Reviewerly/exHarmony huggingface dataset:
Development of Task-Based Instructional E-Module for Data Processing Unit of Information Technology Courses

Information technology has become a necessary skill for work, and it is a compulsory public course for students at Zhumadian Vocational and Technical College, where data processing is one of the learning units. Teaching methods and materials are crucial to improve the learning effect in information technology courses, and task-based teaching is being promoted by more and more educators as a teaching method that can effectively improve the teaching effect in information technology courses in higher education institutions. This research work attempts to develop a task-driven instructional e-module for students at Zhumadian Vocational and Technical College and to test its functionality and usability. The research selects 5 experts and 22 lecturers of information technology courses for the samples. The experts fill in an expert evaluation form to assess the E-module’s functionality, while the lecturers fill in a questionnaire form to evaluate its usability, and three experts ensure the validity of the expert evaluation form and the questionnaire. The SPSS 26.0 is used for data analysis to evaluate the e-module. The results of this research showed that five experts considered the e-module to be highly functionality in terms of format, content, language, media display, and interactivity. The 22 lecturers who participated in the questionnaire also agreed that the e-module had a high level of usability in terms of usability, ease of use, attractiveness, and clarity. Meanwhile, experts and lecturers agree that students at Zhumadian Vocational and Technical College can use task-based teaching to improve their study of data processing modules in information technology courses. It is hoped that this research can provide some useful reference for further promoting the teaching reform of information technology major in higher vocational colleges.
Augmented Reality Application: An Approaches to Enhance Students Motivation in Learning PC Assembly

One of the new technologies that has begun to spark high interest among educators in the context of pedagogy is Augmented Reality (AR) technology. This paper examines the effect of the use of AR application on student motivation based on 4 dimensions in the ARCS Model, namely Attention, Relevance, Confidence, and Satisfaction. This study uses an experimental study with a one-group pre-post design type. This research examines the differences in students learning motivation before and after using AR applications in learning Introduction to Computer System Course. A total of 41 respondents who used the AR application answered the pre and post questionnaires. The results show that the use of AR application has successfully increased student motivation by 20.10 %. Percentage values for each ARCS dimension and difference tests showed significant values. Based on these results, it can be concluded that the use of technology such AR applications in classroom has affected the enthusiasm and motivation of student. Therefore, an educator has to form a teaching style in teaching millennial student who raised with technology at their fingertips by integrating technology in education to providing better teaching and learning opportunities, especially to the next generation.
The Role of Technology in Promoting Sustainability and Business Performance in Industries and Business Models: The Case of Saudi Aramco

This research paper explores the significant role of technology in promoting sustainability and enhancing business performance in various industries and business models, specifically focusing on Saudi Aramco. The paper investigates how integrating technology-driven sustainability initiatives influences environmental Impact and business performance. The study empirically analyzes how technology adoption influenced Saudi Aramco's ecological Impact between 2019 and 2021. It also examines the key factors contributing to the successful adoption and integration of technology-driven sustainability practices and the challenges and barriers limiting the company's sustainability initiatives. The study adopts a mixed-methods approach, combining quantitative and qualitative analyses to comprehensively understand the intersection between technology, sustainability, and business performance. The correlation analysis reveals that R&D investment has a positive correlation with upstream carbon intensity and a negative correlation with flaring intensity. This suggests that increased R&D investment may lead to higher carbon intensity but lower flaring intensity, indicating the potential of technology adoption to reduce flaring practices. Additionally, a positive correlation is observed between R&D investment and revenues, suggesting that higher investment in technology can increase Saudi Aramco's revenues. The results support the hypotheses that technology adoption significantly influences Saudi Aramco's environmental Impact and business performance. While acknowledging the limitations of a single case study approach, the study leverages the extensive technological initiatives undertaken by the company to inform the broader role of technology in promoting sustainability and business performance. The findings and conclusions drawn from this study target to contribute to the knowledge base around the intersection of business, technology, and sustainability, offering valuable insights for organizations striving to thrive in the twenty-first century.
Python for Web Scraping and Crawling

In an technology dominated via the proliferation of digital content, the extraction and evaluation of statistics from the giant expanse of the World Wide Web have end up imperative for various packages, ranging from commercial enterprise intelligence to analyze endeavors. This studies paper delves into the multifaceted realm of net scraping and crawling, elucidating the pivotal role played by Python in these tactics. Web scraping, the automated extraction of information from web sites, and net crawling, the systematic traversal of the net to index and accumulate information, constitute vital techniques for harnessing the wealth of records to be had on line. Python, with its wealthy ecosystem of libraries and frameworks, has emerged as a preeminent tool for developers and researchers engaged in internet records extraction. This paper explores the fundamental concepts and methodologies of net scraping and crawling, inspecting the moral considerations and legal ramifications related to those practices. It delves into the numerous Python libraries, such as BeautifulSoup and Scrapy, that empower builders to navigate the intricacies of HTML systems and automate statistics retrieval effectively. The studies also investigates the demanding situations and nice practices in internet scraping, considering issues including website online get admission to guidelines, price proscribing, and data integrity. Moreover, the paper explores the programs of net scraping and crawling across various domain names, from competitive intelligence and market research to content aggregation and sentiment analysis. By losing light at the symbiotic courting among Python and net information extraction, this research contributes to the information of the evolving panorama of records retrieval within the virtual age. It emphasizes Python's pivotal function in permitting responsible By shedding mild at the symbiotic courting among Python and web facts extraction, this studies contributes to the understanding of the evolving landscape of data retrieval in the digital age. It emphasizes Python's pivotal position in enabling responsible and green net scraping and crawling practices, supplying researchers and builders with a comprehensive guide to navigate the complexities and moral concerns inherent within the extraction of precious insights from the expansive net frontier.
Dams as an Important Elements in Water Resource Balance Study for Sungai Melaka Basin

A water resource balance study is a precursor in quantifying the availability of water within Sungai Melaka basin. All elements in the water system will need to be analyzed and represented. This includes all surface water resources (i.e. main river and tributaries), storage infrastructures (i.e. dam and bunded storages), and river water control structures (i.e. weir, pumps.) In determining the actual water resource and allocations, the total water availability within the basin must be determined together with the total demand needs to be abstracted from the river system. This includes potable, irrigation for paddy, non-paddy, livestock, fisheries, and tourism demands. Computations of future demand should be determined on the basis of future population’s projection of the demand area. If the water resources available within the basin is deemed to be limited or insufficient, the available resources to cater for current and future demands have to be quantified in determining the surplus or deficit values of the basin water resources. This paper presents the development of the water resource balance system for the NAWABS Sungai Melaka basin. The decision support system provided for the assessment of water availability and water demand through several modelling scenarios is discussed in further detail.
Towards Homological Methods in Graphic Statics

Recent developments in applied algebraic topology can simplify and extend results in graphic statics – the analysis of equilibrium forces, dual diagrams, and more. The techniques introduced here are inspired by recent developments in cellular cosheaves and their homology. While the general theory has a few technical prerequisites (including homology and exact sequences), an elementary introduction based on little more than linear algebra is possible. A few classical results, such as Maxwell's Rule and 2D graphic statics duality, are quickly derived from core ideas in algebraic topology. Contributions include: (1) a reformulation of statics and planar graphic statics in terms of cosheaves and their homology; (2) a new proof of Maxwell's Rule in arbitrary dimensions using Euler characteristic; and (3) derivation of a novel relationship between mechanisms of the form diagram and obstructions to the generation of force diagrams. This last contribution presages deeper results beyond planar graphic statics.
A Local Score Strategy for Weight Aggregation in Federated Learning

Federated Learning is a new paradigm for training a machine learning model without sharing the data. Federated Learning aims to deal with this and achieve a competitive result like in centralised settings. FeTS Challenge is an initiative focusing on federated learning and robustness to distribution shifts between medical institutions for brain tumor segmentation. In this paper, we describe a method based on the local score rate for the weight aggregation of clients. We have reported results based on the Training, Validation and Testing of the FeTS 2022 dataset.
Literacy and Digital Inclusion for the Twenty-First Century. The Case of Peru

The Digital Divide (DD) has historically been one of the socio-cultural and educational problems in different Latin American countries. This research article presents a systematic review focused on Peru. For this purpose, the PRISMA methodology for the selection of relevant literature is applied. A total of 20 scientific papers are analysed. The results point to a scarce scientific production on the subject. In addition, important digital access gaps in the country stand out, where the low level of citizen competence stands out as the main factor. However, its production is increasing in recent years. It is concluded by discussing the potential of new information and communication technologies to favour economic and social development in Latin American countries. In addition, more contextualised research and training in digital skills are called to alleviate the gaps detected.
FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated Learning

This paper presents FedPIDAvg, the winning submission to the Federated Tumor Segmentation Challenge 2022 (FETS22). Inspired by FedCostWAvg, our winning contribution to FETS21, we contribute an improved aggregation strategy for federated and collaborative learning. FedCostWAvg is a weighted averaging method that not only considers the number of training samples of each cluster but also the size of the drop of the respective cost function in the last federated round. This can be interpreted as the derivative part of a PID controller (proportional-integral-derivative controller). In FedPIDAvg, we further add the missing integral term. Another key challenge was the vastly varying size of data samples per center. We addressed this by modeling the data center sizes as following a Poisson distribution and choosing the training iterations per center accordingly. Our method outperformed all other submissions.
3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors

Deformable image registration is a key task in medical image analysis. The Brain Tumor Sequence Registration challenge aims at establishing correspondences between pre-operative and follow-up scans of the same patient diagnosed with an adult brain diffuse high-grade glioma and intends to address the challenging task of registering longitudinal data with major tissue appearance changes. In this work, we proposed a two-stage cascaded network based on Inception and TransMorph models. The dataset for each patient was comprised of a native pre-contrast (T1), a contrast-enhanced T1-weighted (T1-CE), a T2-weighted (T2), and a Fluid Attenuated Inversion Recovery (FLAIR). The Inception model was used to fuse the 4 image modalities together and extract the most relevant information. Then, a variant of the TransMorph architecture was adapted to generate the displacement fields. The Loss function was composed of a standard image similarity measure, a diffusion regularizer, and an edge-map similarity measure added to overcome intensity dependence and reinforce correct boundary deformation. We observed that the addition of the Inception module substantially increased the performance of the network. Additionally, performing an initial affine registration before training the model showed improved accuracy in the landmark error measurements between pre and post-operative MRIs.
From Digital Divide to Digital Inclusion in Colombia: A Systematic Literature Review of Strategies from Education

In the last 20 years, inclusion and digital exclusion in education have undergone significant changes in a country like Colombia. To address this, various strategies have been implemented by the Ministry of National Education (MEN), the Ministry of Information and Communications Technologies (MinTic), and non-governmental organizations. Their objectives have been to reduce the digital divide between urban and rural areas nationwide, promote the use of ICTs and digital skills in education, and facilitate digital inclusion. The results have varied across different regions of Colombia. This chapter first presents the concepts of digital inclusion and exclusion, then describes the specificities of digital exclusion. Through a systematic literature review using the PRISMA protocol and consulting databases such as Scopus, Wos, and official MEN documents, the most successful forms of digital inclusion in the last two decades in Colombia are identified. This review of public policies for ICT promotion necessitates considering the role that the education system has played, is playing, or could play in the future to address these challenges and contribute to reducing digital gaps and fostering digital inclusion nationwide.
Applying Quadratic Penalty Method for Intensity-Based Deformable Image Registration on BraTS-Reg Challenge 2022

Registration of Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem. In this paper, we present our implementation of the Quadratic Penalty Deformable Image Registration (QPDIR) algorithm for the Brain Tumor Sequence Registration (BraTS-Reg) Challenge 2022. The QPDIR algorithm is an intensity-based algorithm which turns computation of deformation field to an optimization problem of minimizing terms of image dissimilarity and regularization. The terms are computed based on processing exhaustive search among image blocks and the optimization is performed using a gradient-free quadratic penalty method. The whole optimization problem is decomposed to several sub-problems and each of them can be solved by straightforward block coordinate decent iteration. The data set of the BraTS-Reg Challenge 2022 has 160 cases. For each case, pre-operative images and follow-up images of 4 different modalities including t1, t1ce, flair and t2 are provided. For each case, we apply QPDIR to register image pairs of each modality to produce the deformation field, and then add the deformation field to landmarks, and merge the predict landmarks of each modality together to compute the final predict landmarks of the case. During the validation phrase, our method produces the average median absolute error(MAE) of 4.425, the average Robustness of 0.734 and the average negative numbers of Jacobi Determinant of 87960.95. The testing phase rank of our method is at 7. Detailed study of case ID 142 are shown in the paper.
Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data Distribution

With the ever increasing importance and requirements to ensure data privacy, federated learning emerges as an promising technology for training deep learning models without having hospitals to share the raw data. MICCAI Federated Tumor Segmentation Challenge 2021 is the first international challenge on federated learning to strengthen the understanding of real-world challenges and create practical solutions in the related area. In the challenge of this year, we proposed a series of new aggregation strategies towards improving the learning performance in the context of non-IID and imbalanced data distribution. We also designed a simple collaborator selection scheme to shorten the training time while achieving a good level of model performance for brain tumor segmentation.
Romania 2000–2022: Educational Policies and Strategic Options for Digital Inclusion. Literature Review

The present chapter aims to explore the evolution and dynamics of Romania's educational strategies and policy regarding the digitization process. Applying a systematic review, we propose to analyze the objectives aimed at by the country’s strategies regarding the digitization of education, but also the way Romanian specialists perceive Romania’s educational documents in the context of accelerated technological development. Four relevant policy documents and five scientific documents (articles) were selected that fulfilled the study objectives and inclusion criteria. The analysis of both categories of documents highlights the inconsistency of the decision-makers regarding the digitization process and the lack of critical analysis among specialists. The present chapter highlights the importance and necessity of clear, coherent policy documents and the greater involvement of specialists in the decision-making process.
Bridging the Digital Divide in Higher Education: Notes from the Emergence of the COVID-19 Pandemic

This systematic literature review aims to provide a comprehensive analysis of the challenges faced by universities in bridging the digital divide in higher education during the COVID-19 pandemic, the solutions that have been learned, and policy recommendations. The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Page et al., 2021) and employs descriptive analysis (Tranfield et al., 2003) and reflexive thematic analysis (Braun and Clarke, 2019) approaches. The inclusion criteria covered scientific research articles published in the years 2019 to 2022 in the Web of Science (WoS) and Scopus databases, in English, that focused on the higher educational context. The database searches initially provided a corpus of 520 articles, which was reduced to 17 articles after screening for duplicity and applying the inclusion criteria. This review offers valuable insights into the challenges faced, the solutions learned, and the policy recommendations for bridging the digital divide in higher education, highlighting the achievements of existing studies and identifying shortcomings and research topics that merit further attention in light of the emerging situation. Further, our rigorous methodology ensures that our findings are based on a comprehensive analysis of the relevant literature, making this study a valuable contribution to the field of digital divide research in higher education.
Information Literacy and Digital Inclusion: Challenges of the Modern Information Educational Environment in Ukraine

The article is devoted to the problems of digital inequality and digital inclusion in the context of digitalization processes in Ukraine. The authors emphasize two components of digitalization processes in Ukraine: digital inclusion and digital exclusion, in the context of changes that have taken place in the country over the past two decades. The research problem is to identify the state of information literacy and digital inclusion of the Ukrainian population, including teachers. During the research, methods of interpreting survey data, comparative methods of survey data analysis, use of online tools for surveying teachers, etc. were applied. The results of two national surveys are presented. The results of the study indicate a low level of digital literacy among the upper classes of Ukraine, which is associated with uneven access to knowledge and various technologies, which contributes to the deepening of the digital gap between different layers of the population. Particular attention is paid to the analysis of the information literacy of vulnerable categories of the population, as presented by measures for informational support for elderly people. The authors present the results of the Ukrainian teachers’ self-assessment of the level of digital competence. Conclusions and recommendations are based on the analysis of the situation in Ukraine and public opinion polls and are aimed to develop solutions to minimize various forms of digital inequality in Ukraine.
Super-resolution reconstruction of remote sensing image by fusion of receptive field and attention

Abstract:Aiming at the problem that the existing models are not enough to extract the spatial details and feature channel information of remote sensing images, a super-resolution reconstruction model of remote sensing images is proposed, which integrates the receptive field and the attention mechanism. In the depth feature extraction stage of the model, several cascaded receptive field and coordinate attention blocks (RFCAB) are designed to fully extract the depth features of the image: Firstly, an RFB-CA module is designed inside the residual in residual dense block (RRDB). The model can use convolution of different scales to extract multi-scale spatial features and make both channel and space dimensions get attention. At the same time, in the process of learning features, the model pays more attention to the useful channels for the current task, so as to improve the feature representation ability of the model. In order to further improve the recovery ability of the model to detail information, a multi-scale fusion module (MSFM) was designed to obtain more detailed features by weighted fusion of features at different levels. In the 2x, 3x and 4x overscore reconstruction of DOTA dataset, the PSNR/SSIM value of this model is increased by 0.13dB/0.003, 0.17dB/0.007 and 0.24dB/0.013 compared with ESRGAN, respectively. In the 2x, 3x and 4x over-fraction reconstruction of AID dataset, the PSNR/SSIM value is increased by 0.15dB/0.004, 0.20dB/0.009 and 0.26dB/0.015 compared with ESRGAN, respectively. The experimental results show that the reconstruction effect of this model is better than other classical algorithms, and it has certain practical significance.