Reviewerly/exHarmony huggingface dataset:
Exploring the educational potential of metaverse for medical education

Interest in the metaverse is increasing as learners participate in learning with presence, improving learning motivation, and interaction. Currently, the use of metaverse in medical education remains in a test-phase of introduction within some companies, while some medical school professors are beginning to utilize it. Therefore, it is difficult to ascertain whether the metaverse has educational potential when used in medical education. This exploratory study investigates the possibility of the educational use of the metaverse. Both the educational usefulness and the examples of its educational usage in domestic and foreign medical schools are explored. As the metaverse is expected to be introduced and utilized more actively in the future, some of its advantages and limitations in medical education have been identified. In light of the vast potential of the metaverse in medical education, the method of educating and preparing medical students should be developed by preparing a new educational foundation.
Penerapan Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma Naïve Bayes

Stroke is a disturbance of brain function, both local and general, that occurs suddenly, progressively, and rapidly due to non-traumatic brain blood circulation disorders that lasts more than 24 hours or ends in death. Stroke is also one of the deadliest diseases in Indonesia. In this study, stroke data was used to explore new information or knowledge in it. The process of extracting new information from a set of data is known as data mining. Therefore, this research aims to classify data related to stroke using the Naïve Bayes algorithm to find out whether the patient has a stroke or not. There are 10 attributes that are included in the causes of stroke, among others, gender, age, history of hypertension, history of heart disease, marital status, type of work, type of residence, glucose level, body mass index and smoking status. The results showed that classification with the Naïve Bayes algorithm can be applied in classifying stroke data resulting in an accuracy value of 92.48% in the Good Classification category.
Pengaruh Estetik Video Game terhadap Minat Gamer dengan Genre Survival

Video games have many genres and for this research, the author focuses on survival genre Video games. Video games with the survival genre give players freedom and try to survive in the world provided by the video game. Because players are free to explore in the video game, the visuals or graphics in the world in the video game will greatly affect the player's interest, but there are survival video games that have good visuals but don't have many interests like Fallalypse and Age of Survival where both video games have a Very Negative rating. However, there is one survival video game that doesn't have good visuals and only relies on unique gameplay but has a lot of interest and that video game is Minecraft. The purpose of this research is to find out the interest of players or gamers in video games in the survival genre. This study used quasi-experimental and qualitative methods on 14 participants with an age range of 18-26 years. After the quasi-experiments and qualitative were completed, the authors were able to receive one conclusion about gamers’ preference in survival video games, gamers prefer playing survival video games. which are not too realistic or similar to the real world in terms of difficulty, but gamers prefer to see visuals or graphics that are realistic or similar to the real world.
WEB AND MOBILE PLATFORMS FOR MANAGING ELECTIONS BASED ON IOT AND MACHINE LEARNING ALGORITHMS

The global pandemic situation has severely affected all countries. As a result, almost all countries had to adjust to online technologies to continue their processes. In addition, Sri Lanka is yearly spending 10 billion on elections. We have examined a proper way of minimizing the cost of hosting these events online. To solve the existing problems and increase the time potency and cost reduction we have used IoT and ML-based technologies. IoT-based data will identify, register, and be used to secure from fraud, While ML algorithms manipulate the election data and produce winning predictions, weather-based voters’ attendance, and election violence. All the data will be saved in cloud computing and a standard database to store and access the data. This study mainly focuses on four aspects of an E-voting system. The most frequent problems across the world in E-voting are the security, accuracy, and reliability of the systems. E- Government systems must be secured against various cyber- attacks and ensure that only authorized users can access valuable, and sometimes sensitive information. Being able to access a system without passwords but using biometric details has been there for a while now, however, our proposed system of ours has a different approach to taking the credentials, processing, and combining the images, reformatting and producing the output, and tracking. In addition, we ensure to enhance e-voting safety. While ML- based algorithms use different data sets and provide predictions in advance.
SURVEY: YOUTUBE SPAM COMMENTS DETECTION AND DELETION

The quality of online social platforms has risen, and spammers have developed a number of strategies to trick consumers into clicking on harmful links. This is accomplished by posting spam to various social media networks' comment sections. This paper suggests a method for identifying spam comments on YouTube, which have grown significantly recently. YouTube operates a spam blocking system, although it consistently fails to do so effectively so our program tries to tackle the problem by not only detecting but also deleting the spam comments by using Machine Learning
PENGEMBANGAN MEDIA ANIMASI INTERAKTIF DENGAN PEMOGRAMAN SCRATCH UNTUK MENGENALKAN KETERAMPILAN BERPIKIR KOMPUTASIONAL

Many kinds of learning media were made by prospective elementary school teachers during the CoViD-19 pandemic, ranging from using text, still or moving images, sound, video, and those made using PowerPoint in the form of simple presentations without using a programming language. Here the researcher aims to offer the development of interactive animation media using block programming with the Scratch application on elementary science material which is rarely used by students and teachers because it requires basic skills and logical thinking of a programmer. This research is development research or R&amp;D using the ADDIE model. The results of this block-based media programming project were tested on 10 elementary school students and teachers. The instruments used were material expert validation questionnaires, media experts, and response questionnaires from application users. The results of the research from interactive animation media developed based on the assessment of material experts obtained an average score of 84,6% which was in the good category and for the results of the assessment of media experts obtained an average score of 81.1% which was categorized as very good. For the results of student and teacher responses obtained an average score of 86.0 in the good category. Based on the research data it can be concluded that the development of interactive animation media using block programming with the Scratch application for science material can be used as a learning medium in elementary school.
Developed Method. Interactions and Their Quantum Picture

By developing the previously proposed method of combining continuum mechanics with Einstein Field Equations, explicit equations of interactions operating in a physical system were obtained. The explicit form of the electromagnetic four-potential was also derived, and the Lagrangian and Hamiltonian densities describing the physical system containing all the fields. A quantum picture of the system was also proposed, which, interestingly, allows for a simple transition to a curvilinear description based on the metric tensor in curved spacetime.
Role of Leadership for Blockchain-Driven Supply Chain Management

The emerging trends in blockchain-driven supply chain management (BCSCM) have gained popularity among practitioners in recent years. The growing scholarly interest in this discipline has unveiled various supply chain management practices; however, there is an implicit gap concerning exploring the role of contextual factors that influence the blockchain-driven supply chain. The current chapter contributes to the literature by investigating the impact of leadership on blockchain that would radically improve supply chain management. In this chapter, we review the extant literature on the linkages among leadership, blockchain, and supply chain management. Over and above, the main objective is to understand the mechanism that how businesses may successfully introduce and integrate blockchain technology into the management of their supply networks through the role of leadership.
INOVATION OF ISLAMIC HOLISTIC MIDWIFERY CARE DOCUMENTATION DIGITAL-BASED AS A LEARNING MEDIA FOR UNDERGRADUATE MIDIWFERY STUDENTS ‘AISYIYAH BANDUNG UNIVERITY

Abstract&#x0D; The era of the industrial revolution 5.0 demands that information technology systems can be utilized in recording and reporting midwifery care. This is intended to facilitate the achievement of the goal of documenting midwifery care. This study aims to design SOAP Digital as a learning medium and find out the use of digital SOAP innovation.&#x0D; This study used an experimental approach post test only control group design. Researchers made a pilot project to design and build innovations documenting digital-based Islamic holistic midwifery care. Digital SOAP operation technology interventions are carried out on the object of study. After that, an evaluation or feed back is given through the G-form on the object. The object of this study is a bachelor of midwifery student of FIKes Unisa Bandung. The results of data collection were analyzed using spss for windows software.&#x0D; The results showed that the digital-based Islamic holistic midwifery documentation application has 2 versions, namely the web version and the android version. This software installation has a capacity of 500 MB and requires less than 1GB of storage. The advantages of designing digital SOAP application innovations are that they are more effective in time (87%), more efficient in using paper (92%), more modern because they can use a PC, laptop, or smartphone (92%), more practical (87%), minimal risk of damaged or lost data (95%), and long data storage (90%). The results of the Spearman rho correlation data analysis test showed p-value of 0.000 &lt; 0.05 so that it can be concluded that there is a correlation between the advantages of digital SOAP applications and student satisfaction.
Towards virtual displays in the interventional radiology suite: a feasibility study

Interventional Radiology (IR) is a rapidly advancing field, with complex procedures and techniques being developed at increasingly high rates. As these procedures and the underlying imaging technology continue to evolve, one of the challenges for physicians lies in maintaining optimal visualization of the various displays used to guide the procedure. Many Augmented Reality Surgical Navigation Systems (AR-SNS) have been proposed in the literature that aim to improve the way physicians visualize their patient’s anatomy, but there are few that address the problem of space within the IR suite. Our solution is the incorporation of an Augmented Reality “cockpit”, which streams and renders image data inside virtual displays visualized within the Hololens two, eliminating the need for physical displays. The benefit of our approach is that sterile free interaction and customization can be performed using hand gestures and voice commands, and the physician can optimize the positioning of the display without the need to worry about physical interference from other equipment. For proof of concept, we performed a user study to validate the suitability of our approach in the context of liver tumor ablation procedures. We found there was no significant differences in insertion accuracy or time between the proposed approach and the traditional method. This indicates that visualization of US imaging using our approach is an adequate replacement to the traditional physical display and paves the way for the next iteration of the system, which is to quantify the benefits of our approach when used in multi-modality procedures.
Optimal visual search strategy with inter-saccade response correlations

Humans process the visual world with varying resolution across the visual field and sample information using eye movements to point the high-resolution central vision (fovea) to regions of interest. What eye movements optimize decisions in tasks such as target detection and localization? In 2005, Najemnik &amp; Geisler proposed the Bayesian Ideal searcher (IS) that takes into account the foveated properties of human visual and employs the optimal fixation selection strategy to maximize visual search performance. In addition, they proposed a computationally simpler model, entropy limit minimization (ELM), that approximates the IS searcher. One limitation of these models is that they were developed with the assumption that the visual system’s internal responses across fixations are statistically independent. This assumption will not hold for search tasks such as in 2D medical images for which the external noise and anatomical noise results in correlations in internal responses across saccadic fixations (inter-saccade response correlations). In this work, we present image-computable foveated IS and ELM models that accommodate inter-saccade response correlations. We demonstrate that for static images, the optimal searchers that account for the inter-saccade correlations (i.e., IS-COR and ELM-COR) significantly outperform the traditional methods that ignore such correlations. Moreover, the novel ELM-COR achieves a similar performance as the IS-COR but runs about 18 times faster. Together, the IS-COR and ELM-COR extend the optimal searcher framework to evaulate human fixations for more realistic search tasks with inter-saccadic correlations.
Framework for Fine-grained Recognition of Retail Products from a Single Exemplar

We propose a framework which allows one-shot fine-grained recognition of retail products in a real store from clean images used in e-commerce websites. We apply a metric learning approach to train the one-shot recognition model. To learn a suitable metric space for classification, we construct a data collection system which efficiently captures a large variety of products from various viewpoints under controllable lighting conditions. This dataset plays a role of an intermediate domain between the clean images and real stores. To expand applicable area of the intermediate domain, we use a domain generalization technique. In addition, we propose the pseudo class generation and metric learning method to enhance fine-grained recognition for retail products such as classification for products with multiple flavors. We demonstrate the effectiveness of each part of technique in our experiments for our target task, and show that our framework leads to high-accuracy recognition.
Breast Cancer Prediction: Impact of Stratified Sampling Approach on Classifier Accuracy

Late identification of breast cancer is a major concern in India. After lung cancer, breast cancer is the second most common cause of mortality for women. In the present study, artificial intelligence is utilised to determine if a breast tumour is benign or malignant. The goal of this paper is to offer a thorough investigation of how K Nearest Neighbor (KNN) is used to identify breast cancer. One of the most basic supervised learning-based machine learning algorithms is KNN. Additionally, we have investigated how stratified sampling affects the classifier's accuracy. The proposed work shows that stratified sampling increases accuracy and F1-scores considerably.
Changing tools, changing habits, changing workflows

Over the last few years, the interlibrary loan (ILL) service of the University of Liège Library has evolved considerably, both in terms of habits and workflows. In this article, we will explain the main stages of this evolution: (1) first reduction in the number of ILL units (from eight to five ) and involved operators (from 15 to 10) within the homemade ILL solution (2015); (2) use of the resource sharing functionality in the new Alma library management system (2015); (3) second reduction in the number of ILL units (from five to only one) and in involved operators (from 10 to six) (2018); (4) subscription to an international broker ILL system (RapidILL) for electronic and digital materials and its integration with Alma (2020); (5) project of peer-to-peer resource sharing for print materials between Alma instances of university and research libraries in Belgium (2022), and (temporary) free ILL service to all University users (2020–2022). The aim of these changes is to harmonise the practices of ILL operators, reduce the quantity of manual and administrative operations and tasks devoted to ILL and supply materials that do not belong to the library collections in a fairer, faster and more fluid way. While all of these changes have been implemented gradually over the years, not all of them have been deployed in a concerted manner among all stakeholders.
The mental health of Basic Health Unit nurses in times of pandemic

Objective: To know the mental health of nursing professionals from a Basic Health Unit exclusively for patients with COVID-19 in the municipality of Boa Vista, Roraima. Methods: This is an exploratory and descriptive study, with a qualitative and quantitative approach, carried out with 19 nursing professionals. The data was collected between April and June 2022, using a semi-structured script of 21 mixed questions. The interviews were analyzed using Bardin's method and the quantitative variables were submitted to the Statistical Package for the Social Sciences (SPSS) version 23.0. Results: The issue of stressors that interfere with mental health, associated with work activity during the COVID-19 pandemic, became evident. These factors had as a consequence the prevalence of psychological alterations such as fear, insecurity, anxiety, and stress. Concerning the diagnoses of mental disorders, anxiety and depression were the ones that stood out. Physical activity, psychotherapy, and religion were the coping measures adopted by the professionals interviewed. Conclusion: During the pandemic and their work practice, the professionals in this study were exposed to various stressors, developed psychological pathologies, but sought to maintain some level of mental health.
Web-Page Content Classification on Entropy Classifiers using Machine Learning

In recent years, the World Wide Web (WWW) has become a global data center, which permits people to store and distribute their information. The information in Web Pages may be related to be personal, official, commercial and business. The users of Web would like to access such information for their needs. Therefore, to use the Web data for any specific purpose, it is necessary to have techniques which will classify the Web Pages so that the suitable data available in Web Page are provided to users. This paper proposes a new technique for classification of Web Pages using level based classification and hierarchical indexing model based on predefined domains: Sports, Politics and education. The method works in two important phases: Training phase and Testing phase. During training phase the dynamic Feature Extraction and Knowledge Representation is performed. During testing phase the features extracted from the Web Pages are used for content matching for Classification. The technique comprises three steps namely: Dynamic Feature Extraction, Knowledge Representation and Classification for randomly distributed Web Pages. During Feature Extraction the important keywords are extracted from Headers and Paragraphs of Web Pages. The Frequency Occurrence of Key Words is determined and the frequency values are multiplied with weights so as to segregate the keywords at different priority levels. The Represented Knowledge is further used for content matching for classification of Web Pages. The percentage of belongingness of the webpage for each such category is calculated using Maximum Entropy Classifier. Maximum Entropy Classifier is considered due to its advantage in search based optimizations. The method is evaluated with three different categories of Web Page such as Sports, Politics and Education. The technique has achieved the Classification accuracy of 91% which is higher than conventional Classification technique.
Health Care and Management based on Block chain and Machine Learning

With the coming of technology, we currently have an immense measure of information accessible in each field, making it conceivable to offer responses to many issues. We will utilize machine learning and block chain technology to resolve issues with healthcare information the executives in this review. With the guide of machine learning, separating just the relevant information from the data is plausible. Using prepared calculations, this is finished. The reliability of information trade turns into a test after this information has been put away. This is where block chain technology is valuable. Block chain technology's agreement guarantees that information is credible and exchanges are secure. By putting the patient at the focal point of the healthcare framework and upgrading the security and interoperability of health information, block chain technology can possibly further develop health care organization. This article fundamentally centres around settling issues with healthcare information the board using Block chain technology and including a few urgent highlights utilizing Machine Learning. The freshest technology, block chain, offers a strong and secure groundwork. With these offices, block chain has gotten some momentum in an assortment of use areas, including the food business, store network the board, energy, government support offices, and healthcare.
Multi-mode Summarization of Surveillance Videos using Supervised Learning techniques

Video summarization has been a prevailing area due to the wide use of surveillance systems. Extracting the contents from these video frames for domain-specific usage has become a tedious task. There are many approaches for the augmentation and detection of video content. A single platform for generating a summary of the surveillance videos will be revolutionary as security surveillance is becoming challenging day by day. This work is based on the various literature available for managing the video content especially for extracting the face, activity, and detecting the Object of Interest (OoI). The agents are identified based on an extensive literature survey on various approaches for object detection, activity recognition, and identity verification. The survey has helped to find out the best-performing algorithm in each of the above-mentioned domain. The work has proposed a unique architecture, Multi-mode Summarization of Survellance video System(MSSVS) where the observer can select agents, and based on this the metatag of the activity can be generated.