Reviewerly/exHarmony huggingface dataset:
Improving Young Learners Vocabulary by Using Digital Technology

This writing, aims at describing the possible ways in increasing young learners’ vocabulary. Vocabulary is a very important part of language. When we learn a language like English, we learn the words in the language. Vocabulary is needed to master the four skills of English. Through vocabulary, we convey our thoughts, emotions and efficiency. Although it is often hampered by lack of grammar or grammatical errors in its use. But in communication it does not become an obstacle as long as the speaking partner understands The methodology of this research is library research. Using previous research as the basis of what is being researched. Understanding the important things to take as material from this research. To improve young leaners vocabularies by technology, need guidance between parents, teacher, and school environment. As a form of response to technological developments, stakeholders are Education sector conducts teacher training, teacher education and teacher professions developing. Vocabulary not only mastered by young leaners but there are others too. When they have oral or written communication language, they use vocabulary to express their thoughts and ideas. Although in reading, one must understand the meaning of words write on the text. Of course, master English vocabulary takes time and process.
A Smart Device with Hand Gesture using Webcam

Abstract: Over time, Human-Computer Interaction (HCI) has experienced tremendous growth. An entirely new era in the AI domain of human-computer interaction was ushered in by hand gesture recognition technologies. One of the easiest and most natural forms of communication is hand gestures. It might be challenging for novice With hand gestures, the user can control cursor functions on the system. The input device for the suggested system is a camera. this application, HCI deployment for educational purposes will enter a completely new age.
Restoration of Obscured Images

Abstract: For the aim of exploring the deep undersea world map, a picture of high quality without interfering objects is preferred. However, within the water, the image quality tends to be hampered by light scattering, water density, and light attenuation. effects. Besides, the dynamic interference may affect the important underwater map. during this paper, we proposed a multi-step and all-around underwater image processing system, especially for the underwater images taken in succession to enhance the image quality, remove the dynamic interference, and reconstruct the image. The first step involves utilizing the dark channel approach together with the improved gray world algorithm for brightness adaptation and color correction. Initially, it identifies and removes a dynamic interference regarding image enhancement. Secondly, we applied an upgraded total variation model to patch the blank at the value of resolution. Finally, the super-resolution of the small print is realized by applying an improved BP network. After simulation and experiments, our system proved to realize ideal results of image enhancement and reconstruction.
Analysis of the Use of Learning Media in Early Childhood Education

The purpose of this study is to describe the use of instructional media in early childhood education. Learning media is considered very important in helping the learning process. This research was conducted using a descriptive research method using a qualitative research approach. The research techniques used are interviews, observation, and documentation studies. The research subjects were teachers at Perwari Kindergarten, Tasikmalaya City. The results of this study are the types of media used by the teacher, including textbooks, blackboards, pictures, swings, blocks, seesaws, catwalks, and tape recorders. The use of media in the classroom includes textbooks, blackboards, blocks, and pictures. And the use of learning media outside the classroom includes skyboards, swings, seesaws, and tape recorders. The learning media used can increase children's motivation to participate in learning.
The Effect Of Picture Series Teaching Media To The Writing Ability Of Grade Eighth Students Of SMP Negeri 9 Pematang Siantar On Narrative Text

The goal of this research is to find out the effect of picture series teaching media to the writing ability of grade eighth students of SMP Negeri 9 Pematang Siantar on narrative text. This research was using a quasi-experimental design to find out this research. The sample of the research were taken from two classes that consist of 58 students, 29 students in experimental class at VIII-8 and 29 in control class at VIII-7. The resercher found the total of mean score of pre-test in experimental class is 56,53 while mean in post-test is 71,2. The mean score of pre-test in control class is 50,79 while mean in post-test is 60,7. After calculated the data from all of the score, the researcher found that t-test score is 4,06. The, the researcher calculated with score on t-table uses significance 0.05 and the value is 1.672. So, the researcher found that t-test is higher than t-table (4.06 &gt; 1.672). It can be conclude that the effect of picture series teaching media to the writing abilty of grade eighth students of SMP Negeri 9 Pematang Siantar on narrative text.
Alternative Use of Blended Learning Model in Mathematics Learning for Elementary School Education

Technological developments in the 21st century have played a major role in advancing the quality of learningin education. This technological development is realized through various efforts and the use of availabletechnology, such as the SIDIKMU digital application at SD Muhammadiyah 1 Paron. The application is used asa learning medium and can be used in blended learning. The benefits of using the SIDIKMU application inlearning mathematics at SD Muhammadiyah 1 Paron show that students are given space to study independently, access applications to find materials, and send personal work results. This study uses a field research approach or field research, the subjects of this research are students of classes V-a and V-b, totaling 19 and 20 participants. The instruments used are observation, discussion notes, questionnaires, student learning outcomes data, and interviews. Research data shows that 70% of the experimental class students stated that they responded as "very interested" in participating in learning with a digital- based blended learning model as a learning resource and the results of the post-test in this class had an average score of 76.78.
Sentences of Elementary School Student Book in 2019 Literature Readings

We can find many reading books for low grade elementary school (SD) age in bookstores. However, these books have not been equipped with the sentence structure used. Therefore, a study is needed to determine the structure of the sentence structure used. The purpose of this study is to find out the sentence structure in the 2019 literacy reading book for elementary students published by the Language Development and Development Agency in 2019. According to Indonesian Standard Grammar, the sentence structure in question is the type of sentence based on the number of clauses, the type of predicate, the category of syntax, and completeness of the elements. In this paper, the intended sentence structure is only limited by the sentence structure based on the type of predicate. The research method uses a qualitative descriptive approach, namely by noting the sentence structure of fourteen 2019 literacy reading books for elementary school students to see the type of predicate. The results of the study are expected to be able to show the types of sentence structure in the 2019 literacy reading books for elementary students. The results of this study are also expected to be used as a reference for teachers. parents, and book writers in selecting and writing appropriate reading books for elementary school-aged children.
Learning Difficulties in Early Children

Learning difficulties are deficiencies that do not appear outwardly. Learning disabilities cannot be recognised in a physical form that is different from people who do not experience learning difficulties. Learning disorders faced by children are grouped into 4 criteria, namely cognitive maturity, low attention, hyperactivity, and mental retardation. Learning difficulties that usually occur in children are dyslexia (difficulty reading), dyscalculia (difficulty with arithmetic), dysgraphia (difficulty writing) and dipraxis (difficulty moving in a coordinated manner)
Effectiveness of Cognitive Monopoly (Monotif) Instructional Media to Early Childhood Numeracy Ability

This study aims to produce an effective cognitive monopoly instructional media to improve early childhood numeracy ability from 5 to 6 years. The study included 12 early childhood respondents who were divided into six groups. Learning activities are carried out every day for eight days, with 10 minutes for each group. The instruments are designed according to numeracy understanding for early childhood ranging from 5 to 6 years. Asses of numeracy ability researcher used a Likert scale questionnaire, use of monotif media. The results showed that the t-count test of 0.67 was more significant when compared to the t-table of 0.58. The mean value of 28.41 means that the results of the post-test test showed an increase of 28.41 units. The findings show that monotif media effectively improves numeracy skills in early childhood
Peningkatan Kemampuan Berbicara Siswa Melalui TENANT dan Objek Nyata

At English Education Department of Education and Vocational Studies Faculty of Universitas Lancang Kuning, it was found that the students had low speaking skills. One of the factors is the students’ hesitation in making mistakes, especially mistakes in grammar. It was Classroom Action Research (CAR). The aimed of this research was to improve the students’ low speaking skills by using TENANT (Teaching English with Natural Acting and Natural Talking) and Real Object. This research had been carried out since February until April 2022. It had three cycles and five meetings within it. There were 24 students all of them who were taught to speak English by using TENANT and Real Object. It was found that there was improvement of the students’ speaking skills under the use of TENANT and Real Object which were based on the students’ speaking based score with their scores from cycle I, II, and cycle III. In conclusion, TENANT and Real Object can help the students to improve their speaking skills. This finding implies that the use of TENANT and Real Object can be used to enhance the students’ speaking skill in English speaking class.
Analisis Banjir Metode Hidrograf Satuan Sintetis SCS dan Nakayasu DAS Pesung, Batam

Design discharge is discharge with a certain return period which is used as a reference or parameter in a water building construction planning. Calculation of the design discharge can be done by using the synthetic unit hydrograph method. The calculation of the planned discharge using the Nakayasu and SCS-CN synthetic unit hydrographs aims to compare the calculation of the maximum discharge in the Pesung Watershed, Batam. The design discharge analysis was carried out without calibrating with AWLR data (observed flow) due to the unavailability of AWLR data at the study site. Thus, in this study using HDRO (high direct runoff) as a comparison. The purpose of this study is to estimate the magnitude of the design flood discharge in the Pesung Watershed, Batam City. The methods used in this calculation of the flood hydrograph is using an empirical formula, namely SCS Synthetic Unit Hydrograph and Nakayasu. The results of the HDRO calculation analysis show that the largest flood discharge occurs in the Nakayasu synthetic unit hydrograph method with details of the SCS synthetic unit hydrograph method, the peak time (Tp) is obtained at 4.46 hours, with a return period flood discharge of 5 – 50 reaching 215,488 – 584,308 m3/ s. Whereas in the Nakayasu synthetic unit hydrograph method, the peak time (Tp) is obtained at 1.47 hours, with a return period flood discharge of 5 – 50 years reaching 360.526 – 986.535 m³/s.
Trainable Regularization in Dense Image Matching Problems

This study examines the development of specialized models designed to solve image-matching problems. The purpose of this research is to develop a technique based on energy tensor aggregation for dense image matching. This task is relevant within the framework of computer systems since image comparison makes it possible to solve current problems such as reconstructing a three-dimensional model of an object, creating a panorama scene, ensuring object recognition, etc. This paper examines in detail the key features of the image matching process based on the use of binocular stereo reconstruction and the features of calculating energies during this process, and establishes the main parts of the proposed method in the form of diagrams and formulas. This research develops a machine learning model that provides solutions to image matching problems for real data using parallel programming tools. A detailed description of the architecture of the convolutional recurrent neural network that underlies this method is given. Appropriate computational experiments were conducted to compare the results obtained with the methods proposed in the scientific literature. The method discussed in this article is characterized by better efficiency, both in terms of the speed of work execution and the number of possible errors. Doi: 10.28991/HIJ-2023-04-03-011 Full Text: PDF
Avaliação do Potencial de Geração de Energia Solar e Eólica no Estado da Paraíba com o Uso de Base de Dados Interpolada

The generation of electricity through renewable sources, such as solar and wind, is constantly growing due to the ecological and economic advantages associated with its use. Thus, the monitoring and analysis of climate variables such as wind speed and solar irradiation is of paramount importance to define the wind and solar generation potential of a region. In this context, the present study highlights the elaboration of a Paraíba State database (PB) making use of spatial data interpolation techniques to determine values where there are no measurements. Thus, from the official National Aeronautics and Space Administration (NASA) database, two interpolation procedures were evaluated. In sequence, the Kriging method for creating a base with exclusive resolution of 0.05◦was applied (latitude and longitude) in relation to the original base for analysis of the wind, solar and hybrid potential of the state. Finally, it was seen, based on the developed index, that the intermediate geographical region of Patos is favorable for solar power generation and the regions of Campina Grande and João Pessoa have a high wind potential, while some cities in the Campina Grande region had good rates for hybrid generation, i.e., wind and solar.
Análise de Imagens Hiperespectrais para reconhecimento de depósitos de minério de ferro utilizando aprendizado não supervisionado

Hyperspectral Imaging Systems (HSI) collect spectral data and spatial images simultaneously and continuously in hundreds of narrow spectral bands to generate a data cube. Specifically in mineralogy, remote sensing techniques using hyperspectral images are becoming increasingly popular on remote mapping of mineral formations. Datasets containing hyperspectral images from sensors such as Hyperion installed on the NASA’s Earth Observing satellite 1 (EO-1) can be found online. Considering these facts, we propose in this work the use of analysis and pattern recognition techniques in data cubes from HSI systems, more specifically from EO-1 Hyperion satellite. The hyperspectral image analysed includes the Carajás mine (Pará, BR) for the purpose of reconnaissance and identification of iron ore deposits using unsupervised learning and K-Means clustering method. The results show that about 6.72% of the analysed area is composed of iron ore, which corresponds to approximately 51km2.
Modelagem e Desenvolvimento de uma Solução de IoT e Gêmeo Digital para Medição de Consumo de Energia Residencial

This work carries out a study that aims to obtain a residential energy consumption management solution based on industry 4.0 concepts and technologies, such as augmented reality and IoT, and the modeling of Digital Twins. This system enables the storage, monitoring and visualization of energy consumption data from an energy meter, having an energy meter device located in a residence in the city of Curitiba-PR. The proposed architecture was divided into three layers: hardware, cloud, and software, resulting from the combination of IoT and Digital Twins architecture modeling. The results are satisfactory, because with the solution obtained it became possible to manage the energy consumption of the residence in question, providing the consumer with the monitoring of their consumption online, enabling decision-making and insights (indications) for actions aiming at efficient consumption in the context of energy efficiency.
Navegação com restrições sociais com base em uma percepção robocêntrica

In the last few years, the application of robotics in environments common to human beings has been increasing. There exists several proposals of socially aware navigation frameworks for mobile robots, however all of them generally focused in a specific aspect of social relantionships between humans and robots; so, there exist a lack of approaches that integrate all the aspects related to social navigation. This work aims to propose an autonomous navigation framework based on the integration of social perception elements (from a robocentric perspective) with proxemics modelling, considering the presence of human beings and the perception of their needs, feelings or intentions. We verified the feasibility of our approach by implementing it in ROS and Gazebo, and making a qualitative evaluation of its performance in two simulated scenarios where we included people with different fellings about robot presence, that triggered changes in the path planned by the robot in real time. So, it was concluded that this framework is feasible for implementing social navigation in mobile robots.
Desvio de Obstáculo para uma Formação de Robôs Utilizando Espaço Nulo

This paper proposes a control system to autonomously guide a formation of a differential-drive wheeled platform and an unmanned aerial vehicle to track a given trajectory in an environment containing static obstacles. Such an environment could be, for instance, an automated warehouse. The controller embeds two partial controllers, a formation controller to move the formation accordingly and a controller to deviate the formation when an obstacle appears in its route. The formation controller is designed considering the paradigm of virtual structure, in this case the tridimensional straight line linking the two robots. As for the obstacle avoidance controller, it should make the robot to navigate through regions of low potential, where the potential is a function associated to the obstacles. When the wheeled robot enters a region whose potential is higher than a predefined threshold the controller should modify the route of the formation to avoid the obstacle. These two controllers are then integrated, using the null space-based behavioral control approach. The use of such an approach allows giving higher priority to the subtask of avoiding the obstacle, leaving the subtask of moving the formation with a lower priority, thus generating a way to move around an obstacle following a path of low potential.
An Approach to Location Estimation and Navigation Support Aided by Depth Estimation Map

Depth sensing is essential to many robotic tasks, with mapping, localization and obstacle avoidance. In small robotic platforms, weight, volume and energy consumption limitations motivate the use of depth estimation using a monocular camera instead of depth sensors. This paper propose an approach for localization of autonomous mobile robots in an indoor environment using monocular vision aided by depth estimation maps from a single RGB input image applied to the concept of Transfer Learning linked to Convolutional Neural Networks (CNNs). The performance of classifiers in estimate the location was observed and compared using of a unique configuration of RGB-D images transformed into a mosaic image. The images were combined with the descriptive power of CNNs in the following scenarios: depth captured by the Kinect sensor and depth estimation generated by the AdaBins block. With the results, the performances of the classifiers were analyzed to evaluate the proposed approach.
Research on strategies for improving primary school students' interest in mathematics learning

In recent years, the term "learning interest" often appears in the public eye, and many scholars and teachers have begun to study students' interest in learning. Therefore, this study conducted a study of primary school students in grades 1 to 6 of Yutan Street Central Primary School in Ningxiang City. The author puts forward thoughts and suggestions for improving students' mathematical learning based on the actual situation of Yutan Street Central Primary School.
Application of data science in the prediction of solar energy for the Amazon basin: a study case

Abstract The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change. Solar energy figures as a natural option, despite its intermittence. Brazil has a green energy matrix with significant expansion of solar form in recent years. To preserve the Amazon basin, the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass, avoiding harsh environmental consequences. The novelty of this work is using data science with machine-learning tools to predict the solar incidence (W.h/m²) in four cities in Amazonas state (north-west Brazil), using data from NASA satellites within the period of 2013–22. Decision-tree-based models and vector autoregressive (time-series) models were used with three time aggregations: day, week and month. The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations. The mean absolute error was selected as the output indicator, with the lowest values obtained close to 0.20, from the adaptive boosting and light gradient boosting algorithms, in the same order of magnitude of similar references.
OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment

Current deep models excel in object detection for classification and localization. However, precise object rotation estimation within the visual context of an input image remains underexplored due to the lack of object datasets with rotation annotations. This paper addresses these challenges by tackling rotation estimation for parked bikes with respect to their parking area. Firstly, 3D graphics were leveraged to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Subsequently, an object-to-spot rotation estimator (OSRE) is introduced by extending object detection to regress bike rotations in two axes. As the proposed model trained purely on synthetic data, image smoothing techniques adopted during deployment on real-world images. The proposed OSRE has undergone evaluation on both synthetic and real-world data, showing promising results. Our data and code are available at https://saghiralfasly.github.io/OSRE-Project/.