Reviewerly/exHarmony huggingface dataset:
Visual Simulator for Mastering Fundamental Concepts of Machine Learning

Machine learning (ML) has become an increasingly popular choice of scientific research for many students due to its application in various fields. However, students often have difficulty starting with machine learning concepts due to too much focus on programming. Therefore, they are deprived of a more profound knowledge of machine learning concepts. The purpose of this research study was the analysis of introductory courses in machine learning at some of the best-ranked universities in the world and existing software tools used in those courses and designed to assist in learning machine learning concepts. Most university courses are based on the Python programming language and tools realized in this language. Other tools with less focus on programming are quite difficult to master. The research further led to the proposal of a new practical tool that users can use to learn without needing to know any programming language or programming skills. The simulator includes three methods: linear regression, decision trees, and k-nearest neighbors. In the research, several case studies are presented with applications of all realized ML methods based on real problems.
“The Great College Escape”-the most difficult choice offered to COVID-19 generation of global students.

Abstract The much hyped “Great Resignation” in the world of economic forecasting has opened a big door for the students to take an easy and timely exit from education. Is it a revolving door or a one-way exit with no easy return. There are more job openings for unskilled workers, luring students to leave their studies incomplete and take over odd jobs. Remote education experience changed hearts and minds of the global students and created a survival mindset. Pandemic without question has caused significant loss of lives with profound disruption to lives and livelihoods. As the pandemic is lingering on with uncertainty and ambiguity, students are tempted to leave studies in search of more money, more flexibility, and more happiness. With the fallout of COVID-19 yet to be fully realized and fixed, the predictive-enrollment models that traditionally used to plan classes won’t hold up in the post-pandemic world. We surveyed the current student population across different continents, Asia, Europe, Russia, US and tried to figure out the mindset of student enrollment and attrition. The perception and trends data from 300 respondents across the globe showed a significant economic impact on international students failing to register for higher education abroad in western hemisphere. Majority of students surveyed believe that the education system may have oversold the value of remote education and classroom technology. Free and open-source technologies available to both teachers and students have created a monster of virtual global education. For students in Eastern part of the world, the pandemic has offered new perks. Most students reported that they are rethinking their education needs and reimagining how knowledge and learning could best shape their careers.
Penentuan Jurusan Kuliah Menggunakan Metode Forward Chaining Berbasis Web

Determining majors is one of the obligations for all students who still want to continue their education. Decisions taken often cause problems when differences in abilities and limitations in thinking result in not graduating on time. Also, we often see majors chosen only because they follow the advice of friends or family and because of the development of the need for human resources in the world of work. Determination of majors should be done by considering the capabilities possessed. Therefore, this application was created which aims to design an expert system that can provide an overview for prospective students about what majors are suitable or in accordance with their abilities. The method used in making this expert system is Forward Chaining, where the facts related to determining the direction are stated first, then draw an appropriate conclusion. By making this application, it is expected to provide convenience for prospective students both in terms of time and cost.
On Resilient Boolean and Vectorial Boolean Functions with High Nonlinearity

Boolean functions and vectorial Boolean functions are the most important nonlinear components of stream ciphers. They should satisfy several criteria such as high nonlinearity, proper resiliency and so on to guarantee the security of the whole system. However, there are some constraints among the criteria, and how to achieve a trade-off between them is an important issue. In this paper, some nonlinear Boolean functions possessing simple algebraic normal form with special Walsh spectrum are proposed. By using these functions, we provide two construction methods on balanced and resilient Boolean functions with high nonlinearity. In addition, based on the disjoint linear codes and vector matrices with special properties, some resilient vectorial Boolean functions with currently best-known nonlinearity have also been given.
Deep Evolutionary Algorithm for Large-Scale Sequence Optimization

Aiming at the problem of slow convergence and poor effect of GA in solving large-scale sequence optimization problems, a deep evolutionary algorithm is proposed in this work.The algorithm uses the trained network to quickly find the initial solution of the problem and injects the initial solution into the GA population for further optimization.Finally, the optimal solution in the final population will be further optimized by the 2-OPT.The proposed algorithm is compared with other algorithms on the tsplib95 instances and industrial sorting sequence optimization dataset.Experimental results show that the proposed algorithm achieves the best optimization performance compared with other algorithms, especially for large-scale sequence optimization problems.
GNSS-R Soil Moisture Retrieval Model Based on Elman Neural Network

Global Navigation Satellite System-Reflectometry (GNSS-R) in soil moisture retrieval, the traditional method mainly includes linear regression and exponential regression methods.To address the defects of conventional methods such as poor prediction accuracy and sizeable computational effort, the Elman neural network with dynamic learning features is introduced.An Elman neural network-based soil moisture retrieval method is proposed to establish a multi-parameter retrieval model.Finally, the model is trained to validate the feasibility of this model.The results indicate that the soil moisture values estimated by the GNSS-R soil moisture retrieval method based on the Elman neural network have minor errors with the actual measured soil moisture values.Based on this model, the coefficient of determination (R 2 ) is 0.8988, and the Root Mean Square Error (RMSE) of soil moisture is 0.0207.When compared to the traditional linear regression model, the soil moisture values predicted by this method are more accurate and closer to the measured soil moisture values, demonstrating the method's validity and reliability.
Effective Example Extrapolation for Long-Tail Relation Extraction

Relationship extraction (RE) aims to identify the relationship between two entities in a sentence and is an important step to complete the knowledge graph (KG).In the medical field, the distribution of data is often unbalanced, for example, it is indisputable that the incidence of common diseases is higher than that of rare diseases, and both the size of departments and the number of cases result in an unbalanced distribution of electronic medical record data.Relationship extraction is more challenging when the relationship categories are distributed in long tails.Data augmentation is a common approach used to address category imbalance.We propose an effective example extrapolation (3E) data augmentation method: 3E generates new synthetic examples by simulating the example generation process of data-rich head relationships and extrapolating to an insufficient number of tail relationships categories.Experiments were con-ducted on the publicly available medical relationship extraction dataset 2010 i2b2/VA and compared with the upsampling meth-od to further validate its advantages in handling long-tail relationships.
Transformer Recommendation Algorithm with Fusion Knowledge Graph

To address the problem that traditional knowledge graph recommendation algorithms only consider the message aggregation of neighbor nodes but not the sequence information of knowledge graph neighbor nodes, this paper proposes a Transformer recommendation algorithm that fuses knowledge graphs.Under the item knowledge graph space, the sequence information is constructed from the target item and its neighbors, and the sequence information is encoded using the Transformer model to capture the features of the item.Finally, MLP is used to integrate the obtained item feature vectors and user embedding vectors and send them to the prediction module to obtain user click-through rate prediction by vector inner product operation.Comparast experiments were performed on three publicly available datasets.On the MovieLens-20M dataset, compared with the optimal baseline KGCN, the AUC and F1 improve by 1.74% and 7.18%, respectively.On the Last.FM dataset, the AUC and F1 improve by 7.07% and 5.92%, respectively.On the Book-Crossing dataset, the AUC and F1 improve by 1.52% and 1.62%, respectively, compared to the optimal baseline.Thus, it is verified that the effectiveness of the algorithm is improved by considering the sequence information of the neighboring nodes of the knowledge graph.
Predicting Factors Influencing Acceptance on Online Learning Platforms during the COVID-19 Pandemic

The sudden shift from the traditional face-to-face classes to fully online learning due to the COVID-19 pandemic stunned the education sector worldwide.The abrupt adjustment led to quick decision on what online learning platform to utilize during the COVID-19 pandemic.The aim of this study was to predict factors influencing students actual use and acceptance of online learning platforms during the COVID-19 pandemic.Through convenience sampling, a total of 712 respondents answered the selfadministered online survey voluntarily.Utilizing random forest classifier with Python 3.8, the results revealed that system and information quality, ease of use, and perceived ease of use of online learning platforms led to high and very high actual use.The results of this study indicated that as long as there is comfort, ease of use, design, user friendly, and accessibility among the online learning platforms, there would be high to very high actual usage, leading to acceptance among students.This study presented a 92% accuracy with 0.00 standard deviation, thus indicating the reliability of the outcome.Lastly, the findings could be applied by other educational sectors worldwide.
A Framework for Domain-specific Distant Supervised Named Entity Recognition

With the development of knowledge graphs in the industrial field, Constructing KGs from domain-specific problems is greatly important.However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph.Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect.Specifically, named entity recognition in domain-specific is a critical task for the natural language process applications.However, a bottleneck problem with named entity recognition in domain-specific is the lack of annotated data.To address this challenge, A domain-specific distant supervised named entity recognition framework is proposed.The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities.The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method.The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks.Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.
Determining the Nature of Index Crimes in Bacoor, Cavite, Philippines

The rapid commercial developments, the number of access roads, and the growing population in Bacoor, Cavite is making the city a highly probable prime location for crime.To ensure effective crime deterrence and reduction even with limited manpower, frequency analysis was used to analyze the nature of index crimes and derive insights on how to approach crime reduction and deterrence in Bacoor city.The results revealed that theft and robbery are the top index crimes, which occur mostly during rush hour times in highly urbanized barangays, especially those with malls.The spikes and dips of index crime occurrences in respect to time, day, and months show that index crime suspects in Bacoor follow a logical fashion, making it easy for authorities to come up with effective interventions on a holistic and tactical level.
Social Medias, Zoom and Microsoft Teams: the New Technologies in French Language Classrooms in the Jordanian Context during the Covid-19

The Covid-19 has had a significant impact on teaching/learning processes around the world. As a result, education practitioners in Jordan and elsewhere have had to adapt quickly and switch to distance education: social media, including Facebook, Zoom, and Microsoft Teams. Social networks play an indispensable role in teaching/learning French as a Foreign Language, while providing a wide range of authentic materials. They make it easy for students to interact and provide the opportunity to discover online tools to develop their skills. Internet and all other forms of computer-mediated communication help learners to accelerate their acquisition, while being up-to-date and independent. However, as a result of our feedback from the field, it turned out that Zoom and Microsoft Teams play an important role in all kinds of interactions between teacher and students and between students themselves. These two applications allow a live interaction; as the classroom is virtual, participants will have the ability to exchange ideas in a synchronous manner. It is possible to create groups for each given course. In this article, we will try to highlight the positive aspects of using social networks, especially Facebook, to improve the skills of our learners in the French language department at the University of Petra. Then, the use of Zoom will be analyzed. Finally, and based on a theoretical and practical analysis of a group created in Microsoft Teams, the latter will be studied in order to show the positive aspects of any insertion of such an application in the Jordanian context.
Continuous Integration for a development process of the information technology of remote monitoring and control

The current state of using CI/CD in commercial and open-source projects is analyzed. CI/CD pipelines are shown to be essential to the modern software development process, where they are used to configure workflows. Centralized and distributed approaches to building a software source code version control system are analyzed. For the web-oriented SCADA project, the choice and use of the version control system are justified. We provide concrete examples of using CI/CD that will allow ordinary developers, not DevOps specialists, to take the first steps to configure workflows for their projects and provide basic steps for configuring task execution using the example of the build and test phase of the source code of a real project that uses several programming languages. Figs: 4. Refs. 20 titles.&#x0D; Keywords: CI, CD, VCS, git, workflow, actions, building, testing, Agile.
Agricultural Information and Communication Center in Disseminating Agricultural Information in Bangladesh: Effects, Problems and Suggestions

The government of Bangladesh established Agricultural Information and Communication Centre (AICC) to communicate with mass farmers quickly. This paper aimed at analyzing the direct and indirect effects of farmers selected characteristics on the effectiveness of AICC in disseminating agricultural information and identifying problem faced by the farmers in getting service from AICC and probable suggestions to overcome identified problems. Data were collected from randomly selected 90 farmers of Faridpur district, Bangladesh using a pre-tested interview schedule. Path analysis was applied to assess the direct and indirect effects of farmers’ selected characteristics on the effectiveness of AICC. On the other hand, Problem Faced Index (PFI) and Frequency of response was used to identify problem and suggestions. The analysis showed that farmers’ use of ICT had the highest direct effect (0.345) in the positive direction followed by ICT using experience (0.245) and ICT using confidence (0.237) on the effectiveness of AICC. The other variables have direct and indirect effects were ICT using confidence and ICT using experience. Limited availability of ICT tools and technology in AICC were most severe problem followed by technical problems of different ICT based media in AICC and low bandwidth speed of internet. The study provided suggestions to increase the efficiency of the AICC in communicating agricultural information.
PENGGUNAAN MEDIA BIG BOOK DALAM MENINGKATKAN KEMAMPUAN MEMBACA SISWA SEKOLAH DASAR

Low interest and ability to read is a serious problem in the field of education continue to work on how to overcome it. One of the media that can be used to improve students' reading skills is Big Book media which can be created according to the teacher's creativity. This research aims to provide information to teachers, principals, and parents regarding the use of Big Book media to improve elementary school students' reading skills. The method used in this research is literature study or library research. The researcher traces the data, then reads and records the results needed to obtain the information. The data collected was analyzed using the content analysis method. The results of the analysis of 15 articles concluded that the use of Big Book media improved students' reading skills. This is based on an increase in reading scores from before using Big Book media to after using Big Book media in learning and the SPSS test was carried out to see the effect.
Leading to Diversity Management: Case of Community School Head-Teacher's Style in Nepal

This study aimed to explore the leadership style of head teacher for the effective diversity management and investigate underlying assumption for respecting difference when act in diversity management the theoretical foundation for this underlying study was constructivist/interpretive the research method adopted for this study was qualitative along with hermeneutics phenomenology. Purposively, four head teachers from rural community schools of Dhankuta District were selected as participant. The participant head teacher were leading schools containing more than four diversities such as age, caste, gender, ethnicity. Profound interview was taken via interview guidelines from the participants based upon the experience in leadership style. Data analysis revealed with the themes of collaboration from the in depth interview and observing an activity of head teacher and services. From the interview protocol the responses had been transcribed and themes were organized to obtain main theme. The major results had been deduced from the main theme. Respect to culture, treating with caste and gender, minorities' advocacy and disability management were the prominent leadership strategies for the succession of school leadership. These strategies are more supportive to diversity management and valuable to conceive as appropriate styles for head teacher to his successful tenure.
Study of MTCNN-based driver gaze information visualization

About 80% of traffic accidents are related to the distraction of the driver's gaze.If the driver does not look ahead on the road, the likelihood of a traffic accident increases significantly.A technology that can be visualized by estimating the driver's gaze position while driving using two cameras and displaying it on the front screen was studied.Since the forward-looking screen is divided into 32 areas, the driver's gaze can be presented by dividing it inside and outside the vehicle.Only 1.4 of the set of correct answer images of the subjects were self-constructed and learned.As a result of the test, the average absolute ratio error was 3.58%, indicating a high success rate, and the time until visualization was 0.65ms, which is a level that can be applied in real-time.If the researched technology is applied to road maintenance and patrol vehicles, detailed information on dangerous roads that cause the driver's gaze to be dispersed while driving on the road can be collected.It will be judged to have a great effect in preventing traffic accidents by detecting and removing unnecessary eye-causing facilities such as advertisement facilities and damaged
Pengaruh Penggunaan Strategi Pembelajaran Aktif Tipe Giving Question and Getting Answer Terhadap Hasil Belajar Matematika Siswa Sekolah Dasar

This study aims at determining the effect of the use of an active learning strategy type of giving questions and getting answers on the mathematics learning outcomes of elementary students. This research is under Non-equivalent Control Group Design of Quasi Experimental Design where purposive sampling is used as sampling technique. The population of this study consists of 44 students with observation, tests, and documentation used as the data collection technique. The research instrument used are test of learning outcomes and observation sheets. The normality test was done by calculating the data in the form of pre-test and post-test mathematics learning outcomes using the Kolmogorov Smirnov formula with a sig level of 5% (0.05), after the data was normally distributed, the homogeneity test obtained from the post-test calculations, then hypothesis testing with t-test using the calculation of students’ post-test data. The results of data analysis shows that the value of sig 2 tailed is 0.000, which is less than 0.05, thus, based on the research hypothesis, if the value of sig 2 tailed ≤ 0.05 t-table, then Ho is rejected and Hₐ is accepted. This shows that there is an effect of using an active learning strategy with the type of giving questions and getting answers on the mathematics learning outcomes of elementary students. The results of this research are expected to be used as a reference for further research on different subjects.
Static Detection of Security Vulnerabilities Using Machine Learning on Graph Information

Taint analysis is widely used in detecting security vulnerabilities in source code.However, it is difficult to obtain accurate results in reasonable amount of computing time due to the nature of static analysis.This paper proposes a new way of detecting security vulnerabilities using machine-learning technology utilizing an already established model, graph2vec.The flow graph of a program is transformed into a vector and then given to the model.The data set is prepared by first constructing simplified program and then applying mutation.The evaluation results show that the model detects security vulnerabilities with the accuracy of up to 99%, positively showing the possibility of applying machine-learning technology to the static detection of security vulnerabilities in source code.