Reviewerly/exHarmony huggingface dataset:
Factors influencing the self-efficacy of online learning in university students

The study’s main goal was to find out how academic evaluation and monitoring, student-teacher interaction, and the virtual teaching process affect a person’s sense of self-efficacy in online learning. The research was carried out during the post-pandemic period, with a sample for the study made up of 272 students from a public university in the Arequipa region of Peru. The methodology that was used is no experimental and has two well-defined phases; first, an exploratory factor analysis was performed, which aimed to condense the information in the original variables into smaller series; second, a confirmatory factor analysis was performed, by modelling structural equations based on variances. It is concluded that academic evaluation and monitoring do not positively influence online learning self-efficacy, because the educational model has deficiencies in the online evaluation criteria due to the distrust of teachers and students; However, the student-teacher interaction and virtual teaching process, if they would positively influence the online learning self-efficacy.
Implement the Fuzzy Controller by Imitating the Tuned PID Controller Using Reinforcement Learning

This article presents how to design a fuzzy controller for controlling engine speed. The fuzzy controller is designed to mimic the effect of the PID controller, where the PID controller is adjusted using reinforcement learning. Controller performance is compared between a controller tuned with control system tuning and one tuned using reinforcement learning. While PID tuning with the control system is fast and effective for model-based systems, reinforcement learning tuning is suitable for highly nonlinear systems. The result of the experiment is to use the reinforcement learning agent to calculate the gain of PID, after which the fuzzy logic learned in tuned PID is generated. Fuzzy logic control is easy to adjust for nonlinear systems control. Fuzzy inference system tuning both using ANFIS and pattern search are compared.
Emotional states and relationship with the educational sense and usefulness of information and communication technologies in university students

The study’s objective was to determine how information and communication technologies, through the sense of utility and the educational sense, relate to the student’s emotional states. Research is not an experiment and is based on numbers. A sample of 102 first-year university students in general studies was used. The methodology that was used had two well-defined phases: first, an Exploratory Factor Analysis was carried out with the objective of condensing the information of the original variables into smaller series; secondly, a Confirmatory Factor Analysis was carried out that allowed internal consistency tests of the model to be carried out using non-parametric statistics; correlation and linear regression tests were performed. It can be said that the educational and utilitarian uses of information and communication technologies give different explanations for changes in emotional states.
Is Training Useful to Detect Deepfakes? : A Preliminary Study.

Generative Adversarial Networks (GANs) constitute a significant breakthrough due to their ability to generate realistic synthetic data. Sometimes, the synthetic creation resembles a real person or their voice, and we call them deepfakes. Deepfakes are used for multiple purposes, including malicious ones. This makes them a major concern due to the widespread lack of information about their existence and because they are increasingly generated more realistically. It is becoming increasingly difficult to differentiate them from real content. In this study, we evaluate human ability to differentiate between a real and a synthetic face. The results indicate that people are unable to differentiate between real images and deepfakes, but that some training slightly improves these results. Therefore, our hypothesis is that human accuracy in identifying synthetic faces could be improved with thorough training on how to detect deepfake faces.
Text mining supporting initial literature review : When you start a new area of R&amp;D – text mining may help you with this task

During a master’s or Ph.D. Thesis, starting a new project or research and development area (R&D), each investigator must get into the subject and scope of interest. For such knowledge, you must perform a literature review and explore what has been published in the area. There are several methodologies, some more scientific as systematic review, and others. Some have a defined protocol but mainly depend on the authors’ analysis. This research includes several database searches to retrieve text to analyze using text mining, to help on the initial step of review – identify keywords. When you are just starting a new R&D and you need to know the buzzwords for further and deeper investigation you can use text mining to help you. This work is a starting point to perform a state-of-the-art in health literacy area. Further than “Health literacy”, the top ten words are “University” and “School” beyond “Medicine”, “Patients”, care and “information”.
A Simple Clustering Method for Binary Data based on a Binary Associative Memory

This paper studies clustering methods for binary data based on nonlinear dynamics in a binary associative memory (BAM) characterized by ternary connection parameters and signum activation function. First, as a set of binary data is given, we select several the center candidates. Applying a simple learning rule to the candidates, we obtain a BAM having multiple fixed points. Second, each datum is applied as an initial point and basin of attraction to a fixed point gives a cluster of the datum. Third, the clustering is evaluated as compared with desired distribution on the clusters. Repeating these three steps, the algorithm explores better clusters. Applying the algorithm to typical examples, the algorithm efficiency is confirmed.
Universities in Era 5.0: the Future Accountant

Recently, research on the role of educational institutions in the Era 5.0 in finding solutions and contributing to the needs of students, organizations and society in general has sparked interest within communities and the nation, due to its power and character of innovation and transformation. Thus, and considering the new challenges posed to universities/higher education institutions by Industry 5.0 and Society 5.0, we intend with this paper to promote reflection on the role of Universities 5.0 in the accounting profession in the Era of Education 5.0.Given the recent discussion on the subject, this paper was developed based on a thematic literature review of the last decade, using the “Scopus” database from Elsevier and “Google Scholar”, to contribute to the discussion of the role of universities in the process of change, adaptation and transformation of professions, as well as in the preparation of the accountant in this digital era, the era of Education 5.0.We have found that the role of universities is crucial in the process of change, transformation and innovation of professions, organizations, and societies. Universities in the Era 5.0 must be at the forefront of the process of sustainable education and training to meet the needs of society. However, there is still a long way to go, and it demands speed in the teaching, pedagogical, organizational and social practices of these educational institutions. We will contribute to the reflection on the potential and implications of new technologies in the dissemination of Social Responsibility and sustainability practices.
Library in django framework to standardize early-stage web application development

The development of web applications with Django does not normally follow a unique structure, however, the start of a new project has certain steps that are somewhat repetitive, which causes the developer to lose this time in general configurations This work proposes the development of a standard template, with enough elements to start the development of Web applications with Django, allowing the developer to focus on the main problem, which is the complexity and functionality of his business and system. The initial results show that the generation of an application, using the proposed library, can mean to the developer an approximate time of between 90 seconds and 105 seconds (about 2 minutes). Allowing a considerable saving of time, which usually takes the configuration and structuring of a Django application.
Feedback in Virtual Software Development Teams

Feedback is an important practice of software development. Effective feedback enables the continuous improvement of individuals and teams. In this paper, we conduct a multivocal literature review, including both academic and industrial/gray literature, aiming to investigate the adoption of feedback processes in virtual software development teams. By including different types of primary studies in this review, we aimed to get the perception of both practitioners and academics about the benefits and challenges related to the feedback process. As a contribution, we summarize a set of good practices to support each stage of the feedback process. Good practices may help feedback givers and receivers to increase the positive outcomes of feedback.
A Data-Driven Blockchain-Based Marketplace to Promote Transparency and Accountability in City-Region Food Systems

In the last ten years, several new challenges have emerged regarding food: from the production crisis caused by wars and the climate change to the unsustainable food waste in the occidental societies or the health problems associated to processed food, additives and other equivalent substances.Resilient and sustainable food systems are needed to survive against all these risks.The Food and Agriculture Organization has envisioned that this only would be possible if we put together large urban centers, the conglomeration of smaller cities in their surrounds and the surrounding and interspersed peri-urban and rural hinterland in a unique City-Region Food System (CRFS).CRFS, however, will be only an opportunity if all food agents in the area collaborate together as part of the same network.This collaboration must overcome the potential conflict of interest and the lack of trust among the different agents, so transparency and accountability must be improved in all transactions.Therefore, in this paper we describe a data-driven blockchain-based marketplace where food agents in a CRFS can share and get transparent and accountable information about other industries, users, or producers in the region.To motivate food agents to join the platform, information in the marketplace is not free and consumers must pay a fee before getting it, generating new income flow for participants.The system is supported by Ethereum network and web technologies.An initial experience was carried out to validate the proposed marketplace in the context of a CRFS living lab in Spain.
Understanding Protection Motivation for Identity Theft in Information Security: An Extension of The Protection Motivation Theory with Anxiety

As technology has progressed, people have begun to perform various daily tasks by using different online applications and services, which has led to a large number of incidents of identity theft. These unpleasant events incur expensive costs for individuals, companies, and authorities, and as a result, identity theft concerns them greatly. Although scholars of information security have devoted their efforts to developing technology to prevent identity theft, it is not clear what factors influence an individual’s security protection motivation. Few empirical and behavioral studies on this topic have been conducted. To fill this gap, this study extends the protection motivation theory with anxiety, elucidating the influences of a future negative event (identity theft) on an individual’s current emotion, which in turn determines protection motivation. This study proposes a research model that explores the influences of threat appraisal (perceived severity and perceived susceptibility) and coping appraisal (response efficacy and self-efficacy) on anxiety, which affects protection motivation. The results in this study provide a more holistic comprehension of identity theft and protection motivation, and can be referred to when developing efficient security guidance and practices.
Enhanced medical images through multi-scale mathematical morphology by reconstruction

Medica1 images are indispensable tools for several medical tasks, by allowing a much more accurate diagnosis to help in decision making. For this reason it is essential to have good quality medical images. However, sometimes they show degradations such as poor contrast or imperfections in details. In this article we present an algorithm that improves the details of medical images, preserves the average brightness, preserves the structural similarity and corrects the problem of poor contrast. This algorithm improves medical images using the top-hat transform by reconstruction, which extracts brightness and darkness features on multiple scales. These features are used to enhance the medical image. The algorithm was tested with medical images from two public databases. Experimental results show that the proposed algorithm improves contrast, introduces little noise, preserves natural brightness, detail and similarity to the original medical image.
A Crossover Operator Based on Building Blocks Preservation

In this study, we propose a novel crossover operator for solving optimization problems in genetic algorithms. Our method preserves existing building blocks in the parent chromosomes, which improves the convergence rate and the quality of the results. We compare the performance of our proposed method with three other crossover operators, including one-point, n-point, and uniform crossovers. We use a set of X test problems from the global optimization literature to evaluate the performance of these four genetic algorithms. To assess the effectiveness of our proposed method, we conduct two types of analysis, including a comparison of the convergence curves and an evaluation of the quality of the results obtained.
Spam Text Detection using Machine Learning Model

This research presents a classification between spam and non-spam messages by removing duplicate sentences or words. meaningless word and various marks The data is then classified by machine learning techniques and compared by differences between the data sets. Quantitative transformations were performed on each model to find the most efficient model. which can filter spam messages efficiently and quickly By getting the best comparison results in terms of information. quantitative conversion and model use The experimental results showed that Of all the tests, the model that performed best was Random Forest, with an average accuracy of 97
A Data-driven Identification of Teaching Patterns

Finding and sharing best practice teaching patterns in computer science education contributes to the improvement of the quality of the teaching in this field. These patterns are obtained from data mining of different types of data from a variety of sources e.g., surveys, interviews, videos, students, lecturers. How to collect, to unify formatting, and to transfer data from these diverse sources to one centralized repository for pattern identification is the aim of this paper.
The Metaverse: Investigating the Motivations and Experiences of Early Adopters in RecRoom

Recent technological developments in the metaverse have made this immersive landscape an imminent reality. Limited existing metaverse research focuses on the adoption of metaverse technologies, but little research has been conducted to understand the experiences of early adopters and the motivators and inhibitors which affect their adoption and continued use, despite their role in widespread adoption. This research highlights the motivations of early adopters in using metaverse technologies, with a primary focus on the experiences of early adopters in using social metaverse applications. A qualitative, inductive approach was employed for research, given its strengths in exploring unforeseen themes and allowing early adopters to raise issues which matter most to them. Research was conducted in the metaverse application, RecRoom, with semi-structured interviews taking place between avatars (n=8). The results suggested that motivations for adopting and using the metaverse at this stage were overwhelmingly hedonic, with networks, interest and COVID-19 playing a key role in adoption. The affordances of socialisation, immersion and extended ability significantly affected enjoyment, while harassment and privacy attitudes emerged as inhibitors to continued use. Alongside capturing attitudes of early adopters at this juncture of metaverse development, this research provided suggestions for metaverse researchers and developers for the next stage of innovation.
Enhancing Keystroke Biometric Authentication Using Deep Learning Techniques

Ensuring the data security and integrity of data is a critical challenge for organizations. The need for effective intrusion detection and user authentication in critical infrastructure systems has become especially relevant recently. Detecting insiders is challenging because they are difficult to detect and identify, so advanced techniques are required to prevent their activities. This paper discusses an approach that uses a Siamese neural network to authenticate users by transforming their biometric typing data or behavioral characteristics into images. To achieve this goal, the paper considers several methods for transforming numerical (tabular) keystroke data into images. The transformed keystroke data is then used to train Siamese neural networks for image similarity detection to distinguish a real user from a possible insider. The experiments conducted in this study show that transforming time series data into images is a robust and effective approach to improve the accuracy of intrusion detection and user authentication. This paper highlights the importance of comparing different methods for transforming numerical keystroke data into images to improve the accuracy of intrusion detection and user authentication. The proposed methodology can be useful for intrusion detection and user authentication, improving the security and reliability of critical infrastructure systems.
Quadruped Stanford Pupper Type Robot Modeling and Simulation

Nowadays there are many research efforts in the field of artificial intelligence applied in all the fields of robotics. There are developed and trained new models both supervised and unsupervised learning. ln order to be able to get a reliable final product, a very important step is modeling and simulation. This paper focuses on the analysis of a quadruped robot, Stanford Pupper type, for which the mathematical model for the locomotor system was developed in order to be then implemented in a simulator that allows the development of machine learning algorithms for training it. There can be implemented in the simulator various use cases, like: learning to walk both supervised and unsupervised scenarios, learning to navigate on a zone with uneven surface and with small obstacles, or learning to climb stairs or other big obstacles.
Investigating the State of Blockchain Adoption in the South African Finance Industry

Significant growth has recently been observed within the DeFi sector. Most existing academic literature focuses on the potential benefits of blockchain adoption but does not address the actual current state of adoption in different countries. The objective of this study was to understand the current state of blockchain adoption within the South African finance industry by investigating factors that influence adoption, current perceptions and use cases within the industry. Using expert sampling and qualitative inquiry, this research identified several factors that positively or negatively influence blockchain adoption, which have been organised by technological, organisational, and environmental categories. The major factors influencing adoption include supportability, scalability, skills, regulations, collaboration, and competition. Perceptions of blockchain were mixed with some citing the technology as disruptive and some questioning its necessity. Various potential use cases were identified, among which digital identity and stable coins emerged as significant. Participants noted that digital identity received the most engagement in the industry.
A Framework for Reviewing and Improving Non-Functional Requirements in Agile-based Requirements

The ill-definition or neglect of non-functional requirements has been frequently reported as a problem in agile requirements engineering. Despite the importance of such requirements for the success of software projects, little research is available for reviewing them in the agile requirements. The few existing proposals are either too heavy-wight, lack agility assessment, or lack clarity on how to be embedded in an agile environment. These factors might reduce their adoption among agile practitioners. To address this problem, we propose a framework for reviewing non-functional requirements in the agile requirements formulated as user stories. Our proposed framework utilizes a combination of natural language processing and artificial intelligence techniques to automate analyzing and predicting non-functional requirements in user stories. Then, the artifacts needed for the requirements reading and reviewing technique are generated to assist requirements inspectors in reviewing non-functional requirements, identifying possible defects, and consequentially making a reasonably informed review decision. Our proposal aims to mitigate the most reported causes of the problem in the literature: the minimal documentation, the functionality-driven nature of agile requirements, and insufficient non-functional requirements knowledge.
Multidimensional Modelling in NoSQL Database : A Systematic Review

NoSQL Database Manager Systems (DBMS) are an alternative to analytical systems and have been used in this regard. As analytical systems often use data multidimensional modelling, the question arises of how to apply this modelling to NoSQL DBMS, which has such differentiated physical characteristics of relational databases. A systematic literature review (SLR) is performed to gather the aspects prompting the application of multidimensional modelling to NoSQL. One hundred twelve were pre-selected, and 67 were classified as relevant according to the research questions defined for this SLR. Three main approaches are highlighted: classical multidimensional modelling, multidimensional modelling with junctions of aggregated facts and dimensions in a single entity, and multidimensional modelling with aggregated facts and dimensions in a single entity, and adding pre-computed summaries. These approaches are explained, and their differences and similarities are detailed.