Reviewerly/exHarmony huggingface dataset:
STUDENTS' MOTIVATION TO LEARN DURING THE PANDEMIC IN THE LEARNING PROCESS FROM HOME

This study examines students' learning motivation during the pandemic in the learning process from home. In the learning process, motivation is needed. Motivation can arise from intrinsic (within the individual) or extrinsic (outside the individual). These two factors are very important and continuous in increasing student motivation. During the COVID-19 pandemic, the learning process is carried out from home, so learning motivation can be influenced by several factors. From the research results obtained, there are many factors that do not support the student learning process during the pandemic. These factors, among others; technological skills, internet network connections, learning media used, student capacity for online learning, and so on. Thus, it can be neglected that student motivation during the pandemic in the learning process from home has decreased.
AVALIAÇÃO DE FATORES DE RISCO PARA DIABETES MELLITUS TIPO 2 ENTRE ESTUDANTES UNIVERSITÁRIOS DE UMA INSTITUIÇÃO DE ENSINO NO RECÔNCAVO BAIANO

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RETROSPECT AND PROSPECT: THE HISTORY OF CHINESE TEACHER PROFESSIONAL DEVELOPMENT MODEL

The teacher professional development model is an important part of teacher education and the key aspect in developing teacher’s individual style, consolidating their professional knowledge, and improving their teaching methods. In retrospect to value connotations, the article explores the evolutionary characteristics of the macro design from governmentled to multi-subject collaboration, the development of teacher groups from scattered groups to integration, and the development of individual teachers from passive to active professionals. As for the prospective view, the authors claim the need to deepen the reform of the teacher professional development model by innovations from the governmental macro-regulation of teacher professional development policies, strengthening the integration concept of teacher professional development model, and integrating the elements of the teacher professional development model, as well as improving teachers’ knowledge, teaching techniques, and reflection on teaching
Animation Frame Colorization with GANs

This research paper presents an innovative approach to alleviate the labor-intensive nature of traditional 2D handmade animation utilizing artificial intelligence techniques. Specifically, we focus on refining the process of image colorization for 2D animations by employing Generative Adversarial Networks (GANs). The proposed method involves leveraging the power of GANs to paint a sequence of black and white frames in a manner that emulates the colors present in a single colored example.
3D Model Simplification through Elementary Geometric Structures using Hough Transform

Current methods for three-dimensional (3D) model simplification often involve intricate algorithms that may compromise visual fidelity or incur high computational costs. Traditional approaches using decimation algorithms may still fall short in terms of achieving storage efficiency comparable to basic 3D geometric primitives. This paper proposes an approach centered on the utilization of elementary geometric structures, such as spheres and cylinders, for efficient 3D model simplification. Our approach capitalizes on the inherent simplicity of such shapes, enabling representation with fewer parameters and minimal storage requirements. The proposed method replaces intricate geometric details with these fundamental shapes, identifying regions suited to substitution using a Hough Transform-based method. Preliminary findings replace a region of the mesh with a single sphere.We present these primary results visually with the use of spheres for three 3D meshes along with their corresponding percentage gain regarding fundamental characteristics such as vertices, edges, faces, and size. For future work, we intend on expanding our technique to map other parts of the model and exploring further elementary geometries with Hough Transform.
Interpretation and Hierarchical methods for Dimensionality Reduction

High-dimensional data analysis is a ubiquitous task in practical and research activities. Dimensionality Reduction (DR) techniques are usually employed as they map highdimensional data to lower spaces and allow for knowledge discovery. This thesis focuses on the interpretability and representation aspects of non-linear DR approaches’ output, such as t-SNE and UMAP. That is, we propose methods for interpreting and hierarchically learning embeddings. To accomplish these goals, the following main research activities were carried out, representing separate but interconnected works: (1) a sampling method in visual space (R2) that can preserve class boundary structures while keeping outliers visible; (2) a technique for understanding cluster formation by leveraging statistical tests on the feature values after dimensionality reduction; (3) we advance the state-of-the-art by adapting SHAP to explain cluster formation after dimensionality reduction; (4) a novel hierarchical DR technique that employs an adaptive kernel for global/local neighborhood learning while preserving context across embeddings.
Learning on graphs and hierarchies

Hierarchies, as described in mathematical morphology, represent nested regions of interest that facilitate high-level analysis and provide mechanisms for coherent data organization. Represented as hierarchical trees, they have formalisms intersecting with graph theory and applications that can be conveniently generalized. However, due to the deterministic algorithms, the multiform representations, and the absence of a direct way to evaluate the hierarchical structure, it is hard to insert hierarchical information into a learning framework and benefit from the recent advances in the field. This work aims to create a learning framework that can operate with hierarchical data and is agnostic to the input and the application. The idea is to study ways to transform the data to a regular representation required by most learning models while preserving the rich information in the hierarchical structure. The methods in this study use edgeweighted image graphs and hierarchical trees as input, evaluating different proposals on the edge detection and segmentation tasks. The model of choice is the Random Forest, a fast, inspectable, scalable method. The experiments in this work are an outline of the original study in the related Ph.D. thesis. They demonstrate that it is possible to create a learning framework dependent only on the hierarchical data that performs well in multiple tasks.
People Tracking Methods Applied to Planalto Palace Security Videos

This paper presents a work in progress with comparative results for five state-of-the-art approaches for pedestrian tracking (Deep OC-SORT, OC-SORT, StrongSORT, BotSORT and ByteTrack) applied to a preliminary version of the UFPRPlanalto801 dataset, composed by footage taken from security cameras in Palácio do Planalto, the official office of the President of Brazil. The videos show images of the protesters invasion occurred on January 8, 2023. We used pieces of the public released footage in order to conduct the experiments in a realworld context. The trackers were evaluated by using IDF1, CLEAR and HOTA metrics. The results show a large number of ID switches and missed associations, and a maximum HOTA score of 0.46, achieved by StrongSORT and ByteTrack methods, which shows how challenging is this type of scenario.
Combining YOLO and Visual Rhythm for Vehicle Counting

Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, which are applied to all video frames, leading to a significant increase in computational complexity. To address this issue, this work presents an alternative and more efficient method for vehicle detection and counting. The proposed approach eliminates the need for a tracking step and focuses solely on detecting vehicles in key video frames, thereby increasing its efficiency. To achieve this, we developed a system that combines YOLO, for vehicle detection, with Visual Rhythm, a way to create time-spatial images that allows us to focus on frames that contain useful information. Additionally, this method can be used for counting in any application involving unidirectional moving targets to be detected and identified.Experimental analysis using real videos shows that the proposed method achieves mean counting accuracy around 99.15% over a set of videos, with a processing speed three times faster than tracking based approaches.
Implementasi Metode Association Rule Mining Pada Penjualan Barang Di Toko Bangunan Ada Mas Menggunakan Algoritma Apriori

The purpose of this research is to implement the association rule mining method on sales transaction data at the store and also to find out the most popular and most sold building materials at the Ada Mas Building Shop. The implementation of the Association Rule Mining Method on the sale of goods at the Ada Mas Building Shop is used to provide information on the results of the sales pattern of building materials every month which can be used as a parameter in managing the inventory of building materials at the Ada Mas Building Shop, besides that it can provide information on the most popular and most sold building materials at the Ada Mas Building Shop. Based on sales transaction data for September 2022 as many as 193 transactions, the results of 2 itemset sales patterns using the Association Rule Mining Method that meet the minimum support of 10% and minimum confidence of 50% are the most popular items, namely Iron 10 KSTY, Iron 8 KSTY, and Iron 6 KSTY.
Penerapan Data Mining Untuk Mengetahui Minat Siswa Pada Pelajaran IPA Mengunakan Metode K-Means Clustering

The purpose of this research is to build a data mining application to find out students' interest in science subjects at SMPN 18 Bengkulu City. With data collection techniques by way of observation and interviews, this study used a sample of data on grades IX.8 science subjects with a total of 30 students at SMPN 18 Bengkulu City. The research method used is the K-Means Clustering method because this method separates data into groups that have objects with similar characteristics. In building this application using Xampp server, PHP programming language and MySQL database. This datamining application is used to make it easier for a teacher or admin in this application to manage student data to determine student interest in science subjects. The results of this study were 3 groups of students' interest in science subjects, namely 12 students with high interest, 17 students with moderate interest and 1 student with low interest in SMPN 18 Bengkulu City.
Pengaruh Sumber Belajar Terhadap Hasil Belajar Pada Mata Pelajaran Ekonomi Kelas XI IPS di SMA Negeri 4 Kaur

This research aims to determine the effect of learning resources on learning outcomes in economics subjects in class XI social studies at SMAN 4 Kaur. The approach in this research is quantitative research. The population in this study were students majoring in social studies class XI at SMAN 4 Kaur with a sample of 32 people. Data collection techniques in this study were observation, questionnaires, and documentation. The data analysis technique uses averages, simple linear regression analysis, and the coefficient of determination uses SPSS version 29. The results of this study show that the average of Learning Resources at SMAN 4 Kaur is 46.65, which is in the 43-50 range. in the Very maximal category and the average of student learning outcomes is in the range of 83-91 which is in the good category. And from the results of data analysis, it shows that there is a significant influence from learning resources on learning outcomes of students majoring in social studies class XI at SMA Negeri 4 Kaur. The magnitude of the value of the coefficient of determination obtained results of 0.83 or 83%. With the meaning that the magnitude of the influence of the Learning Resources variable on learning outcomes is 83%. The deviation from linearity results obtained were F of 1.684 with a sig value. 0.640. Because the Sig value is &gt; 0.005, it can be concluded that the line is linear.
APPLICATION OF THE ROLE PLAYING METHOD TO IMPROVE SPEAKING SKILLS IN CLASS V STUDENTS OF UPT SPF SD NEGERI BADDOKA KECAMATAN BIRINGKANAYA KOTA MAKASSAR

The problem in this research is the low learning outcomes of students in learning speaking skills. The formulation of the problem in this research is how to apply the Role-Playing method to improve speaking skills in class V students at UPT SPF SD Negeri Baddoka, Kecamatan Biringkanaya, Kota Makassar. Based on the problem formulation, the researcher carried out a follow-up with classroom action research consisting of 4 patterns, including: planning, implementation, observation, and reflection using the Role-Playing method. The type of research used is classroom action research (PTK) and the approach used in this research is a qualitative approach. The focus of this research is the application of the Role-Playing method and students' speaking skills. The subjects of this research were 1 teacher and 34 students. Data collection is carried out through observation, tests and documentation. In this research, learning refers to learning steps using the Role-Playing method, namely starting from the learning preparation stage, the learning implementation stage, and the final learning stage. The data analysis technique used in this research is descriptive qualitative. Data from observations of teacher teaching activities and student learning activities experienced an increase, which in cycle I was in the sufficient category and in cycle II it increased to the good category. The research results of students' speaking skills in cycle I were in the sufficient category and in cycle II they increased to good category. Thus, it can be concluded that the application of the Role Playing method can improve speaking skills in class V students at UPT SPF SD Negeri Baddoka, Kecamatan Biringkanaya, Kota Makassar
Ensuring Logistics Integrity: An Ethereum Framework

In this study, we examine the potential benefits and difficulties of integrating blockchain technology based on Ethereum into logistics management systems. Our goal is to offer a thorough grasp of this technology's influence on the logistics sector by looking at its theoretical underpinnings and real-world implementations. Significant outcomes from our research include greater logistic transparency, real-time updates, increased security, and automation of contractual duties. These findings underscore the need to embrace innovation and create a legislative framework that facilitates the implementation of blockchain technology, with broad ramifications for logistics firms and legislators. Our study adds to the expanding corpus of information on the application of blockchain technology in logistics, offering insightful information to scholars, policymakers, and business professionals.
Sentiment Analysis of Consumer Reviews: Unveiling Perspectives and Building a Machine Learning Model for Product Evaluation

The goal of this paper is to classify the unusual and dreadful critiques of the clients over one-of-a-kind products and put up a control analysis version to polarize big amounts of analysis. Our dataset comprises consumer reviews and ratings, which are information we have obtained through user evaluations of Amazon goods. We took the skills from our dataset and developed a large supervised version entirely based on them. These modes not great consist of conventional algorithms mutually with naive bays, linear helping vector machines, and k-nearest fellow resident, but also deep studying metrics which encompass frequent Neural Networks and complication neural networks. We evaluated the correctness of those fashions and got a higher know-how of the inverse attitude earlier to the produce. The initial objective is to describe the situation from the client's perspective and assess the intensity of the emotion. The goal of the second assignment is to build and train a machine learning system that can be used to divide customer evaluations into two categories: excellent and terrible. Regardless of the information that Amazon does now not have an API like Twitter to download evaluation with, it does have links for every assessment on every item for consumption, to theoretically traverse the web page through product IDs. We used Perl script written by way of Andrea Eula to get hold of the evaluation for the stimulation and some poles apart products. The most important script downloads the entire HTML page for the product, and the second searches the record for data about the appraisal, such as the product ID, rating, assessment date, and estimate passage. This study shows that the model had a classification accuracy of 78% and a precision similar to the sympathy and genuine score, but the Kappa do curve was rather low. In the future, more work can be done by improving the key parameters.
Gender Disparities in Utilizing Language Features on YouTube Vlogs

This study delves into gender-specific linguistic variations in YouTube vlogs, analysing language features employed by 30 daily English language video vlogs, evenly split between 15 male and 15 female vloggers. Employing a qualitative research design with content analysis, the study identifies prevalent language features and scrutinizes their typologies and intended functions through a systematic four-step method, encompassing data segmentation, code formulation, category development, and the identification of overarching themes. Taboo and swearing, directive and command language feature prominently among male vloggers. In contrast, female vloggers utilize compliments, hedges, tag questions, and questions to engage with their audience on YouTube. This study offers valuable insights into the distinct language patterns of male and female vloggers, contributing to a nuanced understanding of communication dynamics in online platforms and shedding light on the intersection of gender and language in online contexts.
The ANALISIS TINGKAT KEMATANGAN TATA KELOLA TI PADA SISTEM INFORMASI AKADEMIK MENGGUNAKAN FRAMEWORK COBIT 5 DI SMAN 1 PEMULUTAN

SMAN 1 Pemulutan is one of the schools that implements IT governance in the Academic Information System. Academic SI is an information system used by SMAN 1 Pemulutan in implementing learning for students. Therefore, it is necessary to measure the maturity level using COBIT 5. Problem formulation, how to classify problems into statements according to COBIT 5 and how to determine and measure the maturity level of IT governance. The limitations of the research were only carried out on the Academic SI of SMAN 1 Pemulutan, only using COBIT 5 MEA domains and the research object was only on students and related parties. The aim of the research is to determine the maturity level of Academic SI IT governance in the MEA domain and to find out recommendations for improvement at SMAN 1 Pemulutan. The MEA domain consists of 3 sub domains. Researchers used a questionnaire instrument. The sample used was 30 people. The MEA01 maturity level value is 3.2; MEA02 is 3.3 and MEA03 is 3.3. So the average MEA maturity level value is 3.3, namely Established Process. The results of the recommendations from MEA01 establish guidelines and standard SOPs for implementing monitoring and evaluation of Academic SI, there is a need for monitoring from leadership regarding the duties of each Stakeholder. MEA02 develops control/supervision process mechanisms and quality assurance standards for Academic SI. MEA03 reviews and creates standard guidelines and SOPs regarding changes to Academic SI based on external changes.
PENERAPAN METODE AHP DALAM PEMILIHAN ORGANISASI KEMAHASISWAAN

Students are an element that plays a significant role in the progress of higher education institutions. The role in question is in building ormawa. Ormawa is an organization to accommodate ideas for campus progress in accordance with the ormawa that it participates in. The organization chosen must be in accordance with the student's interests and talents. For this reason, research was carried out to build a decision support system in choosing ormawa. The method used is AHP (Analytical Hierarchy Process), this method is used for hierarchical multi-criteria attributes so that it is easier. After the data is obtained from the questionnaire results, the data is then processed into a pairwise matrix after which the consistency ratio value is calculated. The results of the research show that the alternative UKM Flow Map has a weight of 0.34, UKM Aoklah Production has a weight of 0.57, UKM LDK Atma Luhur has a weight of 0.16, and UKM Volly has a weight of 0.31.&#x0D;
IMPLEMENTASI ALGORITMA APRIORI UNTUK MENENTUKAN STRATEGI PROMOSI SEKOLAH TINGGI SAINS TARBIYAH KOTA PAGAR ALAM

This research aims to implement an a priori algorithm to determine promotion strategies for new students at the Tarbiyah College of Science (STIT) Pagar Alam City. Based on the results of observations and interviews with the committee accepting new students at STIT, the promotion strategy is still carried out thoroughly without looking at target opportunities for places that have potential and those that do not have potential. This is very time-consuming and expensive. The Apriori algorithm is very effective in finding relationship patterns of one or more itemsets in a large data set to determine which study programs are most in demand at the Pagar Alam Tarbiyah College of Science. The process of applying the Apriori algorithm to student data for 2020-2021 uses the Rapidminer application with minimum support of 10% and confidence of 40%, this research obtained information that if the student comes from the PAI study program then comes from the department with a confidence of 44.44%, and if the student comes from SMA N 3 Pagar Alam then comes from the science department with a confidence of 56.09%
THE STUDENTS' DIFFICULTIES IN WRITING DESCRIPTIVE TEXT AT THE TENTH-GRADE STUDENT OF SMK N 4 JAMBI IN ACADEMIC YEARS 2022/2023

The purpose of this research is to find out students' difficulties in writing descriptive text. The problem of this research is students' difficulties in generic structure, language features, lack of vocabulary and arranging sentences in writing descriptive text. The objective of this research is to identify the students' difficulties in writing descriptive text. The research was conducted at SMK N 4 Jambi in the academic year 2022/2023. The population of this research is tenth-grade student of fashion class IV. The sample is all of the fashion class IV at the tenth grade and the total number of students is 33 female students. The type of research is qualitative descriptive. The data were obtained by using an online questionnaire by using google forms. Overall, the result of this research is 51% of students have difficulties with language features. While, 57% of students have difficulties in generic structure, which is the same as those who have limited vocabulary in writing descriptive text. Moreover, the biggest percentages are the majority of students have difficulties in arranging sentences to be paragraphs. Keywords; students' difficulties, writing descriptive text