Reviewerly/exHarmony huggingface dataset:
Emerging Technologies in Education: Security and Data Protection

Information technologies are one of the most prospective fields at universities. The number of young people interested in this science is increasing from year to year. One of the essential parts of IT is security and data protection. Many students perceive this industry as complicated and, therefore, unattractive. If we want to change it, we must focus on introducing emerging technologies in education. These technologies should be chosen so that they motivate the students but at the same time so that the level of acquired knowledge does not drop. Their goal is to make studying this field more attractive and easier for students. The article presents examples of new approaches to security and data protection. This article introduces several standards and concrete tools required for them.
Eye-Tracking System as a Part of the Phishing Training

This article explores the potential use of eye-tracking technology within the context of phishing training. Phishing attacks continue to be a significant cybersecurity threat, and one approach to preventing successful phishing attacks is to conduct phishing training. The article focused on how eye-tracking systems can enhance phishing training by analyzing users' gaze patterns when interacting with phishing emails or websites. By studying where users focus their attention, trainers can gain insights into common mistakes and areas of vulnerability. This approach offers data-driven and personalized training, ultimately improving individuals' ability to detect and mitigate phishing attacks. The article focuses on how this eye-tracking system can be utilized in phishing training and its potential to enhance cybersecurity awareness.
Assessing Breast Cancer Risk with 5G-Enabled Data-Sensorized Sample using Random Forest

Breast cancer necessitates precise classification to facilitate optimal treatment decisions. This study evaluates the performance of the Random Forest and Gradient Boosting algorithms in breast cancer classification. Clinical data obtained from medical centers via 5G-enabled devices were subjected to thorough analysis. Both algorithms underwent training, testing, and evaluation, with performance metrics including accuracy, precision, recall, and the F1 score being employed. Notably, key features such as tumor size and hormone receptor status exhibited significant contributions to the classification process. The implications for enhanced diagnosis and personalized treatment approaches are considerable.The study underscores the remarkable effectiveness displayed by both algorithms, highlighting their potential for practical clinical applications and the subsequent improvement of patient outcomes. The integration of AI and 5G technology holds great promise in the advancement of breast cancer care on a global scale.
Game Engine Based Application for Neurorehabilitation in Collaborative Virtual Reality

Virtual reality is used for a wide range of applications nowadays. It can also be used for medical purposes, for example in the process of therapy and rehabilitation of patients. The advantage of applying virtual reality to the rehabilitation process is the possibility of creating interesting and immersive virtual environments and scenarios. Based on an analysis of the technology’s potential uses and current solution, we have created an application in Unity game engine that will allow patients and therapists to collaborate in a shared virtual environment. The application includes several simple scenarios that can be expanded and combined into more complex, engaging sequences in the future, as well as interface to communicate with external environments to assess the patient’s actions via electroencephalography.
User Recognition in Mobile Applications Using Fingerprint Sensors

With the widespread adoption of smartphones and the increasing need for secure and personalized user experiences, user recognition has become a crucial aspect of mobile application development. In remote audiometry mobile applications, it is requested to have a quick user identification or authentication feature for protecting the medical private data. Fingerprint sensors, now commonly integrated into mobile devices, offer a convenient and reliable biometric authentication method. This paper explores using full-sized external fingerprint sensors for user recognition in mobile applications. We discuss the underlying technology of fingerprint sensors, their integration into mobile devices, and the various algorithms and techniques used for user recognition. We also present a case study where we develop a mobile application that utilizes fingerprint sensors for user authentication and discuss the performance and usability of the system.
Exploring Oracle APEX for the University Data Analysis

This paper delves into Oracle Application Express (APEX), a robust platform for web application development, showcasing its merits and suitability for creating a comprehensive Analytical Tool for University Data Management application. This application serves as an integrated solution to analyse various facets of university processes, such as student interactions, faculty engagement, study group dynamics, and subject performance, utilizing visual representations like graphs and charts. Beyond data analysis, the application also works as an Academic Information System, proficiently storing and managing student-centric information, including exam results and enrolled subjects. The initial sections of the paper explore the features and capabilities of Oracle APEX, highlighting its role as a low-code development platform that empowers developers to create sophisticated web applications with efficiency. The focal point of this study is the presentation of the Analytical Tool for University Data Management application created using Oracle APEX. By harnessing various modules and regions within Oracle APEX, the application offers a user-friendly interface that facilitates comprehensive data analysis. The application's ability to present individual and aggregate statistics through graphical visualizations empowers decision-makers with insightful information. The application holds great potential for future expansion utilising Machine Learning methods.
Online Educational Game - Tool for Education in Raw Material

The topic of the presented work is a presentation of the current status of online games in the Briefcase project. The aim of the presented work is to analyze of the current state, show to created proposals that can increase the attractiveness of education in raw materials. The importance of the work lies in the creation of online educational games focused on target groups of pupils, which would increase the motivation of pupils and leave them with pleasant experiences, thanks to which they will remember information for long time. The work is mainly focused on pupils, which are divided into three target groups according to age. The topic, methodology and goals of online educational games are the same. Online games differ only in their difficulty and the amount of information, which, on the other hand, is adequate for the students' level of education. Online games of Briefcase project represent actuality and necessity during teaching at home as well as their suitability for use in teaching in schools as supplementary material.
Application of PID controller and CNN to control Duckiebot robot

The paper presents the design and practical implementation by students of a control system using a classic PID controller and a controller using artificial neural networks. The control object is a Duckiebot robot, and the task it is to perform is to drive the robot along a designated line (line follower). The purpose of the proposed activities is to familiarize students with the advantages and disadvantages of the two controllers used and for them to acquire the ability to implement control systems in practice. The article briefly describes how the two controllers work, how to practically implement them, and how to practically implement the exercise.
Preparing Future Data Visualization Designers for Professional Practice

As the professional field of data visualization grows, so does the importance of preparing students effectively for the demands of real-world practice. Computing education has historically sought to teach and evaluate abstract knowledge (e.g., theories, principles, guidelines, design patterns) and the application of such knowledge to given problems. However, situations faced in professional practice are often messy, dynamic, and uncertain, and do not lend themselves well to the clear and direct application of such knowledge. This leaves a gap between the knowledge learned in the classroom and what is required for skillful practice in professional settings. In this paper, I discuss some historical reasons for this dominant pedagogical perspective, some of the core features of professional practice that are not typically taught in classrooms, and ways in which data visualization design can be taught to be more resonant with the experience of professional practice.
Reflections on Designing and Running Visualization Design and Programming Activities in Courses with Many Students

In this paper, we reflect on the educational challenges and research opportunities in running data visualization design activities in the context of large courses. With the increasing number and sizes of data visualization course, we need to better understand approaches to scaling our teaching efforts. We draw on experiences organizing and facilitating activities primarily based on one instance of a master’s course given to about 130 students. We provide a detailed account of the course with particular focus on the purpose, structure, and outcome of six two-hour design activities. Based on this, we reflect on three aspects of the course: First, how the course scale led us to thoroughly plan, evaluate, and revise communication between students, teaching assistants, and lecturers. Second, how we designed learning scaffolds through the design activities, and the reflections we received from students on this matter. Finally, we reflect on the diversity of the students that followed the course, the visualization exercises we used, the projects they worked on, and when to key in on simple boring problems and data sets. Thus, our paper contributes with discussions about balancing topical diversity, scaling courses to many students, and problem-based learning.
Segmentation of Tissue Regions in Whole Slide Images Using Hand-Crafted Image Features

This paper proposes a method to address the need for accurate and explainable tissue segmentation in whole slide images (WSIs) for computational pathology. The research focuses on developing a machine learning algorithm using hand-crafted image features and a random forest classifier to segment tissue regions in WSIs. Three research questions were formulated and investigated. (RQ1) Can hand-crafted image features be used as the primary features for an explainable ML algorithm to accurately segment tissue regions in WSIsƒ (RQ2) What image features are dominant for classifying whether WSI tiles are within a tissue regionƒ (RQ3) What post-processing techniques are required to improve the accuracy of the tissue segmentation algorithmƒ The proposed algorithm achieved an average accuracy of 98.05%. The results revealed the significant influence of specific image features, such as saturation channel mean and standard deviation, and grey level co-occurrence matrix measures, in accurately classifying tissue tiles within WSIs. More-over, incorporating morphological operations and thresholding as post-processing techniques improved the accuracy of tissue segmentation. The achieved accuracy of 98.05% outperformed existing solutions and demonstrated the effectiveness of the proposed algorithm in reliably segmenting tissue regions from the background of WSIs. This method presents a valuable tissue segmentation pre-processing step that can be used to support future research related to cancer region segmentation.
Relationship Between Motivational Strategies and Gamification User Types in VR Movement Games

Virtual Reality (VR) is a powerful tool for serious games and applications because it is immersive, engaging and enjoyable, can achieve dissociation from negative feelings and pain, and can be used to create personalised experiences. Serious games combined with VR have been used increasingly for contexts like stroke rehabilitation, where they can provide an engaging environment for task-based rehabilitation. Motivation is an important factor for physical rehabilitation, however, one-size-fits-all methods are not universally engaging as some patients lack motivation needed for rehabilitation. In this paper we focus on motivating movements that can be used for stroke rehabilitation. Gamification user typologies, such as the HEXAD model, allow aspects of serious games to be tailored to an individual’s personal needs, potentially enhancing user engagement and motivation. We conducted an experiment on healthy participants which investigated the correlation between personalised upper limb movement games in VR and user engagement. Our results show that for users who had their experiences personalised, engagement and motivation typically was higher (but not significantly) than for participants in the control group. We found statistically significant evidence that personalised VR experiences resulted in higher pressure for Socialisers and lower effort for Philanthropists.
Relative Feature Orientation Filtering in COLMAP Structure from Motion

Structure-from-motion and multi-view stereo systems are based on feature correspondences between images. While these features are typically treated as points, common feature detection/description methods such as SIFT provide orientation and scale as well as location information. Previous work has shown that orientation information in particular can be exploited to improve feature matching and relative pose computation. This work examines the effect of using feature orientation information in the open-source COLMAP structure-from-motion software.. Experiments show that using feature orientation can reduce the time needed for both feature matching and initial scene reconstruction. Furthermore the resulting reconstructions have a lower average reprojection error with orientation-based filtering.
“Choose-your-own” D3 labs for learning to adapt online code

D3 is a popular library for implementing data visualizations, and is often taught in data visualization classes due to its flexibility, expressivity, and the ubiquity of online D3 example code implementing a variety of visualization techniques. However, D3 can be difficult to learn for novices, and it can be especially challenging to make use of online examples, as many of them are either written in previous, incompatible versions of D3, or are written in Observable notebooks, which often require changes to work in standalone JavaScript. We have previously taught D3 to students using guided tutorials, but found that students struggled to apply what they had learned to other visualization types or contexts, and when given the choice of toolkit in final group projects, often opted not to use D3, limiting the expressiveness of those projects. To address this, we introduced a new assignment type: choose-your-own labs. In each of these labs, students were tasked with implementing a visualization technique from that week’s lecture. We provided a suggested code sample, deliberately choosing code samples in older versions of D3 and/or in Observable notebooks, and asked students to get the code sample working in the latest version of D3 as a standalone webpage. Depending on the complexity of updating the code, we sometimes also asked students to adapt the code sample to work with a different dataset. This paper reflects on our experiences using this new assignment. Although students seemed to find the process of updating and debugging real-world example D3 code to be tedious, they generally responded well to the assignment, and were excited to get more exposure to D3. We also observed that the quality and creativity of the final group projects in the class were noticeably improved from previous iterations of the course: more groups used D3 instead of easier-to-use but less-expressive tools (like Tableau), and the variety of visualization types and sophistication of interactions implemented was much improved. We provide suggestions for educators who want to use a similar format in their courses, and provide our materials at https://osf.io/47vfy/?view_only=7e478a23e3e5414086569694279d38fe.
Extreme Sport Analysis Using Object Detection Methods and Unscented Kalman Filters

This paper proposes a method to analyze athlete performance in extreme sport by using CNN object detection methods and unscented Kalman filters.The proposed method employs YOLOv7 pose estimation which uses the pretrained YOLOv7-x6-pose.pt weights file to cast a key-point skeleton over an athlete. This pose estimation method can operate 8 times more effectively than a prior object detection benchmark, detectron2. The resultant video is then passed twice to the YOLOv5n object detection model. On the first pass, the model is used to cast a bounding box around the athlete in each frame and the maximum box width and height value is stored. On the second pass, the model detects and tracks the athlete whilst using the two stored values to crop the video frame, centering the athlete. The use of a CNN object detector, YOLOv5, eliminated problems with tracking failure that occurred in prior research which relied on background subtraction methods. Finally, an unscented Kalman filter is run forward and backward in time on the video to track the athlete in instances where YOLOv5n cannot detect the athlete. Running the filter twice in opposite directions allows the filter to have a smoother trajectory and higher accuracy, improving upon prior research by 4.6% in accuracy.A limitation of the proposal arises when tracking multiple subjects. Future work could be aimed at improving the cropping function to recognize and analyze multiple athletes simultaneously.
National center for digital transformation of education

The demands for competences necessary for the success of a graduate in today's rapidly changing world point to the need for inevitable changes in education. These changes should reflect the development of 21st century competencies, including digital competencies, at all levels of education. The development of digital competences and preparation for the life of a graduate in the digital age is conditioned by the digital transformation of the school. This effective and meaningful digital transformation at schools should be based on the consistent preparation of an action plan in accordance with the vision of the school, the implementation and subsequent evaluation of the action steps. The school digital coordinator became the leader of the digital transformation in primary and secondary schools in Slovakia. Innovative training of digital coordinators, implemented support in the form of online clubs and face- to-face meetings pointed to the necessity of continued, targeted support at the national level. For this purpose, the National Center for the Digital Transformation of Education was established. The aim of the national center, in addition to providing innovative education for the position of school digital coordinator, is also innovative education for school principals focused on the leadership competencies necessary for effective team management and management of the change process. The activities of the center will also include meetings with principals and digital coordinators, individual consultations and online webinars reflecting the needs of the community supporting the digital transformation of the school and teachers. Moreover, the target group is also pedagogical and professional staff, primary and secondary school pupils, students at universities. One of the priorities of the national center is high-quality research and subsequent management in the field of digital transformation of education.
Developing Prerequisite Knowledge for 3D Modelling and 3D Printing

3D printing is a contemporary method of three-dimensional objects’ production. Prior to the printing, the object must be designed in the form of its 3D model - a virtual spatial body. The model is then transferred into a computer program which will control the printing process. Finally, the object is composed using a stepwise addition of micro-particles (grains).3D printing is very appropriate for the individualized production i.e. for fabricating unique objects or those done in small series. A typical lifecycle of these objects is "Design it – Produce it – Make use of it". A growing interest in this extent of production requires preparing enough 3D-printing specialists in a variety disciplines. In our presentation, we discuss what professionals are needed and what modifications of contemporary schooling would fulfil these requirements to a maximum degree.
Beyond Generating Code: Evaluating GPT on a Data Visualization Course

This paper presents an empirical evaluation of the performance of the Generative Pre-trained Transformer (GPT) model in Harvard’s CS171 data visualization course. While previous studies have focused on GPT’s ability to generate code for visualizations, this study goes beyond code generation to evaluate GPT’s abilities in various visualization tasks, such as data interpretation, visualization design, visual data exploration, and insight communication. The evaluation utilized GPT-3.5 and GPT-4 through the APIs of OpenAI to complete assignments of CS171, and included a quantitative assessment based on the established course rubrics, a qualitative analysis informed by the feedback of three experienced graders, and an exploratory study of GPT’s capabilities in completing border visualization tasks. Findings show that GPT-4 scored 80% on quizzes and homework, and Teaching Fellows could distinguish between GPT-and human-generated homework with 70% accuracy. The study also demonstrates GPT’s potential in completing various visualization tasks, such as data cleanup, interaction with visualizations, and insight communication. The paper concludes by discussing the strengths and limitations of GPT in data visualization, potential avenues for incorporating GPT in broader visualization tasks, and the need to redesign visualization education.
Early Recognition of the Speaker’s Age

In this article, we describe a method for determining a person’s real age based on voice recognition. We seek to contribute to the realm of age group classification not reliant on text by investigating voice feature analysis, making use of accessible voice audio datasets, and employing a prototype classification model. Additionally, we aim to explore the initial phase of voice-based age group classification to enhance the customization of interventions.