. It is a contextual extraction and arrangement of text which recognizes and pinpoints subjective information regarding source material and helps to understand the social sentiment of people while monitoring online conversations, comments, tweets, or information on blogs, etc. There is wide utilization of Urdu language in offering perspectives that's why the Urdu language also wants opinion mining as well. In this research, a systematic literature review on sentiment analysis of Urdu language has been performed. This SLR is focusing on explicit research questions and afterward contributions are described appropriately. The findings of the review present a taxonomy that is based on the techniques of sentiment classification. Furthermore, in this SLR, we have extracted all the preprocessing techniques that were used in these 24 papers, the most adopted algorithms by the researchers, the most implemented sentiment analysis approach, and the feature extraction techniques are also extricated. Eventually, a thorough survey is given on all these considerations. After a detailed and deep evaluation, we have computed their accuracy results for better understanding of future researchers.
Pengaruh Media Vidio Pembelajaran Terhadap Motivasi Belajar Siswa Di SMP Negeri 5 Dumai

In this study, the media used by students for learning was video media. The question posed in this research is whether the learning video media at SMP Negeri 5 Dumai has an effect on students' learning motivation. This study uses statistical methods. The data collection instrument in this study used a questionnaire consisting of 15 questions. The sample in this study was 217 students who took part in learning using video media at SMP Negeri 5 Dumai. The learning rate of video media at SMP Negeri 5 Dumai is very good. This is because the instructional video media variable comes from the average percentage of respondents who answer very often is 41.95%, plus the average percentage of respondents who answer often is 54.3% which is the sum of 96.25%, where the range is 75.01%-100%. The students of SMP Negeri 5 Dumai are very motivated. This is because the influence of the instructional video media variable from the average percentage of respondents who answered very often was 44% plus the average percentage of respondents who answered often was 49.16%, so that it was added up with 93.16% results where the range was 75.01 %.-100%. The hypothesis is used to see the effect of the dependent variable, where the independent variable in this study is video media and the dependent variable is student learning motivation. Based on SPSS simple linear regression analysis, it can be seen that simple linear regression Y = 21.578+0.375 X. The results of the above formula can be changed to a constant 21578, namely the consistency value of the video media variable is 21578, and the regression coefficient X is 0.375 which indicates that the increase in the value of video media by 1% will increase learning motivation by 0.375. The positive coefficient indicates that the video media (X) has a positive effect on learning motivation (Y)
Economic Freedom and Education in Sub-Saharan African Countries

This study examined the impact of economic freedom on education in sub-Saharan countries from 1995 to 2020. This work focused on human capital investment using some education performance indicators: secondary school enrolment rates and tertiary school enrolment rates. The empirical model of the study was analyzed within the framework of pooled mean group (PMG) estimator given that the variables had mixed order of integration. The first empirical model of the study showed that there was evidence that all the economic freedom indicators except tax burden and business freedom had positive relationship with secondary school enrolment rates in the long run. Also, the result showed that in the short-run, only trade freedom, property rights, and investment freedom had negative but insignificant with secondary school enrolment rates. The model of the second objective of the study showed that there was evidence that all economic freedom indicators except investment freedom had positive relationship with tertiary school enrolment rates in the long run. On the other hand, the result showed that in the short-run, economic freedom measures had heterogeneous effect on tertiary school enrolment. Therefore, government can improve human capital investment through tertiary school education and other post-secondary school education, as well as the well-trod road between them as these factors played dominant role in most developing economies. Hence, SSA countries should create conducive educational investment climate, lessen tax burden, strengthen thefinancial system and promote free trade to enhance educational performance in SSA.
Energy Trading and Control in Microgrid Network by using Blockchain Technology

In future all Energy resources will be control through the internet (IoT) both home and commercial systems. For home power system, user can installed solar or wind energy system from 1 KW to 10 MW, depend on the area and location. These Energy resources will be control by Microgrid, the microgrid can work in two mode such as stand-alone system or with grid-connected mode. To control losses and trading the energy in microgrid network. The blockchain technology is suitable for microgrid because it has potential to resolve all issues such as energy theft case or billing fraud. The blockchain is distributed ledger technology and it has the potential to improve efficiencies by updating the status of the system. Microgrid combination with blockchain provide complete solution to all previous issues, which were present in conational energy system. In this research work, the Microgrid model with blockchain technology will be analyze for the energy sector to address the issues of the present problems, such as low efficiency, market competition, improve contract system for selling energy and theft case on basis of trusted and secure transaction platform without third party.
KOMPRESI CITRA BERBASIS MULTI-LEVEL THRESHOLDING MENGGUNAKAN DIFFERENTIAL EVOLUTION

Compression in image data needs to be done to manage storage space efficiently. In this study, image compression is performed using the Differential Evolution (DE) algorithm as an approach to achieve an optimal threshold for pixel clustering in images. The performance of this image compression method is evaluated based on the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) parameters. The PSNR of DE image compression ranges from 27 dB to 40 dB with threshold levels between 10 to 40. Meanwhile, the SSIM ranges from 0.83 to 0.97 at threshold levels of 10 to 40. The PSNR and SSIM of DE image compression are both at a good level, resulting in compressed image visuals that closely resemble the original image as the threshold levels increase.