Reviewerly/exHarmony huggingface dataset:
Multi-Criteria Decision Support System for Flood Alert Classification in Smart Cities

People unfamiliar with flood status often find it difficult to understand complex information such as flood warnings from authorities. Decision support systems can process this information and provide more superficial, more understandable reports to the public so they can respond correctly. This research proposes the development of a Multi-Criteria Decision Support System for Flood Warning Classification in smart cities. Identifying flood alert status includes safety, alert, warning, and watchful to improve preparedness and response to flood hazards in a city. The TOPSIS method was used as the main framework in this study to evaluate flood-related data using rainfall parameters, water table height, water discharge, tides, temperature, and humidity. In the decision-making process, TOPSIS will produce a ranking that allows for determining the flood alert status that best suits the actual conditions on the ground. Based on the research results regarding the classification of flood alert status, watchful status dominates, with the highest score of 0.929. This is followed by the alert status, which occupies the second position with a score of 0.193. Furthermore, the warning status is ranked third with a score of 0.185. Meanwhile, the safe status occupies the last position, namely the fourth rank, with a score of 0.070.
Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city.
Decoding Performance Testing Results: Empowering Trust with Explainable Artificial Intelligence (XAI)

The paper advocates utilizing Explainable Artificial Intelligence (XAI) to enhance the trustworthiness of both black-box and interpretable models in the context of performance testing. The proposed methodology involves employing the Shapley Additive exPlanation (SHAP) algorithm as a surrogate model to aid performance analysts in comprehending the decision-making process of black-box machine learning models. By incorporating SHAP around black-box models, analysts can gain insights into the factors influencing the models' pass-or-fail predictions and understand the relative importance of performance data. To validate the effectiveness of the approach, extensive load testing experiments were conducted on a real-world testbed, incorporating industry-standard benchmarks and manual injection of performance bugs. The results demonstrate that the proposed approach significantly improves the trustworthiness of machine learning models by offering explanatory capabilities for their decision-making. Furthermore, the approach can be applied across various domains and requires minimal effort to operate, thus showcasing its generalizability and practicality.
UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection

Fraud detection is used in various industries, including banking institutes, finance, insurance, government agencies, etc. Recent increases in the number of fraud attempts make fraud detection crucial for safeguarding financial information that is confidential or personal. Many types of fraud problems exist, including card-not-present fraud, fake Marchant, counterfeit checks, stolen credit cards, and others. An ensemble feature selection technique based on Recursive feature elimination (RFE), Information gain (IG), and Chi-Squared (X2) in concurrence with the Random Forest algorithm, was proposed to give research findings and results on fraud detection and prevention. The objective was to choose the essential features for training the model. The Receiver Operating Characteristic (ROC) Score, Accuracy, F1 Score, and Precision are used to evaluate the model's performance. The findings show that the model can differentiate between fraudulent transactions and those that are not, with an ROC Score of 95.83% and an Accuracy of 99.6%. The F1 Score of 99.6%% and precision of 100% further sustain the model's ability to detect fraudulent transactions with the least false positives correctly. The ensemble feature selection technique reduced training time and did not compromise the model's performance, making it a valuable tool for businesses in preventing fraudulent transactions.
Lost in “Transl-Hation”: Exploring the Impact of Machine Translation as an Intermediary Tool in Detecting Armenian Hate Speech

As the pervasive spread of hate speech continues to pose significant challenges to online communities, detecting, and countering hateful content on social media has become a priority. Social media platforms typically use machine translation to identify the hateful content of the posts made in languages other than English. If this approach works effectively in identifying explicit hateful content in languages that are predominantly used on social media, its effect is almost insignificant when it comes to Armenian. The present research investigates the effectiveness of machine translation as an intermediary tool in accurately identifying and addressing instances of Armenian hate speech posts retrieved from social networking websites. The study of hate speech posts and comments made by Armenian users in Armenian helps identify that it is often the absence of intricate cultural and linguistic nuances, as well as insufficient contextualized understanding, that impede with hate speech detection in Armenian.
Cultural Itineraries Generated by Smart Data on the Web

The development of storage standards for databases of different nature and origin makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential of the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has a artistic and cultural heritage unique in the world and the italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts.
PELATIHAN DAN PENDAMPINGAN PENERAPAN METODE FONIK MELALUI LAGU DALAM PEMBELAJARAN BAHASA INGGRIS

The project is a community service designed to increase awareness of engaging phonics teaching strategies for young learners. One of the most crucial aspects of English is how words are pronounced. The use of the phonics approach can help kids speak English words more easily. Pagelaran, Ciomas, Bogor Regency was the place for community service in collaboration with the English Kids Club (EKC). The implementation methods used were training and mentoring. Two sessions were performed to complete this project, which was successfully carried out as indicated by the teachers' good feedback and active participation in the activities.
​​EXPLORING THE APPLICATION EFFECT OF SCAFFOLDING INSTRUCTION IN KOREAN LISTENING TEACHING IN UNIVERSITY IN SOUTHWEST CHINA​

Korean listening teaching is one of the most important parts of Korean teaching in universities in China. However, some universities still adopt the old listening teaching mode, and there are many problems in listening teaching, and the teaching effect is not satisfactory. Therefore, Korean listening teachers constantly introduce new and effective teaching methods to solve the problems in listening teaching. This study aims to explore the application effect of scaffolding instruction in Korean listening teaching. This qualitative case study interviewed four Korean listening teachers from Chengdu Institute Sichuan International Studies University, and described their thoughts and experiences. The findings of this study showed that scaffolding instruction can solve some problems in Korean listening teaching. According to the teachers, the application of scaffolding instruction can make the abstract and boring learning content lively and interesting, strengthen the teaching effect of listening, give full play to students' learning enthusiasm and initiative, and improve students' listening ability. Significant constraints were found, including inappropriate teaching materials, limited teaching time, insufficient human resources, teaching skills and training. In addition, the most disturbing aspect is the attitude of students and the lack of support from university, such as the necessity of readjusting curriculum and policies. e.
COMPARISON OF B-SPLINE AND TRUNCATED SPLINE REGRESSION MODELS FOR TEMPERATURE FORECAST

The spline regression model is a nonparametric model and it is applied to data that do not have a certain curve shape and do not have information about it. In this study, the results of the analysis of the B-Spline regression model and the Spline Truncated model were compared on temperature data at several stations on Java Island to obtain the best model that can be used to forecast the temperature for the next few days. Daily temperature data were obtained from BMKG at Semarang, Juanda, Serang, Sleman, Bandung, and Kemayoran stations. The temperature data were modeled with the B-Spline and Spline Truncated regression using the optimal knot point of the GCV, and the best model was obtained. The analysis shows that the B-Spline regression models are better than the truncated Spline models with a fairly small MSE value and a greater coefficient of determination than the truncated Spline model.
METHOD COMPARISON IN THE DECISION SUPPORT SYSTEM OF A SCHOLARSHIP SELECTION

Commonly, the current scholarship selection process has different targets and various criteria for its prospective scholarship recipients. This causes the decision-making process for scholarship selection to be complex, whereas in the general scholarship selection is time-limited. The solution that can be done is to use a DSS (Decision Support System) to improve consistency and speed up decision-making. The available methods for making a DSS used in this study are the Analytical Hierarchy Process, TOPSIS, and the second model using a deep learning approach. The performance of the DSS will then be evaluated using a Confusion Matrix to determine the cost level of each DSS and analyze the strengths and weaknesses of each DSS. The DSS model with the AHP-TOPSIS approach has been successfully created, with the accuracy performance for introducing data on merit, bidikmisi, and independent scholarship schemes are 56.72%, 65.21%, and 95.87%, respectively. While the DSS model with a deep learning approach has been successfully created with accuracy performance of 71.93%, 100%, and 100%, respectively. There are considerable differences between these two approaches. This may be due to the weighting process in the AHP approach which cannot be carried out with precision.
Implicit Profiling Estimation for Semiparametric Models with Bundled Parameters

Solving semiparametric models can be computationally challenging because the dimension of parameter space may grow large with increasing sample size. Classical Newton's method becomes quite slow and unstable with intensive calculation of the large Hessian matrix and its inverse. Iterative methods separately update parameters for finite dimensional component and infinite dimensional component have been developed to speed up single iteration, but they often take more steps until convergence or even sometimes sacrifice estimation precision due to sub-optimal update direction. We propose a computationally efficient implicit profiling algorithm that achieves simultaneously the fast iteration step in iterative methods and the optimal update direction in the Newton's method by profiling out the infinite dimensional component as the function of the finite dimensional component. We devise a first order approximation when the profiling function has no explicit analytical form. We show that our implicit profiling method always solve any local quadratic programming problem in two steps. In two numerical experiments under semiparametric transformation models and GARCH-M models, we demonstrated the computational efficiency and statistical precision of our implicit profiling method.
About the methods of the students competences analysis in distance and blended learning

The rapid development of distance and blended learning formats poses a serious problem for instructors to objectively and reliably assess students’ knowledge. Modern learning management systems often do not provide comprehensive information about the quality of learning in a convenient form for the teacher. A brief review of the leading distance learning systems used in universities of the Russian Federation allows us to make sure of this. The capabilities of popular learning management systems, their advantages and disadvantages in processing information about students’ progress are analyzed. According to the results of the analysis, the necessity of further custom data processing becomes obvious. The methodology of evaluating the quality of students’ knowledge is shown in the example of an own development — the Abins.NET learning management system created at Perm National Research Polytechnic University. The necessity of using a minimum of two independent criteria for evaluating the quality of learning by conducting a correlation analysis between the grade, work time, and bonus points is confirmed. The comparison of bonus points accrual methods in own-developed learning management system, iSpring, and WebTutor was carried out and the conclusion was made about the necessity of automated bonus management (accrual, write-off) according to the criteria that are predetermined by the developer. The conclusions and suggestions on the selection of significant criteria and their calculation when working with any learning management system to assess the success of university students’ learning are formulated.
Empowered Internet of Things for Sustainable Development Using Artificial Intelligence

Everyone has been utilizing the Internet of Things in current years (IoT). Therefore, as the IoT has grown enormously, so too have concerns about Artificial Intelligence. Artificial intelligence (AI) is at the forefront of ubiquitous computing and is utilized to create intricate algorithms to safeguard networks and systems, including IoT devices. But to carry out cyber security assaults, hackers have learned how to make use of AI, and they have even started to deploy antagonistic AI. The goal of this research work is to comprehensively present and summarize the pertinent literature in these fields. It does this by compiling data from a few other surveys and research papers regarding sustainable development using AI, IoT, and attacks with and against AI. It also shows the relationship between IoT and AI.
Effectiveness of Dry Lab Based Augmented Reality to Overcome the Misconceptions of Students on Solar System and Eclipse Learning Topics

In science learning there are many important concepts that must be understood. The solar system is a complex and abstract material because it cannot be seen directly by human senses resulting in students experiencing misconceptions. Interactive learning media is needed to help students understand concepts. One of the learning media that can be applied is augmented reality based on dry lab simulation. This study aims to determine the effectiveness of using Dry Lab simulation to overcome misconceptions of earth and solar system material in junior high school. This research uses experimental research methods with Pre-experimental research models. The overall research results, the average effectiveness was 45.16%, which showed less effective results. Partially for the two sub-concepts of planets and solar and lunar eclipses, the average effectiveness is 65.5%, which shows that the dry lab simulation is effective in changing students' misconceptions
Quantum implementation of the guided image filtering algorithm

Abstract Image filtering involves the application of window operations that perform valuable functions, such as noise removal, image enhancement, high dynamic range (HDR) compression, and so on. Guided image filtering is a new type of explicit image filter with multiple advantages. Here, we report a quantum implementation of guided image filtering algorithm, based on the novel enhanced quantum representation (NEQR) model, and the corresponding quantum circuit has been designed. We find the quality of filtering is consistent with that of the classical one and the time complexity is reduced exponentially from $O(2^{2q})$ to $O(q^2)$ due to the quantization.
The University “Boiling Point” as a Tool for the Development of Education, Science and Technology, as Well as Support for Social Projects of Non-Profit Organizations

Support of scientific, educational and technological initiatives of youth associations and non-profit organiza-tions is an important aspect in the development of Russian science and education. The participation of higher education organizations in the implementation of such initiatives makes it possible to ensure partnership be-tween scientists, representatives of public associations, as well as students and teachers. A system of support for social initiatives of the non-profit sector has been created and is actively developing in Russia. This study will consider the additional potential for the development of the support system for non-profit organizations at the expense of state and non-state organizations, including through the activities of the university “Boiling Point”, which actually performs the functionality of the resource center for NGO support. The team of authors in this study conducted a comprehensive analysis of the support of social initiatives of non-profit organizations and youth associations in the “Boiling Point” of the State University of Management (SUM), which took first place in Russia in terms of the number of events and visitors.
DYMatch: Semi-Supervised Learning with Dynamic Pseudo Labeling and Feature Consistency

A considerable number of approaches based on consistency regularization and pseudo-labeling have been proposed in Semi-supervised Learning (SSL) so far. These approaches significantly enhance the SSL methods by effectively utilizing large amount of unlabeled data to improve the model’s performance. However, existing methods may fail to utilize the unlabeled data more efficiently, mainly due to the challenges faced in pseudo-label estimation. In this paper, we begin by analyzing the impact of the pseudo-label estimation method on training the SSL model. Furthermore, we emphasize that an effective pseudo-label estimation method should reflect the difference of recognition performance among samples from different categories, and also ensure the maintenance of both high-quantity and high-quality pseudo-labels during each training iteration. Based on above analysis, we propose DYMatch, an innovative SSL method employing a dynamic estimation process. Firstly, a dynamic pseudo-label estimation method based on Gaussian mixture model is proposed to dynamically estimate the confidence threshold on different categories. Secondly, a feature-correlation consistency regularization method is introduced to further enhance the learning on unlabeled data. The experimental results show that DYMatch is a simple and effective SSL method, especially when the labeled data is limited.
Rice Husk Silica Research in Indonesia: A Blibiometric Analysis Based on Scopus

Abstract To gain deeper and more understanding the research on rice husk SiO 2 in Indonesia, in this study, a bibliometric software VOSViewer was used to analysis relevant literature in the field. Scientific documents were retrieved from Scopus database between 1 January 2000 to 25 April 2023. Bibliometric indicators, including growth of publications, most prolific authors and institutions, most productive document sources, most cited papers, and cluster analysis were used and were presented using general statistic. Analyse results indicate that the number of documents on rice husk SiO 2 in Indonesia have increased significantly, especially during the last seven years. Professor Wasinton and Lampung University were ranked first as the most prolific author and institution, respectively. Many documents have been published the IOP Conference Series Earth and Environmental Science and a document published in s. In addition, network analysis can be used to identify potential research collaboration among institute to address gaps and challenges, and to avoid unnecessary research overlap.
Leveraging Attention Mechanism to Enhance Culprit Identification in Real-Time Video Surveillance Using Deep Learning

This research presents an efficient approach to improve real-time video surveillance by integrating Attention Mechanisms (AM) within the deep learning model. The study aims to enhance the identification of culprits in surveillance footage. The attention mechanisms enable the model to focus on relevant regions of interest within the video frames, reducing computational overhead and increasing accuracy when compared with the existing works. The proposed work achieves 92% accuracy with an improvement of 0.07, 91% precision with an improvement of 0.03, 94% recall with an improvement of 0.12, and, 92% F1-Score with an improvement of 0.07, and contributes to the field of security and surveillance by providing an efficient and effective method for identifying suspicious activities or individuals, thereby increasing the capabilities of real-time video surveillance systems for enhanced security and safety applications.