Reviewerly/exHarmony huggingface dataset:
Dataset Extension for Neural Networks Training for the Mitochondrial Segmentation Problem of the Brain Electron Microscopy

This paper presents the adaptation of a diffusion neural network to generate a labeled synthetic dataset of electron microscopy of the brain. A model was trained can generate images and markup for them at the same time, which is an undoubted advantage of the chosen approach. Using the trained model, a set of labeled images was generated. The synthetic images are visually very similar to the original ones, the FID similarity metric between the synthetic and original datasets is 27.1. A simplified U-Net segmentation model trained on a mixed data set (original data + synthetic data) obtained a Dice score of 0.856 versus 0.858 on the original training set. Despite the good quality of synthetic data, their use in training the segmentation network does not improve the segmentation results.
Monitoramento de Transformador de Baixa Potência Baseado em Internet das Coisas e Gêmeos Digitais

In this work it is proposed to improve, using the internet of things, a monitoring system based on a digital twin of a low power transformer. A new data acquisition and sending system was developed to communicate with an online database, as well as adding environmental conditions as new inputs in simulations and protection measures performed remotely. To perform the tests a sensor/actuator module was developed to send the data of ambient humidity, ambient temperature, transformer’s current and voltage. All this data was digitized, pre-processed, and forwarded to the simulator, by means of the internet of things. Thus, even though they are physically separated, they exchange information over the internet of things data that was used by the simulation’s module, in which it runs the simulation of the digital twin. In this way, thermal simulations were performed, and, from the interpretation of the results, action commands can be executed to protect the equipment from overload. To validate the results, a thermographic camera was used, measuring the temperature of the transformer in operation. Two points on the equipment were chosen for temperature analysis, one on the winding and one on the core. In the better experience, the mean absolute error between what was measured and what was calculated came 6.18% at the first point and 3.93% at the second point. The improvement of the technique to estimate the transformer temperature with the use of the internet of things, showed the feasibility of this technology with the temperature tests and the system runtime.
Geração de Trajetória para VANT com Resultados Experimentais

This paper proposes a new approach for generating smooth trajectories for unmanned aerial vehicles (UAVs or drones) that can be executed in real time with limited processing embedded hardware. The objective is to perform autonomous aerial image acquisition, ensuring repeatability. In addition, a state feedback control law based on previously identified commercial hexacopter nominal parameters, with integral and feedforward actions was implemented and validated for trajectory tracking in the presence of wind disturbances, in a real and simulated environment.
Predição de séries temporais de radiação solar utilizando modelos estatísticos e aprendizagem profunda na região de Quixeramobim - CE

Weather forecasting is essential in the renewable energy sector, and it is indispensable to use climate information such as air humidity, atmospheric pressure, temperature, wind speed and solar irradiation, which can be considered variables for forecasts in a certain region. In particular, there is a notable potential for the use of photovoltaic solar energy in the Brazilian Northeast due to the high solar irradiation levels in the region and, from the climatic time series, it is possible to train deep learning models that seek to predict it at short and long term. This work aims to statistically analyze and predict a time series of total daily solar irradiation in the municipality of Quixeramobim, Ceará, through machine learning methods. The results obtained indicate that the models predict solar irradiance with low prediction error when compared to existing results in the literature.
Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C

Hepatitis is considered to be one of the most dangerous diseases, which often leads to death if not handled properly. Thus, early detection via precise diagnosis is needed in order to prevent the unfortunate event. This research aims to provide a novel hepatitis C diagnosis based on the machine learning algorithm, which is the classification tree from the decision tree learning and the distance correlation, which measures the Euclidean distance between 2 vectors. In particular, the goal is to develop a low computational cost yet precise algorithm for diagnosing the possibility of whether a person is being infected with Hepatitis C or not. Based on the experiment, the distance correlation-based classification tree algorithm outperforms the classical classification tree algorithm by around 3% while using only 7 features instead of 12 as in the classical algorithm. Furthermore, the algorithm identified albumin (ALB), Creatinine (CREA), Bilirubin (BIL), Aspartate Transaminase (AST) and Cholesterol (CHOL) as significant risk factors in determining whether someone is potentially infected with hepatitis C or not, with Creatinine is identified as the most important parameter among all 5 parameters mentioned above.
Research on the Talent Development Model for Smart Construction in Vocational Colleges

In the context of the new era, human society is ushering in the fourth wave of the industrial revolution, which will have profound implications for various industries, especially the construction industry. The upgrading of the construction industry is imperative to adapt to new technologies and trends. In response to the requirements of this era, smart construction is emerging as the way forward. Major universities are responding to national policies by establishing programs in smart construction to meet the growing demand for talent. This is both a response to the demands of the times and strong support for the future development of the construction industry. Smart construction training programs at universities are crucial, and they need to integrate students with new technologies and trends in education to cultivate forward-thinking professionals with practical skills. The core technologies of smart construction include BIM technology, 3D printing, smart machinery, and artificial intelligence. These technologies will redefine the design, construction, and maintenance management of construction projects. For instance, BIM technology can provide comprehensive project information and collaboration, 3D printing can enable efficient construction, smart machinery can autonomously perform tasks, and artificial intelligence can empower construction projects with greater decision-making capabilities. For universities, nurturing professionals in smart construction requires continuously updating educational curricula and providing practical opportunities to ensure that students can master and apply these emerging technologies. This will help produce graduates with innovative thinking and problem-solving abilities, providing strong support for the future development of the smart construction field. This trend will also promote a more intelligent, efficient, and sustainable development of the construction industry.
Ideological and Political Exploration and Teaching Practice of Engineering Drawing Course

Engineering Drawing is a compulsory basic course for engineering students. It covers a large number of subjects and is quite instructive. By enumerating the typical characters and cases related to the chapter, the learning initiative of students can be stimulated. By citing positive and negative engineering examples, the standardization, science and seriousness of drawing and reading drawings can be emphasized. Taking the craftsman spirit of the new era as the starting point, the craftsman spirit of excellence, dedication, continuous focus, innovation, pursuit of perfection and other essential connotations can blend into the curriculum. Finally, the perfect combination of teaching and educating people can be realized to achieve the trinity of knowledge imparting, ability training and value leading education and teaching goals.
The Effect Of Snowball Throwing Method In Reading Comprehension Of Narrative Text At Grade Ten Of SMA Negeri 1 Siantar

The purpose of this research is to determine the more significant influence of the Snowball Throwing method on the reading comprehension of narrative text for class X SMA NEGERI 1 SIANTAR students. This research uses an experimental design. The problems in this research are the problems of students who have low reading comprehension abilities, students who are not familiar with the vocabulary in the text, and students who do not understand how to find the main idea in the narrative text they read. This research uses an experimental design, meaning that there is a certain experiment that is applied to a population or sample. And the population of this study were two classes, namely students in classes X-3 and X-4, totaling 60 students. The sample size is 30 students in each class. The control group used a textbook while the experimental group was taught using the snowball throwing method. In this study, data analysis showed that the average post-test score for the experimental group was 76, while the score for the control group was 64.16. The influence of the Snowball Throwing Method is more significant than without using the Snowball Throwing Method on the Reading Comprehension of class X SMA NEGERI 1 SIANTAR students in narrative texts. This is proven by the t test of 3.56 and the t table value of 2.001 and df = 58 (Nx+Ny-2 = 30+30-2 = 58). The t test value is greater than the t table value (t test &gt; t table). Therefore the alternative hypothesis (Ha) is accepted and the null hypothesis (Ho) is rejected.
Prediction of direct normal irradiation using a new empirical sunshine duration-based model

In this work, we are interested in presenting a new approach allowing us to express the Direct Normal solar Irradiation (DNI) according to the sunshine duration essentially. This choice is justified by the fact that in addition to the sunshine, duration has a strong correlation with solar irradiation, it is measured in many radiometric stations. Some clear sky models with modifications developed exclusively here are made valid for all types of sky. The proposed model is compared with one of the intelligent models such as the Support Vector Regression (SVR) for daily data from Ghardaïa.
Pengembangan laman pembelajaran menulis simpulan berita kelas VIII SMP dengan strategi text based introduction

Technology-based learning media is favored by students. This research aims to produce a news summary writing page for grade VIII junior high school students with a text-based introduction strategy. The method used is Borg &amp; Gall's research and development modified by the researcher. This study obtained an average score of 85% from validators, an average score of 88% from teachers, and an average score of 85% from students. The material on the page is organized based on the text-based introduction strategy, including the definition of writing the conclusion of the news, the stages of writing the conclusion of the news, and examples of news conclusions. Features in the learning page, namely the material menu, can be added images and videos, the discussion menu as a place to express ideas or ideas, and the assessment menu is used to collect the results of individual student assignments. The learning page is accessed offline using a local network connecting servers and devices.
Kesalahan kala bahasa Perancis mahasiswa dengan kemampuan bahasa tingkat menengah

French tenses are one of the most complex and varied aspects of the language, with different verb tenses and grammatical rules that can be challenging for learners to master. The purpose of this study is to analyze the forms of errors in the use of French tenses by learners with an intermediate level of language proficiency and also the factors of these errors. The method used in this research is descriptive qualitative with a morpho-syntax approach supported by questionnaire and questionnaire distribution techniques in the data collection process. In the process of data analysis, we performed data classification technique followed by mark reading technique. After analyzing the data, it was found that most errors were found in the past tense. The forms of errors found are divided into four types, namely omission of important elements, addition of unimportant elements, misinformation, and errors in arrangement. The factors causing the errors found are linguistic and sociolinguistic factors. One of the linguistic factors found is the adjustment of each verb in French while in Indonesian it is not. Regarding the sociolinguistic factors found, one of them is the lack of time in practicing French by the respondents.
Multimodalitas linguistis-visual dalam morfologi bahasa Indonesia: persepsi pemaduan dalam pengembangan desain pembelajaran

This research aims to describe students' perceptions regarding the integration of linguistic-visual multimodality texts in the design of developing Indonesian morphology textbooks. Data were obtained by applying the nonface-to-face interview method through a list of questions to explore opinions and expectations about the need to integrate linguistic-visual multimodality texts in the design of Indonesian morphology textbooks. Data analysis was carried out by applying the equivalent method which includes identification, classification, and interpretation steps. The findings of this research illustrate that students responded positively to the integration of linguistic-visual aspects in the design of developing Indonesian morphology textbooks. The integration of elements such as images, tables, graphs, photos, text, colors, and infographics in textbook design was assessed very positively by respondents.
Examining the Relationship between Social Media and Intention to Invest in an Investment Scams among Students

The advancement of social media platforms has enhanced communication sharing, interaction and information access. Importantly, the platforms become an integral part of the lives of today's students in updating themselves with current issues around the world and staying informed. Social media offers scammers opportunities to create fake profiles, leading to confusion between genuine investments and scams. However, alongside the benefits, social media also exposed students to deception such as being scammed. Students can be susceptible to falling prey to investment scams that offer high yields with low risk. Students must exercise caution online to avoid falling for fraudulent schemes. Vigilance in evaluating online content is crucial for their financial safety. Therefore, this study was conducted to examine the influence of social media on the intention to invest in investment scams among students. Data was collected among students at Universiti Teknologi MARA, Cawangan Negeri Sembilan. This study employed a quantitative method to collect primary data through an online questionnaire survey. The findings indicate a significant association between social media and intention to invest in investment scams. This study contributes to the existing literature on both social media and investment scam context and offers valuable insights to the policymakers, regulators and social media operators in combatting the fraudulent investment issue.
Pengembangan bahan ajar teks fabel bermuatan nilai peduli dengan pendekatan saintifik pada MTS Miftahul Khair

The purpose of this research is to identify the development process, measure effectiveness, and evaluate the appeal of instructional materials in the form of value-based fable texts using a scientific approach for students in the seventh grade of Madrasah Tsanawiyah Miftahul Khair Loa Janan, Kutai Kartanegara Regency. This research employs the research and development (R&amp;D) method. The research resulted in a module of value-based fable texts with a scientific approach for seventh-grade students, which is overall excellent and aligned with the 2013 curriculum. Expert assessments from subject matter experts, instructional design experts, students, and subject teachers indicate that the module has a clear content presentation, an attractive layout, good readability, and significant benefits in enhancing students' competence. The development of this instructional material has proven to be effective, as evidenced by improved student learning outcomes after using the module. Although some students initially did not reach the minimum passing grade, the post-test results showed significant improvement, influenced by both teacher and student factors. In conclusion, this module has successfully created effective instructional materials to enhance students' competence in writing value-based fable texts using a scientific approach.
STUDENTS’ PERCEPTION TOWARD TOEFL PREDICTION TEST AS ONE OF THE GRADUATION REQUIREMENTS FROM UNIVERSITY

The students at the university under investigation are required to successfully complete the TOEFL Prediction test in order to be eligible for graduation. This study was conducted to assess the perceptions of these students regarding the TOEFL Prediction test as one of the graduation requirements. The research employed a descriptive-quantitative methodology and involved 171 English Study Program students from three different academic batches. A questionnaire served as the primary instrument for data collection, with data analysis carried out using Microsoft Excel and SPSS statistics version 26. The results of the data analysis indicated that the mean scores for most items in the questionnaire were in the high range. Specifically, 25 out of the 29 questionnaire items obtained mean scores above 3.40, demonstrating that students' perceptions in this study leaned positively toward the TOEFL Prediction test policy. However, it's important to note that nearly half of the students recognized that the TOEFL Prediction test is not an easy test. Furthermore, there is room for improvement in terms of students' understanding of the type of TOEFL test provided by the language centre at the university and the benefits of the TOEFL certificate. These findings can provide valuable input for the university's policymakers and administrators. They can use this information to design new initiatives and strategies aimed at assisting students in improving their test-taking skills and their understanding of the TOEFL Prediction test's structure. Such efforts can contribute to enhancing students' overall readiness for this requirement.
TALKING STICK LEARNING MODEL BY ANIMATION VIDEOS IN SOCIAL SCIENCES SUBJECTS

The purpose of this study was to find out the application of the talking stick learning model assisted by video animation can improve student learning outcomes in social studies lessons. Data collection was carried out in class VII B with 32 students. This research is quantitative research with a quasi-experimental approach with a one group pretest-posttest design. Data collection techniques used in the form of observation and tests. The instrument in this study used a learning implementation sheet and 10 multiple choice questions. Data analysis techniques using descriptive analysis and inferential statistical analysis. It is known that student learning outcomes in the normality test are 0.059, which means that the data is normally distributed, with an average pre-test result of 67.34 and a post-test of 86.71. This is evidenced from the results of the paired sample t-test obtained by a t value of -13.519 with a Sig value. (2-tailed) of 0.000 &lt;0.005 means that there is a significant difference between the pre-test and post-test scores. The n-gain result of 0.61 which is in the medium category indicates that the application of the talking stick learning model assisted by video animation can improve student learning outcomes. Therefore, it is necessary to innovate in learning, such as implementing a fun learning model so that students play an active role and improve learning outcomes.
Analisis Risiko Kredit Perbankan Menggunakan Algoritma K-Nearest Neighbor dan Nearest Weighted K-Nearest Neighbor

&lt;p dir="ltr"&gt;&lt;span&gt;Bank is a business entity that collects public funds in the form of savings and also distributes them to the public in the form of credit or other forms. Credit risk analysis can be done in various ways such as marketing analysis and big data using machine learning. One example of a machine learning algorithm is K-Nearest Neighbor (KNN) and the development of the K-Nearest Neighbor algorithm is Neighbor Weighted KNearest Neighbor (NWKNN). The K-Nearest Neighbor (KNN) algorithm is one of the machine learning methods that can be used to facilitate the classification of complex data. The purpose of this study is to determine the results of the application of the algorithm and the comparison of the use of the KNN and NWKNN algorithms in banking credit. The results obtained are that NWKNN is able to predict credit risk better, especially in classifying potential customers with potential losses compared to KNN.&lt;/span&gt;&lt;span&gt; &lt;/span&gt;&lt;/p&gt;&lt;span id="docs-internal-guid-3225d0b5-7fff-da27-d883-e71a565d51af"&gt;&lt;span&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: &lt;/span&gt;&lt;span&gt;Machine learning, KNN, NWKNN&lt;/span&gt;&lt;/span&gt;
Enhancing Cloud Resource Management Based on Intelligent System

Cloud computing is a significant technology model with the strongest potential to change the way IT activities are carried out. The cloud offers low-cost and scalable IT resources. Cloud providers strive to improve business outcomes so cloud systems become more complex. The intelligent cloud has a number of issues, including optimizing the cloud service and designing and distributing resources adaptively and economically. There is a rising trend toward adopting intelligent technologies to better cloud management in particular. This paper introduces an intelligent method to manage cloud resources without delay or trouble. Starting with designing a system based on a genetic algorithm (GA) as a computational tool to achieve the goal and plan a predictive request then depending on the round robin (RR) algorithm to allocate the requests for processing. To evaluate the performance of this method, the proposed algorithm was verified in 145 requests. Where the implementation system achieved a reasonable result.
A Conceptual Review of the Determinants of Employee Innovative Work Behavior

The increasing prevalence of digitalization, competitive markets, quick environmental changes, and the imperative of organizational sustainability have necessitated business organizations to adopt innovative practices. Hence, all organizations must foster a culture of innovative work behavior among their employees to ensure the organization's ongoing survival and advancement. Nonetheless, comprehensive studies that methodically scrutinize the different factors affecting IWB within a unified theoretical model are insufficient. This research is a literature review to find and analyze factors that influence innovative work behavior. The findings of the literature review or existing research indicate that psychological empowerment, employee engagement, self-efficacy, knowledge sharing, perceived organizational support, climate for innovation, HRM practices, and transformational leadership have consistently demonstrated strong associations with employee IWB in various studies. This study additionally proposes a novel conceptual framework that examines the interplay between psychological empowerment, employee engagement, self-efficacy, knowledge sharing, perceived organizational support, climate for innovation, HRM practices and transformational leadership, with a specific focus on their impact on IWB. This framework holds potential for future investigations conducted by other scholars.