Reviewerly/exHarmony huggingface dataset:
Emotional Insights into Online Lectures: A Text-Driven Analysis Using Naive Bayes During the Pandemic Times

The Covid – 19 pandemics first appeared for the first time on 2020 in Indonesia. This pandemic caused many changes in various aspect such as education and social life and it also increasing internet use in Indonesia. As we know, social media is widely used to express many things such as daily activities, expressing complaints, and also use for expressing their emotions for example about online lecture in the form of text such as on Twitter. In this research, an analysis related to the implementation of online lectures in Indonesia has been carried out. This research was conducted to find out opinions related to the existing system by detecting emotions in tweets using the Naïve Bayes algorithm. Based on research conducted by Shaver related to basic emotions in Indonesian, that emotions will be divided into five classes like happy, sadness, angry, fear, and love. In this final project, the five classes are divided into four emotion classes (happy, angry, fear, and love) and three emotion classes (angry, happy, and love). The data used are tweets taken on Github and also web scraping on Twitter which is implemented into the website. The model that has been made tested using Confusion Matrix to find out whether the model used to detect emotions can be said good. The result of final project research show that emotion detection system based on text related to the implementation of online lectures get 80% of accuracy for three class of emotions with test size 0.5, 73.20% for four class of emotion with test size 0.1, and 60.87% for five class of emotions with test size 0.1.
A Comparative Study of Embedded Learning Models IoT-based for real time Mask Detection

Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, facemask detection is a key innovative technology that allows the identificationof the number of individuals wearing face masks. In this regard, this paperprovides a comparative study of several machine learning and deep learningalgorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder,GAN, U-Net GAN) that support mask detection.
Utilizing a pseudo-random Linear Congruential Generator (LCG) S-Box for encoding color images through genetic crossover

This article introduces a novel algorithm crafted for encrypting color images. The algorithm leverages chaotic principles and harnesses fresh substitution tables derived from independent linear congruence generators. These generators are dynamically sized based on pseudo-random vectors used in this technology. The proposed method initiates with the original image, vectorization, employing selected chaotic maps. The primary goal revolves around implementing a genetic operator tailored for image encryption, who integrates an enhanced Vigenère technique, incorporating novel confusion and diffusion functions derived from the previously established substitution tables. To gauge the effectiveness of this approach, numerous color images of varying dimensions and formats underwent testing using our algorithm. The yielded outcomes are both promising and gratifying, furnishing heightened security against recognized attacks.
The formation of conceptual thinking in the participants in the educational process: a systemic approach 

The formation of conceptual thinking in the participants in the educational process: a systemic approach
On the interpretability of part-prototype based classifiers: a human centric analysis

Abstract Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In addition, previous works have had major issues with following proper experiment design and task representation that limit their reliability and validity. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective that solves these issues. The proposed framework consists of three actionable metrics and experiments. The results of these experiments will reveal important and reliable interpretability related properties of such models. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework.
TEACHING LITERATURE IN HIGH SCHOOLS: CURRENT CHALLENGES, PLATFORMS AND APPLICATIONS FOR TEACHING, METHODS AND TECHNIQUES

The article considers the problem of implementing the methodology of teaching literature in institutions of higher education through the prism of socio-historical conditions, the choice of platforms, methods, techniques for teaching. The authors determined that the implementation of an effective, safe and interesting educational process and the training of creative teachers are among the main tasks facing us today. In addition to hard skills, the applicant must also possess so-called soft skills, navigate modern methodological trends and be interesting for students. The dominant problem that became the object of research is the description of today's challenges, the selection of methods, tools (applications) for the realization of the goal set in the name of scientific intelligence. The relevance of the study is absolutely determined by modern historical and social events and the demands of employers in the labor market. The article presents an analysis of applications that can help to competently organize the educational process, analyzes the most popular platforms for organizing classes for future teachers of literature MOOC platforms (Coursera, EdX, XuetangX, FutureLearn, Udacity, Canvas Network, Udemy, Khan Academy), Moodle, Zoom, Google Сassroom, Microsoft Teams, CiscoWeb EX, Padlet, Jamboard, Google Handouts, Trello, Flinga Whiteboard, Linoit, Canva ); emphasis is placed on the formats of classes and general forms of organization of the educational process in the conditions of martial law; The most recent methods and methods of studying the methodology of teaching literature in a higher education institution are described separately, among them: interactive class, virtual reality, use of artificial intelligence, blended learning, 3D printing, design thinking, project-based learning, inquiry-based learning, puzzle learning, flipped learning, peer-to-peer learning, virtual crossover, virtual reflection.
Some ideas of organic criticism of Apollon Grigoryev in the psychological and pedagogical concept of ethical art criticism of the 1970s

At the end of the 1960s, the USSR put forward the task of educating a harmonious and comprehensively developed personality in connection with the transition from socialism to communism. A grandiose state project was launched in the country to educate a new person and develop universal aesthetic and museum literacy, which led to the museum boom of the 60-70s. The scientific result of the educational activity was the emergence of a new direction in art criticism, focused on the explanation of fine art to an unprepared viewer. The author of the article calls this direction "ethical", since its distinctive feature was the assertion of the ethics of art and science about it. The scientists set and solved the task of studying various social groups and developing a language understandable to everyone, teaching perception and understanding of works of art, the development of aesthetic sense and visual intelligence. The art historians of the Russian Museum justified the need to involve various psychological techniques to include art in the lives of as many people as possible, for which the first department of socio-psychological research in the USSR was established in 1974. When studying the concept of ethical art criticism, parallels with the ideas of organic criticism of Apollon Grigoriev, which are considered in the article, are vividly highlighted. Returning to the theme of the organic ethics of art, the unity of the ethical and aesthetic at a new turn of Russian history speaks of its importance for Russian culture.
Probabilistic Modeling for Conditional Statements

A new mathematical framework is proposed in this study to comprehend the impact of program architecture on input random variables, the IF statement was the main topic. The primary idea that is theoretically and experimentally supported in this study is that the part of the joint pmf of a collection of random variables that represents the condition will be shifted to the part that represents the action. After sorting two random variables, the framework is used with four random variables, and the theoretically produced results were realistically validated. The study's equations can be applied to assessing probabilistic models of various sorting algorithms or other intricate program structures. This may also result in future investigations formalizing more precise execution time expectations.
Risks of Using Linux OS with Enabled Firmware Update Mechanism (Linux Vendor Firmware Service)

The article discusses the mechanism for updating the built-in software (firmware) in distributions of the Linux operating system (OS). Considering the import "substitution strategy" in Russia proposed in technology sector and the active transition to this OS, including in solutions related to the digitalization of the energy sector, the problem of additional control over the firmware update process, incl. in used domestic distributions (AstraLinux, AltLinux). At the moment most popular mechanism of automatic firmware update is Linux Vendor Firmware Service (LVFS), article includes brief description of its update process. Using software developed for complete monitoring LVFS metadata were found multiply inconsistencies of current scheme of firmware update — deleting of files from index, replacement of already uploaded images with same id. Also basing on client agent metadata it is possible to organize targeted substitution of binary images transferred by service directly to hardware bypassing built-in monitoring tools. Finally it is concluded that there are potential vulnerabilities in this mechanism and a solution is proposed in the form of organizing own service with additional layer for verification of "firmware" during process of its distribution. If scheme with additional security layer over base of popular and well proved by practice LVFS will be successful it can be used in industrial applications with including in future in appropriate standards as reference model.
Development of Digital Twins for the Design of Integrated Energy Systems

The design of integrated energy systems (lESs) is a complex problem, which is due to the high complexity of the configuration of these systems, a wide range of equipment used and a diverse set of mathematical models and specialized software used for its simulation, the need to simulate a number of decision centers and objects with complex behavior. The use of IES digital twins makes it possible to effectively simulate various directions of their development in the virtual space, which ensures the creation of effective design solutions. The article is devoted to the construction of digital twins for the design of lESs. A new methodological approach to the design of an IES based on its digital twin, developed by the authors, is presented, which includes the following components: the architecture of the software platform for creating digital twins; a set of technologies and tools for its implementation; methods for automated construction of a computing subsystem of a digital twin; methodology for the design of IES based on its digital twin. The results of a computational experiment obtained using the completed software implementation of the elements of an IES digital twin are presented. The results of a computational experiment obtained using the software implementation of the digital twin elements of the IES for a test energy supply scheme are presented. The methodological approach can be applied by research, design and operating organizations that design and develop IES. Its application allows to increase the efficiency of the design process and the quality of the resulting design solution.
Artificial Neural Networks Approach for Cross-Flow Heat Exchanger Fouling Modeling

The traditional estimation methods such as fundamental equations, conventional correlations or developing unique designs from experimental data through trial and error have limits in thermal engineering due to the complexity of problems addressed. Thereby, the purpose of the present work is to explain the effective utilization of the Artificial Neural Networks (ANN) model in heat transfer applications for thermal problems, like fouling in a heat exchanger. The application of the ANN tool with different techniques and structures shows that it is an effective and powerful tool due to its small errors in comparison with experimental data. The feed-forward network with backpropagation technique was implemented in Mechanical Engineering Technologies and Applicatithis study. Based on sensitivity analysis, the performance of the network trained was tested, validated and compared to the experimental data. The results achieved by sensitivity analysis show that ANN can be used reliably to predict fouling in a heat exchanger.
Effect of Addition of Organic Sweet Corn Flour (Zea Mays L. Saccharata) Against Setting Time Test For Alginate

Alginate is one of the most frequently used dental materials; and alginate impression is a simple, cost-effective, and indispensable part of dental practice. However, alginate impression material is still difficult to find in remote areas, this is due to the distance factor and the limited supply of alginate. One of the substitutes for alginate is sweet corn that contain polysaccharide a and are easily available in Indonesia. . This type of research is a laboratory experimental study using 4 groups, consisting of the first group as a control group, namely alginate without being given a mixture of sweet corn flour (10 g). The second group with 60% alginate percentage (6 g) sweet corn flour 40% (4 g) . The third group alginate percentage 55% (5.5 g) sweet corn flour 45% (4.5 g). The fourth group was 50% alginate (5 g) 50% sweet corn flour (5 g). Each group consists of 6 samples. The results of this study were then analyzed using One Way Anova to see if there were differences in each treatment group, the results showed that there were significant differences (p 0.05) in each group. This study also found the group with the fastest setting time, namely the control group, with an average of and the longest setting time is in group 3 with an average of 234 seconds . This can happen because of the polysaccharide content, namely amylopectin in sweet corn flour. The conclusion of this study is that sweet corn flour ( Zea mays L. Saccharata ) can be used as an alginate mixture because it has a setting time according to the American Dental Association (ADA ) standard, which is 2 - 4.5 minutes.
EVALUATING THE EFFICACY OF ALEXNET FOR DETECTION OF THYROID CANCER

The increasing incidence of thyroid cancer cases and the challenge of unreliable false positive diagnostic rates in expert-reviewed ultrasound images emphasize the critical need for precise tumor diagnosis. Convolutional Neural Networks (CNNs), a state-of-the-art deep learning technique, exhibit remarkable capabilities in addressing computer vision challenges. This study introduces a specialized AlexNet CNN model designed for the detection of thyroid cancer in pre-processed ultrasound (US) images. The experimental outcomes reveal that the proposed model achieves an accuracy of 0.5, sensitivity of 0.3846, specificity of 0.7143, Positive Predictive Value (PPV) of 0.7143, and Negative Predictive Value (NPV) of 0.385.
The Role of Self-Regulated Learning and Student Engagement in Academic Activities towards Math Achievement

The purpose of this research is to empirically examine the role of self-regulated learning and student engagement in academic activities in math achievement. The research design used is a correlational survey. The subjects of this study are students at Madrasah Aliyah Negeri in Yogyakarta. The research sample was selected using a proportional stratified random sampling technique, with a total of 96 students. Math achievement was determined from students' daily evaluations, and data were collected using a self-regulated learning scale and a scale of student engagement in academic activities. Data were analyzed using multiple regression analysis. According to the findings, self-regulated learning and student engagement in academic activities significantly predict math achievement. Both self-regulated learning and student engagement in academic activities have a positive impact on math achievement. The combined contribution of self-regulated learning and student academic engagement to mathematics achievement is 37.1%. Self-regulated learning contributes more (17.39%) than academic engagement (19.77%) to mathematics achievement. This study recommends that students, teachers, and parents pay attention to self-regulated learning and student engagement factors.
Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy

Real-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Object detection models with slow inference times are ineffective in real-time. This study addresses this challenge by improving the inference speed of the YOLOv8 model, a leading object detection framework known for its accuracy and speed. We focus on pruning the model's architecture, particularly the P5 head section, which detects larger objects. According to Bochkovskiy's 2020 research, this modification enhances the model's performance specifically for medium and small objects in CCTV footage. The standard YOLOv8 model and its modified version were compared for inference time, mean Average Precision (mAP), and model weight. The pruned YOLOv8 model cuts inference time by 15.56%, from 4.5 ms to 3.8 ms, and reduces model weight. The advantages mentioned above are offset by a 1.6% decrease in mean average precision. This research advances object detection technology by demonstrating architectural modifications' efficacy. These changes make the model faster and lighter, making it suitable for real-time surveillance. The accuracy trade-off is slight. The implications of these findings are crucial for implementing efficient object detection systems in CCTV surveillance. These findings also lay the groundwork for future research to improve such systems' speed-accuracy trade-off.
Development of Augmented Reality Programming Language using Agile Scrum Methodology

The agile scrum methodology for augmented reality development increases project team efficiency. Private campus are frequently confronted with the dilemma of new students with various backgrounds that come not only from vocational high schools but also from high schools. First year students in the informatics study programme come not only from vocational informatics high schools, but also from high schools that specialize in social studies and languages. This is a difficult task in terms of imparting a comprehension of the fundamentals of programming. This study develops augmented reality in order to teach HTML and Javascript. By combining basic principles with gaming, the proposed augmented reality (AR) makes programming interesting. Players must comprehend their programming logic in order to be immersed in a virtual environment by answering coding bug questions. During usability testing, the System Usability Scale (SUS) assesses user happiness and AR knowledge. Participants from various programming backgrounds were tested on their knowledge of programming languages. According to usability research, 59% of people found AR programming languages useful for learning and understanding basic programming languages. AR and Agile Scrum make programming more enjoyable. This study demonstrates how augmented reality can be used to teach programming languages. These findings imply that Agile Scrum and AR methods can improve learning and programming foundations. More research and development could lead to more complete and complicated AR learning environments for programming instruction.
Designing a Virtual Campus Tour using Image Stitching Techniques to Provide Information on College Entrance Test

The University of Bengkulu administers college entrance exams, however some test takers still require assistance in locating the correct room, despite the building being marked. It is crucial to avoid errors in finding the right test room, as it can cause potential students to waste valuable time. Therefore, a more precise and practical solution is necessary to provide information on test locations. This study designs a location-based virtual tour that offers a 360-degree view, providing information on the location of each building and the conditions inside and outside each test room. The virtual tour encompasses 81 buildings, including test rooms, with 28 to 32 images captured at each location, then stitched together using image stitching techniques. The goal of the virtual tour is to create a comprehensive view of the test location and provide more detailed information on the room's condition. Furthermore, the usability of this virtual tour was tested on 140 high school students as potential test participants, utilizing the System Usability Scale (SUS) to evaluate its effectiveness, resulting in a score of 72.19. In other words, the virtual tour was found to be an effective tool in helping users understand the test location.
A Deep Learning Model for Splicing Image Detection

With the advancement of digital technology, manipulating images has become relatively easy through many photo editing techniques. One of the techniques is the splicing image method, which crops parts of images and puts them into another image creating a new composite image. The image splicing detection system is soon regarded as an exciting topic for many researchers to solve the problems of forgery images on the Internet, especially in social networks. ResNet-50 and VGG-16 are powerful architectures of convolutional neural networks, but they reveal many weaknesses when operating on low-end computers. The ultimate goal of this research is to create a model for image splicing detection working well in limited memory machines. The study proposes the model, which is the improvement of VGG-16 applying residual network (ResNet). As a result, the proposed model achieves a test accuracy of 92.5% while the ResNet-50 gives an accuracy of 85.6% after 20 epochs of training 9,319 images from the CASIA v2.0 dataset, which are used for forgery classification. The result proves the efficiency of the proposed model for image splicing detection, especially when working on low-end computers.