Reviewerly/exHarmony huggingface dataset:
PENDEKATAN DATA MINING UNTUK MEMILIH PRODUK TERLARIS MENGGUNAKAN ALGORITMA NAIVE BAYES

Technological advances today can be exploited to process data into more useful information. In data collection, information collection is especially useful to maximize profits and develop marketing strategies. One way to increase profits is by using data mining techniques to help business actors in making decisions about stocks, increased profits and more. The Matahari Department Store is the largest retail platform in Indonesia, one of the retail stores located in Batam is the Matahari Department store Nagoya Hill Batam. The transaction data on the store that is still processed does not use a method that causes the processing of product sales data to be less effective and less efficient. Seeing from the number of transactions, a system is needed to predict the sale of the best-selling product as long as it can determine the correct stock for the products sold and can increase the profit, sale and purchase of the product. This research was conducted with the aim of applying data mining methods using the Naive Bayes Classifier algorithm to select the best-selling products in the outlet of the Matahari Nagoya Hill Batam Department Store. By using the collected sales data, the system is expected to increase profits steadily and avoid shortages of product stocks. Through analysis using the Naive Bayes Classifier method, the study achieved an accuracy of 67% and obtained a bag sales result to be the best-selling sale during January 2023 through March 2023 with a sales percentage of 20%.
RepRCNN: A Structural Reparameterisation Convolutional Neural Network Object Detection Algorithm Based on Branch Matching

A CNN object detection method based on the structural reparameterisation technique using branch matching is proposed to address the problem of balancing accuracy and speed in object detection techniques. By the structural reparameterisation of the convolutional layer in the object detection network, the amount of computation and the number of parameters in the network inference are reduced, the memory overhead is lowered, and the use of the branch-matching method to improve the structural reparameterisation model improves the computational efficiency and speed of the network while maintaining the detection accuracy. Optimisation is also carried out in terms of target screening and loss function, and a new CPC NMS screening strategy was introduced to further improve the performance of the model. The experimental results show that the proposed method achieves competitive results on the PASCAL VOC2012 and MS COCO2017 datasets compared to the traditional object detection methods and the current mainstream models, achieving a better balance between the detection accuracy and detection speed.
A Parameter Estimation of Photovoltaic Models Using a Boosting Flower Pollination Algorithm

An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.
Studies on Association Rule-based Table Data Analysis and its Applications - New Mathematics for Data Sciences - (An Invited Tutorial Paper)

This tutorial paper will survey the research on association rule-based table data analysis and its application. The proposed methods obtain helpful information from discrete table data through rules, which differs from statistical table data analysis by mean or variance. Regression expressions in continuous value table data can characterize past data and predict future data values. The same will hold for rules in discrete value table data. This tutorial paper consists of the following three parts. Part I: Background and Examples of Rule Generation from Tables Part II: Mathematical Research of Rule Generation from Tables Part III: Realization of Software Tools and Applications. We introduce the research trends in this field widely through three parts.
Analisis Model Pembelajaran Discovery Learning dalam Meningkatkan Kemampuan Menyelesaikan Soal Cerita Bangun Ruang Di Kelas V SD N 173435 Doloksanggul

This research was conducted with the aim of knowing the analysis of the use of discovery learning models in improving the ability to solve cube and block word problems in class V SD N 173435 Doloksanggul. This research is a type of quantitative research with a descriptive approach. The population in this study were 1 homeroom teacher of class V and 19 students of class V SD N 173435 Doloksanggul. The sampling technique in this study was a probability sampling technique where each population has the same opportunity to be selected as a sample with certain considerations. The data collection technique used in this research is by using questionnaires, interviews, and documentation. The results of this study indicate that the use of the discovery learning model in improving the ability to solve cube and block word problems in class V SD N 173435 Doloksanggul is effective and well used in improving students' cube and block story problem solving with a percentage of 52.63%.
Examining the Reading Performance and Problems among Students in Indonesian Higher Education

&#x0D; &#x0D; &#x0D; The objectives of the study are to assess the students' reading performance and explore their problems in comprehending English texts. To achieve these objectives, a descriptive research design was employed. The participants consisted of 76 English Department students from the Faculty of Teachers Training and Education at Universitas Sulawesi Barat, Majene, Indonesia. Two instruments were used in this study. First, a test was administered to measure students' reading performance. Second, a questionnaire in the form of a Google Form was employed to gather students' self-reported responses regarding their problems and difficulties in reading English texts. The findings revealed that the students' mean score on the reading performance test was 47.24, indicating a relatively low reading performance among the students. Moreover, the questionnaire responses indicated that most students reported encountering problems and difficulties in comprehending English reading texts. Consequently, it can be inferred that students' reading performance is adversely affected by these challenges. Based on the results, this study recommends the integration of learning media, technological tools, and effective reading strategies to address the identified problems and improve students' reading skills. These practical recommendations aim to provide educators and researchers with actionable strategies to improve the reading proficiency of EFL students.&#x0D; &#x0D; &#x0D;
Research progress of measurement of propagators in path integrals

The propagator plays a central role in path integral theory and therefore has significant value in various fields of modern quantum physics, where path integral representations can be used. However, owing to the fact that it has not been directly measured in experiment, progress of experimental studies of quantum systems based on path integral representations has been seriously limited. Recently, we proposed a propagator measurement scheme based on the direct measurement of the wave function and successfully performed the first experimental measurement of the propagator by using a single photon experiment. Furthermore, in this study, the quantum principle of least action is demonstrated for the first time. This research successfully addresses the technical challenges of path integral experimental studies. In this work, we review the research progress in this field, including a brief introduction to the basic concepts and research progress of direct wave function measurement, and a detailed description of the theoretical model, experimental design, and experimental results of propagator measurement. Finally, we introduce an important application example, which can serve as the experimental demonstration of the quantum principle of least action through propagator measurement. The research progress of propagator measurement reviewed in this work will provide important references for future experimental studies by using this method.
EduGame Maru: Application for Early Mathematics Learning as an Alternative for Optimizing Cognitive Ability for 4-6 Years Children

Mathematics teaching process in kindergarten must adapt to the pandemic situation which requires learning from a distance. This research develops an Android-based application for learning mathematical concepts, named EduGame Maru, consisting of five types of games: recognizing numbers, algebra, statistics, geometry, and probability. This research aimed to create an interactive, innovative, and practical application as a digital medium. The method used in this research was the RD model, with 4D development (define, design, develop, and disseminate). The EduGame Maru application received a validation result of "very feasible for use," with 3.36 points from media expert validators and 3.64 points from material expert validators, so the average was 3.50. Meanwhile, during the testing at Kindergarten Labschool, Universitas Pendidikan Indonesia, Serang Campus, the EduGame Maru application got 3.56 points from the teacher's assessment. In this case, the EduGame Maru application not only trains cognitive abilities but also helps children to understand early mathematical concepts by applying the NCTM (National Council of Teachers of Mathematics) principles. Therefore, it can be concluded that EduGame Maru can help teachers and parents optimize the cognitive aspects of children aged 4-6 years, especially early mathematical concepts during online learning.
Satisfaction of rural and urban high school students with the functioning of the general secondary education system

The modern system of Russian general education is in a state of permanent crisis. The thing is that during the transition from the Soviet educational system to modern educational standards, all the fundamental foundations of the educational process were changed, while the new standards did not receive due verification. As a result, modern educational standards and programs are regularly adjusted, shifting the vector of study from one direction of school subjects to another. In this case, the most vulnerable element in the educational system is the subject of the educational process – schoolchildren, especially high school students – persons studying from 9th to 11th grade. The existence of a crisis in the education system is confirmed by a wide range of educational services on the market, ranging from private tutors to online schools for preparing for the Unified State Exam. At first glance, there is nothing surprising in the fact that a high school student goes to classes with a tutor or studies at an online school – this will allow him to successfully pass the main state or unified state exam and enroll in a particular educational institution on a budget. But at the same time, this means the fact that the knowledge that is given to the student at school does not satisfy the educational needs of the student, namely the key request regarding the successful passing of the state exam.
Dam UAV Image Stitching Technology Studies Based on Weak Texture

Abstract Extraction of image feature point pairs is an important step in the image alignment process. In this paper, we propose a grid-based adaptive threshold feature detection method for weakly textured images using a single threshold extraction algorithm that cannot propose uniformly distributed feature points. Finally, the designed experiments show that the feature points extracted by this method have a lower dispersion than the traditional KAZE algorithm by 70% and a higher alignment accuracy than the traditional KAZE algorithm by 10%.
Urgensi Penerapan Automasi Perpustakaan Pada Perpustakaan Sekolah

(1) Libraries play an important role in all institutions, particularly schools. Libraries in schools exist and must serve as a driving force in realizing and creating the best learning environment possible. To support this, library management services must be revolutionized by developing or applying information technology. (2) Understanding the significance of putting in place an automated system in the school library. (3) The method for writing this article was developed through a review of the literature. (4) stated that there are six important reasons why library automation must be implemented, the first being demands on the number and quality of library services, the second being demands on shared use of collections, the third being demands to streamline human resources, the fourth being demands for time efficiency, the fifth being demands for accuracy information services, and the sixth being the diversity of information managed by the library. (5) The Industrial Revolution, which affected many fields, including school libraries, enabled better service and management. If you pay attention to the legal basis, needs, and rapidly developing technology, library acceleration, particularly good library management, can be carried out by any school library. Management, service, and information accessibility will be more effective and efficient for school library managers and users if the six reasons for the importance of implementing automation in libraries are considered.
Data Exploration

Data exploration is one of the core modules of the competition. It runs through the competition and is also the key to the victory of many competitions. So, what is data exploration? What problems can be solved? First of all, three points should be made clear: that is, how to ensure that you are ready to use the algorithm model for the competition, how to choose the most appropriate algorithm for the data set, and how to define the characteristic variables that can be used for the algorithm model.
Automated Segmentation of Cervical Cell Images Using IMBMDCR-Net

Early screening of cervical lesions is of great significance in pathological diagnosis. Owing to the complexity of cell morphological changes and the limitations of medical images, accurate segmentation of cervical cells is still a challenging task. In this paper, an isomorphic multi-branch modulation deformable convolution residual model is proposed to extract features for enhancing the segmentation of small cells and overlapping cytoplasmic boundaries. Then the regional feature extraction, boundary box recognition, and adding a single pixel-level mask at the last level are integrated and optimized based on the cascade regional convolution neural network (Cascade R-CNN) to complete the segmentation of cervical cells for getting better accuracy. The proposed framework was evaluated on the ISBI2014 cervical cell segmentation competition public dataset. Experimental results show that the average accuracy of the network model in cervical cell segmentation is 81.1%, and the accuracy of small targets is 77%. To some extent, it can assist pathologists in screening cervical cancer in the early phase.
Privacy Preserving Outsourced K-means Clustering Using Kd-tree

Nowadays, more and more resource-constrained individuals and corporations tend to outsource their data and machine learning tasks to cloud servers, enjoying high-quality data storage and computing services ubiquitously. However, outsourcing sensitive data can bring data security and privacy issues, arousing public concerns. In this work, we propose an efficient privacy-preserving outsourced scheme of K-means clustering on encrypted data in the twin-cloud model using the paradigm of secret sharing. The state-of-the-art outsourced K-means clustering scheme using fully homomorphic encryption is efficient but not secure enough. To better solve this problem, we utilize the kd-tree data structure and design a set of secure protocols, presenting a new scheme that is almost as efficient as the state-of-the-art schemes but more secure. In our scheme, the clustering process is performed by two cloud servers without leaking any intermediate information. We provide formal security analyses and evaluate the performance of our scheme on both synthetic and real-world datasets. The experiment results show that our scheme is efficient and practical.
Exploring Preprocessing Techniques for Natural LanguageText: A Comprehensive Study Using Python Code

The paper highlights the significance of efficient text preprocessing strategies in Natural Language Processing (NLP), a field focused on enabling machines to understand and interpret human language. Text preprocessing is a crucial step in converting unstructured text into a machine-understandable format. It plays a vital role in various text classification tasks, including web search, document classification, chatbots, and virtual assistants. Techniques such as tokenization, stop word removal, and lemmatization are carefully studied and applied in a specific order to ensure accurate and efficient information retrieval. The paper emphasizes the importance of selecting and ordering preprocessing techniques wisely to achieve high-quality results. Effective text preprocessing involves cleaning and filtering textual data to eliminate noise and enhance efficiency. Furthermore, it provides insights into the impact of different techniques, such as raw text, tokenization, stop word removal, and stemming, using a Python implementation.
Associated factors with the self-care of people with diabetes mellitus in the COVID-19 pandemic

Objective: to analyze the factors associated with self-care in people with diabetes during the COVID-19 pandemic. Methods: cross-sectional analytical and exploratory online study with 70 participants, using the Summary Diabetes Self-Care Activities Questionnaire. The clinical and sociodemographic profile was portrayed with a descriptive analysis. Fisher's exact test, the Chi-square test and Poisson regression with robust variance were used to determine the association between the variables. Results: after regression, the items Physical Activity, Use of Medication and General Diet from the Summary Diabetes Self-Care Activities Questionnaire showed, respectively, a significant association with the variables Physical Exercise (p&lt;0.001), Occupation (p=0.005) and Age (p=0.01). Conclusion: the physical exercise, medication use and general diet items on the self-care questionnaire were related to the variables of age, occupation, and physical exercise, respectively. Contributions to practice: health services should intensify the investigation of factors associated with the difficulties of people with diabetes, to offer a better quality of life and adequate health promotion for this population.
A Path-Planning Method Based on Improved Soft Actor-Critic Algorithm for Mobile Robots

The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots' interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method.
Economic Impact due Covid-19 Pandemic: Sentiment Analysis on Twitter Using Naïve Bayes Classifier and Support Vector Machine

Covid-19 is an outbreak caused by severe acute respiratory syndrome. Covid-19 first appeared in Indonesia on March 2, 2020, with two confirmed cases and increased to 1285 cases in 30 provinces. One of the impacts of the Covid-19 pandemic is on the economic aspect, which has experienced a drastic decline in income. This study aims to classify public opinion to determine the level of public sentiment on the economic impact of the Covid-19 pandemic and to identify parameters that influence the accuracy of the sentiment analysis classification model. The methods used in this current research are Lexicon, Support Vector Machine (SVM), and Naive Bayes Classifier (NBC). First, Lexicon is used for scoring and labeling the preprocessed data. Second, SVM is used to classify the sentiment, then find the best accuracy using linear, radial, polynomial, and sigmoid kernels. Third, NBC is used to classify sentiment as a comparison method. The results indicated that 255 tweet data consisted of 44 positive tweets (17.25%), 46 neutral tweets (18.04%), and 165 negative tweets (64.71%). Therefore, it can be inferred that the economic impact on the Indonesian people due to the Covid-19 pandemic has a high negative sentiment value. In the performance, SVM yielded a better accuracy of 100%, precision, recall, and F-measure are 1. This study proves that selecting the kernel type and applying underfitting can improve the accuracy of SVM. Also, SVM can perform well on a small amount of training data.