Reviewerly/exHarmony huggingface dataset:
Musical Art Distance Education after Pandemic

Abstract Introduction: The spread of the coronavirus brought necessary changes in education, including the abrupt transfer from classroom instruction to an exclusively online environment. The teachers and pupils/students adapted to the new conditions, and currently, virtual education elements are being implemented in face-to-face teaching to enhance its efficiency. The aim of our research was to monitor distant education in vocational music school environment, describe main difficulties in vocational music online classes, and identify elements of distance learning that can be implemented in music education after pandemics. Methods: The study utilized a mixed research design. The primary method of collecting data was through questionnaires (2020, 2021 and 2022), and analysing essays written by students from the Faculty of Performing Arts at the Academy of Arts in Banská Bystrica, Slovakia (2020). Results: Student respondents strongly believe that while in theoretically oriented courses, online education can replace face-to-face learning, it is no viable option for practically oriented courses in higher music education. More than half of the students do not support the inclusion of elements of distance education in face-to-face teaching. In spite of several challenges, the positive aspects of online education support the implementation of some distance learning elements in face-to-face music education. Discussion: Our findings about vocational music online education do not contradict other surveys (Yurdal, Sahin, Kosan, &amp; Toraman, 2021; Wang, 2023; Al-Mawee, Kwayub, &amp; Gharaibeh, 2021; Bakhov, Opolska, Bogus, Anishchenko, &amp; Biryukova, 2021; Martha, Junus, Santoso, &amp; Suhartanto, 2021). Limitations: The main limitation is the sampling, as it only included participants who had internet access and were willing to participate in the survey. Future studies should encompass more music educational institutions in Slovakia to achieve a larger sample size. Furthermore, in analyzing questionnaires, we did not make difference between participants´ music study fields. Conclusions: Although distance education has shown considerable improvement since 2019 and has undeniable advantages, students pursuing performing arts and composition at the Faculty of Music Arts at the Academy of Arts maintain rather sceptical attitude towards it. They strongly believe that in practical courses, the online teaching cannot fully replace the face-to-face instructions. However, teachers often incorporate certain online teaching elements into their regular classes.
Discussion on the Teaching Reform of Java Programming Course for Computer Science Majors in Colleges and Universities

In the new era, society has put forward higher requirements for the training of talents in colleges and universities, which needs to keep pace with the development of the times in the Java programming course for college computer majors, and constantly improve and optimize the teaching strategy. From the current situation, there are some problems in the teaching of Java programming courses for computer science majors in colleges and universities, such as the teaching content is not updated in time, the teaching method is too single, etc., which needs to be addressed in the teaching reform to solve the current problems. The article explores the importance of teaching reform of Java programming courses for computer majors in colleges and universities and gives effective teaching strategies according to the current teaching situation.
Cognitive Research on the Cultivation of Interdisciplinary Talents for English Majors in the New Liberal Art ——Based on the Learner's Perspective

Previously, English major learning research and curriculum design were usually based on the perspective of teachers and educators, which were adjusted from top to bottom under the social needs. Although it is in view of social needs, it inevitably lacks the demands of English majors themselves. This research shifts the perspective to English majors, surveying the curriculum design from the perspective of English majors, collecting their career plans and demands through questionnaires, and using SPSS (Statistic Package for Social Science) to integrate and analyze the data, so as to take advantage of learners’ ideas to promote educational innovation. Different from the traditional top-down training scheme planning of discipline designers, it will be conducive to adding more practical effects to discipline design from bottom to top by exploring learners' cognitive degree of three factors— "New Liberal Arts", "English Major" and "Talent Training".
Enhancement of Old Historical Document by Image Processing from Gray scale to RGB Scale Conversion

The enhancement of historical documents through image processing stands as a pivotal bridge between the past and the present. In this project, we embark on a journey to rejuvenate these treasured artifacts by utilizing cutting-edge techniques. Central to this endeavor is the conversion of grayscale documents into the vibrant realm of RGB, breathing new life into their visual presentation. The Nonlocal Mean Denoising algorithm takes center stage, diligently removing noise while preserving the document’s authenticity. Accompanied by image division code, we break down these historical scrolls and manuscripts into comprehensible segments, ensuring efficient processing without compromising their essence. At the heart of our methodology lies the profound significance of image histograms. Through these histograms, we delve into the intricacies of pixel intensities, unearthing valuable insights into the document’s characteristics. The outcome of our rigorous efforts is a transformation of these historical gems, making them not only aesthetically pleasing but also accessible to a broader audience, fostering a deeper appreciation of our cultural heritage. As technology and history intersect in this project, we highlight the transformative potential of image processing in preserving and reinvigorating the stories of the past. In an age where the digital realm converges with the analog, this endeavor reiterates the importance of ensuring that history remains alive and tangible for generations to come
Dimensionality Reduction with Variational Encoders Based on Subsystem Purification

Efficient methods for encoding and compression are likely to pave the way toward the problem of efficient trainability on higher-dimensional Hilbert spaces, overcoming issues of barren plateaus. Here, we propose an alternative approach to variational autoencoders to reduce the dimensionality of states represented in higher dimensional Hilbert spaces. To this end, we build a variational algorithm-based autoencoder circuit that takes as input a dataset and optimizes the parameters of a Parameterized Quantum Circuit (PQC) ansatz to produce an output state that can be represented as a tensor product of two subsystems by minimizing Tr(ρ2). The output of this circuit is passed through a series of controlled swap gates and measurements to output a state with half the number of qubits while retaining the features of the starting state in the same spirit as any dimension-reduction technique used in classical algorithms. The output obtained is used for supervised learning to guarantee the working of the encoding procedure thus developed. We make use of the Bars and Stripes (BAS) dataset for an 8 × 8 grid to create efficient encoding states and report a classification accuracy of 95% on the same. Thus, the demonstrated example provides proof for the working of the method in reducing states represented in large Hilbert spaces while maintaining the features required for any further machine learning algorithm that follows.
Comprensión de fuentes primarias históricas en Bachillerato. Un estudio sobre la lectura de textos múltiples contradictorios de la Guerra Civil

Intertextual comprehension is the ability to integrate information from several texts and sources of information. This competence is key in the development of reasoning, although it is challenging for our students. This study evaluates the impact of providing intertextual or textual comprehension aids in the first year of high school (seventeen-year-old students) when they must understand a historical phenomenon with multiple perspectives. They read 5 texts with conflicting perspectives on the Civil War and, afterwards, they wrote an essay, completed a comprehension test and a recall task of sources.Students who received the intertextual aids wrote a more integrated essay than those who received the textual comprehension aids. There were no differences in individual text comprehension or source recall. These results indicate the necessity of helping to make connections between documents for the students to integrate contradictory historical information into a coherent interpretative account. La comprensión intertextual es la capacidad para integrar información de varios textos y fuentes de información. Esta capacidad resulta clave en el desarrollo del razonamiento, aunque es compleja para nuestros alumnos. Este estudio evalúa el impacto de proporcionar en 1º de bachillerato ayudas a la comprensión intertextual o textual cuando deben comprender un fenómeno histórico con múltiples perspectivas. Leyeron 5 textos con posturas enfrentadas de la Guerra Civil y, posteriormente, redactaron un ensayo, realizaron una tarea de comprensión y una de recuerdo de fuentes. Los alumnos que recibieron las ayudas intertextuales escribieron un ensayo más integrado que los que recibieron las ayudas a la comprensión textual. No hubo diferencias en la comprensión individual de cada texto ni en el recuerdo de las fuentes. Estos resultados indican la necesidad de ayudar a establecer conexiones entre los documentos para que los alumnos integren la información histórica contradictoria en un relato interpretativo coherente.
Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

In this paper, artificial intelligence (AI) and the ideas of machine learning (ML) and deep learning (DL) are introduced gradually. Applying ML techniques like deep neural network (DNN) models has grown in popularity in recent years due to the complexity of healthcare data, which has been increasing. To extract hidden patterns and some other crucial information from the enormous amount of health data, which traditional analytics are unable to locate in a fair amount of time, ML approaches offer cost-effective and productive models for data analysis. We are encouraged to pursue this work because of the quick advancements made in DL approaches. The idea of DL is developing from its theoretical foundations to its applications. Modern ML models that are widely utilized in academia and industry, mostly in image classification and natural language processing, including DNN. Medical imaging technologies, medical healthcare data processing, medical disease diagnostics, and general healthcare all stand to greatly benefit from these developments. We have two goals: first, to conduct a survey on DL techniques for medical pictures, and second, to develop DL-based approaches for image classification. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical image classification; for this, we perform a systematic literature review. A review of various existing techniques in terms of medical image classification indicates some shortcomings that have an impact on the performance of the whole model. This study aims to explore the existing DL approaches, challenges, brief comparisons, and applicability of different medical image processing are also studied and presented. The adoption of fewer datasets, poor use of temporal information, and reduced classification accuracy all contribute to the lower performance model, which is addressed. The study provides a clear explanation of contemporary developments, cutting-edge learning tools, and platforms for DL techniques.
Professionalization and self-updating of teachers in the context of the scientific guidelines of neurodidactics

The article contains a theoretical study, which elucidates the current aspects of modernizing higher education and the basic conceptual guidelines of neurodidactics, which are correlated with increasing the quality, professionalism and self-updating of teachers in initial and continuing education. Also, a mini-research of the opinions of the teachers, students in the continuous training and of the master's students / initial training regarding the difficulties encountered in the pedagogical activity is exposed.
Implementation of information technologies at the solving of some probability problems

In this article I describe the simulation of some problems in probability and statistics. These applications are welcome in teaching in this course, as it helps us to calculate theoretical and experimental probability in a very limited time.
Ways to integrate educational software in the didactic act in kindergarten

The use of ICT tools in the education system is considered crucial both for improving the skills of teachers and for adapting pre-school children to the new social requirements. The educational software represents a disciplinary content that can be useful in the training activity and brings benefits to the educational process.
Conditional Objects as Possibilistic Variables

The interpretation of basic conditionals as three-valued objects initiated by de Finetti has been mainly developed and extended by Gilio and Sanfilippo and colleagues, who look at (compound) conditionals as probabilistic random quantities. Recently, it has been shown that this approach ends up providing a Boolean algebraic structure for the set of conditional objects. In this paper, we show how that this probabilistic-based approach can also be developed within the possibilistic framework, where conditionals are attached with possibilistic variables instead: variables attached with a (conditional) possibility distribution on its domain of plain events. The possibilistic expectation of these variables now provides a means of extending the original possibility distribution on events to (compound) conditional objects. Our main result shows that this possibilistic approach leads to exactly the same underlying Boolean algebraic structure for the set of conditionals.
The Factors That Affect Employee Creativity among Staff in a Malaysian Energy Corporation

This article aims to analyze the factors that affect employee creativity among staff in a Malaysian operating in the energy industry that faced the crucial situation impact of the COVID-19 pandemic. The total number of respondents involved in this study is 108 using a questionnaire as an instrument in this study. As a result of correlation analysis, it shows that there are significant relationships between personality and work environment with employee creativity. For reward, there is no relationship with employee creativity. According to the result of multiple regression, R2 was 37.6% which means all the independent variables influence the dependent variable on that amount. The rest of the amount 62.4% is influenced by other factors. The regression result also shows that only the hypothesis on personality and work environment is supported. And other factors that affect employee creativity should be considered in future research. The significance of this study is to highlight the importance of understanding the factors affecting employees’ creativity during Movement Control Order (MCO). From the perspective of the organization, this study helps to better understand its operation and to maximize employee creativity during MCO. On the other hand, the employees may benefit from this study by giving awareness, to educate and support employees during MCO as some need to Work-From-Home (WFH) with problems with internet connection.
A Systematic Review: The Use of Pictures in Teaching Simple Present Tense Sentence Construction to Junior High School Students

The research aims to show the use of pictures to teach simple present tense sentence construction to English young learners. It is important to teach sentence construction to young learners because young learners must begin to make their selves familiar with constructing sentences before they develop and improve their English to the next levels. The method used in conducting this research was Systematic Review to determine the use of pictures in teaching simple sentence construction to English young learners. There were 10 articles from year 2010 – 2022 related to the titles analyzed by the researcher by using Systematic Review. As the result of the research, it is concluded that teaching English using pictures enable young learners to construct simple sentences in English.
Eliciting expert knowledge: Methods and challenges

This chapter delves into the methodology of collecting expert judgement in the AI and the Future of Skills project. It provides an overview of the project&apos;s journey in refining its methodology and discusses associated challenges and considerations. The chapter begins by exploring the different methods of expert knowledge elicitation based on the research literature and discusses their relevance to the project. It then addresses key questions such as the number of experts required for reliable assessments, the framing of tasks for experts, and the aggregation and interpretation of expert judgements. The chapter concludes by offering points of consideration for the project&apos;s long-term trajectory.
Sorting and selecting students between and within schools

This chapter describes how students are selected and sorted into different programmes or tracks, both between and within schools. It discusses the age at which students are first tracked and the types of programmes into which they are tracked (general or vocational). Grouping students by ability, both between and within classes, is also examined. These policies are then related to student performance and equity in the education system.
Incorporando Aprendizado Incremental com MaxSAT em um modelo de Aprendizado de Regras baseado em SAT

This article aims to describe a new incremental model for learning interpretable rules based on MaxSAT, called IKKRR. This new model was based on two other approaches, one based on SAT and the other on MaxSAT. The one based on MaxSAT, called IMLI, presents a technique to increase performance that consists in learning a set of rules by applying the model in a dataset incrementally. This work shows which adaptations were necessary for a model based on SAT to be transformed into one based on MaxSAT. It also shows what was done to make it incremental. Finally, IKKRR and IMLI are compared using diverse datasets. Despite having a smaller number of variables, the IKKRR obtained results comparable to the IMLI.
Aplicação de Algoritmos de Aprendizagem Profunda com Foco na Detecção de Quedas Através da Classificação de Imagens Térmicas

Falls tend to cause physical and psychological damage, especially for people over 65 years of age. Such damage can be aggravated if they live alone and do not get help in the right time. This is one of the motivations for investigating smart devices in homes, acting as a way of monitoring some action or incident. Machine learning techniques have been applied to increase the effectiveness of these devices by looking for patterns in audio signals and in images. When using DNN (Deep Neural Networks) the success rates increase, but these solutions usually use cloud processing, which can bring a feeling of insecurity to the user. This work investigates an environment monitoring system, capable of detecting fall incidents, without invading the user's privacy, as it is based on low resolution thermal images. Some DNN models were tested to choose a model that best fits the problem, looking for architectures with few processing layers so that they can be embedded for local processing. Images from two different sensors were used, resized to the standard size of 64x64 pixels, resulting in an average accuracy of 91.66% for the bidirectional LSTM (Long short-term memory) network, and of 97.88 % using the VGG16 network.
Protótipo de um sistema inteligente para a prevenção de acidentes graves em empresas

In the work routine, accidents cause several losses, both for the contractor and the people hired. At the same time, profit and materials are lost in the companies, the work capacity and, in the worst cases, the worker’s life is affected. From 2002 to 2020, a rate of 6 deaths was recorded for every 100 thousand formal jobs in Brazil. The present work resulted in the evaluation of a computational model using machine learning to prevent serious accidents. Through the use of sliding window algorithms for data standardization, satisfactory results were achieved, in which it was possible to predict the occurrence of severe accidents for periods of up to seven days. Additionally, a prototype of an intelligent web system was proposed, using cloud microservices, using the prediction model created.
Servovisão Direta Diagonal: Nova Formulação e Seus Resultados

This article addresses the problem of vision-based robot control where the equilibrium is defined via a reference image. Specifically, this work considers the class of general direct solutions that diagonally decouple the control error dynamics also in the general case. Hence, those solutions provide for high versatility and accuracy, whilst the decouplings reduce analysis complexity and can improve system performance. In such a demanding context, the existing general diagonal direct visual servoing technique can present a stability problem arising from its parametrization. This study proposes a new parametrization and its new nonlinear estimator to solve such problem. Theoretical analysis of local stability, convergence, diffeomorphism and of that decoupling property are provided. Experimental results are also obtained using a camera- mounted 6-DoF robotic arm in a challenging scenario.
Exploração Autônoma com Múltiplos Robôs Baseada em Fronteiras e Planejamento RRT*

The risks in exploring unknown environments have attracted interest in using mobile robots. In addition, the coordinated and cooperative use of multiple robots to perform these tasks can provide better efficiency in exploration. In this context, this work presents the development of an exploration system with multiple robots for mapping, using a boundary selection method based on the exchange of information among robots. In addition, a planner based on the rapidly-exploring random tree (RRT*) determines the path to the chosen frontier point, carrying out replanning if necessary during the route, always aiming to go to strategic frontier points and avoiding obstacles that may appear along the way. Simulated experiments are presented comparing the use of only one robot for exploration to the use of more than one.