Reviewerly/exHarmony huggingface dataset:
A Mixed methods research on the Development of Core Competencies of Social Service Corps in Social Welfare Facilities

본 연구의 목적은 사회복지시설에 배치된 사회복무요원의 직무와 관련한 핵심역량을 도출하여 사회복무요원의 업무 향상과 서비스의 질적 향상에 기여하기 위함이다. 이를 위해 사회복무요원 378명을 대상으로 설문조사를 실시하고, 복무기관 담당자 11명의 심층면접조사를 종합한 수렴적 평행설계(convergent design)를 수행하였다. 양적연구는 전국 사회복지시설에서 사회복무요원으로 배치된 사회복무요원을 대상으로 역량별 요구수준을 조사한 후 복무기관과 역량별 요구수준을 종합한 구간추정방법을 활용하여 ‘소양 및 태도’에서 3개 역량, ‘사회복지실천 역량’에서 6개 역량, ‘직무수행 역량’에서 4개 역량을 선별 총 13개의 핵심역량을 도출하였다. 질적연구는 사회복지시설에서 사회복무요원 복무 관리를 담당하는 11명의 복무기관 담당자를 대상으로 초점집단인터뷰(Focus Group Interview)를 실시하여 사회복무요원의 직무와 복무 환경변화를 기반으로 주제분석을 통한 핵심역량을 도출하였으며 이를 통해 ‘소양 및 태도’에서 3개 역량, ‘사회복지실천 역량’에서 5개 역량, ‘직무수행 역량’에서 7개 역량이 도출되었다. 본 연구는 사회복지분야의 인적자원으로써 사회복무요원의 역량 향상이 궁극적으로 사회복지시설 및 서비스의 발전에 기여할 수 있다는 점에 주목하여 이들의 핵심역량을 다각적으로 파악하였다는 점에서 의의가 있다.The purpose of this study is to develop the core competencies of social service corps working in social welfare facilities, in order to contribute to the enhancement of their work performance and the qualitative improvement of services they provide. To achieve this objective, a mixed methods research approach was employed, involving a survey conducted on 378 social service corps working in social welfare facilities and in-depth interviews with 11 administrative workers responsible for overseeing social service corps. In the first phase, a quantitative study was conducted targeting social service corps working in social welfare facilities nationwide. The study involved the utilization of an interval estimation method that incorporated the requirements demanded by institutions and competencies, and as a result, a total of 13 core competencies were identified. Specifically, 3 competencies were identified within the domain of ‘Disposition and Attitude,’ 6 competencies were identified within the domain of ‘Social Welfare Practice Competencies,’ and finally, 4 competencies were identified within the domain of ‘Job Performance Competencies.’ Subsequently, during the second phase of the qualitative study, the researchers conducted Focus Group Interviews with 11 administrative workers responsible for managing social service corps duties in social welfare facilities. Core competencies were extracted through thematic analysis based on the duties and changing work environment of the social service corps. The results revealed 3 competencies under ‘Cultivated Literacy and Attitude,’ 5 competencies within ‘Social Welfare Practice Competencies,’ and 7 competencies within ‘Job Performance Competencies.’ The significance of this study lies in its comprehensive presentation of outcomes through the incorporation of both quantitative and qualitative methods, involving not only the social service corps that participated in training but also the administrative personnel that supports the performance of social service corps and utilization of the competencies. This study holds valuable implications as fundamental material for the advancement of the training and education system for the social service corps.
Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods

Hyperspectral image technology in land classification is a distinct advantage compared to ordinary RGB and multispectral images. This technology has a wide spectrum of electromagnetic waves, which can be more detailed than other types of imagery. Therefore, with its hyperspectral advantages, the characteristics of an object should have a high probability of being recognized and distinguished. However, because of the large data, it becomes a challenge to lighten the computational burden. Hyperspectral has a huge phenomenon that makes computations heavy compared to other types of images because this image is 3D. The problem faced in hyperspectral image classification is the high computational load, especially if the spatial resolution of the image also has mixed pixel problems. This research uses EO-1 satellite imagery with a spatial resolution of 30 meters and a mixed pixel problem. This study uses a classification method to lighten the computational burden and simultaneously increase the value of classification accuracy. The method used is satellite image pre-processing, including geometric correction and image enhancement using FLAASH while the corrections are geometric correction and atmospheric correction. Then to lighten the computational burden, the steps carried out are using the Slab and PCA method. After obtaining the characteristics, they are entered into a guided learning model using a support vector machine (SVM) for the five-class or multiclass classification. Moreover, the imbalance learning method is proven to produce increased accuracy. The best results were achieved by the ADASYN method with an accuracy of 96.58%, while the computational time became faster with the feature extraction method.
Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment

It has become a necessity for people to communicate with each other to complete their needs. The exchange of information conveyed in communication often cannot be directly assessed, especially online news. They just get news and are unable to filter out inappropriate stuff. The media website conveys a great deal of information. Popular news websites are one source for keeping up with the newest news. It requires a significant amount of work to deliver news on prominent websites and to choose content that is not incorrect. To crawl the web and analyse enormous data, massive computer power is required, and solutions to lower the process's space and temporal complexity must be created.Data mining is seen to be a solution to the aforementioned difficulties since it extracts particular information based on defined attributes. This research investigated a model to determine the content of false news information in Indonesian popular news. Firstly, preprocessing process from dataset that collected from keaggle. Secondly, we try use classification methods to determined which the optimal method to classify fake news. Thirdly, we use another public dataset for testing method. Furthermore, five machine learning classifiers are compared: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC), and Random Forest (RF). These classifications are utilized independently before being compared based on receiver operating characteristic curves and accuracy. The experimental result shows that DTC has the lowest accuracy of 75.33% and SVM has the highest accuracy of 83.55%.Â
Analyzing the Suitability of English Reading Materials: The Correlation between the Readability Level and Narrative Reading Comprehension

The observed low reading proficiency among Indonesian students can be attributed to several variables, such as the level of readability or the degree of text difficulty. The comprehension of the suitability of texts for students was of utmost importance for English teachers. The correlational research methodology was employed in this study to ascertain the presence or absence of a statistically significant relationship between student reading comprehension and the outcome variable under investigation. The participants of this study consisted of tenth-grade students from SMA Gajah Mada and SMAN 7 Bandar Lampung, who were selected to participate in the research project conducted throughout the 2020-2021 academic year. To acquire pertinent data regarding the reading comprehension abilities of pupils, an assessment specifically designed to evaluate reading comprehension was administered. A total of 12 narrative reading materials were identified in this study, sourced from three different textbooks: the Pathway to English Textbook published by Erlangga, the Talk Active Textbook authored by Yudhistira, and the Bahasa Inggris Textbook developed by the Ministry of Education and Culture. The researcher evaluated text readability using the Flesch Reading Ease Formula, with the subsequent classification of the readability level based on the Common European Framework of Reference for Languages (CEFR) Level. The results of this study revealed a statistically significant positive correlation (r = 0.9054). A selection of six narrative reading materials, deemed suitable for tenth-grade students, was made accessible.
A Lightweight Deep Learning Algorithm for Multi-Objective Detection of Recyclable Domestic Waste

In light of the rapid development of human society, there has been a notable surge in waste production, which has resulted in environmental pollution and degradation. This is a pervasive issue that requires attention. To address the environmental problems caused by waste generation and advance the development of recyclable domestic waste detection, this article proposes waste classification as a solution. Traditional waste sorting methods have proven to be inefficient and prone to errors, hence the need for a more effective approach. A multiobjective recyclable domestic waste detection and classification method based on improved You Only Look Once v5s (YOLOv5s) is proposed in this study. In this study, the network structure is enhanced through the implementation of the Bidirectional Pyramid Network (BiFPN). The coordinate attention mechanism is then incorporated to elevate the accuracy of the model. Additionally, the loss function is refined by adopting the Efficient Intersection Over Union Loss (EIOU_Loss) metric to further optimize network performance. Finally, the introduction of the Ghost convolution module reduces parameter count and significantly improves the real-time detection speed. The waste dataset named Multi-classified Recyclable Domestic Trash Identification Dataset (MULTI-TRASH), which is composed of machine shooting, web crawler, and artificial photography, is used for verification due to its good generalization and representativeness. The mean Average Precision at a threshold of 0.5 ([email protected]) value of 94.8% is achieved by the improved model, which is a 30.72% reduction in the number of parameters and a 1.2% improvement in the [email protected] value compared with YOLOv5s. The effectiveness of the proposed algorithm is proved by a comparison with other target detection algorithms. This study aims to provide technical references for the development of a recyclable domestic waste detection system.
A Study on Strategies for Improving English Application Ability of Chinese College Students

With the deepening of exchanges between China and other countries around the world, high English application ability, namely the comprehensive development of listening, speaking, reading, and writing skills has increasingly become an inevitable requirement for talents by the country and society. This article first analyzes the current situation of Chinese college students’ English application ability, and then examines the importance of improving Chinese college students’ English application ability. Furthermore, it proposes strategies for improving Chinese college students’ English application ability, with the aim of providing some inspiration and assistance for education authorities, university decision-makers, and English teachers in cultivating Chinese college students’ English application ability.
STUDY OF IMPROVING THE IRRIGATION NETWORK SYSTEM FOR THE SALURAN INDUK MADIUN (SIM) IN MAGETAN REGENCY

The Saluran Induk Madiun, abbreviated as SIM, is the primary waterway that irrigates the SIM Irrigation Area. Jati Dam, located in Gondangan Village, Gorang-Gareng District, Magetan Regency, is the primary source of the SIM Irrigation Area. This SIM Irrigation Area gets its main supply from Jati Dam; Jati Dam is the upstream or the beginning of the flow in the SIM Irrigation Area. In conditions before rehabilitation, the Jati Dam had a discharge of 4000 l/s to irrigate the entire SIM Irrigation Area. Especially for the SIM Irrigation Area in Magetan Regency, obtaining water supply from the Jati Dam at 17% of 350,746 l/s, the discharge value can only meet the paddy cropping pattern of 30%. Looking at these problems, the Government of Indonesia is seeking rehabilitation work to increase agricultural barns production. Therefore, it is necessary to research the improvement of irrigation network systems to determine the ratio of water availability before and after rehabilitation, water demand, and the value of increasing agricultural profits. This research uses the F.J. Mock to assess the availability of water discharge FAO Modified Penman Method and the amount of evapotranspiration used to analyze water demand for each cropping pattern. A comparison of the value of the availability and demand for water is presented in the form of a water balance.From the results of the analysis that has been carried out, it is concluded that the highest water availability in the SIM Irrigation Area in Magetan Regency before rehabilitation was 1.37 m3/sec, and the lowest was 0.74 m3/sec. In conditions after rehabilitation, the highest was 5.75 m3/sec, and the lowest was 4.64 m3/sec. It is known that the supply from the Jati Dam, in conditions before rehabilitation, was 17%, and in conditions after rehabilitation, it increased to 28%. The highest irrigation water requirement is the cropping pattern that uses 100% paddy of 3.01 m3/sec. Seeing the value of water availability compared to the still significant water demand, the Jati Dam intake can be reduced from 28% to 20% so that the remaining water can be distributed to the following secondary waterways. Water availability at a percentage of 28% is 5.75 m3/sec; at 20%, it becomes 3.49 m3/sec. This debit can fulfill the highest water demand of 3.01 m3/sec. In conditions before rehabilitation, the availability of water was only able to meet the planting pattern of Paddy (30%) - Paddy (25%) - Secondary crops (100%) with a profit of Rp.26,161,069,519.00. Whereas in conditions after rehabilitation, the availability of water can fulfill the planting pattern of Paddy (100%) - Paddy (100%) - Paddy (100%) with a profit reach up to Rp.115,325,883,740.00.
SRDF_QDAG: An efficient end-to-end RDF data management when graph exploration meets spatial processing

The popularity of RDF has led to the creation of several datasets (e.g., Yago, DBPedia) with different natures (graph, temporal, spatial). Different extensions have also been proposed for SPARQL language to provide appropriate processing. The best known is GeoSparql, that allows the integration of a set of spatial operators. In this paper, we propose new strategies to support such operators within a particular TripleStore, named RDF QDAG, that relies on graph fragmentation and exploration and guarantees a good compromise between scalability and performance. Our proposal covers the different TripleStore components (Storage, evaluation, optimization). We evaluated our proposal using spatial queries with real RDF data, and we also compared performance with the latest version of a popular commercial TripleStore. The first results demonstrate the relevance of our proposal and how to achieve an average gain of performance of 28% by choosing the right evaluation strategies to use. Based on these results, we proposed to extend the RDF QDAG optimizer to dynamically select the evaluation strategy to use depending on the query. Then, we show also that our proposal yields the best strategy for most queries.
Efficient stereo matching using attention mechanism and edge optimization

In practical applications, to improve the real-time performance of end-to-end stereo matching networks, the existing methods build cost volume at low resolution. However, with detailed information missing in low-resolution features, it is difficult to get accurate disparity estimation results in weak texture regions. Besides, smooth L1 loss supervision also results in a loss of accuracy in disparity discontinuity areas. To solve these problems, we propose an efficient stereo-matching network based on multiple attention mechanisms and edge optimization, which can achieve high accuracy in a short time. The multi-scale attention module is applied to enhance the feature expression in detail regions. For weak texture areas, we construct a concatenation cost volume and a multi-level patch matching volume, which can be combined to improve the network’s attention to weak texture regions. In terms of edge optimization, we perform bimodal Laplace modeling of the sampled edge points’ disparity distribution and optimize the edge region of the initial disparity map using likelihood loss to obtain sharp edges. The experimental results show that, on the SceneFlow and KITTI datasets, the proposed network improves by 32% and 27% in accuracy compared with BGNet+.
Virtual Reality Technologies: Analysis of their Application in Electronics Engineering Career Programs

&lt;p&gt;Many problems have arisen in traditional electronics engineering education because of the Coronavirus lockdown. Although these problems have not been solved yet, this paper analyzes the use of virtual reality (VR) technologies as a possible teaching resource to solve them. Even though VR devices are relatively new in the field of teaching, this analysis makes use of numerous research projects, weighing up its positive effects and its drawbacks. Summarizing the results, it is clear that hybrid learning (by means of conventional and virtual reality systems) presents some advantages to traditional learning. However, more research has to be done in order to evince these new devices as an improvement in engineering education. &lt;/p&gt;
Blockchains in the Classroom: A Deductive Journey into the Abyss of Modern Education

&lt;p&gt;The rapid advancement of blockchain technology has permeated various sectors, heralding a new era of transparency, security, and decentralized control. Education, a cornerstone of societal development, is not exempt from this technological tide. This research embarks on a deductive exploration of the integration of blockchain technology within the educational landscape, aiming to unveil the potential benefits and challenges therein. Through a meticulous review of existing literature, theoretical frameworks, and empirical analysis, this study delineates the transformative potential of blockchain in enhancing deductive reasoning and fostering a conducive learning environment. The research employs a mixed-method approach, encompassing surveys, interviews, and case studies across diverse educational settings. Findings reveal that blockchain technology can significantly bolster the integrity and accessibility of educational resources, streamline administrative processes, and enhance deductive reasoning among learners. Moreover, the study unveils the necessity of a robust regulatory framework and stakeholder education to harness the full potential of blockchain technology in education. The discourse extends to provide pragmatic recommendations for educators, policymakers, and blockchain developers to collaboratively nurture a blockchain-augmented educational ecosystem. This work aspires to contribute to the burgeoning body of knowledge at the nexus of blockchain technology, deductive reasoning, and modern education, propelling further scholarly inquiry and practical initiatives in this multidisciplinary domain. &lt;/p&gt;
The Effectiveness of Using Inside Outside Circle Technique towards Students’ Speaking Ability at Tenth Grade Student of Senior High School 8 Muaro Jambi

This study aims to investigate the effectiveness of using Inside Outside Circle Technique Towards Speaking Skill at Eleventh Grade student of Senior High School 8 Muaro Jambi. The research methodology used in this study was quantitative research design with true experimental research. The population of the research was eleventh grade student and the sample was XI IPA 2 as experimental class and XI IPA 3 as control class. The instrument of the research was the oral proficiency test. The data were gathering in this research through pre-test, treatment and post-test. The data were analyzing in this research through t-test formula. As expected, the current study, it was found that the coefficient of t-(observation) = 4,05 where the coefficient of t-(table) = 1,967. It means that there is a significant effect of using Inside Outside Circle technique toward students English speaking skill at eleventh grade student of senior high school 8 Muaro Jambi.
Implementation of AES-256 Algorithm in Android-Based E-Voting Data Security

E-voting is a method of voting and vote counting in general elections using digital devices. There are still many errors and fraud that occur during village head elections in Indonesia. Then the idea was created to carry out village head elections by utilizing existing technological developments, especially Android-based smartphone technology. The data source used in this study is secondary data which uses population data of citizens who will exercise their right to vote, vote data for citizens' voting rights, and data from the results of vote acquisition during the village head election process. In this study, a number of important data were needed in the village head election process which was carried out using documentation techniques. This research produced an Android-based application system for securing e-voting data for village head elections using the AES-256 cryptographic algorithm called Vodes. The application produced in this study was built using the Kotlin programming language on the Android frontend side by utilizing the Android Studio IDE as a tool in the development process. While on the backend side it uses the Javascript programming language using the Node js framework. The database used in the Vodes application uses MySQL. Based on the results of this study, it can be concluded that an e-voting data security application for village head elections has been created using the AES-256 cryptographic algorithm. The application produced in this study has succeeded in properly securing all aspects of the data in the village head election process
Visual Rendering

This chapter addresses visual rendering, which specifies what the visual display should show through an interface to the virtual world generator (VWG). Sections 7.1 and 7.2 cover basic concepts at the core of computer graphics, and VR-specific issues. They mainly address the case of rendering for virtual worlds that are formed synthetically. Section 7.1 explains how to determine the light that should appear at a pixel based on light sources and the reflectance properties of materials that exist purely in the virtual world. Section 7.2 explains rasterization methods, which efficiently solve the rendering problem and are widely used in specialized graphics hardware, called GPUs. Section 7.3 addresses VR-specific problems that arise from imperfections in the optical system. Section 7.4 focuses on latency reduction, which is critical to VR, so that virtual objects appear in the right place at the right time. Otherwise, side effects could arise, such as VR sickness, fatigue, adaptation to flaws, or an unconvincing experience. Section 7.5 explains rendering for captured rather than synthetic virtual worlds. This covers VR experiences that are formed from panoramic photos and videos.
Audio

In the real world, audio is crucial to art, entertainment, and oral communication. Audio recording and reproduction can be considered a VR experience by itself, with both a CAVE-like version (surround sound) and a headset version (wearing headphones). When combined consistently with the visual component, audio helps provide a compelling and comfortable VR experience. Each section of this chapter is the auditory (or audio) complement to one of Chapters 4 through 7. The progression again goes from physics to physiology, and then from perception to rendering. Section 11.1 explains the physics of sound in terms of waves, propagation, and frequency analysis. Section 11.2 describes the parts of the human ear and their function. This naturally leads to auditory perception, which is the subject of Section 11.3. Section 11.4 concludes by presenting auditory rendering, which can produce sounds synthetically from models or reproduce captured sounds.