Reviewerly/exHarmony huggingface dataset:
Evaluations of Design and User Experience in Virtual Reality: A Systematized Bibliographic Review

Virtual Reality (VR) technology provides the sensation of immersion in virtual digital spaces, through interactions that occur in different dimensions of perceptions. Considering the lack of research involving VR in evaluations of Human Computer Interaction (HCI), Emotional Design, and User Experience, the present study aimed to investigate the state-of-the-art in this subject. For this, a systematic literature review was carried out. The question to be answered is: How do users change behavior in visual interaction with different artifacts in real and virtual environments and how that influences the perception (emotional, semantic, and usability) of individuals? Structured analyzes were applied to scientific articles that address the experience of healthy adult participants and their interaction relationship with environments and artifacts immersed in VR. After screening and in-depth reading steps, 27 articles were selected for meta-analysis discussions. The results point to the existence of a scientific gap since the works found and analyzed only partially answered the research question, which, because of this, may indicate an open field for studies involving VR This technology can be a viable tool with potential of assisting and complementing the methodological processes already consolidated in the Design and Human Factors areas.
A Bibliometric Review of Research on Intelligent Personal Assistants: Present Status, Development, and Future Directions

Voice assistants based on Artificial Intelligence (AI) are computer programs that use natural language processing (NLP) and machine learning (ML) algorithms to recognize and respond to voice commands and queries in a human-like way. These voice assistants use speech recognition technology to understand spoken commands and questions, process them, and provide relevant responses. The objective of this study is to examine the intellectual framework and effectiveness of voice assistants and to analyze the literature produced by renowned researchers in terms of authors, keywords, and major organizations. This review seeks to offer valuable insights into the growing body of literature on virtual assistants. In this article, the authors have used the bibliometric method to systematically summarize the current state of voice assistant research. They analyzed 563 articles related to Voice assistants from the Scopus database between 2002 and 2023 using the R-Studio ‘Biblioshiny’ tool. The analysis includes an examination of the core journals, articles, authors, institutions, and relevant countries to determine the most influential voice assistants literature. This study also provides a detailed understanding of Co-occurrence network, Co-citation network and Coword analysis. The authors have also used thematic maps to identify the different research topics of voice literature and have grouped them into four clusters, including Motor themes, Niche themes, Emerging themes, and Basic themes. The article concludes by discussing the limitations of the study and highlighting future research directions.
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Study on Stack Exchange

Context: Technical debt (TD) can bring short-term benefits to software projects, but its presence is associated with issues such as decreasing product quality. Recent literature has proposed various approaches for identifying and managing TD, but most of them focus on the software developers’ point of view. Little is known about how project management practitioners perceive and manage TD items. Goal: This work aims to investigate how project management practitioners discuss and experience TD. Method: To achieve this goal, our work mines, curates, and selects a total of 108 TD-related discussions on the Stack Exchange Project Management (SEPM) Q&amp;A site. These discussions amount to 547 posts and 882 text comments on the subject. We analyze this data set quantitatively and qualitatively, using open coding to derive TD types, indicators, and management practices. Results: We identified 74 indicators used for recognizing debt items and 126 TD management practices. The types of debt most discussed at SEPM are process and people debts. This contradicts studies done with developers where code and design debts are most discussed. Conclusion: The perspective considered by project management practitioners to analyze the TD phenomenon is different from the one considered by other roles in the software development process. Our work organizes the identified TD indicators and management practices into a Sankey diagram, which may assist TD management practitioners and serve as guidance for future research on the subject.
An Deep Convolutional Neural Networks are used to Detect Cyberbullying on Social Networks.

The ramifications of cyberbullying, a horrific kind of inappropriate conduct that may take place online, are upsetting. Speech is the most common form of expression in social networks, despite the fact that it may take other forms. The automated identification of such instances requires the use of intelligent system components. The majority of the existing research has adopted a conventional approach to machine learning to address this problem; however, the majority of the models that have been constructed are exclusively relevant to a certain social network. Deep learning-based models are effective for the identification of cyberbullying events, according to recent study. These models claim to be able to overcome the limitations of standard models and improve detection performance. In this investigation, we focus on the most recent findings that have been published on the subject of interest. We were able to duplicate and validate the findings of this body of research by using the same datasets (Wikipedia, Twitter, and Formspring) as the authors of the original study. Next, we looked at how well the models worked on other social media platforms and applied the previously developed algorithms to a new dataset from YouTube, which consisted of 54,000 postings contributed by 4,000 different users. We also evaluated the performance of the trained models after moving them from one platform to another and then analyzed the results. Deep learning-based models from our company perform much better than machine learning models that were trained on the same data from YouTube. We believe it is essential to take into account the potential impact that individuals’ social media accounts might have on deep learning-based models and to include the relevant data into the overall picture.
KEY ELEMENTS SHAPING THE DIGITAL PROFICIENCY OF PROSPECTIVE ENGLISH EDUCATORS

Article. This article delves into the essential factors influencing the cultivation of digital competence among prospective English educators. The authors underscore the pivotal role of digital competence in the professional training of teachers specializing in this discipline. Given the widespread demand for English as a foreign language, teachers possess a unique opportunity to foster communication and intercultural skills in their students. Nevertheless, as the digital landscape and online communication continue to evolve, the responsibilities of English teachers are also undergoing a transformation.To ensure the coherence of the educational process and its seamless adaptation to the digital era, the creation of an electronic educational resource becomes imperative. This resource would facilitate the targeted development of digital competencies among future English teachers. The article places emphasis on how the integration of digital technologies can elevate the quality of language teaching and enhance the overall effectiveness of the educational process. Through the course of the study, the authors draw conclusions regarding the imperative to nurture the digital competence of English language teachers, ultimately preparing students for seamless integration into a contemporary digital society. This, in turn, guarantees the efficient acquisition of language skills by students. Consequently, the article provides a comprehensive analysis of the significance of instilling digital competence in future English teachers, offering methodologies to achieve this objective within the framework of modern education. Key words: digital competence, English language, education, electronic resource, digitalization.
Authenticity Assessment of Cuban Banknotes by Combining Deep Learning and Image Processing Techniques

The rapid evolution of technology has significantly increased the production and circulation of counterfeit currency, particularly high denomination bills, posing a detrimental impact on society’s commercial and economic sectors. The continuous advancement of domestic technology makes it increasingly challenging to differentiate between genuine and counterfeit printed money. Despite the development of computational tools for detecting counterfeit currency in various countries, no such tools currently exist for Cuban banknotes. This study proposes methods that combines deep learning techniques and other classifiers based on shape, color, and texture features applied to regions of interest, as determined by forensic experts who consider their security measures and characteristics. The evaluation of the proposed methods shows their effectiveness in detecting counterfeit currency, even with the limited availability of both fake and genuine specimens.
The Representation of Multicultural Content Provided by “World-Class Franchise” English Course Textbook Used in Indonesia: A Critical Discourse Analysis Study

Multicultural values in English as a Foreign Language (EFL) textbooks have attracted many researchers, academics, and educators in many studies around the world. However, there is limited literature examining the representation of multicultural content in English course textbooks used in Indonesia, specifically EFL course textbooks from world-class franchise programs. Therefore, this study employs Critical Discourse Analysis (CDA) to examine the impartiality of cultural content representation provided by the EFL course textbook regarding different cultures and ethnic groups. This study provided Halliday’s systemic functional analysis to examine verbal data. While Kress and Van Leeuwen’s visual grammar theory was employed to comprehend, analyze, and explore visual representations from selected EFL course textbooks. This study reveals the EFL course textbook does not reflect the diversity of cultures in Indonesia, as it mainly focuses on the perspectives and traditions of Western and European countries. This creates an unfair and distorted representation of other cultures, especially those from Asia, Africa, and the Middle East, whose values and beliefs are not adequately considered or respected within the EFL course textbook. The implications of this study suggest that the inclusion of cultural sensitivity within EFL course textbooks is crucial in fostering cultural awareness, promoting respect among EFL learners, and creating a more equitable and inclusive learning environment.
Data-Driven Education: Data Processing as a Key to Improving the Quality of Mathematics Education

Education is a major pillar in the development of a nation, and the quality of education becomes a key factor in determining the progress of a society. In this digital era, there is a rapid development in the use of information technology, and this has a significant impact on the education sector. The purpose of this study is for data-driven education: data processing as the key to improving the quality of mathematics education. This research uses a mixed method that combines qualitative and quantitative methods. This approach was chosen to address the complexity of data management and the comprehensiveness of this topic. The research begins with an in-depth literature analysis to understand the potential of Data-Driven Education. By using data as a guide, educators can make smarter decisions, detail instructional strategies, and create an educational environment that allows each student to reach his or her full potential. This is done by combining the results of various data of educator learners who can make informational decisions to improve the quality of mathematics learning. The application of data management in the context of mathematics education can provide concrete solutions to overcome challenges and improve the quality of learning. The right use of data provides the basis for smarter decisions and more adaptive learning, creating a learning environment that fits the needs of each student. By integrating Data-Driven Education effectively, we can move towards a more inclusive, personalized and successful mathematics education. The use of data contributes significantly in improving the quality and inclusivity of mathematics education. The integration of data management in mathematics education is a critical step towards a more adaptive, inclusive, and effective education
UG-schematic Annotation for Event Nominals: A Case Study in Mandarin Chinese

Abstract Divergence of languages observed at the surface level is a major challenge encountered by multilingual data representation, especially when typologically distant languages are involved. Drawing inspirations from a formalist Chomskyan perspective towards language universals, Universal Grammar (UG), this article employs deductively pre-defined universals to analyse a multilingually heterogeneous phenomenon, event nominals. In this way, deeper universality of event nominals beneath their huge divergence in different languages is uncovered, which empowers us to break barriers between languages and thus extend insights from some synthetic languages to a non-inflectional language, Mandarin Chinese. Our empirical investigation also demonstrates this UG-inspired schema is effective: with its assistance, the inter-annotator agreement (IAA) for identifying event nominals in Mandarin grows from 88.02% to 94.99%, and automatic detection of event-reading nominalizations on the newly-established data achieves an accuracy of 94.76% and an F1 score of 91.3%, which are significantly surpass those achieved on the pre-existing resource by 9.8% and 5.2% respectively. Our systematic analysis also sheds light on nominal semantic role labelling (SRL). By providing a clear definition and classification on arguments of event nominal, the IAA of this task significantly increases from 90.46% to 98.04%.
Assessing Dyslexia with Machine Learning: A Pilot Study Utilizing Google ML Kit

In this study, we explore the application of Google ML Kit, a machine learning development kit, for dyslexia detection in the Turkish language. We collected face-tracking data from two groups: 49 dyslexic children and 22 typically developing children. Using Google ML Kit and other machine learning algorithms based on eye-tracking data, we compared their performance in dyslexia detection. Our findings reveal that Google ML Kit achieved the highest accuracy among the tested methods. This study underscores the potential of machine learning-based dyslexia detection and its practicality in academic and clinical settings.
White Blood Cell Classification using Deep Learning

In this paper, a hybrid Convolutional Neural Networks (CNNs)-based system for classifying white corpuscle from blood smear images is presented. The major objective is to effectively classify various types of white corpuscle using CNNs, which will help with early disease identification and medical diagnosis. The suggested method entails gathering a varied dataset of blood smear images, standardizing them through preprocessing, and then using data augmentation approaches to improve model generalization. On the dataset, various CNN architectures are investigated and tweaked for better classification performance, including pre-trained models like ResNet-50and MobileNet-V2. The research places a strong emphasis on thorough evaluation utilizing a variety of measures and comparison with established image processing approaches using deep learning techniques.
A Pay-Close-Enough-Attention Ensemble Classifier for Spotlighting Bogus Reviews

As a result of the proliferation of social media platforms and online shopping, an increasing number of individuals are sharing their shopping experiences on the internet and relying on the opinions of their contemporaries to direct their decision-making when it comes to making purchases. The growing reliance that people place on reviews to help them make judgments about the products and services they buy online has contributed to an increase in the number of instances in which reviews have been falsified. The accuracy of classification in the traditional method is dependent on the label dataset, which is not only challenging to collect and analyze, but also slow, laborious, and reliant on the participation of a human in the form of feature extraction. The length of the text that may be found online makes the model less sensitive to irregularities since the classifier is more likely to overlook important facts. We provide an attention-based semi-Ensemble Classifier as a solution to the problem that was stated before. This classifier is designed to detect fake reviews. Deep neural networks use both labeled and unlabeled data to properly learn input distributions, which ultimately results in enhanced prediction accuracy for online reviews. One way that this is accomplished is by automatically identifying the features that are required for classification. The application to the labeled data set provided by TripAdvisor highlights both the efficiency of the method’s attention mechanisms in the classifier for acquiring an adequate semantic representation and the method’s dependency on a limited training set for identifying false reviews. The results of the trials show that the suggested system can detect fake reviews with a higher level of accuracy than the state-of-the-art semi-supervised algorithms, even when the label dataset is very limited.
Cooperative System of Ground Robots based on dynamic exploration of Drone Occupancy Grid Maps

This study proposes a real-time action planning system for autonomous mobile robots, allowing them to request assistance from other robots when necessary. The system focuses on search and uses a ground robot and a drone to search unknown areas. The drone creates an Occupancy Gird Map and transfers it to the ground robot for optimal path planning. The Behavior Trees used for autonomous robot search reactively select actions to uncertain events by monitoring the state, which is grown using a reachability graph generated in advance by offline planning. The system allows robots to automatically generate tasks and decide which robot should perform them, optimizing battery consumption and avoiding unnecessary movements.
Survey on Various Tool for Analyzing and Detecting Fake Review by using AI

The prevalence of fake product reviews in online shopping platforms has raised concerns regarding the authenticity and reliability of customer feedback. This study proposes a method for detecting fake reviews using a logistic regression model and review-centric features. By analyzing linguistic patterns, sentiment analysis, and reviewer behavior, the proposed model aims to accurately differentiate between genuine and fake reviews. Experimental evaluations on real-world datasets demonstrate an overall accuracy of 82%, showcasing the effectiveness of the approach. The integration of natural language processing techniques allows for the extraction of important features from review texts, which are then utilized as inputs to the logistic regression model. Additionally, considering reviewer behavior, such as the number of reviews, consistency of ratings, and timing, enhances the accuracy of the detection system. The proposed method offers a scalable solution that can be easily implemented in existing e-commerce platforms, bolstering their credibility and protecting consumers from misleading information. By filtering out fake reviews, this approach empowers users to make informed purchasing decisions, ultimately improving customer satisfaction in the online marketplace
Magistracy in the Mirror of the Russian Higher Education Reform

Introduction. The relevance of the research topic stems from the presence of a large number of problems associated with the status of master’s degree in the system of higher education in Russia. Master’s degree, according to the current educational model, is, on the one hand, a logical continuation of Bachelor’s degree, and, on the other hand, a stepping stone to postgraduate education. However, in actual educational practices, both aspects of its status manifest themselves formally, losing their connection with the undergraduate degree and not acquiring it with postgraduate training. The aim of the paper is to determine the prospects of preserving the master’s degree as a level of higher education after a new round of higher school reforms. Methods. The article has predominantly applied nature and is written on the basis of analysis of regulatory documents, regulating formal conditions of Master’s degree as an educational level. Results and their discussion. The author came to the conclusion that the preservation of master’s degree in the form in which it exists now – is inappropriate. In real educational practices it is not so much a vital level of education, as an appendage to the Bachelor’s degree, having not educational, but administrative and managerial functions. Its value lies solely in the possibility of expanding the number of students, which is recognized by&nbsp;the participants of the educational process only in the context of increasing funding for higher education. This stems from the invasiveness of the concept of education reform, which resulted in the emergence of the master’s degree. The transition (or return) to the national system of education actualizes the question of its complete abolition from the system of higher education in Russia.
Illuminating muscle memory's sinister side: a social media case study

When a task is repeated, it becomes part of procedural memory. This type of memory dedicated to movement is called ‘muscle memory’, which allows one to perform actions unconsciously. Within the context of social media, muscle memory builds up if one uses SM applications frequently. In this paper, we investigate the effects of muscle memory within Instagram, and report the following findings: We designed a user study examining the speed and accuracy of using a newly changed interface which showed slower reaction time and more errors. Combining these results with users' perceived feelings lead us to conclude that in specific UX interface changes muscle memory can be applied as a dark pattern.