Reviewerly/exHarmony huggingface dataset:
Probability-Corrected Overlapping Community Detection Algorithm Based on GCN

Recently, graph convolutional networks(GCN) have shown great potential in graph-related tasks. Existing models combining community affiliation graph models and graph neural networks have achieved good results in overlapping community detection tasks. However, these models ignore the heterogeneity of node connection probabilities, which violates the laws of real networks. To address this issue, we propose an extension of the existing models that demonstrates how to consider the heterogeneity of probabilities, resulting in an improved objective function. We also design a community affiliation graph model that estimates node-pair connection probabilities in overlapping community networks based on the community affiliation matrix. We use the traditional NOCD model to generate the community affiliation matrix to estimate the probability for community detection tasks. Normalized mutual information (NMI) is used as the evaluation metric to assess the accuracy of the algorithm. Through experimental evaluation, the probability-corrected model shows significant improvement in performance on real networks compared to the uncorrected model.
Learning Mandarin Language in Ferdinand de Saussure Descriptive Linguistic Perspective at Senior High School

This study aims to describe the learning of Mandarin from the perspective of Ferdinand de Saussure's descriptive linguistics in students of Madrasah Aliyah Ma'arif NU Lampung. The method used by researchers is a qualitative approach with phenomenological methods. The data in this study is in the form of Chinese Language learning in Madrasah Aliyah Ma'arif NU Lampung students. Data sources were obtained from students, teachers, and language tutors at Madrasah Aliyah Ma'arif NU Lampung. Data collection techniques are carried out by observation, interviews, and documentation. Data analysis in this study used phenomenological analysis. The results of this study show that Chinese language learning has a positive impact on students. The positive impact is in the form of increasing Chinese language skills and their involvement in active and fun learning. However, some of the problems encountered in learning Chinese in Madrasah Aliyah Ma'arif NU Lampung are the introduction to Chinese characters (Hanzi) and how to write them.
Pedagogical Ability of Islamic Religious Education Teachers in Using Learning Media

This study aimed to describe the pedagogical ability of Islamic religious education teachers in using learning media at SMA Swasta Dharmawangsa Medan SMA Islam Swasta An-Nizam Medan. This research is qualitative with a multisite model. The research data is Islamic education teachers' pedagogical ability to use learning media. Data collection techniques are observation, interview, and documentation. At the same time, data analysis is done through data reduction, data presentation, conclusion drawing, and data verification. The results of the study explained that in planning, PAI teachers at Dharmawangsa Private Senior High School Medan An-Nizam Private Islamic High School Medan must consider learning objectives, characteristics of students, and available resources. They must also choose media that are relevant to the subject matter and able to facilitate interactive learning. In addition, implementing the use of learning media also requires careful preparation regarding technology and infrastructure. In media-based learning, learners have the opportunity to learn through experience and enrich their understanding through the use of images, videos, and animations. Media also allows learners to access content independently, enabling them to learn the material at their own pace. However, media use can also present obstacles for PAI teachers at SMA Swasta Dharmawangsa Medan SMA Islam Swasta An-Nizam Medan. For example, the media used must be well-designed not to confuse or distract learners. In addition, some learners may need help accessing the media due to technological limitations or lack of access to the necessary resources. The impact of using learning media on learner engagement can be very beneficial. However, media use also requires careful preparation and patience in overcoming technical and practical obstacles. Therefore, PAI teachers must consider the benefits and challenges of media use before using it in learning at SMA in SMA Swasta Dharmawangsa Medan SMA Islam Swasta An-Nizam Medan.
PEMANFAATAN SISTEM INFORMASI UNTUK PENGELOLAAN DATA KOPERASI SIMPAN PINJAM MENGGUNAKAN VISUAL BASIC

Information System for Savings and Loans Cooperatives Using Visual Basic Case Studies at the Semarang Regency Regional Employment Agency is an information system created to overcome data processing problems which are still in the form of manual bookkeeping and even there is no evidence of deposit transactions, loans and installments and existing storage can be overcome by creating an information system for processing data on savings and loan cooperative transactions and creating a more secure and efficient data storage information system. Savings and loan information system design methods are made using R&amp;D, Flowchat, DFD, ERD, and Normalization methods. The program used to create this application uses the Microsoft Visual Basic 6.0 programming language and MySQL as database management. The results of this study indicate that the savings and loan cooperative information system that is applied in the Savings and Loans Cooperative of the Semarang Regency Regional Civil Service Agency can be used to process member data, process savings data, process loan data, process data that has been paid off, process SHU distribution data, and make reports. finances safely and efficiently.
KLASIFIKASI KEPRIBADIAN MENGGUNAKAN ALGORITMA MACHINE LEARNING

Myers-Briggs Personality Type (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This study uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset, the model tested is several machine learning classification models with the help of imlearn under-over sampling techniques for classifying MBTI personality types. This study aims to classify the Myers-Briggs Type Indicator (MBTI) personality type based on text from user posts on the social media platform Reddit. The dataset used in this study consists of around 8,000 posts collected from the MBTI subreddit. Several text processing methods such as tokenization, punctuation removal, and stemming are used to process the raw data before it is entered into the model. The experimental results show that the LSTM model using Adam's optimizer and a learning rate of 0.01 produces good performance with an accuracy of 80.73 compared to other machine learning models. In addition to the LSTM model, XG Boost is also a classification model with the highest accuracy based on 16 personality types producing an accuracy of 60.09 and Logistic Regression with the NS dimension as the best accuracy value of 87.21%.
SYSTEMATIC LITERATURE REVIEW (SLR): PENYALAHGUNAAN WIFI PUBLIK TERHADAP ORANG AWAM YANG ADA DI INDONESIA

The use of the internet on computer network devices in the current technological era certainly very easy for us in daily life to work and other activities in cyberspace. There are so many places to eat or other public places that use computer network devices, namely wifi so that visitors feel at home or feel happy because they have the facilities that are needed by them, namely the internet. However, it is undeniable that there are public wifi devices that have a negative impact because they are free. One solution to prevent internet network users from using negatively charged public wifi can be done by understanding what steps must be taken before and after accessing public wifi. Therefore, the purpose of this study is to provide understanding and learning to ordinary people in Indonesia to minimize or rather eradicate the use of the internet network on public wifi which has a negative impact in public spaces. In realizing the above, the author uses the method of taking 30 journal papers that have been published through Google Scholar with a range of years between 2018 and 2022 to be researched and analyzed using the SLR (Systematic Literature Review) method. SLR is a systematic method used to review a topic in the form of a journal paper to provide opinions on solving problems. Based on this research, it was found that public wifi in computer network learning can improve the problem-solving ability of ordinary users. Based on the literature review conducted, the level of understanding of ordinary people in Indonesia can be developed by learning computer networks on social media and in articles on the internet.
Pengembangan Sistem Informasi Pencatatan Pembayaran SPP Dan Tabungan Berbasis Website

Advances in technology encourage many agencies to be able to maximize the use of technology. One of the institutions affected is the educational institution. The payment transaction process that is still widely used in educational institutions is a manual transaction. Recording transactions manually raises several problems, one of which is transparency in recording.&#x0D; A web-based information system for paying tuition fees and student savings was developed to maximize the use of existing technology. This research resulted in a system for recording tuition fees and student savings which can be easily used and has good transaction recording transparency. This web-based system was developed using the R&amp;D method. Based on the results of the validation carried out, it shows that the system is good and ready to use.
Pelatihan Penggunaan Aplikasi Mendeley untuk Sitasi Artikel Ilmiah pada Jurnal Bereputasi Nasional

This article contains the results of training on using the Mendeley application conducted by Jambi University lecturers and students. This training needs to be carried out to help teachers master the use of the Mendeley application to cite scientific articles in nationally reputable journals. This paper aims to improve the writing of scientific articles for teachers at SMP Negeri 7 Muaro Jambi. The method used in the implementation of this service is participatory story action research (PAR). The participants in this training were teachers at SMP Negeri 7 Muaro Jambi. From the results of the implementation of activities, teachers can understand well about 1) basic material related to plagiarism; 2) material for using the Mendeley application; and 3) skilled at using the Mendeley application for citation of scientific articles in nationally reputable journals. The conclusion of this activity is that it has a positive impact in the form of mastering the use of Mendeley application citations to improve scientific article writing among teachers of SMP Negeri 7 Muaro Jambi.
Literature Study in Numeracy Learning for Children with Special Needs and Policy Alternatives

Conditions in the field show that children with special needs who are blind and slow learners still have many deficiencies in math problems. Therefore, there needs to be an in-depth discussion on this matter. This article will describe the process of teaching and learning arithmetic for students with special needs through article analysis so that later it will be known how to ideally carry out numeracy learning for children with special needs through a policy. This research aims to describe the counting process of teaching and learning of students with special needs. This study uses a systematic literature review method. Data collection was carried out by documenting and reviewing previous articles related to the process of learning numeracy for students with special needs. Articles are searched by searching for articles using the Google Scholar database. Included in the discussion of this learning process are learning methods and models, learning media, and difficulties or challenges faced by students with special needs and teachers in learning numeracy. The final results of the review can be concluded that the process of teaching and learning numeracy between regular students and students with special needs has several differences, including the number of students, the time of delivery of material, and teaching and learning activities. Four alternative policies are proposed: facilitating accompanying teachers, preparing disability-friendly school facilities, collaborating with institutions dealing with children with special needs, and preparing numeracy media.
Pendidikan Karakter melalui Metafora dan Nilai Budaya Sastra Lisan Dolabololo

The purpose of this research is to analyze character education of forms, types, and meanings of metaphors and their relevance to cultural values ​​in the Ternate community. This study qualitative approach by utilizing content analysis methods and integrating them with the theory of metaphor analysis by Knowles and Moon. The research was conducted in Ternate Island District. The research subjects are the indigenous people of Ternate who are in the Ternate Island District, and the traditional holders of the Ternate Sultanate Palace. The data in this study is in the form of traditional Dolabololo expressions obtained from interviews and written documentation. The stages of analyzing the data are through data reduction, data presentation, and data verification stages. The value of cultural character education contained in the ontological metaphor is the teaching to always tell the truth and describe the culture of mutual respect and respect that the people of Ternate have.
METHOD OF DETECTION AND ELIMINATION OF VULNERABILITIES IN INPUT COMPONENTS OF THE DOM MODEL USING fDOM

The article discusses a progressive approach to securing web applications by identifying and eliminating vulnerabilities in input components of the Document Object Model (DOM). The main focus is on the development of fDOM - a formal version of DOM that automates the process of "sanitizing" attributes, actively removing potentially dangerous content, especially in cases involving &lt;script&gt; tags and associated threats such as cross-site scripting (XSS). The article thoroughly examines the existing structure of DOM, identifies key vulnerabilities, and introduces innovations in the form of a sanitize method to prevent potential attacks. Technical aspects include algorithms for cleaning attributes and elements, as well as a procedure for sanitizing input data. The effectiveness of the proposed model is demonstrated through a series of tests and lemmas that confirm the absence of harmful content after the sanitization procedure. The verification of the method for detecting and eliminating vulnerabilities using the formal verification system Isabelle/HOL is also discussed, highlighting the importance of formal methods in securing web applications. The article evaluates the strengths and weaknesses of fDOM, sheds light on potential directions for further development of the model, adaptation for dynamic content, and exploration of integrations with third-party services. Demonstration of the effectiveness of fDOM using a set of tests confirmed its ability to neutralize potentially dangerous code, indicating the success of this approach in providing a higher level of security for web applications.
Contrastive Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation

PDF HTML XML Export Cite reminder Contrastive Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation DOI: 10.21655/ijsi.1673-7288.00309 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:In recommender systems, the cold-start issue is challenging due to the lack of interactions between users or items. Such an issue can be alleviated via data-level and model-level strategies. Traditional data-level methods employ auxiliary information like feature information to enhance the learning of user and item embeddings. Recently, Heterogeneous Information Networks (HINs) have been incorporated into the recommender system as they provide more fruitful auxiliary information and meaningful semantics. However, these models are unable to capture the structural and semantic information comprehensively and neglect the unlabeled information of HINs during training. Model-level methods propose to apply the meta-learning framework which naturally fits into the cold-start issue, as it learns the prior knowledge from similar tasks and adapts to new tasks quickly with few labeled samples. Therefore, we propose a contrastive meta-learning framework on HINs named CM-HIN, which addresses the cold-start issue at both data level and model level. In particular, we explore meta-path and network schema views to describe the higher-order and local structural information of HINs. Within meta-path and network schema views, contrastive learning is adopted to mine the unlabeled information of HINs and incorporate these two views. Extensive experiments on three benchmark datasets demonstrate that CM-HIN outperforms all state-of-the-art baselines in three cold-start scenarios. Reference Related Cited by
ORGANIZATION OF VIRTUAL WORKPLACES OF EMPLOYEES OF A WHOLESALE AND RETAIL COMPANY ON THE BASE OF CLOUD TECHNOLOGIES

Cloud technologies are becoming a necessary and sought after tool in today's world. They enable efficient use of resources by providing the necessary computing power and data storage without the need to own and maintain large physical servers. This makes the infrastructure more flexible and scalable. Cloud technologies allow for a robust level of data security, and remote work software can include measures to protect the confidentiality and integrity of information. Cloud backup also ensures data reliability in case of crashes or data loss. Remote work software allows you to work from anywhere, providing a collaborative work environment for teams that may be located in different parts of the world. This increases the flexibility and mobility of the work process. Remote work makes it possible to attract talented professionals from anywhere, increasing the diversity of teams and work groups. Combined with cloud technologies, such work ensures high productivity through access to the necessary resources. Instead of physical infrastructures, companies can use cloud resources, which allows to save money on equipment and maintenance. Remote work software helps optimize the use of these resources. This approach to work becomes important, especially in the conditions of the modern world, when technologies are developing rapidly, and business processes require flexibility and adaptability. Therefore, during the study, a model of the organization of virtual places of employees based on cloud technologies was created.
ANALYSIS OF NEURAL NETWORK ARCHITECTURES FOR RECOGNITION OF HANDWRITTEN NUMBERS

The accuracy of contemporary systems designed for the recognition of handwritten numbers exhibits significant variability, contingent upon the technologies selected during the development of a specific method. Neural networks have supplanted algorithms reliant on statistical data and datasets for this task, primarily due to their better performance. Several recent publications addressing the challenges of handwritten digit recognition employing diverse types of neural networks have been examined. This paper delves into the analysis of the architecture of two prominent types: dense neural networks and convolutional neural networks along with their respective training methods applicable to the recognition of handwritten digits from images. The study's findings reveal that Dense Neural Networks (DNN), while applicable to the task at hand, demonstrate diminished performance as the size of the input image increases. Conversely, Convolutional Neural Networks (CNN) prove more adept at image analysis, owing to their convolution and pooling operations, thus establishing their priority in this context. Four distinct types of learning applicable to neural networks were explored, namely Supervised Learning, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning. The investigation concludes that Supervised Learning using backpropagation emerges as the most fitting method for training neural networks to address the identified problem. The paper further explores the stages and principles underpinning the operation of the backpropagation method. Building upon the derived results, there is a plan to develop an application leveraging the identified architecture. This application aims to empower users with the capability to efficiently extract digital information from images, irrespective of their resolution. The envisioned outcome is an application that attains high accuracy and processing speed in image recognition and extraction.
Reproduction of Human Potential in Modern Conditions of Education System Functioning

The article discusses various aspects of the reproduction of human potential by the modern education system in Russia and other countries. The author presents a theoretical overview of the positions of researchers writing about human potential, its formation and development, and the influence of the organization of the educational process on it. The author analyses the volumes of current and recent education financing in different countries, trends related to the financing of higher education, and the number of students. The importance of the development of lifelong education for human potential and the features of its financing are considered.
Simultaneous Processing and Fusion of Multiphysics Data and Images

This chapter considers the problem of restoration of blurred images. The image restoration and deblurring problem arises in biomedical, geophysical, astronomical, high-definition television, remote sensing, and other applications. This problem is formulated as the solution of the corresponding ill-posed inverse problem, which can be effectively solved by applying the family of focusing stabilizers. The solution of the joint image deblurring problem is illustrated by using the joint minimum entropy approach. We consider, as an example, the problem of reconstructing blurred images of the brain produced by the Magnetic Resonance Imaging (MRI) method.
Machine Learning Inversion of Multiphysics Data

In this Chapter, we discuss the application of machine learning methods to the solution of the inverse problem. Considering that machine learning algorithms do not require the knowledge of the function or operator they approximate, one can apply the neural network operator to determine the regularized solution of the inverse problem. This operator is called a regularizing neural network operator, and the corresponding network is the regularizing neural network (RNN). This chapter presents the methods of constructing the RNN algorithms using different types of stabilizing functionals introduced in the previous chapters of the book. We also introduce the concept of a knowledge-based neural network. It is based on the ability of the neural network to accurately approximate any mathematical or physical law represented by the forward modeling operator. Thus, a knowledge-based neural network provides a solution based on a priori knowledge about the laws governing the relationships between the model and data. The methods of joint inversion of multiphysics data using a regularizing neural network (RNN) are also considered.