Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF ONLINE EDUCATIONAL PLATFORMS USING WEB INTELLIGENCE

The article discusses the capabilities of web analytics tools for studying the competitive environment. The object of the study is online education platforms. The capabilities of web analytics are expanding using big data, and one of its modern areas is competitive analytics. The analysis tool is the SimilarWeb resource. The paper presents the results of a comparison of five platforms developed in the last decade. The following evaluation criteria were used: traffic and level of involvement, demographic characteristics, marketing channels used and social traffic. The results of the analysis can be used when making investment decisions in the area under consideration, or for using effective models of development and promotion.
Phishing Detection using Base Classifier and Ensemble Technique

Phishing attacks continue to pose a significant threat in today's digital landscape, with both individuals and organizations falling victim to these attacks on a regular basis. One of the primary methods used to carry out phishing attacks is through the use of phishing websites, which are designed to look like legitimate sites in order to trick users into giving away their personal information, including sensitive data such as credit card details and passwords. This research paper proposes a model that utilizes several benchmark classifiers, including LR, Bagging, RF, K-NN, DT, SVM, and Adaboost, to accurately identify and classify phishing websites based on accuracy, precision, recall, f1-score, and confusion matrix. Additionally, a meta-learner and stacking model were combined to identify phishing websites in existing systems. The proposed ensemble learning approach using stack-based meta-learners proved to be highly effective in identifying both legitimate and phishing websites, achieving an accuracy rate of up to 97.19%, with precision, recall, and f1 scores of 97%, 98%, and 98%, respectively. Thus, it is recommended that ensemble learning, particularly with stacking and its meta-learner variations, be implemented to detect and prevent phishing attacks and other digital cyber threats.
FML-MIS: A Scheme of Privacy Protection and Model Generalization for Medical Images Segmentation via Federated Meta-learning

Federated learning enables diverse distributed medical institutions to learn a global prediction model collaboratively while preserving data privacy. However, in practical clinical deployment, the global model may suffer from degraded generalization performance due to the high heterogeneity of medical data from different sites. In this paper, we propose and implement a robust framework for medical image segmentation (FML-MIS) via a federated meta-learning scheme, which can achieve effective privacy preservation and model generalization. Our scheme consists of three components: (1) a federated domain image generation mechanism that synthesizes diverse and realistic images across domains; (2) a meta-learning strategy that leverages the generated images to enhance the model’s adaptability to new domains; and (3) a differential privacy mechanism that protects the model’s security and resists inference attacks during federated training. We evaluate our scheme in a federated learning setting on three public datasets. Extensive experiments and comparison results show that our scheme can effectively utilize rich multi-source data and improve the generalization ability of the model while preserving data privacy. The project code is available at https://github.com/yuwxl/FML-MIS .
Power Data Sharing Model Based on Blockchain and IPFS Storage

With the rapid development of information technology, the electric power industry has also accelerated informatization process. To improve the efficiency of information management and optimize customer service, it is necessary to realize data sharing among electric power enterprises. Due to the privacy, polymorphism, and huge amount of power data, it is very difficult for power enterprises to connect with each other, and it is difficult to realize safe sharing and data storage in the process of power data sharing. To solve the security problem of data sharing, this paper introduces blockchain technology and designs a power data sharing model. Using traditional blockchain technology to store a large amount of data will seriously affect its performance, resulting in its slow running speed. Therefore, this paper designs a data storage network based on IPFS technology, which improves the efficiency of data transmission and storage. Experiments show that this model has good effect.
Characterizing Mobile Applications Through Analysis of DNS Traffic

User privacy may remain vulnerable when using encrypted communication protocols, such as HTTPS, if DNS queries are sent in cleartext over UDP port 53 (Do53). In this study, we demonstrate the possibility of characterizing the mobile application a user is using based on its Do53 traffic. By analyzing a dataset of traffic captured from 80 Android mobile apps, we can identify the app being used based on its DNS queries with an accuracy of 88.75%. While modern operating systems, including Android since version 9.0, support encrypted DNS traffic, this feature is not enabled by default and relies on the DNS provider's support. Moreover, even when DNS traffic is encrypted, the DNS service provider still has access to our queries and could potentially extract information from them.
Penerapan Metode Thresholding Pada Proses Transformasi Citra Digital

Book is a window to the world. By reading books, we can get a variety of knowledge that we do not know. The meaning of books as windows to the world can be doubted today. Given the internet provides a lot of information with a much more attractive appearance. Both books and the internet provide a wealth of information and knowledge. But in fact books are still the mainstay of accurate sources of knowledge. A lot of information on the internet is in fact not supported with accuracy. Even in the world of higher education, books are the main reference source in scientific writing. However, old books that may have started to become dull and the writing can no longer be read resulted in the reader of the book not being able to know the knowledge contained in the old book. Based on observations, it is necessary to build a computerized system so that the writings in the book can be read again. One method that can be used is the Thresholding method. The system is designed with MATLAB programming application. In this process, it is necessary to scan or take photos/pictures from cellphone cameras from old book manuscripts to be processed on the system using the Thresholding method. After carrying out the testing process on several images of old books, better results were obtained so that the writing on the manuscript could be seen more clearly with different threshold values for each image entered into the system.
The Effect Of The Application Of The Use Of Google Classroom As A Learning Media At SMA Negeri 1 Pancur Batu

In today's era, technological developments are very fast so that they can support learning very well, for example using the classroom application where using this application can make it easier for educators and students to carry out the learning process, where in this classroom application you will be able to send files, videos, links , ppt and so on to be used as learning materials given by educators, so that students can open the files provided anytime and anywhere without having to meet or meet with educators. The research method used is a qualitative method, which uses a questionnaire to be given to 30 students where there are questions about students' opinions in using the classroom application. The research results obtained are where students can greatly improve student learning achievement, because it can help students in learning.
Penerapan Model Attention Relevance Confidence Satisfaction Untuk Meningkatkan Hasil Belajar Tematik Siswa Kelas V SDN 104252 Sei Tuan T.A 2022/2023

The problem in this research is the students' classical mastery in class V SD Negeri 104252 Sei Tuan who have not achieved 85% classical completeness. This is because the selection of the learning model that the teacher applies is not in accordance with the material in Theme 2 Clean Air for Health Sub-theme 2 and Sub-theme 3. The implementation of integrative thematic learning that the teacher does has not maximized the use of TPACK-based technology media, and the implementation of learning methods is still dominated by the lecture method so that it is teacher centered. This type of research is Classroom Action Research, with the research subject being Class V students at SD Negeri 104252 Sei Tuan for the 2022/2023 academic year. A total of 26 students, with details of 15 male students and 11 female students. The object of this research is the learning outcomes of students. This research was carried out in two learning cycles. With 4 (four) stages in each cycle, namely planning, implementation, observation/observation, and reflection. The data collection tools are tests, observation sheets, and documentation. The results of the analysis of data from 26 students obtained classical completeness in the teacher's initial observation before using the ARCS (Attention, Relevance, Confidence, Satisfaction) learning model, namely 23% for Science subjects and 19% for Indonesian Language subjects. The acquisition of the first cycle significantly increased with 61.53% classical completeness and was followed up in the second cycle with 92.31% classical completeness. Based on the results of the analysis above, it can be concluded that using the ARCS learning model (Attention, Relevance, Confidence, Satisfaction) can improve student learning outcomes Theme 2 Clean Air for Health Sub-theme 2 and Sub-theme 3 in Grade V Students of SD Negeri 104252 Sei Tuan Academic Year 2022/2023.
Upaya Meningkatkan Hasil Belajar Siswa Melalui Model Problem Based Learning Di Kelas V Tema 3 Subtema 2 Pembelajaran 1 SD Negeri 105322 Desa Mesjid

The purpose of this research was to improve student learning outcomes by using the Problem-based learning model on theme 3, sub-theme 2, learning 1 with the subject matter characteristic of electronic media advertisements and digestive organ disorders in class V SD Negeri 105322 Desa Mesjid, academic year 2022/2023. This type of research is Classroom Action Research, with the research subject being Class V students at SD Negeri 105322 Desa Mesjid for the 2022/2023 academic year. There are 19 students, with details of 7 male students and 12 female students. This research was carried out in two learning cycles. With 4 (four) stages in each cycle, namely planning, implementation, observation/observation, and reflection. The data collection tools are tests and observation sheets. Based on the initial tests conducted, it was found that of the 19 students who took the pre-test, there were 7 students who completed the learning outcomes with a percentage of 37% and 12 students who had not completed the pre-test with an average percentage of 63% with an average of 59. ,4. Researchers followed up in cycle I by using the Problem based learning learning model on theme 3 sub-themes 2 learning 1 subject matter features of electronic media advertising and digestive organ disorders, the acquisition of cycle I results increased to 11 students who completed learning outcomes with a percentage of 58% and there were 8 students incomplete with a percentage of 42% with an average classical score of 71. Based on the temporary conclusion, that student learning outcomes have not been as expected or have not reached a good classical completeness percentage level, so it needs improvement and development in cycle II. In cycle II, the acquisition of student learning outcomes increased to 16 students who completed their learning outcomes with a percentage of 84% and not yet completed 3 students with a percentage of 16% with an average classical score of 83.
The Qualitative Study on Experience of Infection Control Education among Clinical Nurses: Focus Group Interview

The purpose of this study was to examine the experience of infection control education of clinical nurses in small and medium-sized hospitals. Data were collected through focus group interviews with 10 clinical nurses from October 4 to October 14, 2021. The content analysis of in-depth interviews and field notes revealed four themes: 1) perfunctory education influenced by COVID-19, 2) gap between theoretical education and practice, 3) structural issues in hospitals that inevitably resulted in education neglect, and 4) educational experience that facilitated self-growth. The study findings revealed that although infection control education was limited due to lack of personal resources and environmental factors, clinical nurses could acquire new knowledge and experience self-growth through self-reflection. The study highlights the need to support nursing staff, strengthen the competency of infection control nurses, and provide practical and professional education to facilitate infection control education.
O-City: Implementation of an Innovative Multimedia Platform for Promoting Orange Economy

O-City is a non-profit project funded by European Union with the aim of promoting Orange Economy throughout education and collaboration among municipalities, educational entities and businesses. This project has two main assets, the O-City e-learning platform and the O-City World platform. This paper presents the technical aspects of the O-City World platform, which is a digital application that allows interaction among cities, educators and professionals. This platform has a role-based access control with seven different users able to perform different functionalities. Thanks to the collaboration among these stakeholders the platform is growing exponentially.
Bidirectional Sign Language Translation System - bookstall

Currently, there is a huge difficulty in the deaf community to establish direct communication between people or to obtain information, and with the advance of technology, new methods have emerged to make their daily lives easier, a concrete case is VirtualSign. However, this technology needs to be implemented in different areas. The main objective of this project is the development of an inclusive application, to be implemented in a bookstall, where it is possible to make bidirectional translations of texts into sign language and vice versa. The creation of this application uses VirtualSign technology as a sign language recognition system, for this task a camera is used to capture movements and the respective screen to clearly present the respective information. It will serve for better integration of the deaf community. The project developed here was based on the suggestion of Professor Paula Escudeiro and in cooperation with GILT/ISEP. In summary, this project aims to develop an application for a bookstalls that allows bidirectional translations between texts and sign language. It uses VirtualSign technology for sign language recognition. The application aims to improve the integration of the deaf community.
Discrete Uncertainty Quantification For Offline Reinforcement Learning

Abstract In many Reinforcement Learning (RL) tasks, the classical online interaction of the learning agent with the environment is impractical, either because such interaction is expensive or dangerous. In these cases, previous gathered data can be used, arising what is typically called Offline RL. However, this type of learning faces a large number of challenges, mostly derived from the fact that exploration/exploitation trade-off is overshadowed. In addition, the historical data is usually biased by the way it was obtained, typically, a sub-optimal controller, producing a distributional shift from historical data and the one required to learn the optimal policy. In this paper, we present a novel approach to deal with the uncertainty risen by the absence or sparse presence of some state-action pairs in the learning data. Our approach is based on shaping the reward perceived from the environment to ensure the task is solved. We present the approach and show that combining it with classic online RL methods make them perform as good as state of the art Offline RL algorithms such as CQL and BCQ. Finally, we show that using our method on top of established offline learning algorithms can improve them.
Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics

Dynamic multi-person mesh recovery has broad applications in sports broadcasting, virtual reality, and video games. However, current multi-view frameworks rely on a time-consuming camera calibration procedure. In this work, we focus on multi-person motion capture with uncalibrated cameras, which mainly faces two challenges: one is that inter-person interactions and occlusions introduce inherent ambiguities for both camera calibration and motion capture; the other is that a lack of dense correspondences can be used to constrain sparse camera geometries in a dynamic multi-person scene. Our key idea is to incorporate motion prior knowledge to simultaneously estimate camera parameters and human meshes from noisy human semantics. We first utilize human information from 2D images to initialize intrinsic and extrinsic parameters. Thus, the approach does not rely on any other calibration tools or background features. Then, a pose-geometry consistency is introduced to associate the detected humans from different views. Finally, a latent motion prior is proposed to refine the camera parameters and human motions. Experimental results show that accurate camera parameters and human motions can be obtained through a one-step reconstruction. The code are publicly available at https://github.com/boycehbz/DMMR.
Predictive Modeling of Photovoltaic Panel Power Production through On-Site Environmental and Electrical Measurements Using Artificial Neural Networks

Weather disturbances pose a significant challenge when estimating the energy production of photovoltaic panel systems. Energy production and forecasting models have recently been used to improve energy estimations and maintenance tasks. However, these models often rely on environmental measurements from meteorological units far from the photovoltaic systems. To enhance the accuracy of the developed model, a measurement Internet of Things (IoT) prototype was developed in this study, which collects on-site voltage and current measurements from the panel, as well as the environmental factors of lighting, temperature, and humidity in the system’s proximity. The measurements were then subjected to correlation analysis, and various artificial neural networks (ANNs) were implemented to develop energy estimations and forecasting models. The most effective model utilizes lighting, temperature, and humidity. The model achieves a root mean squared error (RMSE) of 0.255326464. The ANN models are compared to an MLR model using the same data. Using previous power measurements and actual weather data, a non-autoregressive neural network (Non-AR-NN) model forecasts future output power values. The best Non-AR-NN model produces an RMSE of 0.1160, resulting in accurate predictions based on the IoT device.
Failure Stories and Surprising Findings: Learning from When VR Did Not Work

It is easy to assume that virtual reality will naturally lead to improved learning outcomes, as there is a tendency to view VR, as an exciting new technology, as a “silver bullet” that can dramatically enhance learning. However, several research studies reveal surprising or counter-intuitive findings – for example, when low-immersion media led to better learning outcomes than VR. We provide a systematic review and discuss specific cases in which results were not as expected. Factors that adversely affect learning include novelty effects, VR sickness, low interaction fidelity, cognitive overload, or other environmental or classroom factors. Through this study, we argue that designers and educators can avoid pitfalls by studying the times when VR did not work as expected. We also provide recommendations based upon these findings. In this way, we can gain insights on how to actually obtain desirable outcomes from the new technology.
Access to Escape - An Immersive Game-Based Learning Experience for Accessibility Education in Virtual Reality

The accessibility of digital systems determines the participation of a large number of people in everyday life. Therefore, it is important to teach this topic to future developers of the said systems. As accessibility can be hard to grasp due to one’s inability to experience the problematic barriers people with disabilities face, Virtual Reality (VR) comes into play. VR technology offers the opportunity to simulate situations encountered by persons with disabilities and enables the possibility of new experiences. To further analyze the suitability of a VR application to teach accessibility, we developed Access to Escape, an Immersive Game-Based Learning Experience for Accessibility Education in Virtual Reality. A first evaluation with 11 participants could show that the VR Escape Room helped to develop a different perspective through the simulated limitation of their senses and sensitize them to the issue of accessibility. The participants reported that they have discovered new connections concerning accessibility. Furthermore, the use of VR was perceived as very enjoyable, especially due to the increase of motivation and the simplification of the topic. Besides that, the results of the User Experience Questionnaire (UEQ) could show that the participants perceived the VR Escape Room as attractive, stimulating and novel. The evaluation demonstrates that a Game-Based Learning Experience in a virtual environment might lead to cognitive as well as affective learning outcomes. The use of the VR Escape Room in education has the potential to improve learning, especially to raise awareness of accessibility.
From Playing the Story to Gaming the System: Repeat Experiences of a Large Language Model-Based Interactive Story

A distinctive aspect of interactive stories is how they enable players to impact the story based on their choices, with repeated playthroughs potentially yielding different stories. While some work has been done to investigate repeat experiences of interactive stories, the focus has mainly been on simpler linear and branching narrative systems. Recent advances in large language model (LLM) systems such as ChatGPT have sparked renewed interest in how users engage with artificial intelligence systems, and how LLM systems could be used in works of entertainment such as games and interactive stories. In this paper, we detail the findings of a qualitative observational study of 10 participants playing and replaying the interactive text adventure game AI Dungeon which is based upon the natural-language-generating LLMs GPT-3 and GPT-J. The study provides insights into how the player’s mental model of the system shifts from assuming the system is a linear or branching narrative to one with seemingly limitless narrative possibilities. This study also uncovers three related sources of motivations for repeat engagement with an LLM-based interactive story, which are impacted by the player’s shifting mental model of the system: narrative closure, narrative incoherence, and expectations for variation between playthroughs. Finally, this study highlights how uncertainty in terms of both how the system works and what objectives the player has could impact the player’s choice of actions when engaging with an LLM-based interactive story such as AI Dungeon.