Reviewerly/exHarmony huggingface dataset:
Model Lessons in the Space of the Museum: Relevance, Interdisciplinarity and Perspectives

The authors of the article analyze various aspects of conducting model lessons in school disciplines in the space of the museum and note its role as a center of interdisciplinary relations for the effective organization of the educational process. The article discusses examples of conducting model lessons in a museum based on participation, inclusion, interactivity, creativity and universality. The authors of the article note that model museum lessons belong to the category of integrated classes and contribute to the growth of students' interest in the learning process, the development of cognitive abilities, and the formation of a holistic interdisciplinary picture of the world. The authors focus on the effectiveness of model lessons in the museum and the prospects for their widespread introduction into the educational process of secondary schools.
Mobile Application for Research Knowledge Sharing and Dissemination: The Case of Nm-Aist Univeristy Tanzania

The utilization of mobile and web applications has surpassed all other platforms in terms of disseminating researchers' knowledge among diverse communities throughout the world.The current method of disseminating researchers' knowledge to the communities in the Arusha region in Tanzania is through meetings, workshops, and focus group discussions held by researchers, agricultural extension officers and community members after every three months or during field study.Yet the strategy is inefficient and ineffective in practice.The purpose of this study was to determine the most efficient and successful method of disseminating knowledge in communities.The study began with a qualitative phase, utilizing an interpretive technique and a qualitative multiple case study research design.The Arusha region in Tanzania was selected as a case study where different social activities were undertaken, including farming, livestock keeping, tourism activities and fishing.Individual participants were interviewed by using a semi-structured questionnaire.In addition, focus group discussions were conducted to gather more information regarding the needs of the mobile application.Through the implementation of the application, the second phase of the study led to the development of a mobile application that includes community members, agricultural extension officers, and researchers that will enable anyone to install the application on their mobile phones to access knowledge regarding activities undertaken in Arusha.According to the findings of the first phase of the research, a substantial percentage of community members own mobile phones, and hence a mobile application would be sufficient.The research also found that most researcher-community interactions occur at the data collection and intervention assessment (field trials) stages.Hence, the mobile application will benefit community members, district agricultural, irrigation, and cooperative officers How to cite this paper:
PROSPECTS OF NEURAL NETWORK APPROACH TO THE PROBLEM OF DAMAGED PAPERS RESTORATION

Nowadays, the damaged papers restoration task is quite urgent, experts spend hours, days or even weeks to restore damaged documents, drawings or other materials that can play the key role evidence in a criminal case. Automation of this process will significantly increase the speed and quality of solving this problem, thereby increasing the efficiency of the work of forensic experts. During the search for existing solutions, no direct analogs were found, but several indirect analogs were found that solve quite similar problems. The first analog is a technology proposed by Haifa University scientists to restore damaged archaeological finds. This technology was successfully tested on real artifacts of the British Museum, which proved its effectiveness in restoring damaged frescoes. These results are promising for the further development of information technology for restoring the integrity of a damaged document, in particular, in the context of the complete restoration of the paper structure based on its microrelief. The second analog is image editing technology using Kohonen maps. This technology effectively performs the basic tasks of retouching images, in particular, removing objects, restoring integrity after removal. Since this technology is used for image processing, it can be used as a basis for restoration of the damaged content of a document after its physical assembly. After all, during the paper structure restoration, the integrity of the content may be partially lost. In this article, each of the above technologies is analyzed in detail, including at the level of mathematical models, their advantages and disadvantages are highlighted, and examples of their real application are given. Based on the advantages of each of the analyzed technologies, an approach to solving the problem of damaged papers restoration is proposed.
Analysis of Internet Public Opinion Popularity Trend Based on a Deep Neural Network

In the context dominated by Internet communication, people’s various emotions can be clearly reflected through network public opinion, whether it is the view of political affairs, the preference for entertainment, or the demand for life. This also allows management or providers to meet their needs more specifically. Based on today’s need to understand the trend of Internet public opinion, this paper describes a deep neural network (DNN). A deep neural network is a machine learning model that is the foundation of deep learning and has a strong ability to mine potential information in data. By improving the loss function of the neural network, this paper reduces the influence of unbalanced data on the classification results and improves the classification effect of the model on a small number of categories. Aiming at the different lengths of Internet text, a more robust model of text sentiment classification is proposed, which makes the HCB-Att model better extract the local information and contextual information of the text. Finally, through comparative experiments, the optimization model used in this paper is proved to be effective for the analysis of network public opinion sentiment.
Animation Activities in Preschool Education: Essence and Content

The urgency of the problem of organizing animation activities in preschool institutions is due to the need to organize effective leisure for preschool children on the one hand, on the other – to prepare educators for the successful conduct of this activity. That is why it is worth considering the essence and content of animation activities in modern kindergartens. Animation in preschool institution is interpreted by the authors as an event or set of activities aimed at recreation, entertainment, relief of psychological stress, recovery within the leisure sphere of man. In turn, animation activity is defined as the activity of designing, developing, organizing and conducting special leisure programs with preschool children. The leading types of animation activity in preschool institution include: sports and health or sports and entertainment, entertainment and entertainment, dance and entertainment, cultural and cognitive or excursion, creative and labor; tourist (tourist and health). A number of animation functions in preschool institution are characterized, the leading ones of which are defined as: entertaining, hedonistic, recreational (health-improving), adaptive, educational, developmental, educational, corrective. The influence of different types of animation on the development of the leading spheres of the preschooler's personality is determined.
Integrating Global Features into Neural Collaborative Filtering

Recently, deep learning has been widely applied in the field of recommender systems and achieved great success, among which the most representative one is the Collaborative Filtering based Deep Neural Network. However, the input of such a model is usually a very sparse one-hot coding vector of users and items. This makes it difficult for the model to effectively capture the global features interaction between users and items. What is more, it also increases the training difficulty, making the model easily fall into a local optimum. Therefore, this paper proposes a two-stage Integrating Global Features into Neural Collaborative Filtering (GFNCF) model. To begin with, the AutoEncoder model with sparse constraint parameters is used to accurately extract the global features of users and items. Following that, the global features extracted in the previous step are integrated into the neural collaborative filtering framework as auxiliary information. It alleviates the sparse input problem and integrates more auxiliary features to improve the learning process of the model. Extensive experiments on several publicly available datasets demonstrate the effectiveness of the proposed GFNCF model.
Mechanism of Inclusive Leadership on Employee Innovation Behavior: A Moderated Mediation

Taking social embeddedness theory as the starting point, this paper examines the influence of inclusive leadership on employee innovation behavior, and explores the mediating effect of psychological distance and the moderating effect of work autonomy.Through hierarchical regression analysis and bootstrap test on the data of 305 knowledge workers, the statistical results support the research hypothesis.The results of this research are as follows.Inclusive leadership has a significant negative effect on psychological distance and a significant positive effect on employee innovation behavior; psychological distance can partially mediate between inclusive leadership and employee innovative behavior; work autonomy moderates the effect of psychological distance on employee innovation behavior, and moderates the indirect effect of inclusive leadership on employee innovation behavior through psychological distance.This paper enriches the research on inclusive leadership style, and reveals the mechanism of inclusive leadership on employee innovation behavior, which has important practical significance for stimulating employee innovation behavior.
An ASP-Based Approach to Scheduling Pre-operative Assessment Clinic

The problem of scheduling Pre-Operative Assessment Clinic (PAC) consists of assigning patients to a day for the exams needed before a surgical procedure, taking into account patients with different priority levels, due dates, and operators availability. Realizing a satisfying schedule is of upmost importance for a clinic, since delay in PAC can cause delay in the subsequent phases, causing a decrease in patients’ satisfaction. In this paper, we divide the problem in two sub-problems: In the first sub-problem patients are assigned to a day taking into account a default list of exams; then, in the second sub-problem, having the actual list of exams needed by each patient, we use the results of the first sub-problem to assign a starting time to each exam. We first present a mathematical formulation for both problems. Then, we present solutions based on Answer Set Programming (ASP): The first solution is a genuine ASP encoding of the sub-problems, while the second introduces domain-specific optimizations. Experiments show that both solutions provide satisfying results in short time, while the second is able to prove optimality faster.
Unsupervised Person Re-ID via Loose-Tight Alternate Clustering

Recently developed clustering-guided unsupervised methods have shown their superior performance in the person re-identification (re-ID) problem, which aims to match the surveillance images containing the same person. However, the performance of these methods is usually very sensitive to the change of the hyper-parameters in the clustering methods, such as the maximum distance of the neighbors and the number of clusters, which determine the quality of the clustering results. Tuning these parameters may need a large-scale labeled validation set, which is usually not applicable in unlabeled domain and hard to be generalized to different datasets. To solve this problem, we propose a Loose-Tight Alternate Clustering method without using any sensitive clustering parameter for unsupervised optimization. Specifically, we address the challenge as a multi-domain clustering problem, and propose the Loose and Tight Bounds to alleviate two kinds of clustering errors. Based on these bounds, a novel Loose-Tight alternate clustering strategy is adopted to optimize the visual model iteratively. Furthermore, a quality measurement based learning method is proposed to mitigate the side-effects of the pseudo-label noise by assigning lower weight to those clusters with lower purity. Extensive experiments show that our method can not only outperform state-of-the-art methods without manual exploration of clustering parameters, but also achieve much higher robustness against the dynamic changing of the target domain.
Evaluation of Vehicle Camouflage Effectiveness Under a Complex Background Based on the Time-Limited Search Model

To comprehensively evaluate the camouflage effect of a camouflaged vehicle in a complex background based on the detection and identification processes of the time-limited search model, the visual saliency degree and feature similarity degree were used to represent the camouflage effect, and a camouflage effect evaluation method was proposed. For the visual saliency degree algorithm, in which the concepts of local saliency degree and target saliency degree were introduced, the visual saliency features of the camouflaged vehicle and background image were manually extracted, multiscale saliency matrices were constructed, and the comprehensive saliency function was used to calculate the visual saliency degree. Based on the bag of visual words model, the feature similarity degree algorithm extracted the local features from public images of the camouflaged vehicles, clustered these features to form a visual word bag, and calculated the feature similarity degree by comparing the local features of the camouflaged vehicle with the visual word bag. Through experimental verification and analysis of the results, the method considering complex landform background characteristics, battlefield target reconnaissance processes, and different camouflage statuses of the vehicle could objectively obtain the camouflage effect value of vehicles, quantitatively determine the advantageous application environment of different camouflaged states, and provide data support for camouflaged state design and concealed area selection.
Exploration of Blended Teaching Mode of Practical Military English based on SAP2C in Big Data Era

The rapid development of information technology has promoted the arrival of "Big Data Era".In the perspective of big data era, blended teaching mode is a diversified and interactive new teaching method.This paper explores the innovative mode of blended teaching of Practical Military English for graduate students.Combined with teaching practice, the author studied how to realize the integration of information technology and practical military English course based on blended teaching mode in the era of big data.In this study, 34 postgraduates from a military academy were taken as the research objects, and the blended teaching mode based on "SAP2C" was implemented in the teaching of practical military English.SPSS was used to analyze the quantitative research of questionnaire survey and qualitative research of interview.It was found that the students had a high degree of acceptance of this teaching mode.
Research on the Cultivation of Big Data Technology Talents in Higher Vocational Based on Studio System

In this paper, by drawing on the experience of vocational education such as "dual system" and "apprenticeship", we deepen school-enterprise cooperation in the process of construction and talent training of big data technology majors, and deepen the cooperation between schools and enterprises by building equipment, teachers, curriculum, teaching materials, etc.Through the reform and construction of common teaching and scientific research, the two sides will build a platform for studio education and promote the formation of a community of destiny.The aim is to improve the vocational ability of big data technology students in multiple directions through the studio education platform jointly built by schools and enterprises, cultivate more big data talents that meet the needs of enterprises and society, and then promote the reform and innovation of talent training mode of higher vocational big data technology majors.
The Connotation of Flipped Classroom and Strategies for Practice in Higher Education

Since the new century, the flipped classroom has gradually become the focus of teaching reform and teaching research.The flipped classroom is the result of the gradual development of higher education in the information age and provides a new way of thinking for higher teaching.The article mainly studied the inner meaning, the theoretical basis of a flipped classroom by reading relevant literature, and how higher education can be better implemented through the flipped classroom.The flipped classroom needs to be optimised in terms of subject curriculum system, development of teaching and research, student assessment and teaching platform to improve the quality of teaching and learning in higher education.It is hoped that this will provide some reference for the practice and research of the flipped classroom in higher education.
Towards Query Processing over Heterogeneous Federations of RDF Data Sources

A federation of RDF data sources offers enormous potential when answers or insights of queries are unavailable via a single data source. As various interfaces for accessing RDF data are proposed, one challenge for querying such a federation is that the federation members are heterogeneous in terms of the type of data access interfaces. There does not exist any research on systematic approaches to tackle this challenge. To provide a formal foundation for future approaches that aim to address this challenge, we have introduced a language, called FedQPL, that can be used for representing query execution plans in this setting. With a poster in the conference we generally want to outline the vision for the next generation of query engines for such federations and, in this context, we want to raise awareness in the Semantic Web community for our language. In this extended abstract, we first discuss challenges in query processing over such heterogeneous federations; thereafter, we briefly introduce our proposed language, which we have extended with a few new features that we did not have in the version published originally.
Accounting of Cryptocurrency

With the development of technology today, the concept of crypto money emerged in 2008. With this development, cryptocurrencies began to be considered indispensable concepts. Thus, countries were expected to take steps in terms of their accounting. While some countries have taken concrete steps in accounting and taxation, some have not yet taken steps. In this study, brief information about cryptocurrencies was given and current developments were explained. Several suggestions have been made for countries that have not yet completed their work in this regard, and why cryptocurrencies should be taxed is mentioned. In addition, by examining the IASB and IFRS recommendations, it was evaluated which asset class cryptocurrencies could enter. With these evaluations, it has been suggested in which asset class cryptocurrencies can be accepted for Turkey and the countries that have not completed their work.
Effectiveness of cloud cover on solar radiation prediction using artificial neural network algorithm

Solar radiation data is highly desirable in various areas, such as agriculture, PV industries, and building performance analysis. There are no commercial tools available for real-time solar radiation prediction using only readily available data such as temperature and humidity. The purpose of this paper is to test if solar radiation can be predicted within acceptable uncertainties using only temperature and humidity, based on ASHRAE Guideline 14-2014. It is also discussed how much “cloud cover” affects solar radiation prediction when using ANN algorithms. Three climate locations are tested in Arizona, Washington, and North Carolina. Studies show that the prediction of solar radiation using ANNs depends primarily on the correlation between cloud cover and global solar radiation, rather than on the cloud cover ratio.
Facebook as a Learning Support Tool for NSSCO Physical Science Grade 12 Learners in Selected schools in the Oshikoto Region, Namibia

The use of Facebook in Namibia is focused on maintaining contact with friends and relatives, conduct sales on Marketplace, and to get updates on daily news (Peters, Winchiers-Theophilus &amp; Mennecke, 2015). The purpose of the study was to explore whether Facebook has the potential to support learning and mastery of Physical Science content to improve learners’ academic performance on the topic of stoichiometry at Grade 12 level in selected schools in the Oshikoto Region. Study hypothesis is: H0 – There is no significant difference in the learners’ academic performance in NSSCO Physical Science on the topic of stoichiometry when Facebook is used as a learning support tool. H1 – There is a significant difference in the learners’ academic performance in NSSCO Physical Science on the topic of stoichiometry when Facebook is used as a learning support tool. The study used a quantitative approach with a quasi-experimental design, constituted of a Non- Equivalent-Groups Pre-test, Intervention and Post-test. The results show there exists a significant difference between the control and experimental score marks when Facebook was used as a learning support tools. The relevance of this study shows that teachers should learn to embrace using Facebook to support learning.
On maximum additive Hermitian rank-metric codes

Inspired by the work of Zhou "On equivalence of maximum additive symmetric rank-distance codes" (2020) based on the paper of Schmidt "Symmetric bilinear forms over finite fields with applications to coding theory" (2015), we investigate the equivalence issue of maximum $d$-codes of Hermitian matrices. More precisely, in the space $\mathrm{H}_n(q^2)$ of Hermitian matrices over $\mathbb{F}_{q^2}$ we have two possible equivalence: the classical one coming from the maps that preserve the rank in $\mathbb{F}_{q^2}^{n\times n}$, and the one that comes from restricting to those maps preserving both the rank and the space $\mathrm{H}_n(q^2)$. We prove that when $d<n$ and the codes considered are maximum additive $d$-codes and $(n-d)$-designs, these two equivalence relations coincide. As a consequence, we get that the idealisers of such codes are not distinguishers, unlike what usually happens for rank metric codes. Finally, we deal with the combinatorial properties of known maximum Hermitian codes and, by means of this investigation, we present a new family of maximum Hermitian $2$-code, extending the construction presented by Longobardi et al. in "Automorphism groups and new constructions of maximum additive rank metric codes with restrictions" (2020).
Linear Complementary Pair Of Group Codes over Finite Chain Rings

Linear complementary dual (LCD) codes and linear complementary pair (LCP) of codes over finite fields have been intensively studied recently due to their applications in cryptography, in the context of side-channel and fault injection attacks. The security parameter for an LCP of codes $(C,D)$ is defined as the minimum of the minimum distances $d(C)$ and $d(D^\bot)$. It has been recently shown that if $C$ and $D$ are both 2-sided group codes over a finite field, then $C$ and $D^\bot$ are permutation equivalent. Hence the security parameter for an LCP of 2-sided group codes $(C,D)$ is simply $d(C)$. We extend this result to 2-sided group codes over finite chain rings.