Reviewerly/exHarmony huggingface dataset:
LighterKGCN: A Recommender System Model based on Bi-layer Graph Convolutional Networks

&lt;p&gt;Recommender systems have been extensively utilized to meet users&amp;rsquo; personalized needs. Collaborative filtering is one of the most classic algorithms in the recommendation field. However, it has problems such as cold start and data sparsity. In that case, knowledge graphs and graph convolutional networks have been introduced by scholars into recommender systems to solve the above problems. However, the current graph convolutional networks fail to give full play to the advantages of graph convolution since they are employed either in the embedding representations of users and commodity entities, or in the embedding representations between entities of the knowledge graphs. Therefore, LighterKGCN, a recommender system model based on bi-layer graph convolutional networks was proposed in accordance with the KGCN model and the LightGCN model. In the first layer of GCN, the model first learned the embedding representations of users and commodity entities on the user-commodity entity interaction graph. Then, the attained user embedding and commodity embedding were used as the data source for the second layer of GCN. In the second layer, the entity v and its neighborhoods were calculated using the hybrid aggregation function proposed in this paper. The result was taken as the new entity v. According to tests on three public datasets and comparison results with the KGCN, LighterKGCN improved by 0.52% and 51.16% in terms of AUC and F1 performances, respectively on the dataset of MovieLens-20M; LighterKGCN improved by 0.67% and 45.0% in terms of AUC and F1 performances, respectively on the dataset of Yelp2018; and the number was 0.67% and 36.35% in AUC and F1 performances, respectively on the dataset of Last.FM.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Data-Driven Modeling of Randomized Controlled Trial Outcomes

Anecdotally, 38.5% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a data-driven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies.
The Practice of Ethical Leadership among Managers of News Websites from Journalists’ Point of View

This study aimed to identify the degree to which Jordanian news websites managers practice ethical leadership from the point of view of journalists working for these sites. The study followed the descriptive-analytical approach. The sample of the study consisted of 317 journalists. To achieve the objective of the study, a 26-item questionnaire distributed to four domains: personal characteristics, administrative characteristics, human relations, and communication with others was applied. The results showed that the degree of ethical leadership practice by the managers of Jordanian electronic news sites from the viewpoint of those working for these news websites was medium. The results also showed no significant differences between the means of ethical leadership among Jordanian news website managers from the journalists’ point of view due to gender. Significant differences were shown in favor of journalists with an experience of 10 years or more. In light of the results, the study recommended training courses for Jordanian news website managers to enhance and practice ethical leadership more effectively.
Performance and energy consumption tradeoff in server consolidation

Server consolidation is one of the techniques used to increase energy efficiency in datacentres. Nevertheless, the server consolidation has an inherent trade-off between performance degradation and energy consumption which has to be quantified to be managed. In this paper, the CiS2 index is proposed to quantify the mentioned trade-off. We validated de use of the CiS2 index through real experimentation. Also, these observations lead us to propose the second contribution, which focuses on the consolidation overhead. We proposed a general method to quantify this overhead and be able to manage its effect on performance degradation. To sum up, this paper improved the management of energy efficiency in datacentres’ servers through the CiS2 index and the server consolidation determination method.
Analyzing The Encountered Problems and Possible Solutions of Converting Relational Databases to Graph Databases

Relational database management systems have been used for storing data for a long time.&#x0D; However, these systems are insufficient to analyze the large and complex structure of the data. Graph&#x0D; databases are becoming more common day by day due to their capacity to contribute to the analysis.&#x0D; Also, graph databases are better at modeling and querying complex relationships than relational&#x0D; databases. To use graph databases with old data stored in relational databases a transfer process is&#x0D; needed. In this study, the problems to be encountered in transferring the data stored in a relational&#x0D; database to a graph database were examined and methods that could be used as solutions to them&#x0D; were proposed. In addition, it is aimed to prevent data loss and data inconsistency that may occur with&#x0D; design errors in relational databases. For this purpose, the normalization process needs to be applied&#x0D; to a relational database before transferring data to a graph database. In our study, we developed a&#x0D; method that converts data to the first normal form during the transfer. But for better data consistency in&#x0D; practice third normal form is the minimum requirement. By using the functional dependencies found,&#x0D; it is possible to make relational databases suitable for higher normal forms. For functional dependency&#x0D; detection, which is normally a very time-consuming and costly process, we developed a method based&#x0D; on a graph database.
Time series forecasting using GMDH neural networks for Chikungunya in Mysore district, India

Infectious diseases are the diseases which are caused by microorganisms.It plays a vital role in day to day life.Infectious diseases prediction is the growing field in the present scenario.Predicting and forecasting the infectious diseases helps as early warning to prepare for medical emergencies.Among Infectious diseases, chikungunya is an infection spread by mosquitos that carry the dengue and Zika viruses.Infectious illness transmission spread not only depends on vector and host but also depends on environmental/meteorological factors which are a complex process that requires advanced computer approaches such as soft computing and artificial intelligence to estimate and anticipate the complex phenomenon to predict and forecast infectious diseases.In order to predict and forecast the infectious diseases, GMDH techniques are used.The data is collected from Mysore district.We have considered confirmed cases of chikungunya from 2006-2019 along with 19 meteorological variables.A lag period of 0-7 was considered.GMDH models showed that for chikungunya cases, the RMSE value was low for 5/7 with less parameter.Here we have considered the Minimum temperature, mean relative humidity and average sunshine as the significant predictors in predicting and forecasting the chikungunya incidences.
Exploration of polygons in a STEAM framework: technology and cultural background

Polygons are an important topic in geometry. We propose and analyse activities for the exploration of their properties, with extensive usage of a dynamical geometry system. They are based on the analysis of monuments, connecting students’ cultural backgrounds with their mathematical learning. Moreover, two opposite directions for work are presented: an analytic way beginning from the monument and exploring its geometric features, and a more synthetic way, using the output of the analysis to build a model with the software. We show how these STEAM activities, based on existing monuments of various shapes, enhance the 4 C’s of twenty-first century skills.
Research on Multi Service Collaboration Mode of University Smart Library Based on Blockchain

The application of blockchain technology in this scenario can effectively improve the collaborative efficiency of multiple service agents participating in smart services. Based on the technology and principle of blockchain, combined with the characteristics of university library services, this paper constructs five different scene structure service blockchain, and integrates the five structures into the multi service collaborative blockchain network of University Smart Library.
A Grover based Quantum Algorithm for Finding Pure Nash Equilibria in Graphical Games

The Grover algorithm and its underlying amplitude amplification process are one of the most essential and widely used building blocks of many other subsequent quantum methods. In this work, we adapt Grover’s search algorithm to find pure Nash equilibria in graphical games. In general, the main complexity lies in the construction of the algorithm’s oracle operator. We show how to set up the oracle from a given graphical game by translating it to a Boolean satisfiability problem, which can subsequently be logically synthesized to an oracle quantum circuit. We investigate the algorithm for random graphical game instances on IBM’s quantum simulator and give an outlook regarding the scaling of it.
Sistem Pakar Diagnosa Penyakit ISPA dengan Metode Forward Chaining

The development of computer technology today continues to experience many changes every year, which are often developed by artificial intelligence, such as expert system technology. An expert system is a computer-based application that can match or imitate the ability of an expert used to solve problems that cannot be solved by ordinary people. His knowledge is taken from books, experience and knowledge. Children often experience ISPA disease caused by viruses and bacteria because children's immune systems are still susceptible to being different from adults. This disease usually begins with a hot body temperature accompanied by symptoms such as sore throat or painful swallowing, runny nose, dry cough and others. So that many parents do not know the symptoms of ISPA disease, as for some ways to prevent ISPA disease are diligently washing hands, increasing consumption of foods containing vitamins, exercise. To make it easier for parents to detect ISPA disease, the authors made this study using the forward chaining method, using this method the resulting system is a system that provides a choice of several symptoms then based on the selected symptoms conclusions will be drawn. This ARI disease expert system uses blackbox testing because blackbox testing is a software testing technique that focuses on the functional specifications of the software or system. Blackbox testing is done on each submenu view, input, edit, print, delete data. So that it will produce an expert system to diagnose ISPA in children online
Key Frame Extraction Method for Minors’ Participation in Online Short-Form Video from the Perspective of Government Administration

With the progress of the Internet era, the audience scope of online short-form video and live broadcast platform is rapidly expanding. In this situation, the physical and mental health of minors is affected and harmed, and serious social problems have been caused. In view of the random images of minors participating in short online videos, the monitoring department should strengthen supervision and control and establish a good network atmosphere. The focus of surveillance is to identify short-form videos in which minors participate, monitor the content of short-form videos, and effectively identify minors. On the basis of analyzing and studying the existing methods of moving object extraction, the method of spatio-temporal information combined with geometric curve evolution is used to improve the effect of moving object extraction. Firstly, the time dynamic information of video frames is fully utilized and the initial contour of the target is segmented by high-order statistical algorithm. Based on the improved watershed algorithm, the spatial frames of the original image are segmented and the secondary contour of the target is obtained by combining the spatial and temporal information. The level set evolution algorithm is used to remove the noise and information void in the secondary target, and the final moving target is obtained. Through the extraction of key frames, it can effectively control the appearance of minors in short-form videos, which can actively guide the benign development of the platform, and put forward governance suggestions based on the perspective of government management.
Application of Decision Tree Algorithm Based on Data Mining in English Teaching Evaluation

In order to bring new opportunities and challenges to teaching and learning English. This paper proposes a study of the use of a data tree-based decision tree algorithm in English language learning assessment. Teaching and learning through online English learning platforms using data mining technology to analyze student learning data, create relevant models, and explore the relationship between English exams and various elements is important for student learning and teacher teaching. Firstly, this paper introduces the function, process, and common machine learning models of data mining. Finally, by using data created by college English tutors and measurements to create and identify student images, through image users, you can learn about student behavior, learning behaviors, etc., provide a foundation after the design experiment, technology training technology in logistic regression model, wooden model making, and postfusion modeling of two models and analyze the factors affecting students’ passing the exam according to the prediction results. The results show that the decision tree model predicts that the score of each question type occurs 72 times, the score of students’ examination occurs 70 times, and the completion of homework occurs 70 times. Trees determined by this approach can have a profound impact on a wide range of indicators of academic excellence and provide a fundamental and useful basis for measuring future improvement.
Determining Travel Time and Fastest Route Using Dijkstra Algorithm and Google Map

Dijkstra's algorithm is commonly used to determine the shortest route connecting a point as a starting node to another which acts as the end node. In this study, the UNP student dormitory acted as the starting node, while the library which is frequently visited by students was sampled from the campus as the end node. Due to the fact that students generally live around campus and move on foot, an alternative route is needed to determine the fastest travel time. Therefore, this study aims to determine the route with the fastest travel time from the start to the end of nodes using the Dijkstra algorithm, in comparison with the route displayed by Google Map. Data were obtained from Google Map, which showed the availability of many routes with the possibility of students taking the fastest travel time. The result showed that the fastest route using the Dijkstra algorithm and Google Map were 14 and 3 alternatives at 15 and 21 minutes intervals. Based on these data, it is concluded that the travel time through the fastest route obtained using the Dijkstra algorithm was 6 minutes faster than data found in the Google Map.
Perbandingan K-Nearest Neighbor dan Random Forest dengan Seleksi Fitur Information Gain untuk Klasifikasi Lama Studi Mahasiswa

&lt;p align="justify"&gt;Accreditation is a quality and feasibility assessment form in carrying out higher education. One of the factors that affect accreditation is the length of student study. In this study, the length of student study is classified by using the best attributes resulting from selecting information gain features. In optimizing the classification algorithm, we process the data by converting the original data into data that is ready to be mined. The next step is dividing the data into training and testing data so that the classification algorithm can be applied. This study gives the best four attributes, with &lt;em&gt;K&lt;/em&gt;-nearest neighbor (K-NN) classification of 86.67% and random forest classification of 100%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;length of study; information gain; &lt;em&gt;K&lt;/em&gt;-nearest neighbor; random forest&lt;/p&gt;
Attitudes and Perceptions of Teaching Staff About the Online Learning During the COVID19 Pandemic: A Case Study of Engineering Education

For a long time, the online learning (OL) was reserved for a particular group of learners: mostly older people with multiple family, work, financial and other responsibilities. These students benefited from the opportunities offered by this type of educational modality compared to traditional face-to-face education. However, the emergence of the Sars CoV virus (and its derivatives) not only generated health problems, but also affected social structures, generating various problems in the field of education The general objective of the present research is to know the perceptions and evaluations that academic teaching staff from a engineering faculty have about the emergency online teaching-learning process that occurred in the context of the COVID 19 pandemic. Based on several previous works related to online learning (OL) and recently in the literature on emergency online learning (EOL), an instrument (questionnaire) was developed and implemented with the participation of teachers from a Chilean university. The research is quantitative-descriptive and had the following specific objectives: To know about (1) the readiness of the teaching staff towards the OL and (2) their interactions with others, to characterize (3) the use of different Self-management skills and (4) the interaction with LMS and ICTs., and (5) to know about the available resources for OL. In general, the results show that teaching staff show mastery in aspects related to self-management of learning, but a low level of motivation and readiness for OL and a "loss" in aspects related to interaction with others (students and peers). These results provide a first approximation to university teachers ' perceptions of OL and allow us to identify several aspects that should be improved. This research was financially supported by DAAD (Project Nr. 57334905).
Rebuilding our Toolkits for the future - How social science research educators changed their teaching in 2020 and 2021 to be fit for a digital future

Adaptation to digital teaching during the COVID-19 crisis were not the same for all disciplines. Subject-specific pedagogical concepts were sometimes more or sometimes less easy to transfer to this new environment. In particular, teaching areas like social science research methods (SSRM) that on the one hand require personal interaction, practical application and individual support of students, and on the other hand have little previous standards and didactic know-how, with digital or virtual teaching elements, faced specific challenges. In a [COUNTRY] interview study, experiences of SSRM educators have been analyzed. Digitization related changes are less apparent on the content level, but pronounced at the didactic level. Main challenges are: encouraging students' attention and participation, providing opportunities for good collaboration on all levels. Chances for the future are seen in the newly expanded the pedagogical toolbox. Implementing tools supporting communication in the virtual space could free resources for direct support for students.
Strategies of English Lecturers in Facilitating Interactional Communication of English Students During the New Normal Period of the Covid 19 Pandemic at UIN Fatmawati Sukarno Bengkulu for the 2021-2022 Academic Year

This research aims to describe the strategy of English lecturers in facilitating interactional communication of English students at the English Tarbiyah Faculty during the new normal Covid-19 pandemic at Fatmawati Sukarno Bengkulu State Islamic University in 2021-2022. This qualitative research aims to describe and analyse the ability, form, uniqueness, and variety of strategies produced by English lecturers in the classroom. The results of this study indicate that there are several strategies used by English lecturers, especially in facilitating student interactional communication. The strategies are providing a new learning environment with students, providing two-way communication with students, facilitating communication media between students (student peer feedback), and activating role play models in the classroom and contextual teaching processes. Several obstacles to facilitating student interactional communication at Fatmawati Sukarno Bengkulu State Islamic University include limited teaching time due to social distance, the learning process is only 60% offline and 40% online so that face-to-face learning is not optimal. English lecturers can improve their teaching competence through several online trainings, online seminars, and online workshops. In fact, the results of the research, it can be concluded that the strategy in facilitating student English interactional communication is very important to be applied in the English class to achieve the aims and objectives of teaching English.
Perbandingan Algoritma Stochastic Gradient Descent dan Naïve Bayes Pada Klasifikasi Diabetic Retinopathy

The purpose of this research is to compare the performance of the Stochastic Gradient Descent and Naïve Bayes algorithms in classifying Diabetic Retinopathy. Diabetic retinopathy is a complication of diabetes that causes damage to the retina of the eye. These disturbances can be detected by early detection through data extracted from eye images. This research uses source data from the UCI Machine Learning Repository, namely Diabetic Retinopathy Debrecen, totaling 1,151 data records with 19 attributes consisting of 18 attributes and 1 target attribute. The validation test uses the Cross Validation method with a total of 10 k. From the comparison of the two proposed methods, the Stochastic Gradient Descent algorithm produces an average test accuracy of 70.16%, while Naïve Bayes produces an average accuracy of 56.74%. From the comparison of the two algorithms, the Stochastic Gradient Descent algorithm is known to be superior in classifying the Diabetic Retinopathy Debrecen Dataset.
Modeling of Educational Services Export Considering National Specifics of Importing Countries

To increase the efficiency of the export of educational services, it is necessary to consider the regional specifics and characteristics of a particular country-importer of education. The research aims to determine the stages of modeling the export of educational services due to the national specifics of the importing country. The paper shows (1) the relationship between the labor and educational services market, and the dependence of successful economic development and the education level of the population, (2) analyses the main segments of the international direction in education, (3) compares the quantitative characteristics of educational services provided by some exporting countries and importing countries demands (on the example of Vietnam). The authors develop the main stages of modeling a nationally oriented export of educational services (11 consecutive steps) relying on a nationally oriented teaching approach and helping foreign citizens with adaptation in Russian higher education. Recommendations of the most promising regions and countries of the multipolar world in terms of export of educational services are offered.