Reviewerly/exHarmony huggingface dataset:
STUDY OF CHILDREN'S LITERATURE AS TEACHING MATERIAL FOR ELEMENTARY SCHOOLS IN LEARNING MORAL CHARACTER OF STUDENTS

Problems regarding character and morals that occur in elementary school students are a hot topic of discussion. The character and moral crisis that is happening to the Indonesian nation today is extraordinary. In recent years, we are often presented with information about how corrupt and depraved the morals of students are. Starting with cases of sexual harassment that occur in children which makes us very sad. The fact that has happened is the shared responsibility of all groups, be it the family, teachers, government, and the social environment where the child is. Thus, children's literature becomes a solution to make improvements to the character and morals of students. Children's literature must be re-presented and intended to the maximum for students. The purpose of this research is to describe how the study of character and moral teaching in the Story of a Fairy Tale and to provide input for children's literature is used as teaching material that is entertaining and fun for students.
Towards Generalization of Machine Learning Models: A Case Study of Arabic Sentiment Analysis

The abundance of social media data in the Arab world, specifically on Twitter, enabled companies and entities to exploit such rich and beneficial data that could be mined and used to extract important information, including sentiments and opinions of people towards a topic or a merchandise. However, with this plenitude comes the issue of producing models that are able to deliver consistent outcomes when tested within various contexts. Although model generalization has been thoroughly investigated in many fields, it has not been heavily investigated in the Arabic context. To address this gap, we investigate the generalization of models and data in Arabic with application to sentiment analysis, by performing a battery of experiments and building different models that are tested on five independent test sets to understand their performance when presented with unseen data. In doing so, we detail different techniques that improve the generalization of machine learning models in Arabic sentiment analysis, and share a large versatile dataset consisting of approximately 1.64M Arabic tweets and their corresponding sentiment to be used for future research. Our experiments concluded that the most consistent model is trained using a dataset labelled by a cascaded approach of two models, one that labels neutral tweets and another that identifies positive/negative tweets based on the Arabic emoji lexicon after class balancing. Both the BERT and the SVM models trained using the refined data achieve an average F-1 score of 0.62 and 0.60, and standard deviation of 0.06 and 0.04 respectively, when evaluated on five diverse test sets, outperforming other models by at least 17% relative gain in F-1. Based on our experiments, we share recommendations to improve model generalization for classification tasks.
Cooperative Navigation via Relational Graphs and State Abstraction

We consider a cooperative-navigation problem in a partially observable MADRL framework. We investigate how agents cooperate to learn a communication protocol given a very large state space while generalizing to a new environment. The proposed solution leverages the notion of structured observation and abstraction, in which the raw-pixel observations are converted into a relational graph that is then used for learning abstraction. Abstraction is performed based on compression using a relational graph autoencoder (RGAE) and a multilayer perceptron (MLP) to remove irrelevant information. The results show the effectiveness of the proposed MLP and RGAE in learning better policies with better generalization capabilities. It is also shown that communication among agents is instrumental in improving the navigation task performance.
Impact of Social Media Towards Employment Decision of Management Undergraduates

Among social media users, university student occupies a prominent place. The major objective is to address how social media influences job decisions, as well as the importance of students operating their own enterprises and how they may affect their business chances and development. This study employed a qualitative technique, content analysis and thematic analysis to analyze the data. The findings suggest that the majority of management undergraduates have a good attitude about using social media for career selections. As a consequence, this research study would provide future support for future job and company selections with the goal of getting favorable enthusiasm via social media
Improvement information and digital competence teachers in the conditions of digitalization of education

Digital educational technologies occupy an increasingly important place among others educational technologies. Thanks to their improvement, effective training is possible remote and mixed forms, which have become predominant since the beginning of the quarantine due to the COVID-19 pandemic. Modern teachers are tasked with constantly improving their digital skills educational technologies. This is also stated in the Teacher's Professional Standard through revealing the content of the teacher's information and digital competence. Process learning can be made more interesting, varied and intensive due to combining a regular lesson with a computer, tablet or other gadget. It will allow the teacher to transfer part of his work to the gadget. The teacher can speed up the writing of definitions, theorems, and other important parts material, as the teacher does not need to repeat the text several times in this case times (the teacher displayed the text on the screen). And the student of education does not need to wait until the teacher will repeat exactly the fragment he needs. The purpose of the work is to investigate the essence of the digitalization of education, to analyze the current global educational trends and to determine the key directions of digitalization of general secondary education in EU countries and in Ukraine. The main research methods were scientific abstraction, comparative and statistical analyzes, logical generalization, structural-functional and strategic analysis for substantiation and determination of directions of digital development of general secondary education. As a result of research, it will be established that the new concept of the development of the digital economy and society for the coming years assumes that the digitalization of education is a modern stage of its informatization. Digitization of secondary education is formed depending on the trends in the development of digital and IT readiness of society. Building a robust infrastructure for learning begins with an understanding of the goals and desired outcomes that support engaging and empowering learning experiences. When based on learning goals, technology infrastructure decisions become clear. The use of digital technologies is necessary to ensure the educational needs of all participants in the educational process.
Solider Health and Position Tracking System

In today's world, warfare is an important factor in any nation's security.One of the important and vital roles is played by the army soldiers.There are many concerns regarding the safety of soldiers.So for their security purpose, many instruments are mounted on them to view their health status as well as their real time location.Biosensor systems comprise various types of small physiological sensor transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for health monitoring This system gives an ability to track the location and monitor health of the soldiers in real time who become lost and get injured in the battlefield.It helps to minimise the time, search and rescue operation efforts of army control unit.This system enables to army control unit to track the location and monitor health of soldiers using GPS module and wireless body area sensor networks (WBASNs), such as temperature sensor, heart beat sensor, etc The data coming from sensors and GPS receiver will be transmitted Wireless using ZigBee module among the fellow soldiers.Further, Wi-Fi network infrastructure has been proposed to be used between the squadron leader and the control unit in high altitude war zones where cellular network coverage is either absent or does not allow data transmission.The collected data will be uploaded on the cloud for further data analysis and predictions using algorithm.
An efficient deep learning architecture for Turkish Lira recognition and counterfeit detection

Banknote counterfeiting is a common practice worldwide.Due to the recent developments in technology, banknote imitation has become easier than before.There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature.The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware.In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images taken under UV light.The results obtained with the proposed algorithm have been compared with the results obtained with state-of-the-art machine learning and deep learning algorithms.It is seen that the results obtained with the proposed algorithm are superior to the results obtained with the state-of-the-art machine learning and deep learning algorithms.The proposed algorithm achieved 100% accuracy on the training set and 99.95% accuracy on the test set while yielding low inference time with relatively few parameters and small file size.
Detecting and Preventing Common Web Application Vulnerabilities: A Comprehensive Approach

Web applications are becoming very important in our lives as many sensitive processes depend on them. Therefore, it is critical for safety and invulnerability against malicious attacks. Most studies focus on ways to detect these attacks individually. In this study, we develop a new vulnerability system to detect and prevent vulnerabilities in web applications. It has multiple functions to deal with some recurring vulnerabilities. The proposed system provided the detection and prevention of four types of vulnerabilities, including SQL injection, cross-site scripting attacks, remote code execution, and fingerprinting of backend technologies. We investigated the way worked for every type of vulnerability; then the process of detecting each type of vulnerability; finally, we provided prevention for each type of vulnerability. Which achieved three goals: reduce testing costs, increase efficiency, and safety. The proposed system has been validated through a practical application on a website, and experimental results demonstrate its effectiveness in detecting and preventing security threats. Our study contributes to the field of security by presenting an innovative approach to addressing security concerns, and our results highlight the importance of implementing advanced detection and prevention methods to protect against potential cyberattacks. The significance and research value of this survey lies in its potential to enhance the security of online systems and reduce the risk of data breaches.
Data Layer

The data layer handles the table design and architecture, as well as storage and access to the data. In Dataverse, like in other tools in Power Platform, the abstraction layer removes a great deal of the complexity of building and configuring the database from the users and allows them to focus on what they need, not how it is done. Creating tables, configuring fields, and managing keys can get a bit confusing for someone who isn’t familiar with SQL development, but is very intuitive in Dataverse. In short, you don’t have to worry about the backend because Dataverse handles that for you. So, in this chapter, we will simply discuss the Dataverse interfaces and how to set up your data structures; we won’t worry about the layer beneath Dataverse where the actual data is stored, or how it operates.
Collaborations: Call for AI/Chat Generated Essays

A description and call for 1000-word essays on collaborations with and about AI-Bots.
Read between the interactions: Understanding non-interacted items for accurate multimedia recommendation

This paper addresses the problem of multimedia recommendation that additionally utilizes multimedia data, such as visual and textual modalities of items along with the user-item interaction information. Existing multimedia recommender systems assume that all the non-interacted items of a user have the same degree of negativity, thus regarding them as candidates for negative samples when training the model. However, this paper claims that a user?s non-interacted items do not have the same degree of negativity. We classify these non-interacted items of a user into two kinds of items with different characteristics: unknown and uninteresting items. Then, we propose a novel negative sampling technique that only considers the uninteresting items (i.e., rather than the unknown items) as candidates for negative samples. In addition, we show that using the multiple Bayesian personalized ranking (BPR) losses with both unknown and uninteresting items (i.e., all the noninteracted items) in existing multimedia recommendation methods is very effective in improving recommendation accuracy. By conducting extensive experiments with three real-world datasets, we show the superiority of our ideas. Our ideas can be easily and orthogonally applied to any multimedia recommender systems.
Face mask detection using machine learning

The COVID-19 epidemic forced governments to adopt worldwide lockdowns in order to limit the virus's spread. Wearing a face mask, it is said, would reduce the possibility of transmission. Due to the growing urban population, proper city management is more important than ever in the modern day to reduce the impacts of COVID-19 infection. To check the mask in public places, however, would require incredibly long lineups and delays. Therefore, it is necessary for an autonomous mask detection system to assess whether someone is wearing a face mask. On the face mask dataset, three different machine learning methods are applied to determine the likelihood of wearing a face mask. The models were assessed using a number of measures, including accuracy, recall, and ROC curve. The main objective of the study is to detect the presence of face masks using deep learning, machine learning, and image processing approaches. All three models—NB, KNN, and CNN—achieved noteworthy accuracy of more than 80%, with CNN showing the best overall performance.
A comparative study of joint video tracking and classification for countering unmanned aerial vehicles

The rapid adoption of autonomous Unmanned Aerial Vehicles (UAVs) for various real-world applications in both industry and the military is driving the need for efficient UAV surveillance and countering systems, as these vehicles create new threats to the safety of people and assets. These systems typically contain a variety of sensors and effectors, including video sensors that are used for both human confirmation of a potential menacing UAV, and visual servicing of effectors used to counter an aerial threat. In this case, the performance of the system depends on the accuracy of the algorithm chain (classification, localization and threat identification) used for video tracking. In this paper, we study an original approach for temporally stable video tracking of targets. Specifically, we use state-of-the-art algorithms for semantic object detection and then consolidate them with a pose estimation method to enhance the perception performance. This paper compares different approaches on real data.
Journals of Vyatka Seminarians of 1905–1907: Verbal and Visual Content of Socio-Political Protest

The article is an analysis of satirical journals published by Vyatka seminarians during the years of the First Russian Revolution; they are a historical source that reflects the social mood of the student youth of those years.The basis for the study of sources was handwritten journals of Vyatka seminarians distributed on a hectograph, materials from all-Russian and regional periodicals, documents characterizing the learning process at the Vyatka Theological Seminary.The analysis of the studied materials that arose in the seminarian environment during the period of censorship restriction and ideological pluralism turned out to be quite promising in identifying the features of the formation of the worldview of young people.The studied illustrative material (first of all, cartoons) made it possible to identify the specifics of reflection and selfreflection of Russian youth (on the example of seminarian students) of the revolutionary era.The authors come to the conclusion that the socio-political protest of Vyatka seminarians, vividly presented on the pages of their satirical publications, was the result of objective social processes.In addition to the revolutionary events, factors such as the outdated system of training and education of seminarians, the predominance of spiritual literature in the educational process, the edifying tone of teaching, disregard for the views and personal position of the seminarian also played a role.Texts and caricatures in hectographic publications testify to the influence of the Social democratic press on seminarians, as well as well-known satirical publications in Russia.The materials involved allow us to state that the protest moods of the students were strong in the Vyatka Theological Seminary, expressed both in the spread of nihilism and unbelief, and in the involvement of some of the pupils in the revolutionary movement.
The Image of a Scientist in the Periodicals of the Russian Empire (“Uchenye Zapiski Imperatorskogo Yur'evskogo Universiteta”)

Since the foundation of the first institution of higher education -the Academic University of St. Petersburg Academy of Sciences opened in 1725, twelve universities have been established in the Russian Empire.One of the objectives of the opening of universities was the development of Russian science, the achievement of inclusion in the world scientific space.The turn of XIX-XX centuries was a time of scientific discoveries and great accomplishments.Imperial University of Juriev was one of the leading scientific and educational institutions of Russian Empire.
Research on Teaching Mode of Digital Transformation of Higher Vocational Education

Aiming at the trend of digital transformation of higher vocational education, this paper discusses the influence of digital transformation on teaching mode, and puts forward a new teaching mode.The research shows that digital transformation has brought more opportunities and challenges to higher vocational education, and the teaching mode needs to be transformed accordingly.The teaching mode proposed in this paper includes diversified teaching resources, personalized learning experience and interactive learning environment.These teaching modes can effectively improve students' learning effect and teachers' teaching quality, and provide new ideas and methods for the digital transformation of higher vocational education.
Research on Blended Teaching of “Computational Methods” in the New Engineering Context Based on the OBE Concept

By introducing the OBE concept into the teaching of computational methods, a blended teaching system based on the OBE concept has been established, which divides the teaching process into three stages: pre-class preparation, in-class instruction, and post-class consolidation.Teaching content construction is carried out in four aspects: teaching design, teaching methods, resource development, and teaching feedback, adapting to the teaching content and teaching methods in the context of the new engineering disciplines.The aim is to stimulate students' enthusiasm for learning and improve the teaching quality of computational methods.
Path planning and obstacle avoidance utilizing chameleon swarm algorithm

Path planning and obstacle avoidance are crucial tasks in the robotics and autonomous industry. Path planning seeks to determine the most efficient path between a start and an end point, whereas obstacle avoidance seeks to avoid collisions with static or dynamic obstacles in the environment. On this work, we utilize the Chameleon Swarm Algorithm (CSA), which is a metaheuristic approach, for path planning and obstacle avoidance on a predetermined map with static obstacles. This CSA extracted the optimal path from several possible different paths, and the results showed that it has slightly superior performance compared to PSO.
Cloud Native Federated Learning for Streaming: An Experimental Demonstrator

This paper demonstrates an implementation of Federated Learning (FL) for streaming applications using cloud-native technology. Compared to a centralized management, by adopting a decentralized approach, the FL method improves convergence time, reduces communication overhead, and increases network energy efficiency. The cloud-native FL architecture presented comprises three sites, each with its own Kubernetes (K8s) cluster. The edge sites run FL Analytical Engines (AEs)/clients for local training and updates, and the central site runs the aggregation server for FL training. Some other relevant workloads deployed at the clusters are the video streaming server, the orchestrator, and monitoring components. As for the RAN, we showcase a multi-gNB setup from which we obtain monitoring data via custom sampling functions. Following the description of the testbed infrastructure and setup, this demonstration presents the real-time visualization of network parameters during FL training, and the enhancement of video streaming through proactive Central Processing Unit (CPU) scaling, made possible by the resource forecasting.
Review on Deep Learning Algorithms for Object Detection

Recently, Computer vision has been one of the widespread forms of object detection. It is challenging to detect an object. When the dataset is trained by a luminous during bad weather like a rainy day, cloudy day, or foggy day and the pictures are taken in a dark place like a tunnel, night hourṣ. The performance would drop. Hence, knowing which algorithm is best to detect an object is necessary. In this paper, the methodologies used to identify an object are well-studied, and deep surveys have been carried out on existing algorithms. Widely used algorithms have been considered, including Faster R-CNN, HOG, R-CNN, R-FCN, SSD, SPP-net and YOLO versions, and sample experiments are carried out.
Applications of Blockchain in Cyber Security Industry

A blockchain is a decentralized record that is used to securely transfer digital currencies, make agreements, and trade. Everyone in the system goes to the most recent duplicate of the encoded record in order to authorize another transaction. A blockchain record is a collection of all prior bitcoin exchanges. Fundamentally, it is a suitable database that keeps up with a continually developing, highly organized information structure of blocks that hold a large number of individual transactions. Blockchain innovation has seen adoption in a variety of organizations, most notably in the use of digital currency. Regardless, innovation is useful in cyberspace. This chapter examined a few application cases of Blockchain in the cybersecurity industry. A majority of specialists are working on the acceptance of blockchain to safeguard IoT (Internet of Things) devices, systems, and information. The chapter has examined the methods proposed by previous analysts through which blockchain can carry the expense of security. Finally, the chapter has proposed the future of a single blockchain for building cyber security applications for coordination and consistency among security arrangements.