Reviewerly/exHarmony huggingface dataset:
Sustainable Development of Promotional Strategies to Increase the New Students at Yapekdi Private Junior High School Dolok Ilir Dolok Batu Nanggar District, Simalungun Regency

The community service activity program for the Universitas Sumatera Utara Team, based on the results of an initial survey at the Yapekdi Middle School, the problem was that there was a lack of students registering as new students in 2021/2022, the promotion strategy carried out by the school had not yet influenced the community to register their children and the condition of some classrooms that were damaged so it was feared that it would have a bad effect on students who were studying inside. Solutions for 2022/23 Promoting sustainable development and providing one year scholarships to 20 students, promotional strategies to attract public interest by distributing flyers, installing banners, and renovating classrooms. Therefore, the school has accepted 20 students for the 2022/2023 school year. Then the outcomes achieved, Mass Media Publications and activity videos. Next plan. Summit Proceedings with ISSN.
Analysis of Learning Motivation of Junior Hight School Students in Terms of the use of Digital Platforms in Online Learning

This study aims to analyze the level of motivation to learn science in terms of the use of platforms in online learning. The research was carried out at Junior Hight School (SMP) Negeri Singingi, Kuansing Regency, Indonesia for the 2021-2022 Academic Year. This research is quantitative descriptive. The type of data obtained consists of primary data and secondary data. The instrument used to measure the level of student learning motivation is using a questionnaire. This learning motivation questionnaire was prepared with reference to the aspect of learning motivation which consists of 8 aspects. The results of learning motivation on the concentration indicator get an average of 64.56% in the medium category, the curiosity indicator on average 64.83% in the medium category, the average spirit indicator 62.14% in the medium category, the average independence indicator at 70.25% in the high category, the average readiness indicator is 77.50% in the high category, the average enthusiasm and encouragement indicator is 64.68% in the medium category, the indicator of never giving up is on average 66.67% in the medium category, and the average confidence indicator is 79.18% in the high category. The results of the study concluded that the overall student learning motivation was categorized as good.
An Approach of Continued Involvement and Continued Development Using Cloud Computing for University-Industry Collaboration

Abstract: Education is strange for any country’s growth. Nowadays technology is growing rapidly. Technology plays a very important role in every domain of industry, especially in higher education where there is a need to improve the quality of education. Continuing updating in technology is essential to adopt by education institutions. Education 4.0 and 5.0 are focused on providing value-based, skill-based education according to the needs of the industry. Cloud computing helps to make a bridge between university and industry. Cloud computing has such a kind of characteristic, service model, and deployment model which helps to continued involvement and development (CI/CD) to UI. Our research discussed the role of continued involvement and continued development by the university and industry using cloud computing.
Development of Instagram-Based Learning Media to Increase Learning Motivation of Tenth Grader in Economics Subjects at SMA Ar-Raudhah

Instagram social media has become a popular social media among students and in it’s use, of course, it must be balanced with its usefulness as used as a learning reference. This study aims to develop Instagram-based learning media in economics subjects and measure the level of student learning motivation towards learning media. The method used is Research and Development with the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development model. The results of this study are based on validators' assessment of Instagram-based learning media which obtained an average validity of 3.89 (material experts), 3.87 (media experts) and 4.49 (learning practitioners) so that they were declared valid and suitable for use. Meanwhile, the results of measurements on student learning motivation increased from the original score of 2415 to 2698 after the implementation of Instagram-based learning media and obtained a gain score of 0.5 which was interpreted to be in the moderate category. This shows that Instagram-based learning media is effective in increasing student learning motivation.
Transforming Healthcare System through AI Sustainability: Chatbots and Emergency Assistance

Abstract: The healthcare sector is facing numerous challenges, including the spread of misinformation on the internet, a shortage of healthcare assistance, inadequate patient management, emergency care, and disease detection. The process of appointments can also be time-consuming and inconvenient for patients who may not be able to visit a hospital or doctor for all minor issues and doubts. The paper suggests using AI chatbots and portals to improve patient access and information, an Emergency Fatality Assistance and SOS system for immediate care, a Report &amp; Medicine Tracking system for better patient outcomes, and AI technology in disease detection for early diagnosis and treatment. This proposed system aims to contribute to the existing literature on the integration of AI sustainability in healthcare services to address these challenges and improve patient outcomes.
STUDY ON EVOLUTION OF GENERAL PURPOSE PROGRAMMING LANGUAGE

This research paper analyzes the evolution of general-purpose programming language over time.The programming language gains popularity based on the number of engineers worldwide, the number of courses available worldwide, etc.There are many programming Languages and this research paper shows that every programming language has its significance This paper shows the common difference between the programming Languages.In this paper the advance features like locales, switch-case with different notation, MVC architecture and LAMBDA expression are discussed.
Detecting banking frauds with analytics and machine learning

Bank fraud is the bodily loss of a Bank or maybe the loss of very sensitive info. For detection, there are lots of machine learning algorithms which can be used. The study shows many algorithms which could be used for deciding transactions as fraud or perhaps real. The information set employed in Bank fraud Detection was utilized in the research. The SMOTE method was used for oversampling, since the dataset was incredibly imbalanced. Moreover, include choice was performed, and the set was divided into two parts, test data and instruction information. The algorithms used in this study were Logistic Regression, Multilayer Perceptron, Random Forest and Naive Bayes. The results show that every algorithm could be used with good precision for fraud detection of banking solutions. For the detection of extra constipation, the proposed model might be used.
Entropy-Guided Assessment of Image Retrieval Systems: Advancing Grouped Precision as an Evaluation Measure for Relevant Retrievability

The performance evaluation of Content Based Image Retrieval systems (CBIR), can be considered as a challenging and overriding problem even for human and expert users regarding the important numbers of CBIR systems proposed in the literature and applied to different image databases.The automatic measures widely used to assess CBIR systems are inspired from the general Text Retrieval (TR) domain such as precision and recall metrics.This paper proposes a new quantitative measure adapted to the CBIR particularity of relevant images grouping, which is based on the entropy of the returned relevant images.The proposed performance measure is easy to understand and to implement.A good discriminating power of the proposed measure is shown through a comparative study with the existing and well-known CBIR evaluation measures.
Musings on Faceted Search, Metadata, and Library Discovery Interfaces

Faceted search is a powerful tool that enables searchers to easily and intuitively take advantage of controlled vocabularies and structured metadata. Faceted search has been widely implemented in library discovery interfaces and has provided many benefits to library users. The effectiveness of facets in library catalogs depends on a complex interaction between facet vocabularies, metadata quality and structure, and the library discovery interface’s capabilities. This article provides a holistic overview of challenges for optimally implementing facets in library catalogs. This supports a systematic approach to refining and enhancing the capacity of faceted search to improve searching and exploring bibliographic metadata.
REDUCTION OF DATA LEAKAGE IN DISTRIBUTED CLOUD STORAGE SYSTEMS USING DISTRIBUTED CLOUD GUARD (DCG)

Cloud storage offers security, affordability, and global data access.Cloud storage is scalable, so organizations may simply add or delete storage.Cloud storage is convenient and safe.Dropbox, Google Drive, and Microsoft OneDrive allow cross-device data storage.Cloud storage providers (CSPs) also encrypt data.If data were dispersed across different CSPs, attackers would need to target numerous CSPs to retrieve the whole set.Hence, attackers struggle to obtain all the data.Email, cloud storage, and other methods can readily exchange data chunks without user consent.Data breaches can occur if security is inadequate.Because there are no access controls or insights into the data exchanged across clouds.Cyberattacks might disclose cloud data.Distributed Cloud Guard (DCG), a cloud security system, leverages advanced analytics to detect data flow irregularities like unlawful data exfiltration to solve this problem.We can then take immediate steps to prevent data leakage.Attackers would have to assault numerous clouds to access all semantically homogeneous data in the same cloud.DCG simplifies data leak detection and mitigation by centralizing data.This project uses Min-Hash and Bloom filter techniques to trademark data hunks for secure storage.Clustering lowers data leaks by distributing data hunks among clouds.
Database in the Study of the History of Estates in the Moscow Region: Filling, Enrichment and Analytics

This article deals with the problem of accumulation and systematization of information in studies devoted to 3D reconstructions of objects of historical and cultural heritage. In the presence of a large number of heterogeneous sources, the researcher may get confused in the accumulated data, which significantly complicates his work. Since in research, the results of which are virtual reconstructions, text sources are used together with graphic, scientific and technical documentation, as well as physical objects undergoing preliminary digitization, the accounting and cataloging of sources cannot be neglected. The solution to this problem allows the most complete and efficient use of all the materials available in the study, which ultimately affects the quality of the reconstruction result obtained. In this article, using the example of a project for the reconstruction of estate complexes in the Moscow region, the author demonstrates the developed system of accumulation and enrichment of source information using modern database management systems and software tools for working with data. The information aggregated and described in the MySQL database is supplemented with information from open sources using web scraping technology in the Python programming language, acquires end-to-end identification and acquires usability in various studies. Standardized information allows you to quickly find the right source from the very top level, and its enrichment provides additional opportunities for analysis and synthesis of all aggregated material.
Development of Science Learning E-Module Equipped with Game On Material Classification of Living Things for Grade VII Students

This research is a research and development with the ADDIE model. In the media and material validation process carried out by lecturers and teachers using instruments in the form of questionnaires, to measure practicality by teachers and students using instruments in the form of questionnaires, and effectiveness for students was measured using learning outcomes instruments consisting of 20 questions. The results of media development in the form of e-modules, namely the results of the validation of material experts obtained 94%, the validation of media experts obtained 90% in very valid criteria. Then for the implementation results in the form of teacher practice results obtained 96.6%, student practice results obtained 96.91% on very practical features and student effectiveness results obtained at 0.71 including the high category calculated on the N-Gain formula and for classical mastery a proportion 93.10% is obtained with a very effective mixture. Based on the results of the research, it can be interpreted that the science learning e-module is equipped with a game about the classification of practical living things in terms of attractiveness, ease of use, material adjustment and effectiveness in terms of KKM achievement.
IMPLEMENTATION OF THE INVERTED CLASSROOM TECHNOLOGY WHEN TEACHING ELECTIVE EDUCATIONAL COMPONENTS IN THE COLLEGE OF TEACHERS

The article examines the peculiarities of using the “flipped classroom” technology in the educational process of the communal institution of higher education “Lutsk Pedagogical College”. The advantages of this educational technology are noted, the essence, peculiarities of organization and content are highlighted. Training future primary school teachers for professional activities using digital online tools.It is noted that the use of this technology in a modern school is an integral component of the organization of the educational process, which determines the relevance of the formation of the skills of future teachers to effectively use modern methods for preparing lessons, organizing remote interaction and communication between participants of the educational process. The work analyzes the latest publications of researchers and practicing teachers and emphasizes that the use of innovative technologies should be taken into account when training specialists in the field of education.With this in mind, the task of practical work was proposed, taking into account the types of activities that a teacher performs during his professional activity: development of methodical materials, handouts and didactic materials, creation of educational videos and their use in the educational process, electronic systems for checking knowledge, tools for demonstration, interactive presentations of various types and video explanations of educational material.During the work on the publication, scientific research methods were used: study, analysis, systematization, comparison and generalization of sources on the research problem, observation of the pedagogical process.&#x0D; Keywords: flipped classroom technology; digital competence; information technology; innovation; innovative methods; primary school teacher; higher education institution; online learning; educational process; professional training; methods and forms of work.
Penggunaan Digital Signature Untuk Absensi Pada Universitas Muhammadiyah Purworejo

Abstract— The student attendance system carried out at the Muhammadiyah University of Purworejo currently still uses paper for both students and lecturers. The use of this paper allows errors to occur when the lecturer recapitulates student attendance data. In addition, the attendance process that still uses paper is considered less effective and efficient. Therefore, it is necessary to create an information system that can apply digital signatures at the Purworejo Muhammadiyah University to prevent Purworejo Muhammadiyah University students outside of lectures or being absent from being able to fill attendance. With this system, it is hoped that this digital signature can be used in academic information systems and support online learning
The Application and Practice of Computer-Aided Translation in English Teaching

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
A realizability semantics for inductive formal topologies, Church's Thesis and Axiom of Choice

We present a Kleene realizability semantics for the intensional level of the Minimalist Foundation, for short mtt, extended with inductively generated formal topologies, Church's thesis and axiom of choice. This semantics is an extension of the one used to show consistency of the intensional level of the Minimalist Foundation with the axiom of choice and formal Church's thesis in previous work. A main novelty here is that such a semantics is formalized in a constructive theory represented by Aczel's constructive set theory CZF extended with the regular extension axiom.
Effect of Classical and Quantum Superposition of Atomic States on Quantum Correlations

In this paper, we report the effect of classical and quantum superposition of atomic states on quantum correlations. Coupled photon pairs generated in a ladder quantum beat laser using coherent-induced classical field and atomic state coherent superposition are considered. Once the quantum coherence get sufficient time, it can generate quantum correlations that include quantum discord, quantum entanglement, and quantum steering, which quickly increase with time until they get their maximum strength. Their strength can be improved further by increasing the number of superposed atoms per unit time, selecting an appropriate amplitude of the classical fields, and managing the amount of temperatures and phase fluctuations. In particular, two-way quantum steering, which is a guarantee for the existence of quantum discord and quantum entanglement, can be achieved by increasing the rate of atomic injection from 2 kHz to 25 kHz even if the mean temperature of the heat bath is considered. The maximum achievable strength of quantum correlations is enhanced by increasing the rate of atomic injection and choosing an appropriate parameters of the coherent-induced classical field in the open quantum system which is treated by using the density operator approach.
Generalization in the Face of Adaptivity: A Bayesian Perspective

Repeated use of a data sample via adaptively chosen queries can rapidly lead to overfitting, wherein the empirical evaluation of queries on the sample significantly deviates from their mean with respect to the underlying data distribution. It turns out that simple noise addition algorithms suffice to prevent this issue, and differential privacy-based analysis of these algorithms shows that they can handle an asymptotically optimal number of queries. However, differential privacy's worst-case nature entails scaling such noise to the range of the queries even for highly-concentrated queries, or introducing more complex algorithms. In this paper, we prove that straightforward noise-addition algorithms already provide variance-dependent guarantees that also extend to unbounded queries. This improvement stems from a novel characterization that illuminates the core problem of adaptive data analysis. We show that the harm of adaptivity results from the covariance between the new query and a Bayes factor-based measure of how much information about the data sample was encoded in the responses given to past queries. We then leverage this characterization to introduce a new data-dependent stability notion that can bound this covariance.
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.
Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior

3D object reconstruction is important for semantic scene understanding. It is challenging to reconstruct detailed 3D shapes from monocular images directly due to a lack of depth information, occlusion and noise. Most current methods generate deterministic object models without any awareness of the uncertainty of the reconstruction. We tackle this problem by leveraging a neural object representation which learns an object shape distribution from large dataset of 3d object models and maps it into a latent space. We propose a method to model uncertainty as part of the representation and define an uncertainty-aware encoder which generates latent codes with uncertainty directly from individual input images. Further, we propose a method to propagate the uncertainty in the latent code to SDF values and generate a 3d object mesh with local uncertainty for each mesh component. Finally, we propose an incremental fusion method under a Bayesian framework to fuse the latent codes from multi-view observations. We evaluate the system in both synthetic and real datasets to demonstrate the effectiveness of uncertainty-based fusion to improve 3D object reconstruction accuracy.
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification

Scalability is a significant challenge when it comes to applying differential privacy to training deep neural networks. The commonly used DP-SGD algorithm struggles to maintain a high level of privacy protection while achieving high accuracy on even moderately sized models. To tackle this challenge, we take advantage of the fact that neural networks are overparameterized, which allows us to improve neural network training with differential privacy. Specifically, we introduce a new training paradigm that uses \textit{pre-pruning} and \textit{gradient-dropping} to reduce the parameter space and improve scalability. The process starts with pre-pruning the parameters of the original network to obtain a smaller model that is then trained with DP-SGD. During training, less important gradients are dropped, and only selected gradients are updated. Our training paradigm introduces a tension between the rates of pre-pruning and gradient-dropping, privacy loss, and classification accuracy. Too much pre-pruning and gradient-dropping reduces the model's capacity and worsens accuracy, while training a smaller model requires less privacy budget for achieving good accuracy. We evaluate the interplay between these factors and demonstrate the effectiveness of our training paradigm for both training from scratch and fine-tuning pre-trained networks on several benchmark image classification datasets. The tools can also be readily incorporated into existing training paradigms.