Reviewerly/exHarmony huggingface dataset:
OSAS Virtual Reality Lab: An Experience in OSH Training

The widespread use of ICT and connected devices significantly affect learning systems: make, do it by yourself, share, are the keywords of a new shared and open culture. This study highlights how the usage of virtual reality (VR) as learning experiences may be necessary to speed up the transfer of new and distinct information in occupational safety and health (OSH) training. Technology-enhanced knowledge structures now have an experiential role that affects and changes relational, cognitive, and emotional structures. The work intends to focus on an experimental approach based on the use of technology, identified not as an exclusive mediator of learning but as a set of additive processes of knowledge production, through the presentation of a case study on training in the OSH sector. OSAS VR lab has been developed in this Italian experience to generate an innovative visual and aural stimulation, engaging learners with the phenomenon of road accidents, with an emphasis on sleep disorders. This immersive training was tested on a sample of convenience related to the working age group (18–65 years old) with a driving licence. The study shows that innovation tools and methods are appreciated in OSH sector and confirms that they can have positive impacts for information, education and training.
Domain-Specific Automatic Item Generation for Higher Competence Levels: A Comparative Study on Three Cases

Automatic item generation can generate large amounts of assessment items from a single configuration. For items that ask students to demonstrate domain-specific competences in applying methods or analysing artifacts, the generation process also needs to reflect domain-specific properties. The paper presents an item template approach for automatic, domain-specific item generation. Three examples demonstrate how to respect domain-specific properties in the generation process.
First Evaluation of an Adaptive Tool Supporting Formative Assessment in Data Science Courses

Adaptive learning systems are tools that tailor the learning experience to the specific learner. In previous research, we designed an adaptive system to support students while practising exercises regarding the execution of statistical analyses. In this work, we investigated the system’s usability, the adaptation algorithm and the students’ engagement with the system. A quasi-experimental study was conducted with a small cohort of students, and questionnaires were used to collect quantitative and qualitative data. The main results and discussion of the findings, with respect to the research questions, highlighted possible improvements and suggested that the system has good usability and engages the students. Nevertheless, the limits of the study suggest caution: an experiment with a larger sample is currently in progress, and the partial results are similar even if they show a lower expectation.
Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People

Gesture recognition is a developing topic in current technologies. The focus is to detect human gestures by utilizing mathematical methods for human–computer interaction. Some modes of human–computer interaction are touch screens, keyboard, mouse, etc. All these gadgets have their merits and demerits while implementing versatile hardware in computers. Gesture detection is one of the vital methods to construct user-friendly interfaces. Generally, gestures are created from any bodily state or motion but typically originate from the hand or face. Therefore, this manuscript designs an Information Feedback Firefly Algorithm with Nested Deep Learning (IFBFFA-NDL) model for intelligent gesture recognition of visually disabled people. The presented IFBFFA-NDL technique exploits the concepts of DL with a metaheuristic hyperparameter tuning strategy for the recognition process. To generate a collection of feature vectors, the IFBFFA-NDL technique uses the NASNet model. For optimal hyperparameter selection of the NASNet model, the IFBFFA algorithm is used. To recognize different types of gestures, a nested long short-term memory classification model was used. For exhibiting the improvised gesture detection efficiency of the IFBFFA-NDL technique, a detailed comparative result analysis was conducted and the outcomes highlighted the improved recognition rate of the IFBFFA-NDL technique as 99.73% compared to recent approaches.
Automated Gesture-Recognition Solutions using Optimal Deep Belief Network for Visually Challenged People

Gestures are a vital part of our communication. It is a procedure of nonverbal conversation of data which stimulates great concerns regarding the offer of human–computer interaction methods, while permitting users to express themselves intuitively and naturally in various contexts. In most contexts, hand gestures play a vital role in the domain of assistive technologies for visually impaired people (VIP), but an optimum user interaction design is of great significance. The existing studies on the assisting of VIP mostly concentrate on resolving a single task (like reading text or identifying obstacles), thus making the user switch applications for performing other actions. Therefore, this research presents an interactive gesture technique using sand piper optimization with the deep belief network (IGSPO-DBN) technique. The purpose of the IGSPO-DBN technique enables people to handle the devices and exploit different assistance models by the use of different gestures. The IGSPO-DBN technique detects the gestures and classifies them into several kinds using the DBN model. To boost the overall gesture-recognition rate, the IGSPO-DBN technique exploits the SPO algorithm as a hyperparameter optimizer. The simulation outcome of the IGSPO-DBN approach was tested on gesture-recognition dataset and the outcomes showed the improvement of the IGSPO-DBN algorithm over other systems.
Emotional Education in Elementary Schools: A systematic review from 2015 to 2017

This article presents a literature review of emotional education in elementary schools. The topic in question is considered of utmost importance. To this end, articles published on the Web of Science (WOS) during the years 2015, 2016 and 2017. The 37 selected articles, after cleaning up the data, were classified by subject for analysis according to whether they addressed the students or the teachers. 31 of the earmarked documents investigated students and 8 teachers. For the student documents, the articles were classified according to the role that emotional education played: as an end in itself, 18 articles, as a means, 12, and together with other variables, 4 documents. The articles intended for teachers focus on the initial training, as well as on the ongoing training, getting the results of the experience of these and of the development of emotional education in the classroom.
Initial Student Attention-Allocation and Flight-Performance Improvements Based on Eye-Movement Data

At the onset of their flight careers, novice pilots often lack clarity regarding the standard attention-allocation pattern. Therefore, to enhance the efficiency of initial flight training, it is crucial for students to develop a comprehensive understanding of flight control and attention-allocation behavior during the learning process. In this study, flight-performance data and eye-movement data from experienced instructors in no-power stall scenarios were collected to create an attention-allocation training course. An experimental group underwent the attention-allocation training course, while a control group followed the traditional teaching curriculum. The disparities between the flight performance and eye-movement indices of the two groups after they completed their respective courses were compared to evaluate the effectiveness of the training. The finding indicate significant differences between the speed losses, altitude losses, and mean course deviations of the instructors and the control group; these indicators had p-values of 0.01, 0.004, and 0.001, respectively. Moreover, significant differences were observed between the altitude losses and mean course deviations of the instructors and the experimental group; these indicators had p-values of 0.006 and 0.001, respectively. The experimental group, which underwent attention-allocation training, exhibited eye-movement indices that closely resembled those of the instructor group, and its instrument scanning was more strategic, thereby resulting in improved flight performance from that of the control group. Additionally, correlations were observed between flight-performance indices and eye-movement indices of the students. Overall, this study demonstrates the effectiveness of an attention-allocation training course designed specifically for a no-power stall scenario. It effectively enhanced the training outcomes of novice pilots, promoted an appropriate allocation of attention to instrument displays, introduced a novel approach to flight training, and ultimately contributed to aviation safety.
Functional Matrices on Quantum Computing Simulation

In simulating Quantum Computing by using the circuit model the size of the matrices to deal with, together with the number of products and additions required to apply every quantum gate becomes a really hard computational restriction. This paper presents a data structure, called Functional Matrices, which is the most representative feature of QSimov quantum computing simulator which is also provided and tested. A comparative study of the performance of Functional Matrices with respect to the other two most commonly used matrix data structures, dense and sparse ones, is also performed and summarized within this work.
The Effects of Spherical Video-Based Virtual Reality and Conventional Video on Students’ Descriptive Writing Achievement and Motivation: A Comparative Study

This study compares the effects of Spherical Video-Based Virtual Reality (SVVR) and Conventional Video (CV) on students’ writing achievement and motivation. A quasi-experimental method was used in a primary school’s Chinese Descriptive Article Writing courses. Twenty-eight fourth-grade students were randomly divided into two groups. In the SVVR group, students observed the writing scenes using SVVR devices. In the CV group, students watched the writing scenes through conventional video. SPSS software was used for the statistical analysis of the data. The results show that: (1) there is no significant difference ( U = 714, p = .061) in the overall writing achievement between SVVR and CV groups. However, students in the SVVR group performed significantly better in Organization ( U = 693, p = .029) and Content ( U = 609, p = .003) than those in the CV group. (2) Overall writing motivation improved in both SVVR and CV groups. The improvement of writing expectations in the CV group was significantly higher than that of the SVVR group ( t = −2.119, p = .044). Therefore, we suggest that schools: (1) design the integration method of SVVR and writing learning to solve the problem of the gap between students’ immersive situational experience and completing their writing goals. (2) use the immersive and interactive features of SVVR technology to create virtual experience scenes to improve students’ observation and description abilities. (3) add activity clues to guide students to experience SVVR scenes purposefully and plan and transform situational interest into learning ability. (4) further optimize SVVR devices to improve their usability during learning.
Statistical characterization of the chordal product determinant of Grassmannian codes

Abstract We consider the chordal product determinant, a measure of the distance between two subspaces of the same dimension. In information theory, collections of elements in the complex Grassmannian are searched with the property that their pairwise chordal products are as large as possible. We characterize this function from an statistical perspective, which allows us to obtain bounds for the minimal chordal product and related energy of such collections.
CONTENT ANALYSIS ON TWO ENGLISH TEXTBOOKS FOR THE EIGHTH GRADER OF JUNIOR HIGH SCHOOL

This paper reports a study that analyzed the content of two English textbooks used in junior high school in Indonesia. The two textbooks are officially published by Indonesia's Ministry of Culture and Education. The first is "When English Rings a Bell," written by Siti Wachidah, Asep Gunawan, Diyantari, and Yuli Rulani Khatimah and published in 2017, and the second is "Modul Pembelajaran Jarak Jauh pada Masa Pandemi Covid-19 untuk jenjang SMP Mata Pelajaran Bahasa Inggris untuk semester genap kelas VIII” which is published in 2020. The findings revealed similarities and differences between the two textbooks. Two English textbooks differ greatly because they have different goals. The second book is not a revision of the first, but it appears to be the solution to the pandemic. The first textbook is designed to improve language skills through the use of communicative approach, both spoken and written, with English as the medium of communication. However, the second textbook is expected to be beneficial and meaningful for students to achieve the intended competence independently. Despite differences in goal or focus in developing the textbooks, they share similarities in terms of basic competence that students must achieve.
Analysis of the Digital Competence of the Students of Mathematics Sciences of the Francisco de Morazán National Pedagogical University

The use of information and communication technologies has increased progressively in Honduras with the emergence of the pandemic, educational centres have had to adapt their educational offerings to a virtual methodology. This research has been contextualised in students of mathematical sciences at the Francisco Morazán National Pedagogical University. The research question was: What levels of Digital competence do NPUFM Mathematics Science students have and what is the relationship between its dimensions and their age and frequency of use of technological resources? The general objective was: To analyse the Digital Competence of NPUFM Mathematics Science students and its relationship with age and the use of ICT. By means of convenience sampling, 255 students were selected to participate in the research. A questionnaire was used for Digital Competence analysis consisting of six dimensions. A qualitative descriptive and correlational methodology was used for data analysis. Positive and significant correlations were obtained between all the DC dimensions. They were also positively related to age, but not to time spent using ICT on a daily and weekly basis. In addition, a negative correlation was found between age and time spent using ICT resources on a daily and weekly basis.
Comparison of Different Pedagogical Designs for an ITS: The Case of Oral Speech as an Ill-Defined Domain

This paper explores the development of the Elevator Pitch Intelligent Companion (EPIC), an Intelligent Tutoring System (ITS) designed to improve students’ oral communication skills. Three design variants of EPIC were evaluated on a group of 246 engineering students at a Latin American university, focusing on different task selection strategies: one with a single, iterative task, another with different tasks solved in one step, and the last involving tasks with dependent sub-tasks. Evaluations, considering usability, learning, and student engagement, revealed that no one design was superior in all aspects. Each model showcased its unique strengths and weaknesses, which emphasizes the complexity of designing ITS systems for ill-structured domains. The findings provide valuable insights into ITS design for such domains.
Collaborative Learning in Mixed Reality Using WebXR and H5P

Collaboration is an important element in learning activities but it is difficult to facilitate it in remote education. Mixed reality provides the opportunity to create a seamless shared environment with learning materials, co-located students and remote participants. To investigate how students can learn with such a collaboration scenario in mixed reality whilst minimizing the disruption in existing learning workflows, we realized the open-source system Realitybox Collab. It is a reusable H5P module based on the WebXR Device API specification and can be integrated into existing learning management systems like Moodle. It allows lecturers to upload 3D models and to annotate them with other interactive content. Students can then collaboratively view these learning activities in the shared space using virtual reality headsets, augmented reality on the smartphone or on a desktop computer. We evaluated the solution in a user study with 14 students. The results indicate a high usability, a positive attitude of the students towards collaboration with mixed reality and validate the seamless integration of the WebXR system into the existing learning infrastructure. Realitybox Collab is a step towards widespread collaborative learning solutions with mixed reality that are embedded in existing learning management systems and which can be accessed on the Web from any device.
HyTEA: Hybrid Tree Evolutionary Algorithm

Hearing Loss affects an ever-growing number of people of all ages. It can occur due to a multitude of sources such as genetics, diseases, ageing, or noise exposure. If not treated properly and timely it may lead to socioeconomic difficulties such as poor job performance, hardship in finding a job, and social isolation. In this work, we propose HyTEA, a framework based on Evolutionary Computation to create Decision Tree like models to identify people that are likely to be diagnosed with hearing loss, so they can be called for screening by a health professional. To achieve this, we will use historic data about patients who have been diagnosed with hearing problems and complement it with publicly available socioeconomic information. The models created should provide some understanding of the reason a decision is being made since this is key for health professionals. To build Decision Trees we usually rely on greedy induction algorithms which may result in overfitting of the training data. To counter this problem, HyTEA uses a combination of two Evolutionary Algorithms, namely Structured Grammatical Evolution and Differential Evolution to generate Decision Trees. The results show that HyTEA is capable of consistently modelling the problem space and predicting hearing loss with an accuracy of approximately 73%. Additionally, we propose a visualisation tool based on t-SNE to help identify the patients that are being wrongly classified.
An Epistemology on Role of AI-Powered E-commerce Chatbot to Support CX

This paper describes the role of intelligent bots in the e-commerce business. In recent years, we have seen massive advances in the field of artificial intelligence. It’s no surprise that AI and ML is gaining more and more attention in digital commerce. Real-world products and applications can range from product recommendations, order fulfillment and shipping, intelligent chatbots, augmented reality, and sentiment analysis. The landscape for the use of AI-powered chatbots in the e-commerce industry has changed a lot recently. Some of the most popular products and approaches have been flawed by a compromised immune system in a very short space of time by a renewed global push for services and customer experience, and along the way, the COVID-19 epidemic has changed the nature of digital commerce business. Meanwhile, the durability of market forecasting models has disastrously collapsed due to transformed business terrain. With a massive increase in e-commerce sales and services, there's no question about this tremendous AI chatbot usage.
Biometric Masking to Re-establish the Database Through Watermarking with Distortion Control

Earlier days, data is basic in numerous fields like medication, scientific applications, where databases are used beneficially for information sharing. Be that as it may, the databases face the danger of being pilfered, taken or mishandled, which could end in huge amounts of security dangers concerning proprietorship rights, information tinkering and protection insurance. Watermarking is used to install possession rights on social databases being shared. Various strategies that support reversible watermarking strategies are proposed as of past due to display the privileges of holders close by getting better particular information. Most progressive techniques change the essential information to an immense degree, bringing about information quality degeneration, and can't accomplish great harmony between vigor against horrendous assaults and information securing. A stable and reversible database watermarking technique, Genetic Algorithm and Histogram Shifting Watermarking (GAHSW), is proposed for numerical social information. This is used to choose the first-rate riddle key for social occasion database, wherein the watermarking will be added with balanced winding and space. The histogram of the envisioning botch is acclimated to embed the watermark with better strength. Primer outcomes uncover the viability of GAHSW and show that it outperforms the advanced techniques as far as heartiness against resentful attack and defending of information quality.
Dual Sentiment Analysis for Domain Adaptation

Sentiment analysis or opinion mining is a sensitive topic which has gain much attention in recent years. Data used in this study are customer reviews collected from Amazon.com. This is because the reviews contain highly diverse data, and classifying such data is difficult. Bag of words (BOWs) is most used text modeling for statistical machine learning in opinion mining and sentiment analysis. Performance of BOW is not much efficient as it shows lot of deficiencies in handling the polarity-shift problem. In order to solve this problem, a model called dual sentiment analysis (DSA) is proposed. It uses a data expansion technique which creates a reversed review for each and every training and test review. This is again passed to a dual algorithm which uses both the original review and also the reversed training reviews for making the sentiment classifier. Finally, dual-prediction algorithm is used to classify the test based on two sides of a review.
Exponential Aquila Optimizer (EAO)-Based Deep Fuzzy Clustering for Document Retrieval

Finding relevant documents that are appropriate for query optimization is a known challenge in the field of document retrieval. In this study, we created a novel method for document retrieval called exponential Aquila optimizer (EAO)-based deep fuzzy clustering. The suggested method successfully identifies pertinent documents and tries to comprehend how documents relate to queries in terms of their significance for query optimization. Here, we use deep fuzzy clustering to produce cluster-based inverted indexing. In this instance, a created optimization technique named EAO is used to carry out a deep fuzzy clustering training procedure. By combining EWMA and AO, on the other hand, the produced EAO is redesigned. Additionally, for user-based searches like: B. Multigram and semantic queries, complicated query matching is carried out utilizing the Tversky index. With a maximum accuracy of 1, a maximum recall of 0.956, and a maximum F-value of 0.977, the suggested technique performs admirably in terms of performance metrics like precision, recall, and F-value.