Reviewerly/exHarmony huggingface dataset:
A meaningful analysis of the updated Federal State Educational Standards and the problem of implementing their requirements in the work of a teacher

The subject of the study is the updated Federal State Educational Standards (FGOS) of all levels of general education. The object of the study is the formation of a unified educational space in the Russian Federation, one of the leading instruments of which is the Federal State Educational Standard. The study presents a detailed analysis of the requirements of the Federal State Educational Standard for the results of the development of basic educational programs. Special attention is paid to the professional competencies of the teacher, which determine the possibility of forming results within the framework of subject-based learning. The study was carried out on the basis of a meaningful analysis of updated FGOS and other regulatory documents, statistical analysis of FGOS of different generations, monitoring studies of professional competencies of teachers. Based on the conducted research, a number of conclusions were made. It is revealed that the leading update of the Federal State Educational Standard is the concretized and systematized requirements for personal, meta-subject and subject results of mastering the basic educational programs, which are holistically presented in relation to training and upbringing and formulated in an activity form. It is shown that the updated FGOS answer the systemic questions "what to teach?", "how to teach?", "why to teach?", i.e. they have an applied nature. The requirements of the Federal State Educational Standard set the framework of requirements for the professional competencies of a teacher – subject, methodological, personal. The study revealed the deficits of professional competencies, the leading of which is the willingness and ability to carry out professional activities on the basis of a system–activity approach, which acts as a system-forming condition for the implementation of requirements for learning outcomes. The scope of application of the research results is to determine the directions of professional development of teachers in the context of changes in the regulatory framework.
Implementasi Augmented Reality sebagai media pembelajaran perangkat Internet of Things dengan metode Marker Based Tracking

Learning media is an important factor in the learning process. By using the right learning media, someone can easily understand what is being studied. Currently, augmented reality is a technology that is being developed in various fields, one of which is in the field of education. Augmented reality can be an alternative to effective and interactive learning media because augmented reality can combine 2D or 3D objects into a real environment. To learn about the internet of things devices, currently, students can only learn through books or do the hands-on practice. If an error occurs during practice, it can cause damage to the internet of things device. By utilizing augmented reality technology, a new learning media is created by combining 3D objects from internet of things devices into the real environment.&#x0D; This learning media application was tested using the Blackbox and Mean Opinion Score methods. Blackbox testing shows that all functions have been running properly by the functional requirements that have been defined. Testing using the Mean Opinion Score shows that the average student respondent's assessment of the application is stated to strongly agree as much as 60.3% and agree as much as 35.3%.
An Overview of Parallel Symmetric Cipher of Messages

Background:&#x0D; Despite significant developments in communications and technology, data protection has established itself as one of the biggest concerns. The data must be encrypted in order to link securely, quickly through web-based technological data transmission. Transforming plain text into ciphered text that cannot be read or changed by malicious people is the process of encryption.&#x0D; Materials and Methods:&#x0D; In order to maintain the required degree of security, both the cryptanalysis and decryption operations took a significant amount of time. However, in order to cut down on the amount of time required for the encryption and decryption operations to be completed, several researchers implemented the cryptography method in a parallel fashion. The research that has been done on the problem has uncovered several potential answers. Researchers used parallelism to improve the throughput of their algorithms, which allowed them to achieve higher performance levels on the encryption algorithm.&#x0D; Results:&#x0D; Recent research on parallel encryption techniques has shown that graphics processing units (GPUs) perform better than other parallel platforms when comparing their levels of encryption performance.&#x0D; Conclusion:&#x0D; To carry out comparison research on the most significant parallel crypto algorithms in terms of data security efficacy, key length, cost, and speed, among other things. This paper reviews various significant parallel algorithms used for data encryption and decryption in all disciplines. However, other criteria must be considered in order to show the trustworthiness of any encryption. Randomness tests are very important to discover and are highlighted in this study.
Improving Student Balinese Language Learning Outcomes through Interactive Animated Video Based on a Contextual Method

The Balinese language's position as a mother tongue or regional language contributes to establishing the identity of its people. However, the use of Balinese is decreasing over time, and the quantity of speakers is decreasing. This study aims to develop interactive animated video media based on contextual method to improve students’ Balinese Language learning outcomes. Hannafin and Peck's model which comprises three processes (needs analysis, design, development, and execution) was employed in this development study project. The information was gathered using tests, questionnaires, observations, documentaries, and surveys. Data analysis techniques include the t-test, inferential statistical analysis, and quantitative descriptive analysis. Three students participated in the solo trials, nine in the small group trials, and sixteen in the outdoor tests of this study. Experts in learning media, design, and content served as the subjects. The results of the individual trial, small group trial, field trial, learning content expert test, learning design expert test, learning media expert test, and the learning media expert test were all excellent, with percentage scores of 96.66%, 94.66%, 90.22%, and 98.125%, were used to determine the validity of the interactive animated video media. The t-test illustrates the effectiveness of interactive animated video media by showing significant variations in student learning outcomes between the pre-and post-use media. Since the post-test results of the children are above the National Minimum Completeness Criteria, it is clear that interactive animated video media is an effective tool to assist Balinese Language learning process.
Some C# Fundamentals

In this chapter, we will present a brief introduction to the C# syntax that will be used in subsequent chapters.
Introduction

This work is intended to be used as an introduction to Web applications development using ASP.Net (Core) MVC, primarily for undergraduate students.
More on Controllers and Views, Introduction to Razor Syntax

In this chapter, we will update the project seen in the previous chapter. In particular, we will revisit model classes and controllers and demonstrate how to make use of Razor views.
Work with Images (Optional)

In this chapter, we will update the project seen in the previous chapter. In particular, we will demonstrate how to work with images: store them in a database, and retrieve them from the database to display them in webpages.
Application of Genetic Algorithm in Predicting Mental Illness: A Case Study of Schizophrenia

Medical data mining is one of the most widely used fields of data mining. The process of discovering new knowledge from a database can be described as a technique. As a result of the decline in performance, schizophrenia is one of the most prevalent diseases that cause a lot of financial and social losses in society. In order to do this, we will investigate the most effective predictors of schizophrenia incidence and then attempt to predict the age of onset of schizophrenia. In this study, a number of common classification methods were used, including genetic algorithms, support vector machines, decision trees, and neural networks. According to our results, the combined model combining genetic algorithms and support vector machines is more efficient than any of the other models. In the study, 189 patients participated, 121 of whom were males, 68 of whom were females, and their mean age was 38 years old. On average, fathers were 32 years old when their children were born. On average, mothers were 28 years old. With an accuracy of 98.1%, a combined model of genetic algorithms and support vectors was used to determine the age of schizophrenia onset in children.
Metaverse Applications in Education: A Systematic Review and a Cost-Benefit Analysis

PurposeMany educators are increasingly acquainting themselves and becoming adept with interactive technologies like augmented reality and virtual reality. Some of them are also looking forward to using Metaverse applications, as they want to benefit from its immersive three-dimensional capabilities. Therefore, the purpose of this study is to critically review the extant literature to investigate how, why, where and when the Metaverse can be used for educational purposes. This study also discusses opportunities, challenges and risks related to this disruptive technology.Design/methodology/approachA Preferred Reporting Items for Systematic Reviews and Meta-Analyses rigorous protocol is used to search, extract, scrutinize and synthesize content from high-impact articles focused on the use of the Metaverse technology in the realms of education. Afterwards, this study theorizes on the costs and benefits of using this interactive technology with students.FindingsA number of researchers are already experimenting with virtual technologies that are very similar to the Metaverse, in different contexts. This research indicates that most students are lured by immersive multi-sensory three-dimensional environments as well as by virtual reality applications that could simulate real-life situations and provide engaging experiences with virtual representations of people, places and objects. On the other hand, this study reveals that educators ought to consider the potential pitfalls of the Metaverse, including privacy breaches and security risks, as well as possible addictions and the development of mental health issues, among others.Practical implicationsStudents and educators can use the Metaverse to catapult themselves in a simulated digital universe that could reconfigure their sensory inputs, definitions of space, time and points of access to information. This research calls for the development of regulatory instruments, including sound principles, guidelines and procedures that are intended to safeguard and protect Metaverse users.Originality/valueThis contribution implies that there is scope for educators to continue developing the Metaverse’s virtual spaces to improve their students’ motivations, aptitudes and learning outcomes. This study clarifies that the use of the Metaverse in education can create infinite possibilities to enhance their knowledge, competences and abilities through its immersive applications. Yet this paper also raises awareness about possible challenges in the short term as well on other risks associated to the prolonged use of this captivating technology.
Real-Time Face Mask Detection Using Convolution Neural Network and Computer Vision

One strategy to prevent COVID-19 as a result of the pandemic is to use a face mask. Although many countries have made it a requirement for citizens to do so, the majority of people continue to disobey this order. In the current situation, police frequently check for face masks in public locations and fine anyone found not to be wearing one. The existing system identifies the person wearing the mask or not, but our proposed method identifies the person those who are not wearing the mask properly and also identifies the multiple person wearing the mask or not. On the other side, some governments have implemented technology to identify individuals wearing face masks and communicate their information to a patrol team so that they may catch them. The proposed model identifies individuals who are in the public without face masks. The proposed approach is able to identify these persons using facial detection technologies, and the data is then combined with a database of public identity information to gather information about the individual and deliver the fine amount to his home address and mobile numbers. Using the convolution neural network (CNN) model, we have identified people wearing and not wearing masks. When compared to many other algorithms, CNN can more precisely recognize data down to the pixel level. In the implementation of our model, the activation functions for the hidden and fully connected layers, respectively, were rectified linear unit (ReLU) and softmax. Two convolution layers with 100 filters each were used. The 91.21% accurate Cascade classifier is used to recognize faces. Adam is the optimizer, and cross-entropy serves as the loss function. More than 1500 images were used to train the model, which comprises classes with and without masks. The public will start wearing masks in public areas because of the terror that this AI-based mask recognition system instils in them, helping to stop the spread of diseases that are otherwise beneficial to society.
Early Detection of Covid-19 Using Wearable Sensors’ Data Enabled by Semantic Web Technologies

The huge increase in the count of devices and the connections among them has birthed the Internet of Things (IoT). The capabilities of wearable health devices are immense, and their potential has especially become apparent during the Covid-19 pandemic. The physiological changes recorded using the activity tracker bands, and smartwatches can be modeled as a data format that supports semantics, i.e., Resource Description Framework (RDF), to help enable interoperability. The objective is to observe and analyze this monitored data to establish how prediction models based on it can be utilized for early identification of infection (pre-symptomatic detection) and effectively raise alerts for early isolation. In this paper, sensor data of heart rate, step count, and sleep hours from users’ Fitbit trackers have been used for showing how Covid-19 infection can be detected early than by using symptoms’ data.
Intellectual Movie Recommendation System Using Supervised Machine Learning Method

One of the AI applications that has attracted the most attention from researchers around the world is the recommendation system. Since the beginning of the Internet era, recommendation systems have been widely incorporated into our daily lives. It is still difficult to give new users the proper recommendations. This paper suggests a novel and sophisticated movie recommender model to address this problem and to suggest highly rated movies to the new users. Moreover, we have applied a number of supervised machine learning techniques, including as KNN, DT, RF, GNB, and LSVM, to evaluate the performance of the proposed model in terms of precision, recall, F1-score, and accuracy. In simulation, all the techniques are performing well on the proposed model and on the given datasets, however, the LSVM and GNB is proving higher accuracy along with precision, recall, and F1 measure.
An Enhanced Encryption Scheme for Cloud Security

Cloud computing is a technique that connects a large number of systems with either public or private network, providing dynamic scalability for data storage, building applications, and other services. The consumers are charged only for the resources they consume. Cloud computing provides enormous applications and benefits in the IT field, but all the facilities need to be completely secured and protected. Removing security risks and maintaining privacy are essential for securing cloud computing. Several encryption algorithms are used for encrypting data in the cloud. Homomorphic encryption is an advantageous technique in cloud computing because it allows the cloud service providers to work on encrypted data. Thus, it ensures the privacy of sensitive data and denies access to data by cloud providers. Nevertheless, this homomorphic encryption technique has a problem with key sharing and management. Hence, to provide efficient key management, the technique of particle swarm optimization is used along with homomorphic encryption. Data to be encrypted is taken as input and treated as the initial population. PSO generates the key for encryption; then, the data is encrypted using homomorphic encryption. We have used MATLAB to implement this proposed encryption algorithm. Performance is analyzed on the basis of the time taken to execute and the utilization of resources. The observations made suggest that the enhanced algorithm executes well and provides better results as compared to the existing algorithm. It shows less time taken to execute and requirement of less number of resources. Hence, the proposed scheme ensures cloud security efficiently.
Image Interpolation-Based Steganographic Techniques Under Spatial Domain: A Survey

Whenever any data is embedded inside any image, then there is always some distortion caused, which caught the attention of attackers. As a consequence of this, there is always a need to reach a balance between the image quality and the embedding capacity of the picture. The researchers working in the field of data hiding have two basic objectives: one is to boost the performance of the suggested algorithms, and the other is to give a better capacity for embedding. This survey paper focuses on various traditional interpolation methods and interpolation-based data hiding techniques for the past few years. Also, the goal of this survey is to offer an in-depth evaluation of current up-to-date approaches, as well as their benefits and drawbacks if any. This survey paper concludes with some research recommendations that can be used in the future to enhance both embedding capacity as well as image visual quality.
Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation Systems

Many industries use recommendation systems (RS) to identify product recommendations when users actively participate on e-commerce sites. Recently, massive growth in both goods and consumers has faced serious challenges. Numerous websites present the consumer with numerous options at once, creating a lot of confusion. In addition, finding the right product or active user is an essential part of RS. Products are already recommended based on consumer preferences and sociodemographic trends. A hybrid action-related recommendation based on K-Nearest Neighbor Similarity (HAR-KNN) combines the ease of hybrid filtering with the development of feature vectors to improve the user behavior matrix. To categorize attributes, it uses both quality and quantity classifiers. Additionally, the proposed methodology overcomes shortcomings in earlier approaches to evaluating user preference for goods and feature analysis. The SOM AND KNN classification technique has been approved for the purpose of locating information about user behavior online and in real time for a specific user group containing a large amount of data in relation to the commonalities among many users and target users. A test result is evaluated by using highly predictive metrics such as precision (P), recall (R), F, as well as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE).
Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents

Linked Data is used in various fields as a new way of structuring and connecting data. Cultural heritage institutions have been using linked data to improve archival descriptions and facilitate the discovery of information. Most archival records have digital representations of physical artifacts in the form of scanned images that are non-machine-readable. Optical Character Recognition (OCR) recognizes text in images and translates it into machine-encoded text. This paper evaluates the impact of image processing methods and parameter tuning in OCR applied to typewritten cultural heritage documents. The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) to tune the methods' parameters. Evaluation results show that parameterization by digital representation typology benefits the performance of image pre-processing algorithms in OCR. Furthermore, our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results. In particular, Adaptive Thresholding, Bilateral Filter, and Opening are the best-performing algorithms for the theatre plays' covers, letters, and overall dataset, respectively, and should be applied before OCR to improve its performance.
TokenDoc: Source Authentication With a Hybrid Approach of Smart Contract and RNN-Based Models With AES Encryption

This study proposes a hybrid architecture in which duplicates in the dataset are identified using a deep-learning neural network to authenticate the source. The blockchain provides distinct metadata to the information source to allow the sender and the recipient to verify that the information is accurate and cannot be modified or altered. The system also uses RNN-based LSTM and GRU model neural networks to determine whether the method performs better for these applications and uses an Ethereum blockchain to perform transactions between the two parties. The system also used an advanced encryption standard (AES) to protect the transactions. The AES uses encryption and decryption keys. This system structure is implemented on raspberry pi devices to determine its feasibility in a small system flowchart. The dataset for this study is the “Image encryption and decryption dataset” from the Kaggle website, and the results are better than GRU, with a 0.9635 accuracy rate for this dataset. The smart contract module is verifiable, available, tamper-resistant, and usable, and AES provides a more robust encryption method for the system.
A Novel L-CLAHE-Based Intensification Filter for Enhancement of Underwater Images and Pipeline Tracking

The high contrast degraded underwater images often demand extensive pre-processing to attain reliable information for object detection and pipeline tracking. The prototype system developed employs CLAHE, a variant of adaptive histogram equalization, with an adaptive clip limit based on entropy. To yield an enhanced image, CLAHE is applied to the L* channel alone, resulting in improved L- CLAHE and better results. Validation of the improved L-CLAHE method is carried out using real-life underwater images captured using DTG2 Pro ROV from Kochi backwaters based on PSNR, SSIM, EPI, and PFOM quality measures and outperforms the conventional histogram equalization and adaptive histogram equalization techniques. These images are subjected to intensification and filtering for further enhancement as well as object detection and pipeline tracking applications. The proposed algorithm is compared with CLAHE, MSRCR, and L-CIF methods. The improved L-CIF algorithm has a high entropy value compared to the existing algorithms while the EME metric has been evaluated close to unity indicating the improved performance of the fast-guided filter. The method developed also helps in improving the average pixel intensity of the input images.