Reviewerly/exHarmony huggingface dataset:
РОЗРОБКА МОДЕЛІ ЦИФРОВОЇ ОСВІТИ У КОНТЕКСТІ ЄВРОПЕЙСЬКОЇ ПРОГРАМИ DIGICOMP 2.0

The relevance of developing a model of digital education in the context of the European DigiComP 2.0 programme is that it attempts to explore the “21st century competences” widely used in Europe, corresponding to skills in the use of digital technologies. Conceptual definitions of the terms “skills and abilities” show that skills are the ability to apply learning outcomes adequately in a given context, while abilities refer more to the ability to achieve goals. Despite the conceptual differences, these terms are used in the literature to refer to the concepts of educational digitalisation. The purpose of this article is to conceptualise the development of a digital education model in the context of the European programme DigiComP 2.0. To achieve this goal, it was necessary to solve the following tasks: 1) to analyse the model of digital education in the context of the European programme DigiComP 2.0; 2) to identify digital resources as new opportunities for developing a model of digital education; 3) to develop strategies for the formation of digital skills of the 21st century in the context of the formation of a model of digital education. The research methodology includes an analysis of scientific literature, which presents disparate empirical facts of the digitalisation of education; synthesis of scientific ideas, concepts, theories that allowed to bring them into the system and form a model; methodology of structural and functional, cross-cultural analysis, systemic and analytical methods, forecasting and modelling. The research has shown that the digital education model in the context of the European DigiComP 2.0 programme is aimed at supporting the sustainable and effective adaptation of the education and training system in the EU Member States to digitalisation, which dominates in advanced developed countries. It is concluded that the digital education model includes: a long-term strategic vision of the European-style paradigm of quality, inclusive and accessible 21st century digital competence training; addresses the challenges and opportunities posed by the COVID-19 pandemic, which has led to an unprecedented use of technology to develop digital education and training; strengthening cooperation at the EU level in the field of digital education in all sectors using digital technologies, supporting the digitisation of teaching methods and pedagogy, as well as the formation of the necessary infrastructure for the development of digital education and distance learning.
Correlation between Teachers’ Digital Competency and their Self-Efficacy in Managing Online Classes

Adoption of online teaching platforms for reaching learners and including technologies in lesson delivery was never so crucial before the Pandemic. The shift from regular classroom teaching to distant learning during the pandemic presented many challenges to educators. This study explored the correlation between teachers’ digital competency and their self-efficacy during distant teaching. This research employed a quantitative approach, and a questionnaire was administered to a sample of teaching staff from Karachi's southern region. 155 respondents participated in this research including 85 Nursery teachers and 70 from lower primary level(I–II). The questionnaire included questions on factors for digital competency and teachers’ self-efficacy in separate sections. The digital competency section assessed teachers' proficiency in using digital tools, platforms, and technologies relevant to online teaching. The self-efficacy section measured teachers' beliefs in their capabilities to perform specific tasks related to two factors, which were instructional strategies and students’ engagement. Data analysis included correlation analysis to find the relationship between teachers' digital competency and self-efficacy , instructional strategies and students engagement .Correlation values (.839) , (.727) and (.764) where p &lt; .001 revealed statistically significant relationship between the research variables, concluding the significant role of digital competencies in online teaching. The findings further highlighted the importance of professional training in equipping teachers with technical knowledge and digital competence in order to use online teaching tools with high self-efficacy, which will eventually improve their overall performance in online lesson delivery. Additional qualitative and quantitative research is suggested on further factors that influence teachers’ self-efficacy in their online teaching through digital resources.
Enhancement of Entanglement via Incoherent Collisions

In contrast to the general thought that the collisions are intrinsically dephasing in nature and detrimental to quantum entanglement at room or higher temperatures, here, we show that in the conventional ladder-type three-level electromagnetically induced transparency (EIT) configuration, when the probe field intensity is not very weak as compared to the pump field, the entanglement between the bright pump and probe fields can be remarkably enhanced with the increase of the collisional decay rates in a moderate range in an inhomogeneously-broadened atomic system. The strengthened entanglement results from the enhancement of constructive interference and suppression of destructive interference between one-photon and multi-photon transition pathways. Our results clearly indicate that the collisions offer a promising alternative to enhance entanglement at room or higher temperatures despite of the dephasing nature, which provides great convenience for experimental implementation, and opens new prospects and applications in realistic quantum computation and quantum information processing.
XWalk: Random Walk Based Candidate Retrieval for Product Search

In e-commerce, head queries account for the vast majority of gross merchandise sales and improvements to head queries are highly impactful to the business. While most supervised approaches to search perform better in head queries vs. tail queries, we propose a method that further improves head query performance dramatically. We propose XWalk, a random-walk based graph approach to candidate retrieval for product search that borrows from recommendation system techniques. XWalk is highly efficient to train and inference in a large-scale high traffic e-commerce setting, and shows substantial improvements in head query performance over state-of-the-art neural retreivers. Ensembling XWalk with a neural and/or lexical retriever combines the best of both worlds and the resulting retrieval system outperforms all other methods in both offline relevance-based evaluation and in online A/B tests.
Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space

Air transport poses significant environmental challenges, particularly regarding the role of flight contrails in climate change due to their potential global warming impact. Traditional computer vision techniques struggle under varying remote sensing image conditions, and conventional machine learning approaches using convolutional neural networks are limited by the scarcity of hand-labeled contrail datasets. To address these issues, we employ few-shot transfer learning to introduce an innovative approach for accurate contrail segmentation with minimal labeled data. Our methodology leverages backbone segmentation models pre-trained on extensive image datasets and fine-tuned using an augmented contrail-specific dataset. We also introduce a novel loss function, termed SR Loss, which enhances contrail line detection by transforming the image space into Hough space. This transformation results in a significant performance improvement over generic image segmentation loss functions. Our approach offers a robust solution to the challenges posed by limited labeled data and significantly advances the state of contrail detection models.
Discovering Spatio-Temporal Rationales for Video Question Answering

This paper strives to solve complex video question answering (VideoQA) which features long video containing multiple objects and events at different time. To tackle the challenge, we highlight the importance of identifying question-critical temporal moments and spatial objects from the vast amount of video content. Towards this, we propose a Spatio-Temporal Rationalization (STR), a differentiable selection module that adaptively collects question-critical moments and objects using cross-modal interaction. The discovered video moments and objects are then served as grounded rationales to support answer reasoning. Based on STR, we further propose TranSTR, a Transformer-style neural network architecture that takes STR as the core and additionally underscores a novel answer interaction mechanism to coordinate STR for answer decoding. Experiments on four datasets show that TranSTR achieves new state-of-the-art (SoTA). Especially, on NExT-QA and Causal-VidQA which feature complex VideoQA, it significantly surpasses the previous SoTA by 5.8\% and 6.8\%, respectively. We then conduct extensive studies to verify the importance of STR as well as the proposed answer interaction mechanism. With the success of TranSTR and our comprehensive analysis, we hope this work can spark more future efforts in complex VideoQA. Code will be released at https://github.com/yl3800/TranSTR.
Blockchain-based Cloud Data Deduplication Scheme with Fair Incentives

With the rapid development of cloud computing, vast amounts of duplicated data are being uploaded to the cloud, wasting storage resources. Deduplication (dedup) is an efficient solution to save storage costs of cloud storage providers (CSPs) by storing only one copy of the uploaded data. However, cloud users do not benefit directly from dedup and may be reluctant to dedup their data. To motivate the cloud users towards dedup, CSPs offer incentives on storage fees. The problems with the existing dedup schemes are that they do not consider: (1) correctness - the incentive offered to a cloud user should be computed correctly without any prejudice. (2) fairness - the cloud user receives the file link and access rights of the uploaded data if and only if the CSP receives the storage fee. Meeting these requirements without a trusted party is non-trivial, and most of the existing dedup schemes do not apply. Another drawback is that most of the existing schemes emphasize incentives to cloud users but failed to provide a reliable incentive mechanism. As public Blockchain networks emulate the properties of trusted parties, in this paper, we propose a new Blockchain-based dedup scheme to meet the above requirements. In our scheme, a smart contract computes the incentives on storage fee, and the fairness rules are encoded into the smart contract for facilitating fair payments between the CSPs and cloud users. We prove the correctness and fairness of the proposed scheme. We also design a new incentive mechanism and show that the scheme is individually rational and incentive compatible. Furthermore, we conduct experiments by implementing the designed smart contract on Ethereum local Blockchain network and list the transactional and financial costs of interacting with the designed smart contract.
Aplikasi Bimbingan Belajar Bahasa Inggris Tingkat Sekolah Dasar (SD) Dengan Fitur Live Chat Dan Automatic Essay Scoring

In the current pandemic, parents want to do additional learning for their children. Therefore, a research was conducted with the title of Application of English Tutoring for Elementary School Level (SD) with Live Chat and Automatic Essay Scoring features and is expected to help the learning carried out by teachers and students. The hope of this application can be useful and can make it easier for teachers to find places to do learning. The system created today is a scoring system in the form of an essay where when students solve problems in the current system it will produce points per question worked on and the total points will produce the correct number of points. Experiments were carried out with several students with several similar questions to find out whether the designed system worked as expected or not. By conducting three trials on students with the same 10 questions and with K-Gram 3, 4 and 5. 4 which was carried out, the results were 70% and for the accuracy results from the K-Gram 5 experiments carried out, the results were 65%.
Even shorter proofs without new variables

Proof formats for SAT solvers have diversified over the last decade, enabling new features such as extended resolution-like capabilities, very general extension-free rules, inclusion of proof hints, and pseudo-boolean reasoning. Interference-based methods have been proven effective, and some theoretical work has been undertaken to better explain their limits and semantics. In this work, we combine the subsumption redundancy notion from (Buss, Thapen 2019) and the overwrite logic framework from (Rebola-Pardo, Suda 2018). Natural generalizations then become apparent, enabling even shorter proofs of the pigeonhole principle (compared to those from (Heule, Kiesl, Biere 2017)) and smaller unsatisfiable core generation.
Development of an algorithm for obtaining data from thematic internet resources

With the rapid development of the Internet, users are actively sharing their personal data and other information on many social networks. Information on the Internet should be analyzed to make sure that it is reliable and does not pose a threat to the public. Based on this, there is a need to collect, monitor and analyze this information. Data collection is a complex task, depending on the structure of each web page. Since not all resources allow you to collect information, you have to use many methods. The proposed article shows effective ways of using syntactic analysis to obtain information. The method of semantic analysis (parsing) of the contents of web pages is explained using a program written in Python based on the BeatifulSoup library. In addition, the focus is on methods of collecting information through other APIs, using tools to emulate user behavior in the browser. An algorithm for extracting information from thematic Internet resources using the BeatifulSoup + Requests library is presented. As a result, information was obtained from Englishand Russian-speaking hacker and carding forums.
Monitoring Crowd Movement for Anomaly Detection Using Scale Invariant Feature Transform

In order to detect the potentially dangerous arrears and the situation of a crowd in public security systems, the automated analysis of crowd monitoring using surveillance video is playing vital role. Even though many works are focused on the analysis related to the crowd behavior analysis, complexity in algorithm, real time working module and predefined rigid automatically selection rules are the major problems in the behavior analysis crowd detection. This work proposed a real time algorithm to detect the global anomalies in Scale Invariant Feature Transform(SIFT) based on holistic approach. Significantly deviation in the normal behavior from the previously stored data set, that is people running away from the crowd or suddenly gathering into a particular point were consider an the anomalies are the framework of the approach. The experimental result shows that, compared with the existing methods, the proposed method could able to run in real time and have less complexity in algorithm.
Funding the Higher Education System: International Experience and Russian Practice

Relevance. Education is a significant factor in economic growth. However, the discussion about the principles for distributing higher education funding is still open, and the cases of individual countries are not sufficiently covered in research literature. Research objective. The study aims to determine the principles of financing based on the case studies of Russian universities. Foreign financing mechanisms are analyzed and compared with Russian practice that has similar foundations. Financing mechanisms are classified according to their distribution principle. Data and methods. The statistical base for the study is the data of a large-scale higher education monitoring project of 2019-2021. The study was conducted in more than 650 Russian universities. In order to determine the principles of financing, a correlation analysis is carried out to identify the correlation between the indicators. Universities are grouped by regions with different socio-economic characteristics, subgroups of universities within the regional division were identified. Results. The distribution of funding among Russian universities is based on the principles of quasi-competition and equalization. Universities located in regions with low indicators of socio-economic development are mainly financed to achieve equalization of educational activities, and, as the socio-economic situation in the region improves, funding is channeled into equalization of research activities. Another more obvious conclusion is that research activities of universities that participate in state programs are funded based on competition, while other universities have lower correlation between indicators, which leads us to the assumption that other universities’ research activities are funded based on the principles of equalization. Conclusions. The novelty of the study is the results that enrich the understanding of the principles for funding distribution in the Russian higher education system. Contrary to most studies of the concentration of resources around a limited number of institutions, the study concludes that resources and funding are distributed based on equalization, supporting the less competitive units of the system, and directing funding to regions with less stable socio-economic characteristics.
PEMBUATAN PROGRAM SISTEM PENJUALAN HELM BERBASIS WEB PADA TOKO HELM KARTINI

Helmet Sales System Program at the Kartini Helmet Shop, located in Bekasi City, aims to create a helmet sales program in the form of a website that can process transactions online. Customers can process orders, payments and deliveries online through the helmet sales website which is opened with a web browser. Store owners can print sales reports to see the results of their product sales, customer reports to see customers who bought the most and financial reports to see revenue earned. In making this program using the Waterfall method. In designing this program using Context Diagrams, Data Flow Diagrams, Entity Relationship Diagrams, Relationships Between Tables and Table Specifications. The programming languages used in making this program are HTML and PHP with the CodeIgniter Framework and use the MySQL database.
HUBUNGAN MOTIVASI DAN IKLIM KELAS TERHADAP HASIL BELAJAR FIQH DI MTSN 1 PESISIR SELATAN

The purpose of this study is to determine the correlation of the variables X1, X2, and Y. By using the quantitative-correlational method, this study intends to determine the relationship between learning motivation, classroom climate, and learning achievement at MtsN 1 Pesisir Selatan. The research results show that; 1) there is a positive and substantial relationship between classroom climate and student learning outcomes, with a correlation coefficient of 48.4%; 2) there is a positive and significant relationship between the variables of learning motivation and student learning outcomes at MTsN 1 Pesisir Selatan by 0.3 percent while other variables influence 99.7 percent; 3) there is a positive and significant relationship between classroom climate variables and student learning outcomes at MTsN 1 Pesisir Selatan by 48.4 percent while other variables influence 51.6 percent. So it can be concluded that there is a positive and significant correlation between research variables
Large-capacity Fragile Watermarking Scheme for Color Images Based on Prime Number Distribution Theory

Abstract Color image digital watermarking technology is an important security protection technology to realize secret communication and content authentication. To better achieve the purpose of color image copyright protection, this paper proposes three watermark embedding schemes for color images based on the prime number distribution theory, embedding the RGB three channels of the color carrier image into binary watermark images of the same size to realize large-capacity watermark embedding. The scheme uses the Rivest Cipher 4 (RC4) encryption algorithm to securely encrypt the image information. During the watermark embedding process, the appropriate pixel value is selected for pixel replacement according to the division of the prime number set within 0-255. The experimental results show that the PSNR values of the three schemes are all greater than 40dB, and the NC values are all close to 1. Compared with the existing algorithms, the scheme proposed in this paper has a large embedding capacity, which can resist common image processing attacks while ensuring good imperceptibility of the watermark, and has good tamper detection and localization capabilities.
Resource Allocation for Blockchain-Enabled Fog Computing with Deep Reinforcement Learning

With the developments of the Internet of Things, the demands of low latency, high bandwidth and high-performance computing has increased higher. Therefore, the distributed computing named Fog Computing has proposed to solve the problem above. Fog computing can provide lower transmission latency, faster response time and less network congestion. However, the fog devices are unable to guarantee the security and privacy of data transmission, due to they are vulnerable to attack. Blockchain technology works as a decentralized public ledger to store and share transactions. Blockchain can improve security and protect data privacy of Fog Computing. Moreover, there are still issues in the blockchain-enabled Fog Computing, the two main issues are the energy consumption and computing efficiency. Thus, in this paper, we propose an optimization framework for blockchain-enabled Fog Computing systems to optimize resource allocation. Besides, we adopt the dueling deep reinforcement learning to obtain the optimal resource allocation strategy, with dynamically selecting the fog server, offloading decision, block size. Simulation results show that the proposed framework can reduce the energy consumption and computation overhead of the system, as well as can improve the computing efficiency.
Sistem Rekomendasi Kamera Mirrorless Dengan Metode Simple Multi Attribute Rating Technique (SMART)

Photography are an activity that can be done through a lot of other media, some uses DSLR (Digital Single Lens Reflex) camera, Mirrorless and even from their smartphones. Choosing a mirrorless camera by those who doesn’t have the knowledge of the specifications of a camera might be a problem by itself. The problem itself rose because there’re a lot of criterias that need to be considered in getting the right one and due to lack of information. That is why recommendation system based on website are developed which can help users choosing the right product using SMART (Simple Multi Attribute Rating Technique) method. SMART method are a method for taking decisions that are multi attribute used for supporting decisions which have other alternatives. In developing this website it uses 5 brands of camera that is Canon, Fujifilm, Nikon, Panasonic and Sony. The validity test based from UAT (User Acceptance Rate) of 35 respondents gives a result of 85.71% accuracy that agrees that the recommendation system is appropriate to what user wants and proves that the system gaves the recommendation based on what user wants and beneficial to be used.