Reviewerly/exHarmony huggingface dataset:
Debriefings on Prehospital Care Scenarios in MedDbriefer—A Tool to Support Peer Learning

Across the healthcare professions, many students don’t get enough practice doing simulated clinical interactions during course labs to feel confident about passing certification exams and treating actual patients. To address this problem, we are developing MedDbriefer, a web-based tutoring system that runs on a tablet. MedDbriefer allows peers to engage in supplemental clinical scenarios on their own. With its current focus on paramedic training, one student “voice treats” a simulated patient as the leader of a mock emergency medical services team while a peer uses MedDbriefer’s checklists to log the team leader’s verbalized actions. The system then analyzes the event log and generates a debriefing, which highlights errors such as assessment actions and treatment interventions that the team leader missed or performed late. This paper focuses on how the system analyzes event logs to generate adaptive debriefings.
Identification of the Problem of Neural Network Stability in Breast Cancer Classification by Histological Micrographs

This paper discusses the actual neural network architectures used to classify breast cancer according to histological micropreparations. A comparative analysis of models and frequently used datasets was conducted. The main methods of augmentation and data preprocessing were also identified. The main goal of the study was to test the trained models on data other than the training set. ResNet 152, DenseNet121, Inception_resnet_v2 models, and the transfer learning approach were chosen for training. As a result of the testing, it was concluded that training on data without color normalization of images and with standard augmentation (rotation and flipping) makes the models vulnerable to changes in the input data. In further studies, we plan to improve the models’ stability to changes in the color gamut of images using various methods of data preprocessing.
Using Web Technologies to Implement a Modular Integrated System for Augmented Tourism Destinations

Identifying and promoting attractions and points of interest to visitors, especially through multimedia and – lately - AR/VR technologies, has been shown to enhance the pre-traveling and on-site overall experience. Throughout this paper, the development of a modular, web-based, generally applicable platform, consisting of interconnected modules and based on open-source technologies is presented. Not long ago, the deployment of engaging, rich-media user experiences required special equipment and native development tools. The present work demonstrates, how recent advancements in web technologies and modern web browsers (e.g., the WebGL), enable the deployment of such cutting-edge services, on mainstream mobile devices, using exclusively well-established web technologies like HTML, CSS and JavaScript.
Embracing Blockchain Technology in Logistics

In today’s increasingly interconnected world, the logistics and supply chain management industry faces numerous challenges, such as inefficient processes, lack of transparency, and vulnerability to fraud. This paper explores the potential of blockchain technology as an inno- vative solution to address these issues and optimize logistics processes. By leveraging the unique features of blockchain, such as decentraliza- tion, immutability, and smart contracts, we discuss the transformative effects on key aspects of logistics operations, including traceability, trans- parency, data integrity, and security. The paper highlights various use cases and real-world applications of blockchain in logistics, emphasizing its benefits in reducing operational costs, improving efficiency, and fos- tering trust among stakeholders. Finally, we address the challenges and limitations of adopting blockchain technology in the logistics industry, providing recommendations for overcoming these hurdles and paving the way for a more sustainable and efficient future in logistics and supply chain management.
A Web Tool for K-means Clustering

The K-Means clustering finds many applications in different domains. Researchers and practitioners utilize K-Means through specialized software or libraries of programming languages. This implies knowledge on these tools. This paper presents Web-K-Means, a user-friendly web application that simplifies the process of running the K-Means clustering and identifying the optimal number of clusters via the elbow method. Web-K-Means allows users to upload datasets, select relevant features, and finally execute the algorithm. The application displays the graph generated by elbow method, suggests a value for the number of clusters and presents the cluster assignments in a data table along with an exploratory data plot. Thus, the users can easily perform clustering analyses, without specialized software or programming skills. Additionally, Web-K-Means includes a REST API that allows users to run clustering tasks and retrieve the results programmatically. The usability of Web-K-Means was evaluated by the System Usability Scale (SUS) and experiments were conducted for the CPU times evaluation. The results indicate that Web-K-Means is a simple, efficient and user friendly tool for cluster analysis.
Using CAD to Preserve Local Traditional Weaving Craft in Teaching Mathematics

Addressing the dearth of research integrating Computer-Aided Design (CAD) software in teaching mathematics for cultural preservation, this study aims to determine the usefulness of using CAD in teaching the undergraduate course “Problem Solving, Mathematical Investigation and Modeling” with the output of creating traditional handloom weave drafts to be used as prototypes by the local handloom weaving community in Argao, Cebu, Philippines. It used the descriptive design, which utilized qualitative data-gathering procedures: online open-ended surveys and focus-group discussions to determine its impact on the students, designers, and the weavers. Findings show that the use of CAD developed the students’ problem-solving skills, deeper appreciation of the traditional handloom weaving craft, and skills in creating weave designs. CAD promotes visual representation of the mathematical weave patterns that created authentic learning. The designers evaluated the student-CAD weave outputs to have aesthetic and commercial value, while the local weavers described the designs to exude sophistication, marketability, and uniqueness.
Exploring the Use of Augmented Reality in Teaching History to Students with Attention-Deficit Hyperactivity Disorder

Augmented reality is an emerging technology that aims to enrich the elements of the real world with virtual information through the use of appropriate devices. These new technological devices, namely smart mobile devices such as smartphones and tablets, can serve as a link between augmented reality and the user. Augmented reality technology has not left the education industry unscathed, attracting the interest of many teachers and researchers, since it is seen as an important, cutting-edge learning and educational tool. Augmented reality provides students with unique learning experiences by supplementing the sensory stimuli of sight and hearing with three-dimensional images, sounds, videos, etc. The purpose of this study is to investigate the effectiveness of augmented reality in high school history classrooms for the teaching of students with attention-deficit hyperactivity disorder (ADHD). The evaluation was conducted with semi-structured interviews with students who have been diagnosed with ADHD, using a questionnaire at the end of the procedure. Qualitative analysis was mainly used to conclude. The method used to carry out this work is the case study. Research findings show that augmented reality technology can make a positive contribution to the learning process. Through the answers of most students, it seemed that they accepted augmented reality and considered it essential for learning better. Furthermore, student behaviors such as attention, engagement and learning motivation showed an improvement. Finally, the educational objectives of this didactic intervention were achieved to a satisfactory degree according to the results of the test.
Case Study of Organization of Decision-Making and Feedback Synthesis in Intelligent Tutoring Systems with a Cross-Cutting Approach

This paper describes an approach to support the decision making process in the Intelligent Tutoring Systems (ITS) and provide graphical presentation of the ITS decisions. The discussion focuses on the issue of explainability of the decisions made by the ITS and their explanation to a human learner. Highlighting the most significant aspects in the interpretation of the learning situation and their further use by the ITS intelligent scheduler is based on the mapping mechanism and the cross-cutting approach to switching between the maps. The feedback synthesis in the form of the dialogue is based on parametric maps and their visualization using CMKD notation. The maps are combined into an atlas, which is used as the basis for decision-making when switching from the combined map to the particular maps. An example of learning situation analysis using the cross-cutting approach in the experimental ITS is discussed in detail.
A meta-discursive analysis of engagement markers in <i>QAnon</i> anti-immigration comments

Abstract To better understand QAnon’s anti-immigration rhetoric, the study conducted a meta-discursive analysis of one of the group’s active Telegram channels by drawing on Hyland’s (2005) model of interaction. Specifically, engagement markers in their immigration-related discourse were analyzed to see how they contribute to endorsing the group’s macro conspiratorial arguments. The results illustrate a complex rhetorical manipulation and nearly unanimous expression of hate toward immigrants, with the use of ‘directives’ and ‘questions’ being the most prevalent metadiscursive markers. The former suggests that the commenters were more assertive or commanding in their tone, while the latter located within rhetorical persuasion, aimed at stimulating negative attitude toward immigrants. Conversely, a low frequency of the ‘shared knowledge’ markers indicates a lack of interest to establish credibility and constructive dialogue with the audience. The study aimed to unpack the nature of anti-immigration extremist discourse on social media, and its potential to incite violence among public.
APPLICATION OF THE K-MEANS CLUSTERING METHOD FOR PERFORMANCE ASSESSMENT BASED ON THE COMPETENCY OF EDUCATORS

The ability of lecturers to teach for 1 (one) semester needs to be evaluated. Evaluation is a student assessment through a student academic information system. Review using the system is separate because many students need to care about the filled things. There the author tries to analyze the values ​​from the questionnaire results distributed to students using the Principal Component Analysis (PCA) technique and the K-Means method, where PCA reduces data. At the same time, K-Means assumed the values ​​closest to the study's results and concluded that of the 3 (three) clusters, the maximum distance was 0.5475 in cluster 1 and cluster 3.
A Novel Gabor Filtering and Adaptive Histogram Equalization Method for Improving Images

The correct information may only sometimes be effectively conveyed by images due to various factors, such as excessively bright or dark lighting and low or high contrast. As a result, picture improvement has become an essential part of digital image processing. This proposed method aims to develop an algorithm for improving photos captured in dark environments. This letter presents a new picture-enhancing approach that combines median and Gabor filtering using the wavelet domain with histogram equalization working over a spatial domain. The proposed method in this paper combines spatial and transformed domains for image enhancement and has been simulated using MATLAB. The simulation results of two different photos show that the suggested approach extends the histogram over a wide range of grayscale, offering a superior improvement to the original image. The novel proposed algorithm aims to improve image quality and visibility, making identifying essential details within the image easier. Further, the proposed technique's success is manifested by examining the produced photos' contrast and brightness. The findings reveal that the suggested technique beats the other strategies for improving low-contrast photos.
BioBERT-Based Model for COVID-Related Named Entity Recognition

In natural language processing, information extraction from textual data is an important task. Named entity recognition, is the most popular task of information extraction, especially in the context of the medical domain. The entity extraction task aims to identify the entities and categorize them (Cho and Lee in BMC Bioinform 20:1–11, 2019) into predefined categories. With the emergence of COVID-19, COVID-related digital resources increased drastically and new types of entities are being introduced, which are semantically similar; also, new entities are being introduced which were earlier unknown. Now due to this, the task of entity extraction has become more challenging for which well-defined models developed earlier are not suitable for extracting such entities. Earlier research suggested that the state-of-the-art models were generic and focused less on domain-specific knowledge. Thus, it becomes important that the research progresses in a direction that considers biomedical domain knowledge for named entity recognition. The paper thus aims to identify the entities of biomedical nature specifically on the COVID benchmark (Cho and Lee in BMC Bioinform 20:1–11, 2019) dataset which was released by the University of Illinois. The experiments were performed using the biomedical domain-specific model BioBERT. Further, we have compared different versions of pre-trained weights on the BioBERT model and the experimental results show that the (Lee et al. in Bioinformatics, 2019) BioBERT-Base v1.1 (+PubMed 1M) weighted version outperforms the other models.
An Insight to Features Selection using ACO Algorithm

The richness and diversity of network data provide a number of problems for data classification technology. The choice of features has always been a significant and challenging issue in categorization technology. Feature Selection (FS) method is the most important part of the data pre-processing steps in data modeling, as we know that all the data is not useful in final processing, so it is important to find out only the essential features as a subset from the superset of available features. Irrelevant and redundant features may add the overhead to the extraction of necessary information which becomes difficult due to the processing and storage needs. We may narrow down the available data to only the relevant information by using feature selection. The main aim of FS process is to reduce the number of features, remove irrelevant, noisy and redundant data from the dataset and provide better results and accuracy as compared to the complete set of features. In order to speed up processing, cut training time, prevent learning from noise (overfitting), reduce dimensionality, increase predictive accuracy and lower computational complexity, it is necessary to choose a good feature selection method based on the number of features investigated for sample classification. The goal of this work is to identify the most widely used feature selection optimization strategies that deal with improving classification performance in terms of accuracy and efficiency. This paper highlights various methods of FS and additionally suggests a feature selection method based on ACO.
Exploring Integration Mechanism of Music Instructional Design and Education Informatization

INTRODUCTION: In the new era of information society, the knowledge reform of school music education is getting more and more attention. Traditional teaching methods need to make full use of information technology. They cannot meet the current learning needs because the learning of information technology teaching is an essential means to realize modern learning. , therefore, the application of information technology in music education meets the requirements of the times.&#x0D; OBJECTIVES: To improve traditional teaching methods by utilizing modern teaching techniques so that students can gain more knowledge. With the help of modern technology and information technology, secondary music education can change the students' audiovisual experience, enrich their musical experience, and deepen their understanding and memorization of knowledge.&#x0D; METHODS: In this paper, use a neural network algorithm to explore the integration mechanism of music teaching design and education information technology, optimize the traditional information technology teaching platform, improve the stability of the music teaching platform, and further improve the level of network teaching from the feedback of students and teachers.&#x0D; RESULTS: This study's results found that this experiment's educational platform could motivate students to manage teaching and actively participate in music teaching and lessons. However, due to the real-time nature of information technology, it removes doubts between the parties and facilitates communication between teachers and students. The primary goal of music education is to perceive the attraction of music in terms of sight and sound, to relax the mind and body, and to develop a musical personality. From this perspective, it is also necessary to utilize modern information technology to change teachers' traditional teaching methods in classical performance and singing.&#x0D; CONCLUSION: With the help of modern technology and information technology, secondary music education can change the audiovisual experience of students, enrich their musical experience, and deepen their understanding and memorization of knowledge. The information system can motivate students to manage teaching and actively participate in music teaching.
Removing Stegomalware from Digital Image Files

Steganography, a data hiding technique, has trended into a lucrative means to hide malware within digital media and other files to avoid detection. Such malware hidden by means of steganography is known as stegomalware. Detecting stegomalware has been difficult and indeed removing such malware from the file is a big challenge. A tool has been created (Verma V, Muttoo SK, Singh VB Detecting stegomalware: malicious image steganography and its intrusion in windows. In: International conference on security, privacy and data analytics. Springer, Singapore (2022). 10.1007/978–981-16–9089-1_9) to detect such malware hidden within widely used JPEG file format. This paper introduces new and significant functionality to our tool (Verma V, Muttoo SK, Singh VB Detecting stegomalware: malicious image steganography and its intrusion in windows. In: International conference on security, privacy and data analytics. Springer, Singapore (2022). 10.1007/978–981-16–9089-1_9) to remove such malware from the file after detection, unlike the existing techniques limited to detection. The tool proposed in this paper has rendered the malicious image files benign with a success rate of 99.92%.
UDCGN: Uncertainty-Driven Cross-Guided Network for Depth Completion of Transparent Objects

In the field of robotics, most perception methods rely on depth information captured by RGB-D cameras. However, the ability of depth sensors to capture depth information is hindered by the reflection and refraction of light on transparent objects. Existing methods of completing transparent objects’ depth information are usually impractical due to the need for fixtures or unacceptably slow inference speeds. To address this challenge, we propose an efficient multi-stage architecture called UDCGN. This method progressively learns completion functions from sparse inputs by dividing the overall recovery process into more manageable steps. To enhance the interaction between different branches, Cross-Guided Fusion Block (CGFB) is introduced into each stage. The CGFB dynamically generates convolution kernel parameters from guided features and convolutes them with input features. Furthermore, the Adaptive Uncertainty-Driven Loss Function (AUDL) is developed to handle the uncertainty issue of sparse depth. It optimizes pixels with high uncertainty by adapting different distributions. Comprehensive experiments on representative datasets demonstrate that UDCGN significantly outperforms state-of-the-art methods in terms of both performance and efficiency.
GCD Thresholding Function Applied on an Image with Global Thresholding

In this paper, we have developed and applied a new threshold function over an image globally and found the results to be quite promising. The method utilizes the feature of calculating the greatest common divisor (GCD) of the pixels within blocks formed in the image. The results obtained when compared with other standard thresholding techniques show us further insight with regard to the robust quality and performance of the newly devised thresholding function. In this paper, 3 progressive algorithms are presented with their particular challenges and shortcomings. Of these, the third algorithm is the main successful implementation of this thresholding technique.
The implications of cybercrime for national security

This study aims to identify the general aspects concerning the elements of correspondence between the notion of cybercrime and the field of national security by analysing the regulatory and institutional framework. Electronic information concerning national security requires concrete measures for its identification and control, as its alteration, theft, or modification may constitute a source of vulnerability for the security of the State and its citizens. In this respect, the architecture of the regulatory framework we are considering concerns national security provisions and the protection of classified information.
Symmetric Secret Key-Based Quantum Key and Its Distribution Over the Networks

Quantum key distribution (QKD) is an information-theoretically secure symmetric secret-key negotiation system. QKD stands for “quantum key distribution.” Recently, QKD networks have advanced to the point where they have graduated from the domain of theoretical study and are beginning to make their way into a few practical applications. A network is formed by QKD nodes, which can communicate with one another either wirelessly or utilizing light-speed optical connections. Any two QKD nodes have the ability to negotiate the distribution of secret keys to a large number of users located in a variety of different places. This is done to provide both long-term security and forward secrecy. Following the presentation of a foundational overview of QKD and a few developments in QKD, and in this study, we will look into how QKD networks have evolved and where they have found practical use. In conclusion, we make some proposals as to the direction that future research should go, as well as some recommendations for the most effective methods of constructing QKD networks.