Reviewerly/exHarmony huggingface dataset:
GEOSPATIAL DASHBOARD FOR PROBLEM REPORTER IN JAMBI PROVINCE

Community involvement in providing problem reports is essential to improving the quality of life in cities and villages. Jambi already has "SiKESAL" as a problem-reporting application in Jambi, but it has not been able to provide information on the spatial distribution of locations through a single dashboard display. This research aims to apply the Geospatial Dashboard application as a spatial-based problem-reporting application using ArcGIS technology from ESRI. Sampling as information on problem topics for the development of the Geospatial Dashboard includes road damage, availability of disability facilities in houses of worship, illegal waste disposal sites, illegal parking, and road damage. The development of this Geospatial Dashboard presents the distribution, number, range, trend, and centre of problems so that it can be used for direct monitoring, decision-making, and solving related departmental problems. The study results show that the development of the Geospatial Dashboard can create data integration between sectors in real-time or quickly and assist local governments in solving various problems in Jambi Province.
USABILITY TESTING OF THE HI JOBS! APPLICATION USING THE PERFORMANCE MEASUREMENT AND THE CONCURRENT THINK ALOUD TECHNIQUES

Hi Jobs! is a mobile application that will help people find work because it provides various information concerning job vacancies. This application is still relatively new and has never been tested before. However, this application already has several criticisms and suggestions from users regarding problems that had occurred when using the Hi Jobs! application. The problems that occurred were related to aspects of application usability. Therefore, usability testing is needed to determine the effectiveness, efficiency and user satisfaction when using the Hi Jobs! application using Performance Measurement and Concurrent Think Aloud techniques. The test was conducted on 20 participants who were divided into two categories, advanced and novice. The results of the study showed that advanced participants had a success rate of 96% with a total of one failed task, while novice participants had a success rate of 89% with a total of four failed tasks. The average time for participants in the advanced category was 30 seconds and the novice category was 37 seconds. Based on the results of interviews conducted, there were problems with 4 application features of the Hi Jobs! application. In conclusion, this application was very effective but it was not efficient nor has it meet user satisfaction because there are some features that are difficult to find. Therefore, recommendations for improvement have been made based on the received criticisms and suggestions to meet user satisfaction.
Degree of Readiness of Teachers in Ukraine to Use ICT in Their Professional Activities: 2019‒2022

The article presents a comparative analysis of Ukrainian teachers' readiness to incorporate ICT into their professional activities.It presents the results of a longitudinal study that began before the Covid-19 pandemic, continued during the pandemic and after it, including a series of teacher surveys.The article explores changes in teachers' attitudes toward digital tools during this period; shows the dynamics of the expansion of tools and assesses changes in teachers' self-assessed ICT competence.Additionally, it identifies the digital resources for the implementation of professional functions, most favoured by Ukrainian teachers.Statistical analysis revealed that, following the implementation of distance learning, 1.4% of teachers reported high-level ICT competence, while 2.1% reported sufficient competence.However, a considerable 14.6% rated their ICT competence as inadequate.This period of distance learning unveiled gaps in teachers' ICT proficiency, prompting a more accurate assessment of their ICT competence.The study demonstrates that, based on the needs of full-time, distance and blended learning, teachers increasingly rely on ICT tools for lesson preparation, interactive content development, and student progress monitoring, facilitated by digital services offering virtual classrooms and electronic journals.Obviously, the demand for ICT tools is greatest in the context of distance learning.However, even during onsite education, Ukrainian teachers widely use digital resources.
SMART PUBLIC OPINION MONITORING AND ANALYSIS SYSTEM FOR ETHNIC REGIONS: A MULTI-MODAL APPROACH USING MACHINE LEARNING

In ethnic regions, public opinion plays a vital role in shaping social stability and harmony. However, monitoring and analyzing public opinion in these regions can be challenging due to the complex linguistic and cultural factors involved. To address this issue, this study proposes a Smart Public Opinion Monitoring and Analysis System for Ethnic Regions, which utilizes a multi-modal approach for data acquisition and machine learning techniques for analysis. The system collects public opinion data from multiple modalities, including text, speech, image, and video, and uses intelligent speech processing and text translation to analyze non-textual data. The sentiment analysis module evaluates the polarity of public opinion, while the topic modeling module identifies key topics and keywords of public interest. The system also includes an early warning and risk detection module that uses machine learning algorithms to detect potential risks and generate alerts in real-time. Finally, the data visualization module presents the analysis results in an intuitive and user-friendly manner. The proposed system has been tested on a dataset of public opinion data from ethnic regions, and the results demonstrate its effectiveness in monitoring and analyzing public opinion in different modalities. The system can help government agencies and relevant stakeholders to respond quickly to potential risks and maintain social stability in ethnic regions.
Conservation Value of Gunungan Wayang Kulit Purwa for Science Learning Materials

This study aims to explore the concepts of ecology and biodiversity, the value of environmental preservation in gunungan wayang kulit purwa, and its correlation to science learning or environmental education in a science subject at the junior high school level to increase environmental awareness. This research employed a qualitative descriptive approach. This research included ethnographic research associated with symbolic interpretation in anthropology. The informants of this study were a puppeteer (dalang), a javanology expert, a gunungan wayang kulit purwa creator, and a biodiversity expert chosen by purposive sampling. Data were collected through semi-structured interviews, literature reviews, and content analysis. Data triangulation was applied to ensure the data validity. The data were analyzed using a spiral analysis model, including managing, reading, marking, describing, classifying, interpreting, and visualizing data continuously. The study results show that the gunungan wayang kulit purwa contains the conservation value and concept of ecology and biodiversity. This concept has a correlation with the material on ecology and biodiversity in the independent curriculum in a science subject at schools. The ecological concept includes biotic and abiotic components, symbiosis, pollinators and predators, food chains, food webs, competition, and the value of environmental preservation. The findings in the form of a link between ecological concepts and biodiversity in the gunungan wayang kulit purwa have implications for the potential use of gunungan wayang kulit purwa as a learning resource for science education in schools or environmental education to increase environmental awareness of the young generation.
Analysis of Twitter User Sentiment on Presidential Candidate Anies Baswedan Using Naïve Bayes Algorithm

Indonesian hold presidential election in 2024. One of the most discussed topics by public is the presidential candidates. The discussion about the presidential candidate certainly reaped various kinds of responses from public, ranging from support to statements of disapproval. This research was limited to the candidacy of Anies Baswedan as a presidential candidate before a vice president candidate as his pair was selected. The purpose of this study is to conduct a sentiment analysis of public responses regarding Indonesia 2024 presidential candidate Anies Baswedan using tweets data from October 2022 to January 2023 using the naïve bayes classifier algorithm. This is expected to provide an overview of the public opinions on Twitter. Three test models were carried out with differences in the division of the amount of training data and test data, respectively 60%:40%, 70%:30% and 80%:20%. The test results showed the highest accuracy level was obtained by the 3rd model using training and testing data of 80%:20% with an accuracy value of 76.21%. Further research is recommended to conduct sentiment analysis on the pairs of Presidential and Vice-Presidential candidates who have been officially registered with the General Election Commission using various other classification algorithms.
Realtime Monitoring and Analysis Based on Cloud Computing Internet of Things (CC-IoT) Technology in Detecting Forest and Land Fires in Riau Province

Forest and land fires in Riau are natural disasters that always repeat every time they enter the dry season. The solution of this research is to apply the leading technology of cloud computing internet of things (CC-IoT) to find out more quickly the existence of forest or land fires. This study uses Particle Argon (Photon) to connect to the internet and several IR Fire Detector sensors, DHT22 MQ2 and GPS Neo 6m. Particle Argon can receive input and perform processing so that it is connected using the CC-IoT concept to a web server so the users can monitor land conditions in real time. Based on the test results, it can be concluded that a fire detector using fire parameters (2000 = Normal and 2000 = Danger) , temperature (≤37 = Normal, 38 – 45 = Alert, and 46 = Danger), humidity (≤50 = Dry, 51 = Humid) , smoke (≤ 1700 = Normal, 1700 = Danger), and soil moisture can work well ( 3500 = Dry Moisture Content, 1500 to 3500 = Medium Moisture Content, and 1500 = High Moisture Content). The fire detection tool developed can detect fires in real time and also has a fire early detection function that is useful for anticipating land conditions to prevent fires. The results obtained from the test are that the sensor can read indications of fire, smoke, soil moisture with a success rate of 93% and send location data and sensor values to the website. The use of sensors has their respective roles so that if there is a problem with one of the sensors, the tool has an alternative sensor and can continue to function.
Assistance in the Preparation of Scientific Articles in the National Journal for Teachers

Writing scientific papers aims to express ideas, thoughts and information in the form of writing and a writer is able to distinguish between opinions and facts. The purpose of the service is to improve the ability of teacher professionalism by equipping teachers in terms of strategies for compiling scientific papers correctly in accordance with writing guidelines, and equipping teachers in procedures for publishing scientific articles in accredited journals scientific publications for teachers who are prerequisites for promotion and position, so as to motivate teachers to make more quality scientific work. The method of implementing assistance in writing scientific articles includes activity steps that emphasize the learning process. This mentoring activity is built on the basis of active participation (participation) of trainees in all aspects of training activities, starting from planning, implementation, to the assessment stage of learning activities in training. The implementation of assistance in the preparation of scientific articles in the National Journal for teachers of SMKN 1 Sawit Boyolali is divided into three groups of speakers as follows, namely: 1) Format of writing scientific articles 2) Mendeley, 3) Online Journal System. The results achieved in this activity are that teachers have increased the motivation of teachers to write papers, conceptual articles and research articles and can increase the understanding, ability and skills of teachers writing scientific papers. Teachers have a basic understanding of how to identify, select and formulate topics and titles, compile outlines, collect writing materials, organize, and conceptualize writing, as well as scientific writing and editing using mendeley software. 63.6% of teachers have the intentionto write articles in scientific journals. Teachers enthusiastically participate in community service activities that are attended by 100% of the stipulated conditions. Teachers are able to understand the rules and procedures in writing scientific papers, the process of stages in making articles and guidance in publishing articles with the help of mendeley software.
Amortizing intractable inference in large language models

Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.
Implementasi Metode MAUT Dalam Pemilihan Aplikasi Pembuatan Media Pembelajaran Dimasa Pandemi Covid 19

Corona Virus disiase 2019 (covid 19) is an infectious disease caused by SARS-CoV-2 a type of Corona virus. This virus is transmitted through physical contact and causes symptoms of respiratory problems, colds, fever, dry cough, and attacks the human physical immunity. covid 19 has disrupted the activities of daily human life, including in the field of education. To prevent the spread of covid 19 the government proposes that during the covid 19 pandemic the teaching and learning process takes place online or online. In the online learning process so that students are more active, creative and enthusiastic, a system is needed decision support that can determine and choose the application of learning media during the covid 19 pandemic, a decision support system that can solve the problem using the MAUT (Multi Attribute Utility Theory) method and produce an appropriate decision based on the results of the data obtained such as alternatives and the criteria so as to produce a decision, namely the application of the best learning media in the era of covid 19, namely the Sony Vegas Pro application with a score of 0.9025.
FinChain: Adaptation of Blockchain Technology in Finance and Business - An Ethical Analysis of Applications, Challenges, Issues and Solutions

Blockchain Technology is a distributed database technology that has emerged as a ground-breaking technology with several possible solutions to critical applications, say from supply chain management, agribusiness, marketing to healthcare industry including internet of medical things. Although it started as a digital coin (popularly known as bitcoin), it is slowly influencing business, marketing policy and society. We have presented an in-depth study and ethical analysis of how blockchain is applied over the economic and financial sector including banks, credit unions and other retail giants. During our research, we have also investigated how blockchain technology can affect financial institutions around the world and businesses including large and small businesses. Our contributions included the following: (i) classifying blockchain models and architecture for finance and business markets (ii) analyzing recent and relevant works for finance applications and business solutions using blockchain (iii) discussing the advantages of using blockchain technology in financial institutions like banks, government firms. education sectors (iv) pointing out challenges and issues of blockchain technology for finance and business organizations (v) summarizing future research on integration and adaptation of blockchain technology along with C2C, B2C, and B2B with finance and recommendations for improvement.
The Implementation of Balanced Literacy Approach To Early Childhood Education Units As An Effort To Strengthen Learning: A Phenomenological Study After The Covid-19 Pandemic In Indonesia

Research by Indonesian Ministry of Education, Culture, Research, Technology in 2020-2021 found a significant decrease of learning outcomes in the first grade of elementary school students as a result of the Covid-19 pandemic. The Early Childhood Education as the foundation phase plays a crucial role in the learning recovery, especially literacy. This study explores and analyzes the experiences of Early Childhood Education (PAUD) teachers in applying balanced literacy approach as an effort to strengthen learning. This research refers to the literary processing theory by Clay (Ronning, 2020: 17) regarding the acquisition of literacy skills in early childhood. The method used is qualitative phenomenology with 10 teachers from several regions of Indonesia as informants. The analysis was about the experience of teachers applying balanced literacy before and during the pandemic, the obstacles faced, and the solutions carried out. These findings become the basis for designing the product of this study, namely a conceptual model of early literacy strengthening program with balanced literacy in PAUD. This model is expected to be a contextual alternative solution for strengthening effective literacy in the post-pandemic period and minimizing the impact of learning loss in Indonesia.
Neur2RO: Neural Two-Stage Robust Optimization

Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively. This results in a nested min-max-min optimization problem which is extremely challenging computationally, especially when the decisions are discrete. We propose Neur2RO, an efficient machine learning-driven instantiation of column-and-constraint generation (CCG), a classical iterative algorithm for 2RO. Specifically, we learn to estimate the value function of the second-stage problem via a novel neural network architecture that is easy to optimize over by design. Embedding our neural network into CCG yields high-quality solutions quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital budgeting. For knapsack, Neur2RO finds solutions that are within roughly $2\%$ of the best-known values in a few seconds compared to the three hours of the state-of-the-art exact branch-and-price algorithm; for larger and more complex instances, Neur2RO finds even better solutions. For capital budgeting, Neur2RO outperforms three variants of the $k$-adaptability algorithm, particularly on the largest instances, with a 5 to 10-fold reduction in solution time. Our code and data are available at https://github.com/khalil-research/Neur2RO.
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning

Knowledge graph-based dialogue systems are capable of generating more informative responses and can implement sophisticated reasoning mechanisms. However, these models do not take into account the sparseness and incompleteness of knowledge graph (KG)and current dialogue models cannot be applied to dynamic KG. This paper proposes a dynamic Knowledge graph-based dialogue generation method with improved adversarial Meta-Learning (KDAD). KDAD formulates dynamic knowledge triples as a problem of adversarial attack and incorporates the objective of quickly adapting to dynamic knowledge-aware dialogue generation. We train a knowledge graph-based dialog model with improved ADML using minimal training samples. The model can initialize the parameters and adapt to previous unseen knowledge so that training can be quickly completed based on only a few knowledge triples. We show that our model significantly outperforms other baselines. We evaluate and demonstrate that our method adapts extremely fast and well to dynamic knowledge graph-based dialogue generation.
Pengembangan Metode Pembelajaran Interaktif Berbasis Adobe Ilustrator Dalam Pembuatan Desain Poster Pada SMK Telkom Medan

Education is self-development work related to the development of skills, intelligence, character, creativity and soft skills of the younger generation. Multimedia-based learning media can be used to increase students' understanding of using Adobe Illustrator. Half of the members of class 10-4 TKJ Telkom Medan have a high interest in the subject of poster design or Adobe illustrator[1]. But they are not proficient in using the features in Adobe Illustrator in making posters. So that this research aims to introduce the features in Adobe Illustrator and their functions to students so that they know about making calligraphy through the Adobe Illustrator application. Utilization of the Adobe Illustrator application program can improve student soft skills and creativity. So they can pose as they want
Is there a relationship between emotional Intelligence and students' mathematics learning outcomes?

Students with good emotional Intelligence have adaptability and can develop social relationships with teachers and other students more easily. It is predicted to have an impact on student academic achievement. The study aims to determine whether there is a positive influence of emotional Intelligence on the mathematics learning outcomes of students at one of MAN in Yogyakarta on geometric lines and series. This study uses a quantitative approach to the type of correlational research. This research was conducted at one of MAN in Yogyakarta. The population in this study was class XI Agama 1 and XI Agama 2. The sampling technique used simple random sampling with a sample of 42 students. The instrument used in this study is a questionnaire of emotional Intelligence and a written test of learning outcomes, both of which had passed the content validity and reliability tests. The data analysis technique in this study uses simple regression analysis that has gone through classical assumption tests such as the normality test, heteroscedasticity test, and linearity test, followed by hypothesis testing such as t-test, coefficient of determination, and model interpretation. Data analysis was carried out with the help of the SPSS 25 software application. The results of this study indicate a positive influence of Emotional Intelligence on students' mathematics learning outcomes, with the magnitude of emotional Intelligence on learning outcomes of 12.8%, while other factors outside of this research influence the remaining 87.8%.