Reviewerly/exHarmony huggingface dataset:
Projek Based Learning dan Media Loose Parts dalam Melejitkan Kemampuan Literasi Steam pada Anak Usia Dini

The background to the problem in writing this Best Practice is because the students' ability in STEAM Literacy is very low, teachers are not varied in using approaches and learning models. The purpose of this Best Practice is to create effective, creative and imaginative learning, in introducing STEAM literacy to students and to be able to provide motivation to play naturally for children and inspire teachers to be more innovative and varied in presenting learning. Based on these problems, the teacher applies learning using a project-based learning approach to boost STEAM literacy skills and differentiate. As for the results of this Best Practice, students become more free in expressing their ideas and ideas and can solve the problems they face. Children can develop STEAM literacy skills and differentiation as well as learner-centered learning. By applying Loose Parts media, it is hoped that children will be more creative, imaginative, collaborative in accordance with the characteristics of children, that the child's world is play by playing will gain knowledge and skills and attitudes in everyday life. this it is hoped that it will form a future generation that is tough, competent, with Pancasila personality ready to face challenges in the era of globalization.
A High-Speed, Area-Efficient Transfer Method Using a Reverse Carry Propagate Adder

The most important component of any electronic device has historically been the arithmetic and logic unit. An efficient algorithmic function, such as addition and multiplication, which is required for an arithmetic as well as logic unit to be significant in the current improvement. For performing modular arithmetic in several cryptography and pseudorandom bit generator (PRBG) algorithms, the three-operand binary adder is the fundamental functional unit. In this paper, this study purposes a reverse carry propagate adder A carry input signal is more important than the carry output signal because, in the RCPA structure, the carry signal flows counter-clockwise from the most significant bit to the least significant bit. In the presence of delay changes, this carry propagation technique results in greater stability. Three implementations of the reverse carry propagate full-adder cell with different delay, power, energy, and accuracy levels are introduced by this study.As a result, it continues as one of the greatest options for creating huge arithmetic circuits with little increase in area and minimal power and energy usage.
IOT Integration in two-axis sun tracking system with LDR sensors in a Wheatstone bridge Circuit

Nowadays, using photovoltaic (PV) cells is among the power generation methods that absorb solar energy and convert it into electrical energy. The sun moves from east to west during the day and its radiation angle changes relative to the Earth in different seasons. So, the output power of PV panels changes as well. The output power of PV panels increases by being located in a position perpendicular to the angle of the sun’s rays. This study aims to design and implement a dual-axis solar tracker (DAST) to increase the output power of the PV panel. This simple system has high efficiency and adjusts the PV panel based on solar radiation by moving simultaneously on two axes. An analog controller is used for its control system. DAST control system is a closed-loop system that uses Wheatstone bridge circuit function along with light-dependent resistors (LDRs). A small DAST was designed and built to validate the proposed system, the performance of which was verified. Based on the experiments, I-V and P-V specifications were obtained. Finally, it was found that the output power of the PV panel using solar tracker was higher than that of the fixed panel. Further this paper discusses the implementation of IoT (Internet of Things) for maneuvering and remote monitoring the orientation of the solar panel from anywhere.
Exploring the Practice of Cultivating Business English Talents from the Perspective of Artificial Intelligence

Abstract In the context of the development tendency of artificial intelligence, the paper describes the opportunities and challenges artificial intelligence brings to business English talent training, innovates and reforms innovative business negotiation talent training methods, and develops independent innovation according to the future development trend of artificial intelligence. The paper discusses the practical activities of training business English talents using artificial intelligence. It mainly focuses on the indicators of building the entity model of business English talents training through artificial intelligence technical RBF ne ural network, grasping the genetic algorithm from four aspects such as the number of gender chromosomes, integral function formula calculation, genetic algorithm, RBF neural network parameters to optimize the entity model and genetic algorithm, and optimizing the limitation of RBF neural network according to the genetic algorithm. The RBF neural network entity model with the genetic algorithm is used to implement detailed analysis and scientific research exploration of the business English talent training evaluation system. The database deviation index and precision index values under the two entity models were implemented for comparison operation. The experimental results show that under the deviation index, the RBF neural network entity model, according to the genetic algorithm, converges quickly and longer than the RBF entity model. Regarding the precision index, the genetic algorithm RBF neural network has a higher precision than 0.94 for every 60 teams, while the RBF model has a precision of 0.84. This research allows for a more accurate analysis of the strengths and weaknesses of school students in business English. Teachers need to improve their academic performance and quality based on their strengths and weaknesses to facilitate the shaping of more excellent business English talents for China. It is important to explore the way of training business English talents under artificial intelligence to accelerate the innovative development of English education and training in China and the trend of English development.
The Effect of Applying Index Card Match (ICM) Active Learning Strategy on Student Learning Outcomes in Information and Communication Technology Subjects in Class IX of SMP Negeri 19 Padang

This study aims to determine the effect of student learning outcomes by using an Active Learning strategy type index Card Match (ICM) on ICT learning outcomes for class IX Negeri 19 Padang students. This research is in quantitative form with a quasy experimental approach. The type of data in this study is in the form of student learning outcomes and the data source is student scores. Then the data is processed using the T-Test. Based on the research results, the average value of the experimental group with the ICM strategy was obtained 80 .70 , higher than the average value of the control group 63.83 . The calculation of the t-test states that t count is 5.57 while t table = 2.0 0 , t.count &gt; t.table. This means that there is a significant difference in the learning outcomes of class students who use the Active learning strategy of the Index Card Match (ICM) type (experimental class) with the learning outcomes of class students who use conventional strategies (control class).
INDONESIAN ICT-BASED TEACHING OF ENGLISH-SPEAKING SKILL: A QUALITATIVE SYSTEMATIC REVIEW

The fact that technology is inevitable nowadays in education has shifted the traditional teaching method to modern. It offers teachers various accessible facilities to support English learning based on context and needs. This paper is objected to eliciting how the teaching of English-speaking skills is integrated with Information and Communication Technology in the Indonesian context. It employs a qualitative systematic review method that synthesizes the existing studies related to the topic involving its' procedures and how those procedures bring success to learners. It has six main steps; (1) topic formulation, (2) overall study design, (3) sampling, (4) data collection, (5) data analysis, and (6) data presentation. The findings provide some insights for education developers to design methods and activities for speaking. Moreover, future researchers are suggested to explore rarely discussed areas, including young learners and unpopular technologies related to English teaching. It is also found that Information and Communication Technology based teaching speaking in Indonesia has significantly developed through the years, especially as a learning material using video, movies, and podcasts.
Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction

This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuable insights. It is possible that additional data points, such as holidays and weather conditions, will further enhance the accuracy of the model in future research. As a result of implementing XGBoost, Jakarta's transportation system can optimize resource utilization and improve customer service in order to improve passenger satisfaction. Future studies may benefit from additional data points, such as holidays and weather conditions, in order to improve XGBoost's efficiency.
A Novel Approach for Identification of Figurative Language Types in Devanagari Scripted Languages

Poetry can be defined as a form of literary expression that uses language and artistic techniques to evoke emotions, create imagery, and convey complex ideas in a concentrated and imaginative manner. It is a form of written or spoken art that often incorporates rhythm, meter, rhyme, and figurative language to engage the reader or listener on multiple levels. There is no automated system that can identify figures of speech (FsoS) in poetry using Natural Language Processing (NLP) methods. In this research paper, the authors categorized four types of FsoS व्रत्या अनुप्रास (Type of alliteration), छेकानुप्रास (Type of alliteration), अन्तत्यानुप्रास (Rhyme), and पुनरुक्ति (Repetition) using two custom algorithms, Koshur and Awadhi (KA and AA), developed specifically for three different language corpora of poems (Koshur (K), Awadhi (A), and Hindi (H)). To evaluate the effectiveness of these algorithms, the authors conducted tests on three languages using four distinct approaches: with stopwords without optimization, with stopwords with optimization, without stopwords without optimization, and without stopwords with optimization. Authors have put lots of effort into identifying FsoS in not only one single language but in three Devanagari scripted languages. This research work is the first of its kind. The average accuracy without stopwords was not up to the mark. The authors then optimized both algorithms and again tested them on the same corpora with or without stopwords, resulting in a significant increase in accuracy.
Hussein Search Algorithm: A Novel Efficient Searching Algorithm in Constant Time Complexity

Hussein search algorithm focuses on the fundamental concept of searching in computer science and aims to enhance the retrieval of data from various data warehouses. The efficiency of cloud systems is substantially influenced by the manner in which data is saved and retrieved, given the vast quantity of data being generated and stored in the cloud. The act of searching entails the systematic endeavor of locating a particular item within a substantial volume of data, and searching algorithms offer methodical strategies for accomplishing this task. There exists a wide array of searching algorithms, each exhibiting variations in terms of the search procedure, time complexity, and space complexity. The choice of the suitable algorithm is contingent upon various aspects, including the magnitude of the dataset, the distribution of the data, and the desired temporal and spatial intricacy. This study presents a novel prediction-based searching algorithm named the Hussein search algorithm. The system is designed to operate in a straightforward manner and makes use of a simple data structure. This study relies on fundamental mathematical computations and incorporates the interpolation search algorithm, an algorithm that introduces a search by-prediction method for uniformly distributed lists, it forecasts the precise position of the queried object. The cost of prediction remains consistent and, in numerous instances, falls under the O(1) range. Hussein search algorithm exhibits enhanced efficiency in comparison to the binary search and ternary search algorithms, both of which are widely regarded as the best methods for searching sorted data.
Accelerating innovation: the transformative impact of 5G

This research paper aims to explore the impact of 5G technology on innovation in various industries. As the fifth generation mobile technology, 5G promises to deliver faster speeds, lower latency, greater connectivity and improved reliability. These resources have the potential to change the way businesses operate by providing new applications, services and business models. Through an in-depth analysis of research papers, expert opinions and industry publications, this article highlights the key elements that 5G expects to drive innovation, including healthcare, transportation, manufacturing, smart cities and entertainment. Additionally, the article examines the challenges and opportunities associated with the widespread adoption of 5G technology and provides insight into the steps needed to maximize its transformation potential.
Optimized Combination of E-Commerce Platform Sales Model and Blockchain Anti-Counterfeit Traceability Service

With the rise of e-commerce and the increasing prevalence of counterfeit products, there is a pressing need for robust anti-counterfeit measures. This research aims to explore the optimized combination of an e-commerce platform sales model and a blockchain-based anti-counterfeit traceability service strategy. By integrating the strengths of e-commerce platforms and blockchain technology, this study proposes a comprehensive approach to enhance consumer trust, reduce counterfeit products, and improve supply chain transparency. The research investigates various e-commerce sales models, such as business-to consumer (B2C) and consumer-to-consumer (C2C), to identify their effectiveness in combating counterfeiting. Additionally, the study explores the implementation of blockchain technology to establish a decentralized and immutable traceability system that ensures the authenticity and integrity of products. The proposed strategy leverages smart contracts, cryptographic hashing, and consensus mechanisms to enable real-time product verification and anti-counterfeit tracking throughout the supply chain. Through this optimized combination, it is anticipated that consumer confidence will be bolstered, counterfeit products will be mitigated, and stakeholders across the e-commerce ecosystem will benefit from improved transparency and accountability. The findings of this study contribute to the development of effective anti-counterfeit strategies and provide insights for e-commerce platforms and stakeholders seeking to enhance trust and combat counterfeit activities.
Sentiment analysis of domestic movie reviews based on a discrete regression model

Abstract In the context of the rapid development of big data information digitization, how to use big data technology to analyze the sentiment of domestic movie reviews is a hot topic of current concern. In this paper, we use two similarly themed Chinese movie reviews as the basis and use discrete regression models to study the emotional tendency of movie reviews. Firstly, the ROC curves of the regression model are plotted based on the convolutional neural network to construct the text classification task metrics and the logistic discrete regression algorithm. The prediction probability at the bottom of the curve is 56.3%, and the 95% confidence interval is [56%, 56.6%], indicating the model’s high accuracy. Then, the factors affecting effective tendency were analyzed by the logistic discrete regression model, and both user level (UL) and per capita disposable income (PCDI) had significant effects on affective tendency (ET) (both p-values were less than 0.001), and the coefficients of both influencing factors were less than 0. This indicates that user level (UL) and per capita disposable income (PCDI) have significant negative effects on affective tendency (ET) have a significant negative effect on affective tendency (ET), i.e., the higher the user level, the more likely it is to have a bad review, i.e., the higher the per capita disposable income, the more likely it is to have a bad review. This study provides a comprehensive and objective evaluation of domestic movie reviews and provides a guiding reference basis for the development direction and progress of Chinese movies.
Implementing a Blockchain, Smart Contract, and NFT Framework for Waste Management Systems in Emerging Economies: An Investigation in Vietnam

The management and disposal of various types of waste (including industrial, domestic, and medical waste) are worldwide issues, which are particularly critical in developing nations such as Vietnam. Given the extensive population and inadequate waste treatment facilities, addressing this challenge is of utmost importance. Predominantly, the majority of such waste is not processed for composting but is instead subjected to elimination, thereby posing severe threats to public health and environmental safety. Furthermore, insufficient standards in existing waste treatment plants contribute to the rising volume of environmental waste. Emphasizing the process of waste recycling instead of total elimination is an alternate strategy that needs to be considered seriously. However, the implementation of waste segregation in Vietnam is still not sufficiently prioritized by individuals or organizations. This study presents a unique model for waste segregation and treatment, leveraging the capacities of blockchain technology and smart contracts. We also scrutinize the adherence or non-compliance to waste segregation mandates as a mechanism to incentivize or penalize individuals and organizations, respectively. To address this, we employ Non-Fungible Token (NFT) technology for the storage of compliance proofs and associated metadata. The paper’s primary contributions can be delineated into four components: i) presentation of a waste segregation and treatment model in Vietnam, utilizing Blockchain technology and Smart Contracts; ii) application of NFTs for storage of compliance-related content and its metadata; iii) offering a proof-of-concept implementation rooted in the Ethereum platform; and iv) executing the proposed model on four EVM and ERC721 compliant platforms, namely BNB Smart Chain, Fantom, Polygon, and Celo, to identify the most suitable platform for our proposition.
Research on Improving Piano Performance Evaluation Method in Piano Assisted Online Education

With the continuous progress of science and technology and the popularization of the Internet, online piano education has gradually emerged. This educational model provides piano learning resources and communication platforms through the network platform, so that students can learn piano at home anytime and anywhere. However, there are still some problems in the evaluation method of piano assisted online education, which hinders the development of piano assisted online education. Aiming at the problem that piano assisted online education is difficult to evaluate correctly, this paper proposes to integrate the bidirectional long and short memory network into the instrument digital interface piano performance evaluation model, and to integrate the attention mechanism into the bidirectional long and short memory network, hoping to improve the evaluation accuracy of the model. In the comparison experiment of the evaluation model based on the bidirectional long term memory network, it is found that the accuracy of the bidirectional long term memory network model is 0.91, which is significantly higher than the comparison model. In addition, through the empirical analysis of the model, it is found that the piano online education course integrated with the model can improve students' performance level scores and promote their participation enthusiasm. The above results indicate that the digital interface piano performance evaluation model can not only be used to evaluate digital interface piano performance more accurately, but also to promote the development of online piano education.
The Medical Image Denoising Method Based on the CycleGAN and the Complex Shearlet Transform

Medical image denoising plays an important role for the noise in the medical images can reduce the visibility, thereby affecting the diagnostic results of the doctors. Although good results have been achieved by the well-known deep learning-based denoising methods for their strong ability of learning, the loss of structural feature information and the well preservation of the edge information have not attracted considerable attention. To deal with these problems, a novel medical image denoising method based on the improved CycleGAN and the complex shearlet transform(CST) is proposed. The CST is used to construct the generator to embed more feature information in the training process and the denoising process is modeled to adversarial learn the mapping between the noise-free image domain and the noisy image domain. With the mechanism of the recurrent learning from the CycleGAN, the proposed method does not need the paired training data, which obviously speeds up the training and is more convenient than other classical methods. By comparing with five state-of-the-art denoising methods, experiments on the open dataset fully prove the accuracy and efficiency of the proposed method in terms of the visual quality and the quantitative PSNR, SSIM, and EPI.
The Influence of the Effectiveness of Visual Learning Media on Economic Learning Outcomes across Interests of Class X Students at SMAN 1 Kampar Kiri

This study aims to determine the effect of the effectiveness of visual learning media on economic learning outcomes across interests of class X students at SMAN 1 Kampar Kiri. The method used in this study is a correlational method with a quantitative approach. The sample used in the study was 73 people. The data collection method used in this study used questionnaires and tests, test instruments to determine learning outcomes and questionnaires to determine student responses to the use of visual power point media. The analytical techniques used were descriptive analysis, classical assumption tests and simple linear regression. The research results obtained showed that the effectiveness of visual learning media had a positive and significant effect on student learning outcomes with the regression equation Y = 4,272 + 0.813 (X)X1 + e and contributed 48% and the rest was influenced by other factors not examined.