Reviewerly/exHarmony huggingface dataset:
"Visualizing Socioeconomic Data in India: A Comprehensive Methodology for Choropleth Map Creation"

Choropleth maps are a valuable tool for visualizing spatial patterns in socioeconomic data.In this paper, a methodology is presented for creating choropleth maps and demonstrate its implementation using the Socioeconomic Highresolution Rural-Urban Geographic Platform for India (SHRUG) dataset.This methodology comprises three main stages: data selection and preparation, data processing, and map creation.It emphasizes the value of documentation and reproducibility and offers thorough explanations and step-by-step procedure for each stage.Examples of choropleth maps produced using SHRUG data, encompassing a variety of socioeconomic variables at the village and town levels are presented to illustrate the utility of this technology.The example shows the possibilities of using SHRUG to look at socio economic problems in India and the effectiveness of choropleth maps in exposing geographical patterns and trends in complex data.The examples provided here serve as a starting point for researchers interested in studying the rich data available through SHRUG and the methodology can be adapted to different datasets and contexts.In order to choose visualizations that may be utilized in a variety of fields, including the social sciences, economics, and public health, where choropleth maps are frequently used for data visualization, the research addresses the difficulties of working with big data sets.The recommendations for improving the usability of SHRUG data, including documentation, metadata, and software tools for data processing and visualization are also presented.Overall, this work emphasizes the significance of open-access data sources like SHRUG and offers a comprehensive and accessible guide for creating choropleth maps, with specific examples utilizing SHRUG data.
Implementation of Convolutional Neural Network and Long Short-Term Memory Algorithms in Human Activity Recognition Based on Visual Processing Video

Human Activity Recognition (HAR) is an interesting research topic, especially in identifying human movement actions focusing on video-based security surveillance. Symptom of an illness from a movement. The use of HAR in this research is the key to better understanding the various semantics contained in the video to find out the pattern of a human movement, especially in sports movements. In this study, a combination of the CNN and LSTM method algorithms was applied by using several variations of the model parameter values on the dropout layer and batch size to convert the pattern in the video into image form to produce a HAR model. Data processing at the convolution layer is used to extract spatial features in the frame. The extraction results are fed to the LSTM layer on each network for modeling the temporal sequence of human movement. In this way, the network on the model will learn spatiotemporal features directly in end-to-end data training tests to produce a robust model. The test data used are 10 sports activities obtained from related research from the University of Central Florida (UCF). The results showed that the performance was quite good, although there were still errors in the classification of sports activities because they had similarities in the movements of the activities carried out. The classification results show a loss value of 0.4 and an accuracy of 0.94. In further research, what needs to be corrected is the loss value which is still high so that several times the test results show an error in the classification of sports activities that have similarities in the movements of the activities.
Hemp-Alumina Composite Radar Absorption Reflection Loss Classification

The Radar Absorption Material (RAM) method is a coating for reducing the energy of electromagnetic waves received by converting the electromagnetic waves emitted by radar into heat energy. Hemp has been studied to have the strongest and most stable tensile characteristics of 5.5 g/den and has higher heat resistance compared to other natural fibers. Combining the characteristics of hemp with alumina powder (Al2O3) and epoxy resinÂ couldÂ provide aÂ stealthÂ technology system that is able to absorbÂ radarÂ waves more optimally, considering that alumina has light, anti-rust and conductive properties. The electromagnetic properties of absorbent coatings can be predicted using machine learning.Â Â This study classifies the reflection loss of Hemp-Alumina Composite using Random Forest, ANN, KNN, Logistic Regression, and Decision Tree.Â These machine learning classifiers are able to generate predictions immediately and can learn critical spectral properties across a wide energy range without the influence of data human bias.Â The frequency range of 2-12 GHz was used for the measurements.Â Â Hemp-Alumina composite has result thatÂ the most effectiveÂ structure thicknessÂ isÂ 5mm,Â used as a RAM with optimum absorption in S-Band frequencies of -15,158 dB, C-Band of -16,398 dB and X-Band of -23,135 dB.Â The highest and optimum reflection loss value is found in the X-Band frequency with a thickness of 5mm which is equal to -23.135 dB with an absorption bandwidth of 1000 MHz andÂ efficiencyof 93.1%.Â From this result, itÂ isÂ proven that Hemp-Alumina Composite isÂ very effective to be used as a RAM on X-Band frequency.Â Â Based on the results of the experiments, the Random Forest Classifier has the highest values of accuracy (0.97) and F1 score (0.98). The F1 score and accuracy of Random Forest are 0.96 and 0.97, respectively, and do not significantly differ from KNN.Â
(PANDEMIC Covid-19): A Shooter Game for Education - the Impact Measurement of War Games on Virus Eradication Lessons for Students

(PANDEMIC Covid-19) is an educational shooter game inspired by the Covid-19 pandemic which occurred from the end of 2019 until early 2022. There are 2 game modes, namely Third-Person Shooter, or TPS, and First-Person Shooter, or FPS. This study was carried out to highlight the absence of a shooter genre game used in the student learning process. The research methodology in the development of this game applied the pressman method, and the stages include planning, analysis, game development and artificial intelligence, implementation, as well asÂ Â evaluation. Furthermore, the testing phase used software testing techniques based on the ISO 9126 standard and involved a total of 100 participants. The age range was between 17 and 20 years, while the participants' gender percentages were 55% male and 45% female. Some of the factors tested include functionality, reliability, portability, usability, efficiency, and maintainability. There were 2 choices only in this test, i.e. agree and disagree. The functionality factor had an agreed rate of 85%; reliability 79%, portability 86%, usability 83%, efficiency 79%, and maintainability 87%. Therefore, it was concluded that this game is suitable for use in student learning in the shooter genre. Furthermore, this research was inspired because shooter games have not been developed for the student learning process. This game genre is currently used for hobbies and for profit by developers and professional players. Further research should develop game levels, enable features to play online together with other users, and should be extended to Android and IOS.Â
Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study

Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more search area for the search agents in the early phase of the algorithms and resulted in a faster convergence speed. The objective of this comparative study is to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA in terms of the two criteria mentioned even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.
A Study on the Path of Teaching Ability Improvement of Physical Education Teachers in High Schools

The improvement of the teaching ability of physical education teachers in colleges and universities is crucial to promote the improvement of physical education teaching quality. However, there are some problems with the teaching ability of college physical education teachers at present, such as the lack of advanced teaching concepts and methods, the lack of teaching experience and practice, and the lack of subject knowledge and teaching skills, which directly affect the advancement of physical education teaching. This paper discusses the paths and methods to improve the teaching ability of physical education teachers in colleges and universities, to improve the teaching level and teaching effect of teachers and better serve the learning and growth of students.
Optimization of Vehicle Object Detection Based on UAV Dataset: CNN Model and Darknet Algorithm

This study was conducted to identify several types of vehicles taken using drone technology or Unmanned Aerial Vehicles (UAV). The introduction of vehicles from above an altitude of more than 300-400 meters that pass the highway above ground level becomes a problem that needs optimum investigation so that there are no errors in determining the type of vehicle. This study was conducted at mining sites to identify the class of vehicles that pass through the highway and how many types of vehicles pass through the road for vehicle recognition using a deep learning algorithm using several CNN models such as Yolo V4, Yolo V3, Densenet 201, CsResNext â€“Panet 50 and supported by the Darknet algorithm to support the training process. In this study, several experiments were carried out with other CNN models, but with peripherals and hardware devices, only 4 CNN models resulted in optimal accuracy. Based on the experimental results, the CSResNext-Panet 50 model has the highest accuracy and can detect 100% of the captured UAV video data, including the number of detected vehicle volumes, then Densenet and Yolo V4, which can detect up to 98% - 99%. This research needs to continue to be developed by knowing all classes affordable by UAV technology but must be supported by hardware and peripheral technology to support the training process.
Pre-Trained CNN Architecture Analysis for Transformer-Based Indonesian Image Caption Generation Model

Classification and object recognition in image processing has significantly improved computer vision tasks. The method is often used for visual problems, especially in picture classification utilizing the Convolutional Neural Network (CNN). In the popular state-of-the-art (SOTA) task of generating a caption on an image, the implementation is often used for feature extraction of an image as an encoder. Instead of performing direct classification, these extracted features are sent from the encoder to the decoder section to generate the sequence. So, some CNN layers related to the classification task are not required. This study aims to determine which CNN pre-trained architecture or model performs best in extracting image features using a state-of-the-art Transformer model as its decoder. Unlike the original Transformerâ€™s architecture, we implemented a vector-to-sequence way instead of sequence-to-sequence for the model. Indonesian Flickr8k and Flick30k datasets were used in this research. Evaluations were carried out using several pre-trained architectures, including ResNet18, ResNet34, ResNet50, ResNet101, VGG16, Efficientnet_b0, Efficientnet_b1, and Googlenet. The qualitative model inference results and quantitative evaluation scores were analyzed in this study. The test results show that the ResNet50 architecture can produce stable sequence generation with the highest accuracy value. With some experimentation, finetuning the encoder can significantly increase the model evaluation score. As for future work, further exploration with larger datasets like Flickr30k, MS COCO 14, MS COCO 17, and other image captioning datasets in Indonesian also implementing a new Transformers-based method can be used to get a better Indonesian automatic image captioning model.Â
Deanthropological aspects of inauthentic virtuality

Introduction. The influence of virtual reality on the processes of deanthropologization becomes an obvious fact of the development of numerical rela- tions that determine philosophical concepts in the direction of posthumanistic discourse. In this type of discourse, a person is reduced to a numerical operation, an intermediate procedural link in the network structure of a numerical relationship with other objects of the digital network. The purpose of the article is to identify the rela- tionship between virtuality and imagination, the distinction between a virtual person and a vividly imagining one. Methods. The author uses the methods of decon- struction, which identifiy new philosophical meanings of established understand- ings of virtuality and related anthropological types, and the phenomenological method of studying the issues of virtuality and anthropological reality, forming new perspectives for studying the phenom- enon of virtuality through an appeal to the constitu- tive role of consciousness. Scientific novelty of the research. The scientific novelty of the study is represented by the discovery of a new anthropological distinction between homo imaginabundus and homo virtualis, implying on- tological differences of these types in their appeal to the subjective and objective constitutive role of consciousness. Results. If the anthropological type homo imagi- nabundus is vividly imaginative and is associated with the phenomena of authentic virtuality and sub- jectivity, then homo virtualis refers to inauthentic virtuality only as one of its possible variants without connection with the self—affecting component as such. Conclusions. Anthropological reality is in a state of bifurcation, from which it can emerge in several ways, the most likely of which will be the ways of deanthropologization in algorithmic flows and codes of the digital environment, or the way of preserving the phenomena of self-action, vivid im- agination associated with subjectivity and the field of authentic virtuality.
Development opportunities for mobile and ICT learning in teacher education

Learning is nowadays a continuous intellectual readiness to be able to cope with the current needs of the world of work.Students' knowledge must be adapted accordingly and they must be capable of continuous development throughout their lives [8].Changes in teaching-learning habits have already been observed at the beginning of the 21st century and those involved in education need to adapt to these changes with the appropriate transition to digital education what is more important today than ever before.We asked our teacher-candidate students in an online questionnaire at the beginning and at the end of the "Teaching Methods in Mathematics" course.Our non-representative survey provided valuable data for course development.The aim of the survey was to find out students' perceptions of the methods and tools they had learned in education.In the questionnaire at the beginning of the semester, we inquired our students about the types of work and methods they were familiar with, and then they gave their opinions about the different ICT tools, educational programmes and applications and how they were used.At the end of the semester, we interwieved them again whether they still held similar views on the subject or they had managed to change their views during the course.
POTENTIAL OF KHABAROVSKY KRAI, JEWISH AUTONOMOUS OBLAST AND THE AMUR OBLAST FOR FLUORITE MINERALIZATION

The available data on the presence of fluorites in the Khabarovsky Krai, the Jewish Autonomous Oblast and Amur Oblast have been synthesized. Fluorite deposits and occurrences are classified as rare-earth-fluorite, beryllium-fluorite, fluorite-tin-ore, polymetallic-fluorite, and fluorite. Also, fluorite occurs in ore deposits and phosphorite deposits. The features of their localization in different tectono-stratigraphic areas of the region are shown. Fluorite-bearing regions were identified and their potential for development was assessed. Fluorite occurrences that are economically attractive targets for exploration are located along the periphery of the Siberian platform and in the southern part of the Bureya massif. Forecast resources of fluorite were calculated and the prospects for their industrial development were assessed.
NI-CO ARSENIDES AND AG-BI TELLURIDES IN B-PB-ZN SKARNS OF THE PARTIZANSKOE DEPOSIT (DALNEGORSKY ORE DISTRICT, SIKHOTE-ALIN, RUSSIA)

Mineralogical and geochemical studies of the northeastern flank of the Partizanskoe deposit in the zone of its junction with the Dalnegorskoe borosilicate deposit revealed the superimposed Ni-Co arsenide and Ag-Bi telluride mineralization hosted in Pb-Zn ores of the Bolnichny ore body. The typomorphic features of arsenides and tellurides, the sequence and physicochemical conditions of their formation were studied. Rammelsbergite and hessite were found at the deposit for the first time. The authors assume that the arsenide and telluride assemblages formed after the completion of the main skarn-sulphide stage of ore formation. Together with the earlier mineralogical finds of large Bi nuggets and reniform aggregates of native As and Sb in the same junction area, the arsenide-telluride mineralization can be assigned to the geochemically unified Ag-Bi-Ni-Co-As(Sb) mineral assemblage, which in its geochemical features is a miniature of the ores of hydrothermal (five-element vein-type) deposits.
Reinforcement Learning and its application in making Recommendation System

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents are made to take actions in an environment in order to maximize the total reward. RL works on Markov Decision Process which leads to Q-learning. MDP provides a mechanism to maximize the reward in a given environment. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. DRL has applications in many fields like medicine, robotics, games, etc. Combining DL and RL leads to the formation of Deep QNetworks. Another application of RL and the focus of this seminar is personalized recommendation systems. Recommendation systems are trained on user-item interaction to predict the next item that a user can be interested in. In personalized recommendation systems, it is important to consider a lot of factors like user-item interactions, clicks, purchases, loss, etc. DRL does a very good job of reducing the loss in recommendation systems and recommending relevant items. I will explore a technique called self-supervised learning in which a model is made to predict data for itself for its use in recommendation systems. I also summarize 2 frameworks namely SelfSupervised Q-learning (SQN) and Self-Supervised Actor-Critic (SAC) based on self-supervised learning and try to understand how they work differently as compared to non-RL recommendation systems.
Ensiklopedia Tipografi Sebagai Media untuk Meningkatkan Pemahaman Terhadap Tipografi

Typography is one of the important elements in a multimedia product design. It is impossible to learn the science of typography from one course. Many terms exist in typography and can be learned by students to increase their knowledge of typography. One way to make it easier to know these typographic terms can be obtained from the encyclopedia. But the encyclopedia for typography does not yet exist. Based on this, the research is expected to be able to compile data on existing typographic terms which are then summarized into an interesting typographic encyclopedia and can improve student learning in multimedia education. After collecting data in the literature, it was continued to design a good typographic encyclopedia book and the next step was tested on 84 Multimedia Education students at the University of Pendidikan Indonesia who were contracting typography courses at the Multimedia Education Study Program, Universitas Pendidikan Indonesia. From the results of the trial, it was concluded that, through a good typographic encyclopedia book design, students could learn more easily about typographic terms.
THE EFFECT OF PROJECT BASED LEARNING ON SCIENCE PROCESS SKILLS IN EARLY CHILDREN

The results of preliminary observations indicate that learning still uses Project Based Learning Model Learning. So that the child's ability to speak is still lacking, especially telling the contents, communicating with simple words, expressing ideas, retelling the story according to the flow. The right learning model to stimulate children's speaking ability is Project Based Learning assisted by big books, because project learning is carried out in groups and uses big book media so that it can stimulate children's speaking abilities. The purpose of this study was to determine the differences in group learning outcomes using the Project Based Learning model assisted by big books and the Project Based Learning class groups on speaking skills. The study design uses quasi-experimental research. The subjects in this study were all children of group B in TK Al-Muhajirin Malang in the 2018/2019 academic year even semester which consisted of two classes namely B1 totalling 17 children as an experimental class and B2 totalling 17 children as a control class, using purposive sampling. The prerequisite test used in this study was the construct validation test with 3 validators, namely one lecturer as a learning expert, one lecturer as a media expert, and one school principal as a practitioner to assess the validity level of the Daily Learning Implementation Plan (RPPH) and an observation sheet of participants' speaking skills students using big book.
PENGGEROMBOLAN DESA DI KABUPATEN POSO BERDASARKAN SARANA PRASARANA DAN TENAGA KESEHATAN MENGGUNAKAN METODE K-PROTOTYPE DENGAN ALGORITMA GENETIKA

Cluster analysis is a technique for grouping a set of similar objects into one group so that they are not similar to objects in other groups. Cluster analysis is generally applied to objects with numeric data type. but in reality clustering also uses categorical data types. Clustering handling with mixed-type data can be done by applying the k-prototype algorithm, but the determination of cluster center initialization tends to be sensitive. To handle the determination of the initialization of the cluster center, can be applied an algorithm that is genetic algorithm. This study discusses the facilities and infrastructure and health workers in Poso Regency where the infrastructure and legal personnel in the district are adequate but the distribution is not evenly distributed in several areas. The results of this study indicate that in the k-prototype algorithm there are 8 clusters with cluster centers optimized using the genetic algorithm, namely 36,7,99,49,69,104,105,110.