Reviewerly/exHarmony huggingface dataset:
Influence of poverty on digital education in Uganda’s rural secondary schools: reference to covid-19 lockdown: a case study of Kayunga District in Uganda

This study explored the impact of poverty on digital learning, examined the role of parents’ income in the success of digital education in rural schools, and provided suggestions to improve teaching and learning using digital facilities in times of pandemic in rural schools. Uganda’s rural schools lack enough facilities to support the online school system. And there is a lack of in-depth analysis of poverty's influence on digital education. This study used cross-sectional survey data utilizing quantitative and qualitative research approaches. The findings revealed that rural schools in Uganda do not receive efficient digital education where parents and guardians of the school-going children cannot afford the financial costs of the digital devices that could support children’s home digital learning. Parents' income plays an influential role in the digital education of their children. 38.7% of the parents are farmers, and 6.3% of parents are not employed at all. Over 22% of students revealed that their parents supported more than four school-going children. There is a need for governments, institutions and local communities to develop a technological infrastructure for digital learning and develop new curriculums which can support virtual classrooms. Areas that emerged for further research.
ANALYTICAL APPROXIMATIONS WITH EXACT NON-INTEGRAL PART FOR VOLTERRA’S POPULATION MODEL

The present paper is strongly motivated by the brilliant work ofWazwaz [9, Sections 4 and 5] computing analytical approximation in the form of a series truncated at t8 and applying [4/4] Pade approximant to the series. In this paper, we make an attempt to workout analytical approximations in the form of the series truncated at t4 and apply suitable Pade approximations as well as asymptotic approximations with the following features: i) The solution contains exact non integral part. ii) The solution exhibits the population rapid rise along logistic curve followed by decay to zero in the long run. iii) The solution is reasonably comparable with that of Wazwaz [9] using the information from the series with terms only upto t4.
Digital Orientation and Digital Innovation Performance in Moroccan IT Workers: A Moderated Mediated Analysis

This study investigates the links between digital orientation, digital dynamic capacities, and digital innovation performance, while also examining the moderating effects of knowledge sharing and knowledge hiding among Moroccan IT workers. Drawing on the knowledge-based view and dynamic capability theory, the study utilizes a cross-sectional quantitative design and involves a sample of 350 employees from Casablanca, Morocco. Data analysis is conducted using Smart PLS for measurement and hypothesis testing. The results reveal that knowledge sharing moderates the positive relationship between digital orientation, digital dynamic capabilities, and digital innovation performance. Conversely, knowledge hiding negatively moderates the relationship between digital innovation performance and digital dynamic capacities. This study contributes to the development of a knowledge-based perspective and dynamic capability theory, offering practical implications for businesses and IT professionals seeking to enhance their digital competencies in a rapidly evolving digital environment. To gainqa a more comprehensive understanding of the factors influencing digital innovation performance, future research should address the limitations of this study, including its cross-sectional and quantitative nature, as well as its focus on IT workers from Casablanca, Morocco. Future studies may employ longitudinal and qualitative methods and explore the moderating impact of other variables.
AN OVERVIEW OF ONLINE GAME ADDICTION AMONG INDONESIAN EARLY ADULTHOOD DURING THE COVID-19 PANDEMIC

The COVID-19 pandemic has brought about changes in human life, including changes in human behavior related to the use of digital technology. Indonesia is the third country with the highest number of online game players in the world. Excessive use of online games can lead to online game addiction in users. Online game addiction can happen to anyone, including early adults. This study aims to determine the excessive use of online games and the level of addiction in early adulthood during the COVID-19 pandemic. The method used in this study is a quantitative method with cross-sectional design based on descriptive analysis. The sampling technique that is used is the purposive sampling. Participants in this study were early adults with the age range of 18-25 years who played online games as many as 100 participants. The measuring instrument used in this study is The Indonesian Game Online Addiction which consists of 7 questions with a likert scale interval from one to five. The level of online game addiction is relatively high at 47 participants (47%) in early adulthood during the COVID-19 pandemic. It can be concluded that the level of addiction from online game users for participants with an early adult age range 18-25 years is relatively high, with the majority of players being male and female, which are almost equal. For further research, it is recommended to further develop research on online game addiction in early adulthood.
Pengembangan Aplikasi Evaluasi Pembelajaran Wizer.Me pada Mata Pelajaran IPS di MTs Darussalam Aryojeding

This study aims to determine and analyze the development, feasibility and practicality of the Development of Evaluation Media Using the Wizer.me Application on Student Learning Outcomes in Social Studies Subjects at MTs Darussalam Aryojeding.The type of research used in this research is research and development (Research and Development). The 4D model includes 4 stages, namely: Define, Design, Development, and Desiminate. The subjects in this study were students of class VIII A and class VIII C who participated in social studies learning at MTs Darusalam Aryojeding 2021/2022, with a total of 82 students.&#x0D; The results of the study found that: 1.) Media development Evaluation includes validation from experts, namely media experts, education experts, linguists and IT experts. So that media validation data is obtained which is then processed with the Interaclass Coefitien Correlation (ICC) formula, assisted by processing using the SPSS version 22 application for Windows, the results are 0.974. if the value is more than 0.5 then it can be concluded that the level of validation is sufficient. 2.) Evaluation media using Wizer.me was declared very feasible after analyzing data processing using Microsoft Excel so that a total score of 1312 was obtained with an average level of 4.0 and a percentage level of 88% with the predicate "Very Eligible". 3.) The evaluation media using Wizer.me was declared practical after processing the research data obtained from the questionnaire, and a practical test was carried out so that a total score of 881 was obtained with an average level of 4.45 and a percentage of 86% in the "Very Practical" category.
Creating a Drag-and-Drop Project with TypeScript

In this chapter, you create an awesome drag-and-drop project in TypeScript. It is a fairly large project in comparison to the earlier projects you've tackled so far (see Figure 8-11 for the completed project).
Evaluation of blended learning effectiveness by students of different academic programmes

As modern universities actively use various models of blended learning that combine traditional and digital learning technologies, it seems necessary to find their optimal integration and develop appropriate criteria for assessing the effectiveness of such integration in achieving educational objectives. The research is aimed at identifying trends and searching for reasons for the efficient, optimal or inefficient use of educational technologies when organizing the learning process in Russian universities in a blended format. The paper presents the results of the analysis of student assessments of the efficiency of the blended learning technologies (BLT) based on the unique system of criteria and methods introduced by the authors to study such efficiency. The main research method used was a survey in which 295 students took part from various academic programmes of St. Petersburg State University and Northern (Arctic) Federal University. Respondents were asked to evaluate the efficiency of BLT according to the effective, didactic and resource criteria. Based on statistical data processing, the following conclusions are drawn: students evaluate the employed BLT as optimal; readiness and interest of teachers (according to students) in creating comfortable and favorable conditions for blended learning is quite high, despite the existing difficulties in organization and technical maintenance of the equipment at the university level. At the same time, the survey revealed heterogeneity in the assessment of each criterion and determined differences in the assessments given by the students from different academic programmes.
The introduction of digital technologies into the practice of physical education of students at the university: the results of a survey of lecturers and students of Mari State University

The organization of physical education at a university differs from the organization of teaching other disciplines; therefore, a special approach to the introduction of digital technologies into the practice of physical education at a university should be used. To develop and critically understand the further digitalization of the process of physical education of university students, it is important to obtain empirical data on what experience of using digital technologies in the practice of physical education at the university teachers and students have accumulated, and what problems they have encountered when used these technologies. The author carried out a comprehensive survey of teachers (n=20) and students (n=50) of the Faculty of Physical Education, Sports and Tourism of Mari State University to study their experience and attitude towards the use of digital technologies in the physical education of university students. The survey results indicate a generally positive attitude of teachers and students towards the introduction of digital technologies into the process of physical education. Most students consider the introduction of digital technologies into the process of physical education useful and relevant. As the main difficulties associated with the process of physical education digitalization, the students specified the following: 1) the majority of them lack experience in using digital technologies specifically in the field of physical education; 2) difficulties of self-organization; 3) increasing the volume of intellectual workloads. The majority of lecturers surveyed do not have sufficient experience in using digital technologies in their professional activities; there is a lack of time to master completely the opportunities offered by digital technologies, which is associated with a heavy professional workload.
Image deblurring method based on feature fusion SRN

This article proposes a SRN algorithm of feature fusion to solve the problem of image motion blur. First, an Attention Residual Module (ARM) is designed to add channel attention between residual units to increase feature extraction capabilities. Second, a feature pyramid structure is constructed to improve the representation ability of the network. Then, a multi-scale coordinate attention feature fusion structure is built to improve the deblurring effect of the model. Finally, optimising the loss function improves the robustness of model to discrete points and increases the stability of the model. The testing is performed on the GOPRO dataset. Our algorithm is the best, with PSNR and SSIM reaching 34.72 dB and 0.97. Tested on the foreign object data set, the PSNR and SSIM of our algorithm have been greatly improved, and compared with other methods, it has a great advantage in detailed texture recovery.
Language-based transfer learning approaches for part-of-speech tagging on Saint Petersburg Corpus of Hagiographic texts (SKAT)

The article describes an experiment about training a part-of-speech tagger using artificial neural networks on the St. Petersburg Corpus of Hagiographic Texts (SKAT), which is being developed at the Department of Mathematical Linguistics of St. Petersburg State University. The corpus includes the texts of 23 manuscripts dating from the 15th–18th centuries with about 190,000 words usages, four of which were labelled manually. The bi-LSTM, distilled RuBERTtiny2 and RuBERT models were used to train a POS tagger. All of them were trained on modern Russian corpora and further fine-tuned to label Old Russian texts using a technique called language transfer. To fine-tune transformer-based language models it was necessary to tokenize the texts using byte pair encoding and map tokens from the original Russian-language tokenizer to the new one based on indices. Then the model was fine-tuned for the token classification task. To fine-tune the model, a tagged subcorpus of three hagiographical texts was used, which included 35,603 tokens and 2,885 sentences. The experiment took into account only the tags of the parts of speech, the classification included seventeen tags, thirteen of which corresponded to parts of speech, and the remaining four marked punctuation marks. To evaluate the quality of the model, the standard metrics F1 and Accuracy were used. According to automatic evaluation metrics, the RuBERT model showed the best result. Most of the errors were related to incorrect generalization of linear position patterns or to the similarity of word forms in both the extreme left and extreme right positions.
Transmedia project as a format of creative communication: Process mechanisms and successful performance markers

The article is devoted to the development of a procedural scheme for the implementation of a mechanism for successful creative communication in the format of a transmedia project, which acts as an activator of creative environment features during the interaction between specialists of different professional fields and recipients of the relevant content. The transmedia project is considered in the research as a communicative heterogeneous space for the creation of fictional worlds and universes that exist simultaneously in various media formats and are coherently interconnected by certain creative components that allow effectively achieving the communication goal. The study offers an empirically tested procedural scheme containing a logical sequence of certain elements: the type of project, a typical feature of a transmedia project, a relevant creative communicative indicator and a target marker of communication effectiveness. The novelty of the approach lies in the possibility of updating the procedural scheme proposed in the framework of the transmedia project in the real communication space through the identification of dominant target communicative markers. Using a critical thematic analysis of three different types of transmedia projects implemented in various industries, a modeling scheme of a universal procedural mechanism for the effectiveness of creative communicative activity is proposed, empirically derived and emphasizing individual features of a creative communicative act, with a projection of success or failure markers of communication, identified according to the dominant target principle of identifying relevant creative elements.
Efficient Panoramic Video Coding for Immersive Metaverse Experience

Efficient panoramic video coding plays a crucial role in the metaverse and Web 3.0 by enhancing content delivery, accessibility, and scalability. However, panoramic video is viewed in the spherical domain, while it is coded in the typical two-dimensional plane. Such a framework renders the compression-distortion metric unable to align well with the spherical-distortion perceived by viewers, resulting in inefficient rate-distortion optimization (RDO) in the coding process. Additionally, independent RDO on an individual panoramic video frame is also inefficient as it disregards the distortion propagation caused by the inter-prediction of video coding. To address these issues, a temporal-dependent spherical-distortion model is proposed for efficient panoramic video coding. Using the geometric projection principle, an independent mapping model between the spherical-distortion and the compression-distortion is first established for individual frames. Subsequently, the temporal-dependent spherical-distortion model for consecutive frames is deduced based on the inter-prediction structure. This model is then employed to guide the RDO process for panoramic video coding. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods, achieving an average bitrate reduction of 4.2% compared to the reference software VTM with the 360Lib extension.
Collusion-based Poisoning Attacks Against Blockchained Federated Learning

The emerging metaverses can be regarded as the current wave of the digital civilization revolution due to it build a new connection between the physical and virtual space, and such connections make it possible for virtual space to interact with real word. However, some concerns, such as privacy data leakage incedents, still hinder the widespread deployment of the metaverse. The blockchained federated learning (FL) architecture can train a federated global model towards multiple stakeholders without the need for centralized management servers and the risk of privacy data leaking as a new paradigm to address the metaverse dilemma. Although many studies have been proposed for the great development potential of blockchain and federated learning for metaverse, few studies, if not none, have explored security threat against the blockchained FL architecture. This work report a prominent phenomenon that the combination of FL and blockchain may pose a more serious security threat, and first to propose Collusion-based Poisoning Attacks (CPA) in the blockchained FL architecture. To be specific, the malicious participants manipulate the model training process so that poisoned global models are aggregated with the most significant probability during the FL training phase. Then submits to the blockchain system and enables the poisoned models to be affected for all participants due to the strong consistency of blockchain. Experimental analysis demonstrates that the proposed CPA can achieve superior performance on multiple aspects and has better performances than the state-of-the-art attack method on stealth and robustness. This research can shed light on the hidden vulnerabilities of the blockchained FL architecture and lay the foundation for build a more open and secure metaverse.
Combined RF-Based Drone Detection and Classification

Abstract: Despite various positive uses, drones are also utilised for illegal purposes like as drug trafficking, weapon smuggling, and posing dangers to security-sensitive locations such as airports and nuclear power plants. Existing drone localisation and neutralisation solutions are predicated on the drone having previously been discovered and categorised. Despite significant progress in the sensor sector over the last decade, no viable drone detection and classification approach has been suggested in the literature. The frequency signature of the sent signal is used in this research to identify and classify drones using radio frequency (RF). Using commercial drones, we built an unique drone RF dataset and provided a comprehensive comparison of a two-stage and integrated detection and classification framework. Both frameworks' detection and classification results are demonstrated for a single-signal and simultaneous multi-signal situation. We demonstrate that the You Only Look Once (YOLO) framework outperforms the Goodness-of-Fit (GoF) spectrum sensing framework in a simultaneous multi-signal situation, and that it outperforms the Deep Residual Neural Network (DRNN) framework in classification
Integrating Machine Learning with Cryptography to Ensure Dynamic Data Security and Integrity

Abstract: Keeping user data secure is very important in the modern era, as there are growing concerns on data privacy across the world. With very strict laws in place such as General data Protection Regulation (GDPR) in Europe and California Consumer Privacy Act (CCPA) in California, it has become a necessity for businesses and government agencies across the world to secure data in use, irrespective of whether it is at rest or in transit. Also, secure data transfer is a mandatory requirement for organizations which handle confidential data. Data privacy breach and data leak can impact the organizational value and reduce customer loyalty. In this paper, various methods of securing data transfer using a combination of Cryptography and advanced Machine Learning techniques have been discussed. The high-level idea is that ML should complement the existing cryptography methods used in practice, to build a robust and impeccable secure platform making it almost impossible for intruders to get into the system.
LINEAR AND NON-LINEAR WAVELET APPROXIMATIONS OF FUNCTIONS OF LIPSCHITZ CLASS AND RELATED CLASSES USING THE HAAR WAVELET SERIES

In this paper, linear wavelet approximation of a functions f belonging to Lipξ, and Lip(ξ, p), 1 ≤ p &lt; ∞, have been determined by using Haar scaling function and Haar wavelet series. A non-linear wavelet approximation of function belonging L2(R) and Lipα class has been obtained. The comparison of the order of linear and non-linear approximations have been studied. It is observed that a non-linear approximation error is better and sharper than linear approximation error.
New Method for Neuro Cureness

In this paper, I proposed new idea for patients who have injured neurons in brain. Nowadays Neural Network and Artifitule Intelligence has been subject of many researchers to solve Neural Networks. By information of CNN and its theory it will be solved some questions about NeuroScience.
Retracted: Evaluation Model of Physical Education Teaching Effect Based on AHP Algorithm

[This retracts the article DOI: 10.1155/2023/9363403.].
Retracted: GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

[This retracts the article DOI: 10.1155/2022/2538807.].
Retracted: Optimization of College Teachers’ Performance Management Using Artificial Intelligence Technology

[This retracts the article DOI: 10.1155/2023/4203776.].
Double Momentum Backdoor Attack in Federated Learning

&#x0D; &#x0D; &#x0D; Federated learning is conceived as a privacy- preserving framework that trains deep neural networks from decentralized data. However, its decentralized nature exposes new attack surfaces. The privacy guarantees of federated learning prevent us from inspecting local data and training pipelines. These restrictions rule out many common defenses against poisoning attacks, such as data sanitization and traditional anomaly detection methods. The most devastating attacks are usually the ones that corrupt the model without altering the performance of the main task. Backdoor attacks are prominent examples of adversarial attacks that often go unnoticed in the absence of sophisticated defenses. This paper sheds light on backdoor attacks in federated learning, where we aim to manipulate the global model to misclassify the samples belonging to a particular task while also maintaining high accuracy on the main objective. Unlike existing works, we adopted a novel approach that directly manipulates the gradients’ momentums to introduce the backdoor. Specifically, the double momentum backdoor attack computes two momentums separately based on malicious and original inputs and uses them to update the model. Via experimental evaluation, we demonstrate that our attack scenario is capable of introducing the backdoor while successfully evading detection.&#x0D; &#x0D; &#x0D;