Reviewerly/exHarmony huggingface dataset:
Research on College Mathematics Auxiliary Teaching System Based on Computer Platform

With the development and popularization of computer technology and the Internet, people have entered an informationbased society, and the traditional teaching mode cannot meet the needs of teaching.Now online teaching has become a common education method.The long-distance real-time interaction function of online teaching allows students to study without restrictions on time and place.This paper builds a Web-based auxiliary teaching system, and builds a system platform for communication between students and teachers.In the system constructed in this research, the B/S threetier structure is adopted, and the mathematical knowledge database is constructed by using the SQL database system, and the construction of the computer university mathematics aided teaching system under the .NET framework is realized.This system has strong practicability for university mathematics courses.
Effect of Hydrogel as Autolysis Debridement Media on Wound Healing in Diabetic Ulcer Patients in Holistic Homecare

Background: Applying hydrogel as a topical dressing in modern wound care which will help the autolysis debridement process, so that patients feel comfortable in the wound care process. Objectives: To Knowing the description of Wound Healing in Diabetic Ulcer Patients before and after Hydrogell Administration. Methods: Pre-test and post-test research was used in a quasi-experimental research design. Before measuring the wound, the first activity in the follow-up of this study was giving a pre-test. A final post-test was administered following the hydrogel administration procedure to compare the wound before and after treatment. Results: After the statistical test was carried out, the p-value = 0.000 (p-value &lt;0.05), and the t result was 22.477. This shows the effect of Hydrogell as an autolytic debridement medium on wound healing in diabetic ulcer patients. Which means H0 is rejected and Ha is accepted. Conclusion: Based on the results of research conducted that there is effectiveness of Hydrogell for autolysis debridement in the wound healing process in diabetic ulcer patients.
Digital transformation of the educational environment of a preschool educational organization

This article discusses the theoretical justification of the transformation of the educational environment of a preschool educational organization. The article reveals the main definitions of the relevant topic. The article also substantiates the relevance of the use of digital technologies in the educational environment of kindergarten and describes the features of their use.
Special Issue on Logic-Based Artificial Intelligence

Since its inception, research in the field of Artificial Intelligence (AI) has had a fundamentally logical approach; therefore, discussions have taken place to establish a way of distinguishing symbolic AI from sub-symbolic AI, basing the approach instead on the statistical approaches typical of machine learning, deep learning or Bayesian networks [...]
Interaction of educational organizations of general and secondary vocational education with youth communities through social networks

The potential of using social networks in building the interaction of educational organizations of general and secondary vocational education with youth communities is quite diverse: this includes communication, the provision of educational content, the broadcast of experience, and educational influences. In terms of building this interaction, it is important to understand the content and organizational mechanisms of its implementation, such as: the study of the specifics and content of the most popular communities in social networks; the use of various formats of interaction with young people and their communities in social networks; research and consideration of the motives for using social networks by young adults; participation in the creation of social network content; understanding the specifics of digital interaction.
Development of Local Culture-Based Pop-Up Book Media to Improve Students' Reading Literacy in Elementary Schools

This study aims to develop local culture-based pop-up book media to improve the reading literacy of grade V students in elementary school. This type of research is development or Research and Development (RD) using a design model from Borg Gall. Data collection is carried out by observation, interviews, questionnaires, tests and documentation. The research was conducted at SD Muhammadiyah 1 Kudus. The data analysis technique used is a quantitative data analysis technique. The results showed that the practicality of the media obtained an average percentage of practicality of 97% of the "very practical" category and the results of the T test on the pre-test and post-test of reading literacy achieved a Sig.(2-tailed) score of 0.000. The score showed that there was a difference in the average of pre-test and post-test results in the reading literacy of the students of this study. Then the results of the N-Gain pre-test and post-test reading literacy obtained a score of 0.567 with the category "Medium". The N-Gain score identified an increase in students' reading literacy towards a better one. So it can be concluded that the Local Culture-based Pop Up Book media is effective for improving students' reading literacy. Keywords: [Media Pop Up Book, Local Culture, Reading Literacy]
Implementation of Minimum Competency Assessment in Learning to Decipher Fairytale Messages for Elementary School Students

Education is currently to prepare students who have 21st century skills, the government conducts a minimum competency assessment which includes assessments on literacy and numeracy. This study aims to analyze the results of the implementation of the minimum competency assessment for level 2 class content of fictional texts in learning to describe the message of fairy tales for elementary school students. This research was conducted at SD Negeri Petompon 01. The research method used was quantitative research. The results showed that students were able to work on questions in the form of a minimum competency assessment in learning to decipher fairy tale messages. Working on questions in the form of a minimum competency assessment with a stimulus for each question in the form of a stimulus in a story or picture can help students decipher the message of a fairy tale well. Tests on the minimum competency assessment questions showed an increase in reading literacy in the students of the experimental class at SD Negeri Petompon 01 showing that 90% of students achieved completeness in working on the minimum competency assessment questions. Keywords: Minimum Competency Assessment, Fairy Tales, Literacy
Dzongkha Next Words Prediction Using Bidirectional LSTM

Dzongkha Development Commission of Bhutan (DDC) is trying to computerize Dzongkha. However, the computerization of Dzongkha poses numerous challenges. Currently, the support for Dzongkha in modern technology is limited to printing, typing, and storage. Typewriting a single Dzongkha word requires several keypresses. As a result, typing Dzongkha is tedious. In this paper, the Dzongkha word label prediction was studied. The purpose of the study was to further reduce keystrokes and make Dzongkha typing much faster. The dataset encompasses different genres curated by DDC. The dataset consisted of 10000 sentences and 4820 unique words. Next, 52150 sequences were generated using N-gram methods followed by vectorizing text using embedding techniques. Different RNN-based models were evaluated for the next Dzongkha words prediction. Two Bi-LSTM layers with 512 hidden layer neurons gave the best accuracy of 73.89% with a loss of 1.0722.
Fully complex-valued deep learning model for visual perception

Deep learning models operating in the complex domain are used due to their rich representation capacity. However, most of these models are either restricted to the first quadrant of the complex plane or project the complex-valued data into the real domain, causing a loss of information. This paper proposes that operating entirely in the complex domain increases the overall performance of complex-valued models. A novel, fully complex-valued learning scheme is proposed to train a Fully Complex-valued Convolutional Neural Network (FC-CNN) using a newly proposed complex-valued loss function and training strategy. Benchmarked on CIFAR-10, SVHN, and CIFAR-100, FC-CNN has a 4-10% gain compared to its real-valued counterpart, maintaining the model complexity. With fewer parameters, it achieves comparable performance to state-of-the-art complex-valued models on CIFAR-10 and SVHN. For the CIFAR-100 dataset, it achieves state-of-the-art performance with 25% fewer parameters. FC-CNN shows better training efficiency and much faster convergence than all the other models.
Identification of Diatoms in the Upstream, Middle, and Downstream Ciliwung River as a Diagnostic Tool for Drowning

Drowning is death caused by asphyxia due to the aspiration of fluids into the airways or lungs, resulting in airway obstruction within 24 hours. One of the examinations to establish the diagnosis of drowning victims is a laboratory examination of diatom identification. This research aims to know the morphological description and types of diatoms that live along the Upper, Middle, and Lower Ciliwung Watersheds, to facilitate the identification of drowning victims found in the Ciliwung River. The research design is qualitative descriptive. The data in this study were the results of observations and analysis of Ciliwung river water samples at the Research Laboratory of the Faculty of Medicine, Indonesian Christian University. The study's results found 17 different species at the Katulampa Dam, 19 at the Ciliwung Bridge, and 14 at the Manggarai Sluice Gate. From a total of 22 species of diatoms found, overall, the widest distribution was Nitzschia sp, Navicula sp, Fragilaria sp, and Cyclotella sp. The distribution and types of diatoms found are closely related to current velocity and water discharge, temperature, and river turbidity.
Density Based Traffic Management Using Deep Learning and Computer Vision

Our project is focused on developing a traffic control system that will dynamically adjust signal timing in response to traffic congestion. Traffic congestion is an acute problem in numerous world cities, and it's time to replace our current time-dependent traffic management system with one that has decision-making capabilities. Current traffic management systems are fixed, which can be hugely inefficient when one lane is occupied more than the others. To optimize this problem, we've created a framework for controlling traffic efficiently that takes into account the density of traffic at any given moment. Sometimes higher traffic density at one side of the junction demands longer green light times – rather than the allotted time. So that all lanes can move along smoothly. We propose here a mechanism in which the green light of a stop sign or traffic signal doubles as the glowing indicator for when it's safe for drivers to cross based on the number of vehicles captured by cameras stationed around the area. Once the number of vehicles is calculated, this information is sent to a microprocessor (Raspberry Pi), and the red-light timer for that particular lane is dynamically adjusted accordingly.
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

Abstract. This work introduces the novel short-term nowcasting model MACIN, which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASI). With a synthetic setup based on simulated cloud scenes, the model and its components were validated in depth. We trained a convolutional neural network to identify clouds in ASI images and derive their height and motion using sparse matching. In contrast to other studies, all derived cloud information from both ASIs and multiple timesteps are combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times up to 20 minutes. For the cloudmasks derived from the ASI images we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud base heights is 4 % and cloud motion error is in the range of 0.1 ms−1. For the DNI nowcasts, we found an improvement over persistence for lead times larger than one minute. Using the synthetic setup, we computed a DNI reference for a point and also an area of 500m × 500 m. Errors for area nowcasts as required, e.g., for photovoltaics plants, are smaller compared to errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup.
MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text

Modal verbs (e.g., "can", "should", or "must") occur highly frequently in scientific articles. Decoding their function is not straightforward: they are often used for hedging, but they may also denote abilities and restrictions. Understanding their meaning is important for various NLP tasks such as writing assistance or accurate information extraction from scientific text. To foster research on the usage of modals in this genre, we introduce the MIST (Modals In Scientific Text) dataset, which contains 3737 modal instances in five scientific domains annotated for their semantic, pragmatic, or rhetorical function. We systematically evaluate a set of competitive neural architectures on MIST. Transfer experiments reveal that leveraging non-scientific data is of limited benefit for modeling the distinctions in MIST. Our corpus analysis provides evidence that scientific communities differ in their usage of modal verbs, yet, classifiers trained on scientific data generalize to some extent to unseen scientific domains.
Information system design for higher education evaluation based on student's perspective

The quality of Indonesia's higher education institutions has not excelled compared to Southeast Asian countries such as Singapore, Malaysia, and Thailand. Based on 2020 data, the percentage of higher education institutions in Indonesia that have been accredited is 59%. It is crucial to evaluate and improve the factors that affect the quality of higher education institutions based on the student's perspective, the primary customer of higher education institutions. This study aims to evaluate the quality of higher education institutions based on students' satisfaction in real-time. Furthermore, this research will propose a design of an information system that can automate the evaluation process. The findings of this study can be applied to the existing system, providing information and analysis to support the policy-making process.
Application of the Naïve Bayes Classifier Algorithm to Analyze Sentiment for the Covid-19 Vaccine on Twitter in Jakarta

The epidemic of a new disease caused by the coronavirus (2019-nCoV), commonly referred to as COVID-19, has been declared a global virus epidemic by the World Health Organization (WHO). President Joko Widodo has officially ratified Presidential Decree No. 99 of 2020 concerning the provision of vaccines and the implementation of vaccination activities. Twitter is a social media platform that allows users to share information and opinions directly with fellow users. Tweets given can be in any form, either positively or negatively, so one of the methods used is sentiment analysis. Sentiment analysis helps determine an opinion or comment on an issue, whether the response is positive or negative. The Naïve Bayes algorithm is used in sentiment analysis because it is suitable for tweets or text data that is not too long or short text. The initial stage of sentiment analysis is text pre-processing which consists of Cleaning, case folding, tokenizing, and stopword removal. Then the data is labeled manually. The analysis results are visualized as bar charts, pie charts, and word clouds. Then the word weighting is carried out using the term frequency-inverse document (TF-IDF), and classification is carried out using the Naïve Bayes classifier. From the test results, the accuracy value of the confusion matrix is 82% from 2600 tweet data with 80% training data composition and 20% test data.
Decision support system for employee recruitment optimization

In a production process, various production factors can affect the resulting performance. Employees, as a factor of production, play an important role in the production process, so it is necessary to get a high-quality workforce. In this regard, the recruitment process is very important, so it needs to be carried out strictly and thoroughly. The problem that is often encountered in the recruitment process is that applicant files are still handled manually, which allows errors to occur in the recruitment process. This error will impact the resulting performance because the industry has employees who do not fit the criteria. Therefore, a Decision Support System is needed in the recruitment process so that decision-making can be done quickly and accurately, reducing errors. This research aimed to find a decision support system application in employee recruitment. The recruitment process can be faster, more precise, and optimal because we get prospective employees who fit the required criteria. The method used to achieve the research objectives was the Analytical Hierarchy Process (AHP) to develop weights or priorities. The results obtained in this study were an application that shows the ranking of accepted prospective employees based on assessments in the recruitment process. In addition, the test results showed that 95% of the application could meet the Usability and Efficiency characteristics, and 70% meet the overall characteristics.
Penerapan Model Pembelajaran STAD (Student Teams Achievement Division) untuk Meningkatkan Hasil Belajar Materi Dimensi dan Struktur Pembelajaran IPS di Kelas V Sekolah Dasar Negeri 05 Sungai Pinang

The background of this research is that children do not like social studies lessons and children feelbored with the material. This research aims to improve social studies learning outcomes in dimensions and structure material through the application of the STAD (Student Teams Achievement Division) learning model for fifth-grade students at SD Negeri 05 Sungai betel nut. This research is a classroom action research (CAR) which is carried out in 2 cycles and 4 stages consisting of planning, implementing, observing, and reflecting. the subject of this study was the fifth children of SD Negeri 05 Sungai Pinang, data collection techniques used observation and assignments in the core activities, to obtain information about students' activeness in participating in the teaching and learning process and seeing social studies learning outcomes in material dimensions and structure. Social studies learning outcomes on dimensions and structure are said to have increased. It can be seen from the results of cycle II that there was an increase of 86, 66 %. This increase is due to changes in the use of learning models that are by the conditions of students so that students more easily understand the material and learn more actively in collaboration. As well as increasing the sense of responsibility in carrying out the task.
The Surprising Factor about GDP

This paper involves a search about GDP (gross domestic product) factors, such as social support and life ladder.Some studies about how to analyze are also included in this article.Four different methods have been implemented in this paper: factors analysis, Linear regression, Multiple regression, and clustering.The figures reveal that these factors are helpful in GDP but to various extent.Gross domestic product (GDP) significantly influences each country in many fields.Each country they have different GDP.This paper needs to analyze the relationship within it.Factor Analyzing is the most significant factor, so it does the most research on it.
Financing Strategies for a Fashionable Product Supply Chain with Capital Constraints under Blockchain Technology

The major members of the fashionable product supply chain are small and medium-sized enterprises, which have difficulties in obtaining loans.However, the traditional supply chain finance is relatively difficult for them.Blockchain technology can provide feasible financing methods for small and mediumsized enterprises.Hence, we establish a multi-level Stackelberg game and analyze equilibrium strategies of members in supply chain.Through analysis, we find that equilibrium strategies of members are subjected to supplier's discounts rate.In addition, the conditions for choosing blockchain-supported finance mode heavily rely on the marginal blockchain cost and disutility of customers' perceived value of fashionable products.
VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition

We propose a combined three pre-trained language models (XLM-R, BART, and DeBERTa-V3) as an empower of contextualized embedding for named entity recognition. Our model achieves a 92.9% F1 score on the test set and ranks 5th on the leaderboard at NL4Opt competition subtask 1.
The Content Construction and Future Development Options of All-round, Whole-process, and Whole-coverage Budget Performance Management

With the deepening of the reform of China's fiscal and taxation system and the advancement of the establishment of a modern fiscal pillar, China's budget performance management has become a problem worthy of study.Only good budget management can provide a basic guarantee for the realization of performance goals and the high-quality supply of public goods.Based on the overall requirements of all-round, full-process and full-coverage budget performance management, and reviewing the development history of China's budget performance management reform, this paper deeply analyzes the existence of all-round, full-process and full-coverage budget performance management in actual practice.The purpose is to provide a reference path selection for the next step to optimize the implementation of government budget performance management in an all-round, whole-process, and full-coverage manner.It aims at at promoting the modernization of the national governance system and governance capacity, and improve the budget performance management system.
Application of Game Theory in Selecting Covid-19 Immunity Methods

This paper aims to do a comprehensive analysis through research on the relationship between shield immunity and the city of herd immunity.And then the paper makes some evaluations and suggestions for the policy formulation in Shanghai by using game theory.To come up with the conclusion on the topic, this paper mainly uses game theory method combined with some basic data analysis approaches, which include the questionnaires and the interviews.The data used in this paper mainly reflects the relations of the two streams of data sources.Also, the results shows that there is a connection between shield and herd immunity, and the effect is remarkable.These findings give a clear path to the conclusion and possible recommendations for the work.Finally, according to the discussion, it is recommended that the application of the policies and the formulation of legislation by the Shanghai authorities that should reflect on the advantages of the two methods.