Reviewerly/exHarmony huggingface dataset:
Instructors’ Conceptions of the Efficacy and Motives for Online Instruction in Open and Distance Learning Mode

This paper employs the Community of Inquiry Theoretical Framework to examine the instructors’ conceptions of training efficacy and motives for online instruction in open and distance learning mode at the Open University of Tanzania (OUT). Using a hermeneutic phenomenology research design, data were collected from 25 instructors at the Open University of Tanzania through in-depth interviews, which were corroborated with questionnaires. Findings from the study indicated that, generally, continuous professional development significantly impacts how instructors teach in the online environment. Although most participants rated training as an effective strategy for online instruction, the programmes lacked regularity and fell short of more relevant training programmes. More specifically, findings indicated that the training package missed some essential contents required by instructors. A wide range of motives for conducting online training is elaborated. This paper recommends that institutions invest in relevant regular training programs for online instructors and ensure comprehensive training packages. Keywords: distance learning, online instruction, online learning, open and distance learning, open learning
Motivations for Consuming Avatar-Specific Virtual Items on The Zepeto Gaming Platform

Metaverse provides the experience of Dual-spacization of life in the real world and digital environments and allows users to have an immersive, three-dimensional presence with their avatars. Due to the popularity of the Metaverse and the early presence of video game giants within it, special virtual items for avatars have also received increased attention. Zepeto, the largest platform in South Korea and Asia with users from all over the world, including Iran, is one of the metaverse game platforms collaborating closely with global fashion leaders. Due to the growing popularity of Korean wave products such as K-Pop among Iranian children and adolescents, Zepeto has become a popular gaming universe. This article addresses two issues in light of these findings. First, within the framework of the consumption values theory, users' motivation to consume virtual items specifically for avatars in Zepeto is investigated, followed by an examination of the potential repercussions of their consumption. The target population for this article was Iranian Zepeto users. Using the methods of thematic analysis and random sampling, semi-structured interviews were conducted with 35 Iranian Zepeto users. The results indicate that in the Zepeto game, consumption has become a mode of representation, and users experience the consumption of virtual fashion items from various brands during their adventures in virtual worlds.
Technological Prospection of Intellectual Property and Innovation of High School Educational Technologies

The focus of this technological prospection is to carry out an evaluation of patents and computer programs aimed at basic education, along the lines of the National Common Curricular Base (BNCC) and that allow interactive teaching-learning between legal guardians, students and teachers. In the methodological approach, a bibliographical review was carried out in scientific articles and the use of technological prospecting methods in the search for anteriority and similarity, aiming to cover nationally and internationally, in the patent databases and computer programs of the European Patent Office (ESPACENET), World Intellectual Property Organization (WIPO) and the National Institute of Intellectual Property (INPI), with the use of Boolean descriptors and operators. The systematization analyzed the pertinence with the theme, excluding patents without relevance with the filters performed, patents repeated in the databases and those that do not have a deposit record. Thus, this work aimed to observe the gaps regarding the registration of patents and computer programs in official bodies on systems and applications intended for the educational area, assisting in decision-making with the mapping of existing technologies deposited in official bodies and encouraging for the improvement and development of education. The results obtained showed a constant growth in the number of patents granted over the years, with the largest domain of specific deposits for the basic education area of China (68.75%), which reflects being the country that presents the best index in PISA 2018 (Program for International Student Assessment). On the other hand, Brazil, which has the second highest number of patents granted (6.73%), either by patents or by computer programs, after data mining, is in 53rd place in the PISA 2018 evaluation. In this way it was also possible to observe a gap between the TMDICs that allow interaction in collaborative activities between students, teachers, and legal guardians in the process of meaningful learning. In this way, the need for more encouragement for TMDICs, public educational policies and the effective participation of society and family in school units, especially in public schools, is demonstrated.
Solutions to Enable the Use of TIC in Education in Regions with Low Internet Connectivity: a Literature Review

The lack of Internet access increases the digital divide in the educational sector of developing countries, specifically in vulnerable and remote areas. In this article, a literature review was conducted to identify and to characterize LMS solutions adopted in regions with low Internet connectivity to leverage ICT integration in education. What are the LMS solutions to make the use of ICT in education feasible in regions with low Internet connectivity? Results found that, the Moodle app is the only solution that considers social isolation and can be used in distance education, in asynchronous activities, where there is no stable Internet. This result shows that it is worth investigating other solutions that considers social isolation and can be used in e-learning, with limited Internet access. Other solutions consider access to digital content on a local network, experienced by people living in remote areas with very little or no access to the Internet. The results of this study can be used to consider actions, projects and policies related to e-learning and ICT dissemination, mainly to people in vulnerable situation.
Vocational and Technological Education: Challenges and Perspectives in Teaching-Learning Articulation

Vocational and technological education (VTE) serves as a significant avenue for workforce integration. In the context of networked public policies, VTE has emerged as an alternative to foster new work-related skills. This study aims to assess the perceived effects of an integrated professional training model from the viewpoint of program managers and agents. The chosen focus is the Basic Education and Professional Education Program (EBEP), a collaboration involving Sesi, Senai, and the federal government. Employing a qualitative approach, the research involved semi-structured individual interviews with program collaborators, supplemented by document analysis and participant observation spanning 2016 to 2022. The findings underscore EBEP's social value, providing quality vocational education across urban and peripheral regions. The model emphasizes collaborative training with a civic approach and competency-based methodology, geared toward entrepreneurial job roles. Nonetheless, increased investment and scrutiny of other vocational education initiatives at local and national levels are imperative.
Play as a Learning Resource for Literacy Skills: a Systematic Review

The main objective of this research is to analyze the impact of game-based learning on literacy. For this, following inclusion and exclusion criteria, a systematic review has been carried out in the Scopus, ScienceDirect, Researchgate and Web of Science databases with date restriction (2013/2023). A total of 10 articles met the inclusion criteria. The results show the game's effectiveness, showing benefits in the acquisition and development of literacy, as well as other benefits such as greater motivation and positive emotions at school. Lastly, it should be noted that there is still much to be researched on this strategy to take advantage of all the benefits it generates.
Mobile Applications Developed as Educational Products for Mathematics Education: State of the Art

The use of mobile technologies has become increasingly present in people's daily lives, exerting a significant influence on forms of behavior and information consumption. They are often the subject of debates about their integration into didactic-pedagogical contexts in school institutions, since they exert pressure for changes in the teaching model, aiming to improve the quality of education. In this sense, with this work we seek to expose results of a bibliographic investigation of the "state of the art" type and of a qualitative nature, with the objective of identifying, selecting and discussing data from professional master's dissertations that resulted, as educational products, in the development of mobile applications aimed at basic level mathematics education, having as a time frame research completed from 2010 until the end of 2022. The question that guides this research is the following: what is the incidence of the production of mobile applications as educational objects in the context of Professional Masters?. Data collection was carried out in the repositories of the Catalog of Theses and Dissertations of the Coordination for the Improvement of Higher Education Personnel (CAPES) and the Portal of Educational Products and Objects (eduCAPES) in which descriptors were used combined with the Boolean operator "AND". The returned results allowed us to select four academic productions and their educational products framed in criteria that value the objective of this work and within the theme addressed. Despite the strong discussion about the use of mobile technologies in education, the productions are still few, within the scope of professional master's degrees, and lacking in consistency and methodological refinement, revealing that there is a need to develop more studies that explore this field of knowledge.
Energy Efficient Virtual Machine Placement in Dynamic Cloud Milieu Using a Hybrid Metaheuristic Technique

Energy consumption in cloud datacenters is an alarming issue in recent times. Although handful of researches have been conducted in this domain during virtual machine placement in cloud milieu, efficient techniques are still scarce. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable datacenters as well as servers in a cloud environment, while optimizing the energy consumption. The method we propose has been compared with certain relevant hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively.
Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams

This study evaluates the performance of large language models, specifically GPT-3.5 and BARD (supported by Gemini Pro model), in undergraduate admissions exams proposed by the National Polytechnic Institute in Mexico. The exams cover Engineering/Mathematical and Physical Sciences, Biological and Medical Sciences, and Social and Administrative Sciences. Both models demonstrated proficiency, exceeding the minimum acceptance scores for respective academic programs to up to 75% for some academic programs. GPT-3.5 outperformed BARD in Mathematics and Physics, while BARD performed better in History and questions related to factual information. Overall, GPT-3.5 marginally surpassed BARD with scores of 60.94% and 60.42%, respectively.
An FPGA Smart Camera Implementation of Segmentation Models for Drone Wildfire Imagery

Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels. Thus, a significant amount of research has been carried out to investigate and apply computer vision techniques to address this problem. One of the most promising approaches for wildfire fighting is the use of drones equipped with visible and infrared cameras for the detection, monitoring, and fire spread assessment in a remote manner but in close proximity to the affected areas. However, implementing effective computer vision algorithms on board is often prohibitive since deploying full-precision deep learning models running on GPU is not a viable option, due to their high power consumption and the limited payload a drone can handle. Thus, in this work, we posit that smart cameras, based on low-power consumption field-programmable gate arrays (FPGAs), in tandem with binarized neural networks (BNNs), represent a cost-effective alternative for implementing onboard computing on the edge. Herein we present the implementation of a segmentation model applied to the Corsican Fire Database. We optimized an existing U-Net model for such a task and ported the model to an edge device (a Xilinx Ultra96-v2 FPGA). By pruning and quantizing the original model, we reduce the number of parameters by 90%. Furthermore, additional optimizations enabled us to increase the throughput of the original model from 8 frames per second (FPS) to 33.63 FPS without loss in the segmentation performance: our model obtained 0.912 in Matthews correlation coefficient (MCC), 0.915 in F1 score and 0.870 in Hafiane quality index (HAF), and comparable qualitative segmentation results when contrasted to the original full-precision model. The final model was integrated into a low-cost FPGA, which was used to implement a neural network accelerator.
Flood Prediction with Optimized Attributes and Clustering

An emergency is a situation that poses an immediate risk to health, life, property, or environment. Most emergencies require urgent intervention to prevent a worsening of the situation. So, it is always better to predict the emergency before its happening and to take action for optimizing the loss. In this work, we tried to predict the flood by analysing the monthwise rainfall index of a particular area. First, we tried to find the months which have more contributions towards predicting the flood. For this, we used Particle Swarm Optimization (PSO) as feature selection technique and then applied classification algorithms such as J48 and Random Forest (RF). The experimentation was done for both without and with feature selection on the considered dataset. The results obtained without feature selection indicate that 70.34% and 78.81% of data are correctly classified and with feature selection 66.10% and 76.27% respectively in J48 and RF. Then we removed the class attribute from the dataset to see the effect of results when the class is not available and we applied K-mean and Density Based clustering techniques on the same dataset. It was observed from the results that Kmean with manhattan distance approach and Density Based clustering without feature selection classifies accurately 72.03% and 72.88% of data respectively. Similarly, when Kmean and Density Based clustering were used with feature selection, it was found that Kmean and Distanced Based clustering result in correct classification of 70.03% and 68.64% of data. We had also compared the model building time for both classification and clustering techniques using without and with feature selection. It was noticed that although the accuracy percentage was decreased with feature selection in both the cases, however, the model building time was reduced by 29%, 50%, 78%, and 60% in case of j48, RF, K-Mean, and Density Based techniques respectively.
EXCHANGE OF GEO-SPATIAL DATA THROUGH GEO-WEB

Applications for the web, desktop, and mobile are made using the Arc-GIS server. Arc-GIS for server offers end users apps and services for managing, visualising, and analysing geographical data.The proposed work deal with sharing of GIS data to any user using the provision of Arc-GIS server. It provides the facility of publishing Arc-map data in the global server and local server which facilitates user to share and access the Geo spatial data i.e vector data and Raster data.Intelligent online maps offer a distinctive method of viewing complex data. They assist you in mining large datasets, uncovering hidden patterns, and pounding up seemingly unconnected facts. Using Arc-GIS server Online, data may be created on a desktop, transferred to the cloud, and shared
Unifying Assurance A Framework for Ensuring Cloud Compliance in AIML Deployment

Intrusion poses a significant challenge in Cloud networks, necessitating the development of efficient mechanisms to mitigate intrusions and enhance system security. To address this, we propose a novel Artificial Bee-based Elman Neural Security Framework (ABENSF). This framework involves rescaling the raw dataset using preprocessing functions and integrating an optimal fitness function based on artificial bees into the feature extraction phase to identify and extract attack features. Additionally, the monitoring mechanism in ABENSF enhances network security by proactively preventing attacks. By employing tracking and monitoring functions, known and unknown attacks can be effectively thwarted. We validate the proposed framework using the NSL-KDD dataset in Python software and conduct a comparative analysis to assess its performance against existing techniques. Our results demonstrate that the developed model outperforms other methods in terms of attack prevention and overall security enhancement.
Data Guardianship: Safeguarding Compliance in AI/ML Cloud Ecosystems

AI has found widespread application across various sectors, including security, healthcare, finance, and national defense. However, alongside its transformative advancements, there has been an unfortunate trend of malicious exploitation of AI capabilities. Concurrently, the rapid evolution of cloud computing technology has introduced cloud-based AI systems. Regrettably, vulnerabilities inherent in cloud computing infrastructure also pose risks to the security of AI services. We observe that the integrity of training data is pivotal, as any compromise therein directly impacts the efficacy of AI systems. Against this backdrop, we assert the paramount importance of preserving data integrity within AI systems. To address this imperative, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cyber security framework. Leveraging block chain technology and smart contracts emerges as a fitting solution to tackle integrity challenges, owing to their characteristics of shared and decentralized ledgers. Smart contracts facilitate automated policy enforcement, enable continuous monitoring of data integrity, and mitigate the risk of data tampering.
The traditional core of DOM (Document Object Model) and its formal representation in fDOM

This article discusses the differences between two models of web documents: the traditional DOM (Document Object Model) and fDOM (formal DOM). The discussion begins with a description of Core DOM, which represents the structure of web documents as a hierarchical tree of nodes. Each node represents elements, attributes, and textual content, allowing for navigation, modification, and deletion of document components. The visual structure of DOM is illustrated using HTML code. The use of Isabelle/HOL, a higher-order logic system, for working with fDOM is then considered. This allows for the formalization of the mathematical properties of fDOM, ensuring their correctness and resistance to vulnerabilities. An example of code in Isabelle/HOL demonstrates how operations on web document elements can be defined and verified. The article also compares DOM and fDOM, highlighting differences in their approaches to representation and interaction with web documents. It is emphasized that while the traditional DOM provides a flexible interface for web documents, fDOM offers a more rigorous, mathematical representation, focused on precision and strictness. The discussion concludes with the advantages and disadvantages of fDOM. Among the advantages are formal verification, strong typing, consistent behavior, clear definitions, and enhanced security. However, increased complexity and development time, less flexibility, higher resource costs, and a steep learning curve are noted as disadvantages of fDOM. The conclusion emphasizes that fDOM offers a reliable, verified foundation for web applications but also introduces additional development requirements, making it particularly useful for applications where correctness and security are important.
Mitigating partial content update issues on web pages through SPA parameter optimization

The experience of recent years has shown that the number of active web applications developed according to the fundamental principles of single-page applications continues to grow steadily despite a decrease in the number of newly created ones. This indicates a shift in focus from the importance of developing anew to the necessity of maintaining, servicing, and optimizing the existing end product. Furthermore, with the advancement and improvement of web development technologies, the potential expectations of users increase, necessitating the provision of a worthy user experience and the enhancement of application competitiveness. A significant role in this process is played by the partial content update process on web pages, widely used in the development of single-page applications. Most modern JavaScript frameworks designed for client-side web development employ the «out-of-box» technique, thereby providing the developer with complete discretion in deciding on approaches to its application. However, the automatic use of the web page content update mechanism does not guarantee desired results and often leads to average or sometimes unsatisfactory values that could have been avoided. This article provides an analysis of the characteristics of the performance parameters of a single-page web application, explores alternative methods to solve the stated problem, investigates potential «problematic» areas of the client-side of applications, compares the functioning of content update mechanisms of the most popular JavaScript frameworks Angular and React.js, and formulates recommendations for optimizing the overall application performance. The article concludes with tables of Core Web Vitals metrics for evaluating the effectiveness of the conducted optimization. Finally, potential vectors for further research are proposed.