Reviewerly/exHarmony huggingface dataset:
A preferenciák és attitűdök változása a zongoraoktatásban a pandémia első évében

"Online education introduced in the spring of 2020 changed the methods of piano education fundamentally. At SZTE Vántus István Secondary School of Music and SZTE Béla Bartók Faculty of Arts there is both attendance and online education.. During the last year we have gained a lot of experience about which methods are useful, and which practices we needed to abandon for the interest of professional training: using digital tools and platforms; changes in the composition of curriculum, and the preferences of reporting; changes in course of lessons. Instead of demonstrative methods, verbal explanations and new forms of technical and musical processes gained a bigger role. The oppotunities of using ICT tools and online communication in the higher piano education of the future. Overally it can be stated that there are effective solutions in digital and hybrid education, although their efficiency is not adequate. It is positive that the students’ improvement did not halt, they were able to show progress. This study is about our new opportunities, methods, rules and the pedagogical aspects."
Crypto Investment and Trading

Tim Draper, a renowned venture capitalist and crypto proponent, has made a significant investment by buying 29,656 bitcoins confiscated by the US government from the Silk Road darknet marketplace site for $18.7 million ($632 per bitcoin) back in 2014. Even with today's Bitcoin price, which has seen better days, he’s still made an enormous profit on crypto, incommensurable with returns on the stock market.
The Role and Impact of Federal Learning in Digital Healthcare

During the COVID-19 pandemic, IoT and machine learning played a very important role in assisting doctors by remote patient monitoring. Machine learning and deep learning algorithms are used to process the data that are generated by IoT devices. However, there was major concern about the privacy of the patient data that is generated. The data that has been generated by the devices was sent to central servers which may cause data privacy issues. FL (federated learning), a type of machine learning, was created to address this problem. It provides a solution for data governance and privacy by processing the data rather than transferring the data to another location. The performance of FL models is better when compared to the models that are trained on datasets maintained centrally. In this work, certain insights are given on some of the challenges faced by the healthcare industry while employing digital healthcare techniques and how FL (federated learning) can improve the digital healthcare as well as how patient data can be preserved.
Development of Digital Materials for Diversity and Social Inclusion Practices

When addressing educational and social inclusion, it is fundamental to take into account several aspects, such as the fact that not only people with some difficulty benefit from them, the right of everyone to education, and the incorporation of those individuals excluded from the system based on gender, culture, race, or personal characteristics. Thus, the development of digital materials from and for the practice of diversity and social inclusion is an integrating element that, from formats such as augmented, mixed, or virtual reality, provides different possibilities in those areas where they are least integrated. This chapter addresses, firstly, the concepts of social inclusion and diversity, the influence and impact of information and communication technologies (ICTs), and the digital materials used in the educational scope from different analytical approaches.
A Visualization Dashboard for COVID-19 Tweets Sentiment Analysis

The COVID-19 pandemic has compelled the world to come to a standstill. Everyone including governments, researchers, organizations were caught off-guard. Social scientists and psychologists all try to understand the sentiment of the public so that they can help social organizations and governments to avert situations that ought to become worse if a negative sentiment persists among the commonality. With government-issued lockdowns in place during the pandemic, the public was mostly confined to their homes. So, the public started to share their status updates, discussions, photos, and videos over social media. Social media became the go-to place to obtain the public's sentiments and insights on the COVID-19 pandemic. This chapter introduces the utilization of the Twitter API to obtain tweets in real-time based on hashtags relating to the COVID-19 pandemic in order to gain insights on the sentiments of people at specific times. Each tweet received will be analyzed for emotional tone and sentiment. All data is stored in a Cloudant database.
A Novel Model for the Semantic Enrichment of an Ontology Alignment System

In many applications, ontology alignment is a difficult challenge, and it is a major concern for interoperability and domain specialists in information systems. Although there are various methods for ontology alignment, most of them ignore ontological links such as subsumption and aggregation. Furthermore, traditional alignment methods provide little or no information about the underlying structure of the correspondences between ideas that they identify, limiting them to basic links between matched concepts. However, many actions, such as ontology mergers, ontology evolution, or data conversion, require more detailed information, such as the actual relationship type of correspondences matches or information about the cardinality of a correspondence (one-to-one or one-to-many). We employed an enrichment technique to build an upgrade to the present ontology alignment tool (Falcon AO++) that recognizes and adds more semantic information to a created ontology mapping in this study. The improved mapping now supports equal, is-a, inverse is-a, part-of, has-a, and related semantic connection types. To enable semi-automated mapping to correct and detect more sorts of correspondences, the enrichment technique leverages a variety of linguistic, structural, and background knowledge. As an outcome, more expressive mappings may be created. In terms of precision, recall, and f-measure, Falcon-AO++ and eFalcon-AO perform better by 18.1 percent, 20.2 percent, and 18.6 percent, respectively.
Rancang Bangun Website Project Management Untuk Project Specific Repository Checklist Pt Anabatic Digital Raya Menggunakan Metode Rad (Rapid Application Development)

The development of the digital era is currently progressing very rapidly. Website-based applications can be a solution needed by companies that want to develop a more integrated online communication and information system. PT Anabatic Digital Raya is improving the efficiency and effectiveness of the Compliance Officer (CO) team in the process of checking project documents. By building a project management website application for the Project Specific Repository Checklist file by applying the RAD (Rapid Application Development) method, the goal to be realized will be achieved, namely increasing the efficiency and effectiveness of the Compliance Officer team.
Leadership Styles of Sales Managers and their Effects on Sales Team Performance: The Augmenting Effect of Issue Leadership

What is the most effective style of sales leadership in a B2C sales environment? This study was designed to find the answer to this key question. We focused on examining the effects of four leadership styles (paternalistic leadership, transactional leadership, empowering leadership, and issue leadership) of sales managers on team sales performance (subjective team performance and revenue growth rate). Specifically, we analyzed the augmenting effects of each leadership style to examine each style’s discriminatory effect on sales performance at the team level.&#x0D; The data were collected from 130 directly-managed branches of J company which operates B2C distribution business nationwide. In order to control for any potential common method biases, we diversified the sources to three―branch sales managers, sales team members, and objective sales data―and collected the two-wave (T1 and T2) data with the two-month interval, with different measurement points for the independent and dependent variables. Furthermore, we obtained the revenue growth rate data from the company's management database and included it as an objective team performance.&#x0D; The findings of the current study could be summarized into two points. First, the issue leadership style of sales managers was the only style that had the statistically significant augmenting effects on the subjective team performance after controlling for the effects of the other three leadership styles. Second, among four leadership styles of sales managers, the issue leadership style was the only style that had significant augmenting effects on the objective sales performance, i.e., the revenue growth rate. The results verified that the sales manager's issue leadership had a significant augmenting effect on team sales performance (subjective team performance, revenue growth rate). We discussed the implications and limitations of this study and suggested how this study results could lead to future studies.
TaRCoS: Providing a Combat Simulation Tool for Tabletop Role-Playing Games

Role-Playing Game (RPG) is a game where players pretend to be a character acting in an environment and story created by the narrator and the players. During a tabletop RPG game, there are events, usually battles, in which players waste a lot of time to get a result for the action they choose to perform. To optimize this aspect, this paper presents the Tabletop RPG Combat Simulator (TaRCoS), a software tool able to perform tabletop RPG combat and show the winner of modeled matches in a tabletop RPG system. It eliminates the expenditure of time to get a battle result, such as the excessive calculations of dice rolls, and provides data for balancing purposes of specific characters during an initial combat configuration for modeled matches. As a result, TaRCoS improves the dynamics of playing tabletop RPG games, avoiding unnecessary efforts to define battle results and allowing players and narrators to be concerned with the description of the scene, the construction of desired game elements, and the player immersion in an amazing game story.
Multi-Object Classification for Deep Learning Analysis in CNN and MLP

Researchers have argued that an attentional mechanism is required to perform many vision tasks. In this paper, we propose an approach to object classification that is based on deep learning analysis in CNN. It was implemented on the tensor and descent database, for the classification of an object. The neural network takes as input and gives, as output, the estimated class. The entire structure can learn, from a wide variety of examples, how to classify scan-path patterns in a supervised manner and then recognize objects in digital images. This model provides a solution to the problems of selection in an image and information routing through the visual processing hierarchy. This approach is described in some detail and a performance example of scan-path classification is shown. The results confirm that our proposed system is both robust and fast.
An alternative construction of the Hermitian unital 2‐(28, 4, 1) design

Abstract In this paper, we give an alternative construction of the Hermitian unital 2‐(28, 4, 1) design in such a way that it is constructed on the isotropic vectors in a unitary geometry of dimension 3 over the field . As a corollary, we can construct a unique 3‐(10, 4, 1) design (namely, the Witt system ).
Reinventing acquisition process of print books in university libraries

The paper aims to find the best practices of print book procurement in select central university libraries in northern India, including getting maximum discounts and supply of ordered books, and empanelling vendors. Through online questionnaires, selected libraries' book procurement procedures have been surveyed. Further, acquisition staff were consulted, and websites were accessed to learn book procurement procedures at central universities. Data collected from select seven university libraries were recorded in Microsoft Excel worksheets, aggregated, and summarized to create consolidated datasets for analysis. It is found that five universities buy books from empanelled vendors by setting minimum and maximum discount rates on print books. Vendors are increasingly being asked for discount quotes through expressions of interest. University libraries don't seem to buy books through tendering process or through government e-market place (GeM).
MALARIA DETECTION USING DEEP LEARNING

Every year, hundreds of millions of people throughout the world succumb to the fatal disease known as malaria. If the treatment is not given in a timely manner, disaster may result. The microscopic method is still the most used, despite recent improvements in malaria detection. The speed at which malaria diagnoses may be made is, however, constrained by the precision of microscopic diagnostics, which is reliant on the expertise of the microscopes. Deep learning methodologies can provide a more accurate diagnosis of an illness. Comparing the Adam-based model to earlier models created for recognizing contaminated cells. In a variety of accuracy criteria, the model outperforms the majority of previously produced models. The fact that the model just has a few layers contributes to its simplicity. This uses less computer power and processing time. Moreover, we test our model on two types of datasets infected and uninfected cells.
SMART WEARABLE GADGET FOR VISUALLY IMPAIRED WITH GPS TRACKER

According to the research dispensed by the globe Health Organization (WHO), it absolutely was found that there a minimum of 285 million people across the world who are visually-impaired. This device offers an ETA (Electronic Travel Aids)-smart guiding device in the form of a VR box for providing this group with direction effectively and safely due to the difficulties of travelling. A multimodal fusion-based obstacle-avoiding algorithm is described that differs from prior systems in that it makes use of the Camera to identify people and detect barriers. Through GPS Module we are able to find the placement of user. Once an obstacle on the trail of the user is detected, it alerts the user through the audio messages. Thus, helping the user to navigate safely.
A Study on the effectof Personal Characteristics, Choice Factors, and Social Support on Institutional Commitment and Recommendation Intention of Adult Learners in Adult Universities - focusing on the mediating effect of s’ Institutional Commitment

This study surveyed 233 first-time graduates from 9 adult-only colleges in South Korea.&#x0D; As a result of the study, first, it was found that the younger the age of adult learners of lifelong education in the university, the more positive the attitude toward concentrating in the university to which they belong. Second, educational selection factors had a positive effect on university immersion in the order of educational quality and social reputation.&#x0D; Third, social support has been demonstrated as a factor that has a positive effect on university immersion. Fourth, this study tried to examine the mediating effect of Institutional Commitment between college selection factors and intention to recommend others. Among the four college selection factors, educational quality and social reputation showed a partial mediating effect.&#x0D; It is worth noting that the two factors, financial accessibility and occupational factors, showed a complete mediating effect, in which college immersion plays a mediating role.&#x0D; Fifth, it was found that social support influenced the recommendation intention of others through university immersion. This study examines the factors that should be considered to promote adult learners' Institutional Commitment and academic performance in the future lifelong education-related operation of universities and provides implications for designing and operating strategies for universities that seek to improve the adult-friendly constitution.&#x0D; In particular, it has significance in that it expanded the basis of the study in that it revealed more in-depth that Institutional Commitment is a variable that plays an important role.
A Study on Calligraphy of Haseo(河西) Kim-In-hoo(金麟厚)

Kim-In-hoo wrote the introduction to the Collected Poetry by the Sages of the 1519 Purge. He showed that calligraphy could be a medium for realizing the "Tao" just as the "Writing" was a means of expressing the "Tao". He wrote a "Boating Song of the Nine Bends at Wuyi" by Zhu Xi and wrote an explanation of the poem to his sons-in-law Cho Hee-moon and Yang Ja-jing. The shape of this character is based on the Regular Script of Wang Xizhi, but it is unique that Ahn Jinkyung's style of writing is added.&#x0D; Kim's cursive script is typical of his calligraphy. His cursive script strongly reflects the influence of Zhang Bi, which is close to the wild cursive script. Kim placed importance on the spirit that appears in the handwriting. His vigorous appearance in cursive script express his deviant spirit through cursive script.&#x0D; Jo Hui invited Kim In-hoo, Jeong Sa-ryong, and Shin-jam to his pavilion. At that time, one or two people wrote seven poems for them, which is very sensuous. It has not been revealed so far who wrote it, but it was confirmed that it was a cursive script by Kim. This work remains the only cursive script handwritten by him. Therefore, along with "Thousand-Character Classic in Cursive Style" it is valuable as a representative handwriting of Kim's cursive script.
The Effect of COVID-19 Knowledge, Subjective Norms, Resilience on Preventive Behavior in Health College Students

Objectives: This study was to investigate factors influencing preventive behavior against COVID-19 in Health college students.&#x0D; Methods: This study were convenience sampling of 165 students in Health College Students departments at Jeollado and Gwangju Metropolitan City area.&#x0D; Results: Factors affecting COVID-19 Preventive behavior were subjective norms and resilience. The higher subjective norms and resilience, the higher COVID-19 preventive behavior.&#x0D; Conclusions: In order to increase the preventive health behavior of students, it is necessary to provide health education to raise the knowledge level of COVID-19 and to provide health training programs to strengthen subjective norms and resilience.
Effects of Creative School Environments and Future Goals on Children's Creativity: The Mediating Effects of Perceived Happiness

This study examined the effects of the creative school environments (friend support, teacher support, teacher's controlled attitude) and future goals (extrinsic goals, intrinsic goals) on children's creativity through perceived happiness. For analysis, 10th data (age 9) from the Korean Children Panel were used. Mediation analyses revealed that social support from friends and teachers was positively related to children's creativity through the mediating effects of perceived happiness. On the other hand, teachers' strict and controlled attitudes negatively affected children's happiness, which lowered students' creativity. In addition, the mediating effect of perceived happiness on the relation between children's future goals and creativity was statistically significant. It suggests that children with intrinsic future goals (e.g., helping others, building a happy family) are happier, in turn, more creative than those with extrinsic future goals (e.g., making a lot of money, being famous). The theoretical and practical implications, as well as the limitations of the current study, are discussed.
Design and implementation of measurement system for workshop measurement room based on web

In view of the inaccurate statistics of quality information, the accumulation of measurement tasks can not be measured in time, and the traceability of abnormal parts can not be realized in the process of parts inspection in the metering room of a manufacturing enterprise, a measuring room parts measuring system was designed based on B/S architecture. In view of some problems that exist in the process of measuring parts in the measuring room, this system provides solutions through modules. The system uses Java programming language and selects MySQL database for data storage. The front-end measurement system page uses Thymeleaf template engine and the back-end uses SpringBoot framework to realize the business logic. Through the practical application in a manufacturing enterprise, the system can meet the business needs of the enterprise to solve the problems of low efficiency and inaccurate data filling in the measurement process.
Detection and Application of Mobile Target Image Based on Artificial Intelligence

In the 21st century, with the rapid development of high technology, artificial intelligence technology is becoming more and more mature, and its application in various aspects is becoming more and more extensive. Image recognition is an important feature of artificial intelligence. For mobile target image detection and application, the reasonable application of artificial intelligence technology and related research analyzes the changes brought by mobile target image detection and application and adopts its optimal scheme which can improve the innovation of mobile target image detection and application. Moving object image detection and tracking are the algorithm process of intelligent dynamic analysis. It also provides a lot of help for the development of many research aspects. This paper analyzes and studies the detection and application of mobile target images from the perspective of artificial intelligence and improves the algorithm of the current target image detection. The improved artificial intelligence algorithm will be applied to future mobile target image detection and application. The results show that in the nonoptimized detection and reconnaissance system of moving targets, the program can run in parallel after the system is optimized, and the running time of each image is kept between 5 and 8 seconds, thus improving the system’s performance. Through the continuous trend of linear graphics, the number of actual test frames increases, the line graph tends to be stable, and the performance is improved by about 20–30 times, which shows the feasibility of the algorithm.
Optimizing CNN Architecture Using Genetic Algorithm for Classification of Traffic Signs in Real Time

With the notion of smart cities transforming cities into digital societies and making people's lives easier in every way, Intelligent Transportation Systems have become an integral element among all. The Intelligent Transportation System (ITS) attempts to improve traffic efficiency by reducing congestion and ensuring the safety and comfort of commuters in real time. Traffic sign detection and recognition is one of the multifaceted conjunctive fields of research in ITS. In this paper, we address the issue of the TSR (traffic sign recognition) problem, i.e., classification of traffic signs along the roadside which plays a crucial role in developing advanced driver assistance and autonomous driving systems. CNN's network design has a huge impact on its performance and convergence. As a result, we use the Genetic Algorithm (GA) to automate the task of selecting a high-performance CNN (Convolutional Neural Network) Architecture for the GTSRB (German Traffic Sign Recognition Benchmark) dataset. The model is optimized through GA using multiple network configurations in the search space. Our model takes into account the limitations of the dataset, and we use certain data augmentation approaches to address the issues. We were able to attain an average accuracy of 98.2% which demonstrates the state-of-the-art performance on the publicly available dataset.