Reviewerly/exHarmony huggingface dataset:
Penerapan Model Discovery Learning pada Pokok Bahasan Balok Dan Kubus untuk Meningkatkan Pemahaman Konsep Siswa

This research uses a qualitative approach which aims to determine the application of the discovery learning model to the subject of blocks and cubes to increase the conceptual understanding of class VIII students at SMP PGRI 02 Pakisaji. The subjects in this research were 16 students in class VIII for the 2023 school year. The data used are: concept understanding test results, observation results, interview results, and field notes. The data collection steps used in this research are: providing a concept understanding pretest, providing discovery learning and observation, concept understanding posttest, four interviews. The data analysis used in this research is: data reduction, data presentation, drawing conclusions and verification. The research results showed that the percentage of students' understanding of mathematical concepts increased from 9.95% in cycle I to 31.8% in cycle 2 with a KKM ≥70. Teacher and student activity during learning obtained a total average of 66% in cycle I, increasing to 88% in cycle II and student activity during learning obtained a score of 50% in cycle I, increasing to 85% in cycle II. Based on the research results, it can be concluded that Discovery Learning can improve students' understanding of concepts in class VIII
A Convolutional Neural Network Model and Software Tool for Classifying the Presence of a Medical Mask on a Human Face

A model of a convolutional neural network, a database for training a neural network, and a software tool for classifying the presence of a medical mask on a person’s face, which allows recognizing the presence of a medical mask from the transmitted image, have been developed. The structure of the neural network model was optimized to improve classification results. In addition, the development of the user interface was carried out. The developed application was tested on a set of random images. The resulting model demonstrated high accuracy and robustness in solving the task of classifying the presence of a medical mask on a person’s face, which allows automating measures to protect people from the spread of diseases. The implemented application meets the requirements for speed and quality of classification. Further improvement of the classification quality of CNN can be done by collecting a larger dataset and researching other CNN architectures.
Implementasi Metode Tebak Kata dalam Meningkatkan Keterampilan Berbicara Siswa

Speaking skills play an important role in giving birth to a future generation that is intelligent, creative, critical and cultured. Through mastery of speaking skills, students are able to express their thoughts intelligently according to the material and situation when they are speaking. The aim of this research is to find out how the implementation of the word guessing method improves students' speaking skills. The type of research used is classroom action research (PTK) with two cycles. The research subjects were 25 people consisting of 11 men and 14 women. Data collection techniques in this research were obtained through observation and tests. The research results obtained in cycle I were 4 students or 16.00% "very good" speaking skills, 13 students or 52.00% "good" speaking skills, 8 students or 4.00% "fair" speaking skills, none students' speaking skills were "poor", and there were no students with "very poor" speaking skills, in cycle II there were 15 students or 60.00% "very good" speaking skills, 10 students or 40.00% "good" speaking skills, no there were students with "fair" speaking skills, no students with "poor" speaking skills, and no "very poor" speaking skills. So it can be concluded that the word guessing method can improve the speaking skills of class IV students at SD Negeri 0508 Tangga Bosi
Exploring the Impact of Teachers' Technology Integration and TPACK Competencies amidst COVID-19: A Mixed-methods Study for Future Educational Preparedness in School

COVID-19 and the subsequent closure of the schools across Bhutan, to check the spread of the virus forced the Ministry of Education and Skills Development to transition to online teaching and learning. Teachers were mandated by emergency policies to teach students online through the use of technology, social media, and other social network platforms. Technology integration is influenced by both first and second-order barriers and the quality of technology integration ranges from low-quality integration to high-quality integration. Using survey data from a sample of 110 teachers and interviews with 26 teachers in five districts of Bhutan, this research attempted to identify and delineate teachers technological pedagogical content knowledge and the draw its relation to the quality of technology integration. Sequential explanatory mixed-methods design, which enables researchers to first collect quantitative data and analyze the data to draw first set of findings was used. The findings of the survey were used to guide the interviews conducted during the second phase of the research. Results indicate that teacher are neither proficient in the various dimensions of TPACK nor required too much assistance to navigate the challenges to technology integration. The interview data did not reveal high-quality technology integration in the teaching learning process. Recommendations for policy and practices are provided.
Evaluating the efficacy and drawbacks of traceability technology in the quality assurance of the food supply chain

The food industry can significantly benefit from blockchain technology by increasing transparency and visibility throughout the supply chain because it enables end-to-end data traceability in the food retail network. A blockchain does not require a central authority to control all data, reducing organizations’ need to disclose their proprietary information. All supply chain participants share only the data essential for a specific record or transaction. In this study, peer-reviewed journal articles were used as secondary data sources for data collection regarding blockchain usage in the food sector. After analyzing the collected data, key findings were organized into relevant themes to address the study’s objectives using content analysis. Organizations can adopt a cost-benefit analysis in the food industry when traceability technology is combined with blockchain. Transparency increases revenue and profitability, especially in segments where consumers are willing to pay. However, several risks and challenges must be addressed, including vulnerabilities, piracy, contractual disputes, and hacking. Using smart contracts, blockchain automates production processes and facilitates real-time order settlement. Even the most secure cryptocurrencies on the blockchain can be traced to varying degrees, emphasizing the importance of blockchain transaction reliability. To address privacy concerns and enable data-sharing capabilities, food companies should integrate blockchain with the Internet of Things (IoT). Using blockchain technology in food supply chain and packaging operations can also enhance transparency and traceability by enabling consumers to scan product codes using a mobile app.
On-Chip Memory Access Reduction for Energy-Efficient Dilated Convolution Processing

Dilated convolutions have recently become increasingly popular in deep neural networks. However, the inference of these operations on hardware accelerators is not mature enough to reach the efficiency of standard convolutions. Therefore, we extended a dedicated accelerator for dilated convolutions to reduce the number of energy-intensive accesses to the on-chip memory. We achieve this by applying the principle of feature map decomposition to an output-stationary compute array with a strided feature loading. Our solution shows a 50% reduction in memory accesses for an unpadded 3 $$\,\times \,$$ 3 kernel and a dilation rate of 9 compared to a recently proposed dilated convolution accelerator. We also support flexible parameter selection for kernel sizes and dilation rates to meet the requirements of modern neural networks. The energy consumption of the additional hardware modules is less than the savings achieved by the reduced memory accesses. This results in a relative energy saving by a factor of 4.77 for dilated convolutions with unpadded 3 $$\,\times \,$$ 3 kernels.
Quantum Computing Research Lines in the Italian Center for Supercomputing

Quantum computing is widely seen as an evolution step in computer science, with the potential for disruptive changes in how we think about problem solvability. The significant perspective societal gains have pushed for the creation of nation-wide research efforts, gathering together a diverse set of competences, ranging from fundamental physics, to electronic and computer engineering, and to pure computer science. In this paper, we provide an overview of the perspectives and research directions of the Italian Center for Supercomputing, and in particular its efforts towards advancing research in all aspects of quantum computing. Besides a general overview of the center itself, and its components, we also provide a glance on some of the current research directions.
Machine learning methods on quantized vectors

Vector quantization is one of the important issues in digital images. There are many studies conducted on quantized vectors or images. On the other hand, machine-learning approaches are a popular issue today. In this study, the classification performances of machine learning approaches on reduced image vectors are examined. Firstly, Corel 1K data set were reduced to 64 colors with octree and histogram feature vectors extracted. Classification was carried out using various machine learning approaches on the relevant vectors. As a result of the classification, the success of the methods was examined.
English Need Analysis for Mataram Children Forum in Mataram City

As an international language, English has become highly important in supporting Mataram Children Forum (MCF) members' participation in raising children's voices at the national and international levels. However, there is no program that can help to develop their understanding of English skills. This research aims to find out the English levels and English needs of MCF members, the population of which were all members of the MCF while the samples are all the 15 active members with 3 additional samples from facilitators selected through a purposive sampling technique. All data were collected using triangulation techniques, including tests with the CEFR guideline, interviews, and questionnaires. They are then analyzed qualitatively and quantitatively based on the data specifications. The research reveals that (i) the members’ level of English was still low (7% at the pre-beginner level, 47% at the beginner level, 33% at the elementary level, and 13% at the intermediate level); (ii) their basic need is to be able to communicate orally in order to participate in the fulfillment of children's rights; (iii) their desire is to be able to communicate and understand English not just for organizational purposes, but also to help them in achieving their future goals; (iv) they tend to have issues in English comprehension, particularly at speaking and listening skills, which limits their level of participation. Therefore, it is recommended that the MCF members make an English learning program that could help meets their needs and function in raising children voice.
Beans classification using decision tree and random forest with randomized search hyperparameter tuning

Dry-beans are a food with high protein. Dry-beans can be used as processed food products for emergency conditions such as famine, natural disasters, and war. Dry-beans can be used as a long-lasting product. To identify types of beans, manual work certainly requires a lot of time and effort. Therefore, creating a system that can classify beans in a computerized system is necessary. In this study, we classified beans using public data from Koklu. The data consists of sixteen features, seven classes with 13,611 rows. The data for each class of bean is unbalanced, so it is necessary to carry out a balanced dataset using random oversampling. Machine learning for classification using Decision Tree and Random Forest. Apart from that, hyperparameter tuning with randomize search for the number of trees 50, 75, 150, 200, and 300. The test results show that the Random Forest’s accuracy, precision, recall, and f1-score reach 0.9658 respectively. The best parameter number of trees is 300.
Optimasi PID Kontroller Pada Sistem Pengaturan Irigasi Menggunakan Metode Bat Algorithm (BA)

Provision of irrigation water on agricultural land aims to meet crop water needs. In its utilization, irrigation water must be used optimally. An automatic irrigation system is needed that is able to provide water for plants with the expected conditions. PID (Proportional, Integral and Derivative) is widely used for optimization of control systems. In irrigation control system and sensor connection with software program (Matlab 2013b). PID simulation was applied to the irrigation prototype used to increase efficiency and determine the amount of irrigation water to regulate the provision of irrigation water as needed. As a comparison controller, conventional PID (PID-Konv) was used, Matlab auto tuning PID (PID-Auto), and Bat Algorithm tuning PID (PID-BA). The simulation results show that PID-BA is the best method in the simulation with the smallest overshot (0.043) and the fastest settlingtime (84 seconds).
Research on the application of nanomaterials in new energy batteries and future development prospects

Nowadays, new energy batteries and nanomaterials are one of the main areas of future development worldwide. This paper introduces nanomaterials and new energy batteries and talks about the application of nanomaterials in new energy batteries and their future directions. Nanomaterials can bring human technology to a new level and bring many new functions to objects. It can be objected that are lighter, stronger, and higher. Because of the smaller surface area, the product made by nanomaterials is more stable. And they have higher sensitivity, as well. However, they have disadvantages too, such as pollution, and poor nanotechnology humans controlled. But in any case, nanomaterial plays a vital role in the technological development of mankind. A new energy battery is also one of the future development goals of mankind, it is an energy-saving battery that can reduce the pollution of the environment. But poor charging speed and poor continuity are its weaknesses. However, it does not stop it from gradually replacing the original traditional battery and becoming the mainstream battery in the future. Nanomaterials play a key role in improving new energy batteries improving the stability of batteries, accelerating battery charging, and so on. It can help people to understand nanomaterials and new energy batteries and their applications. It can also help to understand their future research directions and the market development of nanomaterials and new energy batteries.
Research on building design and construction application based on BIM technology

Building Information model (BIM) is a kind of digital building information model. Through BIM technology, building information can be presented to each department of building engineering in the form of three dimensional (3D). In traditional building information management, design part and construction part are independent of each other. There is no unified standard and information management system between the two sides. As a result, the construction information between the design department and the construction department cannot be shared in time, and both sides are prone to make mistakes in the negotiation of construction issues. These mistakes not only delay the construction, but also increase the construction cost. With the development of BIM technology, each department of building can understand the details of the building more intuitively through the building information model. Designers and builders can communicate effectively in construction projects and coordinate problems in the construction process.
Aplikasi Manajemen Kinerja Pegawai Dengan Metode Sosiometri pada PT. Siba Prima Utama Feed Mill

Employee performance appraisal is one of the main processes in managing human resources in a company. PT. Siba Prima Utama Feed Mill evaluates employee performance to assist leaders in making decisions that affect increasing work productivity. Currently, the assessment process is done manually. It causes problems of subjectivity, inaccuracy, and lack of transparency. Employee performance appraisal application is designed to facilitate a computerized appraisal process. The assessment method includes performance results-based evaluation that can define clear and measurable performance goals and involves the sociometric method to map social interactions between employees. This helps direct employee focus and ensures assessment objectives are met. The application is equipped with practical feedback features through a transparent environment. Application development is built using the waterfall method and through black-box testing and SUS (System Usability Scale). The black box test results were declared valid. The SUS test yielded 78 points that were categorized in the excellent standard, which means the application was feasible properly. The employee performance application system is a solution to improve the performance appraisal process more efficiently and objectively.
Research on the Path of Digital Transformation in Chinese Higher Education

In the era of digital economy, digital transformation is an inevitable choice for higher education to adapt to the future development of education and meet the personalized learning needs of students, a necessary way to improve the quality of talent cultivation in colleges, and an urgent requirement to achieve the connotative development of higher education. With the in-depth promotion and application of information technology in the field of education, the role of technological innovation in transforming the shape of education, expanding educational opportunities and innovating learning styles has become increasingly prominent. The article analyzes the intrinsic implication of digital transformation in the development of Chinese higher education and the current problems, and puts forward the implementation path for the digital development of Chinese higher education in the context of digital transformation. The in-depth promotion and application of information technology in education should be promoted through the development path of building a digital education resource platform based on technology integration, a data-driven education and teaching model as the core, an open sharing education and teaching mechanism, and a service-oriented education management system, so as to accelerate the transformation of education mode, expand educational opportunities, innovate the learning mode, and improve the quality of talent cultivation.
Blockchain in Realistic Areas of Application and Difficulties Encountered: A Survey Study

Blockchain technologies have grown in popularity over the last few years, with various experts touting the technology's potential applications in a range of businesses, markets, organizations, and governmental institutions. In the brief history of blockchain, an astounding number of incredible implementations have been done in terms of how it may be utilized and the potential effect it may have on a range of sectors. And, because of the great number and complexity of these characteristics, addressing the blockchain's potential and complications can be difficult, especially when seeking to address its purpose and fit for a certain activity. The blockchain's practical skills in fixing multiple challenges that are currently preventing further progress in various industrial fields are significant benefits. Securing and sharing transactional data, automating and optimizing supply chain procedures, and enhancing transparency throughout the value chain are just a few of the issues that companies are concerned about. Blockchain technology efficiently overcomes these challenges by leveraging distributed, shared, secure, and permission-based transactional ledgers. This paper concentrates on the many industrial application domains that blockchain technology offers. In addition, it examines and investigates the benefits, drawbacks, and challenges of incorporating blockchain into various industry applications, as well as defines the criteria for using blockchain in multiple industry applications and provides a brief overview of the technology.
Review on Hybrid Swarm Algorithms for Feature Selection

Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedures, the majority of hybrid algorithms enhance classification accuracy.
IDENTIFICATION OF MENTAL HEALTH WORKERS IN LAMONGAN WITH MACHINE LEARNING

COVID-19 has caused a global health crisis, with increasing numbers of people being infected and dying every day. Various countries have tried to control its spread by applying the basic principles of social aggregation and testing. Experts agree that physical and mental health are interrelated and must be managed and balanced. The government must pay attention to balancing physical and mental health during a pandemic. The Ministry of Health has issued a guidebook for Mental Health and Psychosocial Support (DKJPS) during the COVID-19 pandemic. Based on the mental health conditions of the community or medical personnel, we are trying to create a system for mental health analysis for medical professionals based on the results of questionnaires using the machine learning method (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression). A total of 24 question questionnaires were submitted to respondents. This study aimed to create a machine learning model (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression) to identify the mental health of medical personnel during the COVID-19 pandemic. The results of this study are machine learning models that have the highest accuracy in identifying health workers' mental health and are 100% SVM.