Reviewerly/exHarmony huggingface dataset:
HUBUNGAN ANTARA POLA ASUH PERMISIF DENGAN MOTIVASI BELAJAR REMAJA DI SMP YAYASAN MARDI LESTARI MEDAN

This study aims to determine the relationship of the origin pattern : permissive with youth learning motivation at the SMP Lestari Medan Gatot Subroto Fondalition SNIP The setting that became the target og this writing was because the author saw many teenagers at Yayaan Maari Middle Scholl who had low learning motivation. This happnes because student have a strong punuhisit pattern such as parents who do not know how to learn about children‟s learning at scholl, anda teach their children who they are so that the parenting style is not fulfilled The subject of this students at Mardi Lestari Foundation Junior High School Medan as many students as many as 52 people this subject is students Mletode this method uses data collection in this study using the technique and sampling technique in the research using a smple of fingers where mpel does not meet from 100 or starts 100 the results of this plot represent babu ola znih permissive is the category lingp „‟as hingh as (82,62%) or 44 people, and motivation to study in category high amounting to (64,46%) or 33 people grade score you negatife.
Breast Histopathology Images Multi-Classification using Ensemble of Deep Convolutional Neural Networks

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Machine Learning Approaches for Prediction of various Cancer types

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Addressing the Digital Divide with Educational Systems in Mexico: Challenges and Opportunities

This scientific article presents a systematic literature review conducted following the PRISMA method to analyze the challenges and opportunities faced by the Mexican education system in effectively integrating Information and Communication Technologies (ICTs) to address the digital divide. The aim of the study is to examine digital inclusion in higher education within the Mexican context and provide insights relevant to the country’s educational system. The review included studies published between 2002 and 2023, focusing on the Mexican educational system and written in English or Spanish. Various databases, including Scopus, Web of Science, ERIC, Redalyc, and SciELO, were searched, along with other relevant sources. The review process, carried out from November 2022 to April 2023, involved organizing and compiling retrieved documents, removing duplicates, and applying inclusion criteria. Data extraction was performed manually, and a thematic analysis will be conducted to identify common themes and patterns across the selected studies. The results of the review highlight significant challenges, such as limited access to ICTs, lack of digital skills, and socioeconomic disparities, while also identifying opportunities through the development of digital literacy programs, implementation of inclusive policies, and collaboration among stakeholders. The findings emphasize the importance of addressing the digital divide in Mexican education and call for the formulation of comprehensive policies and strategies that consider technological barriers and socioeconomic inequalities. By effectively integrating ICTs, Mexico can foster a more inclusive and digitally empowered education system.
RANCANG BANGUN ALAT PENCATAT AKTIFITAS JANTUNG MENGGUNAKAN ARDUINO UNO BERBASIS ANDROID

The electrical activity of the heart is the basis elektrokardiograph (ECG) in observing the condition of the heart , a series of ECG consists of amplifier biopotensial of an instrument ECG became the main component in this study , records heart activity is still a burden to the medical world in the use and the results of the expected output .In this study biolistrik signal received from the ECG to be controlled via arduino , and data received through arduino will be sent via Bluetooth and android will be shown on the display , Bluetooth serves as the sender and the data receiver ( RX,TX). Results released by android is a graphic display that can be stored in the form of images in JPEG format on a regular basis or in real time so as to facilitate and reduce the use of paper in printing graphics imaginable on ECG in general that still use paper as the output.
Analysis of Vulnerability Levels to the Flash Flood Based on Social Economic and Institutional Factors in Wasior, Teluk Wondama, West Papua

One form of disaster mitigation is to know the vulnerability of areas that are at risk of disaster socially and economically. Studies on vulnerability to flash floods are necessary, as it might prevent material losses and fatalities. Wasior District at Teluk Wondama Regency experienced a flash flood in 2010 causing negative impacts such as fatalities and large material losses. To anticipate flash floods that might occur in the future, studies to evaluate the vulnerability to the flash flood are needed. This study aimed to: 1) analyze the level of vulnerability to flash floods in Wasior based on socioeconomic factors post the 2010 flash floods, 2) analyze the level of vulnerability to flash floods in Wasior based on institutional factors. The method used in this study was a combination of qualitative and quantitative methods. Data collection was conducted by observation, interview, and documentation. Data were analyzed by: 1) identifying the socioeconomic indicators of the community and institutions around the watershed, 2) providing weighting scores to the social economic criteria and institutional criteria from the most vulnerable to the least vulnerable, 3) assessing the level of community and institution vulnerabilities in the study site to the impact of flash floods based on the calculation of the weighting scores of socio-economic and institutional indicators. The results showed that the level of socioeconomic vulnerability to flash floods were categorized as moderate (total score 2.084), while the level of institutional vulnerability was low (total score 2.251). The results of this study can be used as a basis for considerations in the implementation of flash flood mitigation in Wasior.
Analyzing Students' Perspectives About School and Respect for Social Rules

The main purpose of the study was to analyze students' perspectives about school and respect for social rules. The objectives of the study were (a) to review the impact of respecting social rules on the overall environment of the school, and (b) to examine students' perceptions about school and respect for social rules. The study was descriptive in nature while a quantitative research approach was used to analyze the perspective of students about school and respecting social roles. The Population of the study consisted of all the secondary school students of the Government sector situated in Urban and Rural Areas of Punjab Province, which is the largest Province of Pakistan with respect to the population of the country. The study presented has delved into the multifaceted world of altruism, unveiling the profound impact of acts of kindness on individuals and their communities.
Analyzing US Airline Customer Sentiment on Twitter using Multinomial Logistic Regression and Feature Reduction

Social media has exerted a substantial impact and ongoing influence on how businesses interact with customers. Within this context, airlines have come to recognize the importance of Twitter as a pivotal avenue for connecting with customers, addressing complaints, and swiftly resolving inquiries. Employing sentiment analysis techniques, airlines can easily identify patterns, improve areas of weakness, and promptly address customer concerns. This study aims to investigate methods for improving the precision of automatic sentiment analysis of airline customers’ feedback. The proposed approach involves utilizing Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF), feature selection and machine learning techniques. Based on experimental findings using the Twitter-airline sentiment database, the implementation of multinomial logistic regression based on refined TF and TF-IDF matrices has exhibited an impressive accuracy rate (81.69%).
FM-IGNN: Interaction Graph Neural Network with Fine-grained Matching for Session-based Recommendation

Session-based recommendation plays a critical role in a number of scenarios, e.g., e-commerce, which predicts user behavior based on sessions. The primary challenge is to match user interests and candidate items accurately. Existing studies mainly learn the representation of session items and candidate items independently, and aggregate session representations through an attention mechanism. Nevertheless, user interests are usually cross-domain, and the characteristics of the items are also multifactorial. Fixed session and candidate item representations lead to suboptimal matching. To some extent, it limits the representation capability of the model. In this paper, we present an Interaction Graph Neural Network with Fine-grained Matching, named FM-IGNN, for session-based recommendation. It incorporates interactive features into item and session modeling to match candidate items and user interests more accurately. We first propose an Interaction Graph Neural Network(IGNN) to learn candidate-aware session item representation and session-aware candidate item representation interactively. We then design a Fine-grained interest Matching (FM) framework to learn user interests related to candidate items, and calculate the matching scores of users and candidate items at multi-levels. Experiments on three benchmark datasets demonstrate that FM-IGNN outperforms the state-of-the-art(SOTA) schemes with a large margin. Specifically, on the Tmall dataset, it achieves relative improvement of up to 20.54%, 16.07%, 13.41%, and 15.52% on the Precision@10, MRR@10, Precision@20, and MRR@20, respectively.
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness

During the past few decades, cognitive diagnostics modeling has attracted increasing attention in computational education communities, which is capable of quantifying the learning status and knowledge mastery levels of students. Indeed, the recent advances in neural networks have greatly enhanced the performance of traditional cognitive diagnosis models through learning the deep representations of students and exercises. Nevertheless, existing approaches often suffer from the issue of overconfidence in predicting students’ mastery levels, which is primarily caused by the unavoidable noise and sparsity in realistic student-exercise interaction data, severely hindering the educational application of diagnostic feedback. To address this, in this paper, we propose a novel Reliable Cognitive Diagnosis (ReliCD) framework, which can quantify the confidence of the diagnosis feedback and is flexible for different cognitive diagnostic functions. Specifically, we first propose a Bayesian method to explicitly estimate the state uncertainty of different knowledge concepts for students, which enables the confidence quantification of diagnostic feedback. In particular, to account for potential differences, we suggest modeling individual prior distributions for the latent variables of different ability concepts using a pre-trained model. Additionally, we introduce a logical hypothesis for ranking confidence levels. Along this line, we design a novel calibration loss to optimize the confidence parameters by modeling the process of student performance prediction. Finally, extensive experiments on four real-world datasets clearly demonstrate the effectiveness of our ReliCD framework.
DSG: An End-to-End Document Structure Generator

Information in industry, research, and the public sector is widely stored as rendered documents (e.g., PDF files, scans). Hence, to enable downstream tasks, systems are needed that map rendered documents onto a structured hierarchical format. However, existing systems for this task are limited by heuristics and are not end-to-end trainable. In this work, we introduce the Document Structure Generator (DSG), a novel system for document parsing that is fully end-to-end trainable. DSG combines a deep neural network for parsing (i) entities in documents (e.g., figures, text blocks, headers, etc.) and (ii) relations that capture the sequence and nested structure between entities. Unlike existing systems that rely on heuristics, our DSG is trained end-to-end, making it effective and flexible for real-world applications. We further contribute a new, large-scale dataset called E-Periodica comprising real-world magazines with complex document structures for evaluation. Our results demonstrate that our DSG outperforms commercial OCR tools and, on top of that, achieves state-of-the-art performance. To the best of our knowledge, our DSG system is the first end-to-end trainable system for hierarchical document parsing.
Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding

Graph convolutional networks (GCNs) play a critical role in improving the performance of collaborative filtering. They leverage the concept of aggregating neighbor information to capture user preferences on bipartite graphs by stacking multiple convolutional layers. However, this requirement for layer stacking often leads to a long training time for convergence, and results in indistinguishable representations with significant performance deterioration due to the problem of oversmoothing. Additionally, the noise of interactions will be amplified by the stacking of convolutional layers through message passing. To address these issues, we propose a simple, plug-and-play-Neighborhood Structure -Embedding approach, named NSE, which utilizes first-order adjacency information to construct structural embeddings. By explicitly incorporating local topologically statistical information before message passing, the embeddings propagated at GCNs have better topology-structure awareness. This leads to an improved optimization path and greater robustness against noise propagation. Experimental results demonstrate significant performance improvements by employing our proposed NSE in graph collaborative filtering models. Particularly, the NSE-enhanced LGCN shows performance gains of 5.06% and 4.86% on the Yelp and Amazon-Books datasets, respectively. The average training convergence speed is improved by 204.8%. NSE-enhanced graph collaborative filtering has also demonstrated excellent robustness against both noise and oversmoothing.
Development of an IoT-Monitoring and Control System for Solar Panel Surface Temperature Regulation Utilizing Water-Cooling Techniques

As the world's energy needs escalate and the availability of finite natural resources diminishes, there is an urgent need to explore sustainable alternatives. Renewable energy sources, including solar power, hold great promise due to their abundance, cost-effectiveness, and environmental friendliness. Solar panels, a cornerstone of renewable energy, have the potential to provide clean electricity, but their efficiency depends on temperature control. This research addresses this challenge by implementing an Internet of Things (IoT) monitoring and control system using the Blynk platform to regulate solar panel surface temperatures. The system's smart design uses water pumps to mitigate excessive temperatures, improving performance and energy efficiency. Extensive testing demonstrated the system's effectiveness, particularly in mitigating temperature spikes, improving system efficiency and sustainability. Our results demonstrate the remarkable potential of water-cooled systems and IoT technology to revolutionize solar energy harvesting, marking a significant step toward a sustainable energy future.
Disambiguation in context in the Russian National Corpus: 20 yeas later

An updated annotation of the Main, Media, and some other corpora of the Russian National Corpus (RNC) features the part-of-speech and other morphological information, lemmas, dependency structures, and constituency types. Transformer-based architectures are used to resolve the homonymy in context according to a schema based on the manually disambiguated subcorpus of the Main corpus (morphology and lexicon) and UD-SynTagRus (syntax). The paper discusses the challenges in applying the models to texts of different registers, orthographies, and time periods, on the one hand, and making the new version convenient for users accustomed to the old search practices, on the other. The re-annotated corpus data form the basis for the enhancement of the RNC tools such as word and n-gram frequency lists, collocations, corpus comparison, and Word at a glance.
FORMATION OF DIGITAL COMPETENCES IN THE CONTEXT OF “DIGITAL HUMANITIES”

The relevance of the digital transformation consists in the fact that it represents a continuous process of strategic renewal that uses advances in digital technologies to create opportunities that update or replace the educational process, approach to collaboration and culture. Digital humanities uses Big Data and analytical tools to study and analyse social, cultural and other phenomena. This can include the use of artificial intelligence, machine learning, and other technologies. Innovations are crucial for the development of digital competences in the context of teaching digital humanities. The digitalisation of society not only simplifies access to resources, but also creates new, previously impossible, opportunities for self-development and self-realisation of each individual. The purpose of the study is the theoretical and practical foundations of digital competence formation in the context of teaching digital humanities. Results of the study. The technological dimensions of the formation of digital competencies in the context of teaching “digital humanities” are revealed. The use of foreign experience elements in the formation of digital competences in the context of teaching “digital humanities” is clarified. The innovative possibilities of personal self-realisation in the formation of digital competences in the process of teaching “digital humanities” are substantiated. It is concluded that the formation of digital competencies in the context of digital humanities makes a significant contribution to the development of education, science and society as a whole, given the interaction of the humanities with modern technologies. Preparing students for a digital society requires the development of digital competences and allows students to adapt to a rapidly changing digital environment in which technology plays a key role in all aspects of life. Possession of digital skills focused on the humanities makes graduates more attractive to employers, as they can combine a deep understanding of society with technological knowledge.
The IEEE 2302–2021 Standard on Intercloud Interoperability and Federation: Seamless Cloud-agnostic Interoperability Management for Resource Discovery, Access and Trust

This column in Cloud Continuum is titled “Frontiers in Software, Architecture, and Standards” or “Frontiers” for short. It will cover emerging design and implementation patterns being used to lay the software, architecture, and standards groundwork for new functional capabilities in the cloud-to-edge continuum. In it, we'll use articles, interviews, and contributions from leading proponents for new cloud tools to explain how they are being developed and designed to work together and the capabilities they provide. Wherever possible, we'll focus on areas that include opportunities for open source development and community involvement in carrying out these advances.
A prototype of a low-cost autonomous mini-vehicle

This work presents the first phase of developing a prototype of an autonomous mini-vehicle on a scale of 1:24. The main contribution is the feasibility and executability of the project using only simple, readily available, and low-cost components. The primary focus was to construct a functional 3D chassis capable of housing all the necessary parts, creating full-scale 3D mechanical components. In addition, we show the first server-client programming using the ROS (Robot Operating System) platform, which allows communication between a central computer and the ESP32 microcontroller. All data that supports this paper are freely available.
METHODOLOGICAL FEATURES OF USING IMMERSIVE TECHNOLOGIES IN PRIMARY SCHOOL TEACHER TRAINING

The article discusses the features of using immersive technologies in the educational process of primary school. In the context of distance and blended learning, in addition to the use of various digital technologies, Internet resources and interactive tools in the educational process, it is effective to use special software tools to visualize, demonstrate and reproduce various phenomena. The need to train future teachers to use immersive technologies and the lack of methodological support for teachers proves the relevance of the chosen research topic. In the article, the authors demonstrate the advantages of using immersive technologies (virtual and augmented reality), virtual laboratories and mobile applications in the training of future primary school teachers in the integrated course "Art". The article presents a sample of existing immersive applications that educators most often use in art classes. Based on the analysis of modern online resources, it is proposed to systematize them in terms of their use in the educational process of higher education institutions under martial law for the training of future primary school teachers. In the conditions of the real educational process, the study was conducted on the basis of Borys Grinchenko Kyiv University and during the internship of students in the secondary school of Kyiv. Kyiv, which made it possible to identify the best software tools for use in primary education institutions and during the training of students majoring in 013 "Primary Education": information and reference (electronic encyclopedias, virtual tours, 360° museums), simulation programs (Crayola Art-Studio multimedia graphic editor, Crello online tool, PaperOne training program), laboratories (Chrome Music Lab (Oscillators, Kandinsky, Sound Waves, etc.) Toyteatre). Ensuring that future primary school teachers are properly trained in the use of immersive technologies is a key to successful informatization of education, which will ensure quality and effective education for future generations.