Reviewerly/exHarmony huggingface dataset:
Research into the capacities of the global digital environment for obtaining cultural and conceptual information at institutional and individual levels

Introduction. The article examines the problem of using the global digital environment as a resource of information about the concepts of German linguistic culture. The purpose of the article is to study the functions of the digital environment, identify its capacities for institutional and individual familiarization with cultural-conceptual information, justify the criteria for selecting media texts, and summarize recommendations for using the environment as a source of information about linguistic concepts. Materials and Methods. The study of the digital environment was conducted during two semesters in 12 groups of undergraduates studying German at MGIMO University of the Russian Foreign Ministry. During the research, the following methods were used: survey and study of written and oral statements of students in the process of their immersion into the digital environment, analysis and systematization of the accumulated experience in teaching the concepts of German culture. Results. The article summarizes the experience of researching the global digital environment as a resource of cultural and conceptual information. The functions of the environment used in teaching a foreign language are determined: informational, educational, motivational,and diagnostic. The prospects for institutional and independent familiarization with information about key concepts of culture are identified. The criteria for selecting media texts used as a resource of cultural-conceptual information are substantiated: the availability of data on the concepts being studied, compliance with the value guidelines of Russian society, relevance and novelty of materials, matching with the level of foreign language proficiency of international relations students. Conclusions. The authors have revealed the capacities of the digital environment for obtaining cultural and conceptual information at institutional and individual levels. Implementing the research provisions in teaching practice will contribute to the effective use of the virtual cultural and linguistic space and improve the quality of language and professional preparation of international relations professionals.
Characteristic features of designing the system of students’ practical training in the context of education transformation

Introduction. The article investigates the problem of enhancing students’ ability and willingness to perform professional tasks. The research aim is to identify the characteristic features of practical training design in the context of educational system transformation. Materials and Methods. In order to solve the research tasks, we used the following methods: questionnaire survey of students (n=159) and teaching staff (n=98), observation of the educational process, analysis of laws and regulations as well as scholarly literature on the subject, and focus group discussions of employers, students, and practical training supervisors. Results. We identified core components of practical student training and comprehensively analysed this concept. The analysis revealed the characteristic features of practical training design during education transformation. Furthermore, the study substantiated the viability of modular and step-by-step designs of the practical training system aimed at the development of students’ ability and willingness to perform professional tasks. Conclusions. We consider the characteristic features of practical training design to be the basis of effective educational design aimed at the development of students’ professional competencies and strengthening the tendencies of practice-oriented learning.
Evaluating the functions of the teacher-producer for preparing prospective economists in the modern context of education system digitalization

Introduction. The authors explore the problem of enhancing the communication culture of prospective economists in the context of digital transformation and interaction with artificial intelligence systems. The purpose of the article is to identify the role of the teacher-producer in organizing teacher-student interaction in the system of economic education (secondary school-university), as well as to evaluate the functions of pedagogical production necessary to improve the communicative culture of future economists in the era of digitalization. Materials and Methods. The Pareto principle is used as the methodological basis of the study, according to which 20% of teaching resources provide 80% of the obtained educational result. The sociological survey was conducted among students and academic staff of economic faculties at the Financial University under the Government of the Russian Federation, Astrakhan State University, State Social and Humanitarian University (Kolomna) and schoolchildren of specialized classes (gymnasium 19, Omsk). Results. The analysis of empirical data demonstrates the relevance of the role of the teacher-producer in the economic education system in the era of digitalization, as well as the positive attitude of the younger generation towards the prospects for the introduction of artificial intelligence. The functions of pedagogical production are highlighted, taking into account the digital specifics of modern economic education. Using the Pareto principle confirms that mastery of the functions of pedagogical production (information, communication, organizational) in the system of economic education, constituting about 20% of the component composition of pedagogical functions, determine 80% of the educational result in terms of increasing the level of communicative culture of future economists. Conclusions. The authors revealed and described the functions of pedagogical production are highlighted, taking into account the digital specifics of modern economic education.
Artificial Intelligence and Customer Experience: Key Takeouts From Telecoms Sector in Zimbabwe

The purpose of this study was to glean key learnings from the use of artificial intelligence on customer experience with emphasis on Zimbabwean telecoms companies. The study employed qualitative research design, using semi structured interviews with customers, staff, and management of the telecoms companies in Zimbabwe. To unearth the key themes and trends and insights between artificial intelligence and customer experience thematic analysis was applied. The research evaluated the models employed by the Zimbabwean telecoms companies in transforming the customer experience landscape and hurdles they face in moving customers to the new channels. The challenges experienced in the new trajectory were explicitly highlighted. Case studies of other telecoms companies who explored the same avenue were chronicled. The various merits and skepticism of artificial intelligence on customer experience were clearly evacuated paving way for future research areas to completely harness the potential in the discipline of customer experience and its contribution to competitiveness.
On the transitivity of Logical Consequence without Assuming Monotonicity

We generalize Ripley’s results on the transitivity of consequence relation, without assuming a logic to be monotonic. Following Gabbay, we assume nonmonotonic consequence relation to be inclusive and cautious monotonic, and figure out the implications between different forms of transitivity of logical consequence. Weaker frameworks without inclusiveness or cautious monotonicity are also discussed. The paper may provide basis for the study of both non-transitive logics and nonmonotonic ones.
Thematic Analysis through Artificial Intelligence (AI)

Thematic analysis, a well-enforced qualitative analytic method, is likely to continue evolving with the adoption of AI technologies. This how-to report does not delve into the details of thematic analysis itself, as there are ample existing studies on the topic. Instead, it acknowledges the potential impacts, dynamics, and pitfalls of AI in thematic analysis while offering valuable advice, particularly for novice analysts, on how to incorporate and document AI tools in each phase of a thematic analysis. The author underscores the importance of not allowing AI to overshadow the analyst's critical evaluative and interpretive skills but instead supporting the use of AI as an aid in thematic analysis, enhancing the depth and breadth of analysis, provided certain criteria are adhered to. This approach ensures that AI serves as a complementary tool, augmenting rather than replacing human analytical inquiry.
Adiabatic elimination in the presence of multiphoton transitions in atoms inside a cavity

Various approaches have been used in the literature for eliminating nonresonant levels in atomic systems and deriving effective Hamiltonians. Important among these are elimination techniques at the level of probability amplitudes, operator techniques to project the dynamics on to the subspace of resonant levels, Green’s function techniques, the James’ effective Hamiltonian approach, etc. None of the previous approaches is suitable for deriving effective Hamiltonians in intracavity situations. However, the James’ approach does work in the case of only two-photon transitions in a cavity. A generalization of the James’ approach works in the case of three-photon transitions in a cavity, but only under Raman-like resonant conditions. Another important approach for adiabatic elimination is based on an adaptation of the Markov approximation well-known in the theory of system–bath interactions. However, this approach has not been shown to work in intracavity situations. In this paper, we present a method of adiabatic elimination for atoms inside cavities in the presence of multiphoton transitions. We work in the Heisenberg picture, and our approach has the advantage that it allows one to derive effective Hamiltonians even when Raman-like resonance conditions do not hold.
The COVID-19 Tweets Classification Based on Recurrent Neural Network

Due to its extensive use in both public and commercial contexts, sentiment analysis on Twitter has recently received much attention, particularly concerning tweets about COVID-19. Information about COVID-19 has been widely spread over social media, resulting in various views, opinions, and emotions about this pandemic, significantly impacting people's health. It is exceedingly challenging for the authorities to find these rumors on these public platforms manually. This paper proposes a framework for text classification using the RNN model and its updates, such as LSTM, BiLSTM, and GRU. This study aims to determine the best recurrent network model for handling cases of Twitter data classification. We utilized Twitter data relevant to COVID-19 and the lockdown with four classification classes (sad, joy, fear, and anger). In addition, this study aims to prove whether GloVe pre-trained word embedding can increase the accuracy of model predictions. The training and testing datasets were split into 80% and 20%, respectively. Therefore, in this experiment an early stopping technique was used with a limit of 15 epochs and a minimum delta of 0.01, meaning that training will be stopped if there is no improvement of 0.1% accuracy after 15 epochs. We used the f1-score average to measure the accuracy of the classification task results. The test results show that the BiLSTM model with GloVe word embedding yields the best f1-score compared to other models. Moreover, in all model testing, the f1-score value of the 'fear' class displays the highest accuracy compared to other classes.
PENGARUH PENGGUNAAN APLIKASI TIKTOK TERHADAP POLA PIKIR KREATIF REMAJA (STUDI PADA SMK NEGERI 3 KOTA BEKASI JURUSAN TEKNIK KOMPUTER DAN JARINGAN)

TikTok application is currently very popular and has a pretty good influence as a place to express yourself. The TikTok application can be used to sharpen creative minds when viewing interesting content. Therefore the research is interested in discussing this matter further. This research is discussed using the theory of new media or new media. This study uses a quantitative method with a positivism paradigm. In this study, data was obtained by distributing questionnaires to students of SMK Negeri 3 Bekasi, TKJ Department, class of 2022-2023. The sampling technique is by purposive random sampling. The data analysis technique used by researchers in this studywas one sample Kolmogrov Smirnov with the help of the SPSS 22.0 program. The results of the research can be seen in the simple linear regression test with the results from the R square test it is known that the use of the tiktok application has an effect of 0.083. This states that the use of the tiktok application affects the creative mindset of teenagers at SMK Negeri 3 Bekasi City, Department of Computer and Network Engineering by 8.3%. The results can be explained that tcount &gt; rtable then it is determined that Ho is rejected statistically significant. It can be seen from the results of the t test, the significant value is less than 0.05 (0.000 &lt;0.05) meaning that there is a strong influence between the independent variable of the tiktok application and the dependent variable of a creative mindset. So it can be concluded from this study that the use of the TikTok application has an influence on the creative mindset of adolescents at SMK Negeri 3 Bekasi majoring in Computer and Network Engineering.
Beam dynamics corrections to measurements of the muon anomalous magnetic moment

The Muon $g-2$ experiment at Fermilab is making progress towards its physics goal of measuring the muon anomalous magnetic moment with the unprecedented precision of 140 parts per billion. In April 2021, the collaboration published the first measurement based on the first year of data taking. The second result is based on the second and third years of data taking combined. We discuss the corrections to the anomalous spin precession signal due to beam dynamics effects being used to determine the anomalous spin precession frequency for the second result.
Enhancing Students' Motivation and Learning Outcomes in Thematic Learning Using the Discovery Learning Model for Grade V SDN 31 Kinali

This research aims to determine the increase in student motivation and learning outcomes in thematic learning using the Discovery Learning model for class V SDN 31 Kinali. This research aims to determine the increase in student motivation and learning outcomes in thematic learning using the Discovery Learning model for class V SDN 31 Kinali. Research subjects 18 students where 10 male students and 8 female students. This research was conducted in two cycles. In each cycle there are activities of planning, implementing, observing, and reflecting. Data collection techniques used observation and tests. The technique for checking the validity of the data used is source triangulation, namely comparing and re-checking the trustworthiness of data obtained through different times and tools by comparing the researcher's data with the learning results. The results of research using the Discovery Learning model can increase student motivation and learning outcomes. In cycle I, there were 5 students who completed with a percentage of 28%, while there were 13 students who did not complete with a percentage of 72%. In cycle II the number of students who completed was 15 people with a percentage of 83%, while there were 3 students who did not complete it with a percentage of 17%.
Enhancing the Zebra Optimization Algorithm with Chaotic Sinusoidal Map for Versatile Optimization

In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA)is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibitsgreat optimization capabilities, but the addition of CSM increases its potential even more. This addition greatlystrengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks.CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions,including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM toinvestigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shownby CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatilitythrough multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOAbecomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficultoptimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is arelatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership andfollowing, in which members of the population—zebras in this case—cooperate to solve optimisation issues in thebest possible ways
Factors Affecting Student Satisfaction and Dissatisfaction in a Higher Education Institute

In the current competitive environment, one of the most critical issues that organizations deal with is awareness of customer satisfaction and their opinions regarding the organization's performance, products, and services. Organizations that focus their primary activities on customer satisfaction have a competitive advantage. According to systems thinking, academic institutions are also among the organizations whose survival is closely tied to the satisfaction of their students (customers), and therefore, satisfaction is a factor that must be given greater attention than ever before. The main goal of this research is to identify the factors affecting the satisfaction and dissatisfaction of students at a higher education institute, which is examined in three dimensions: the teaching staff, the administrative staff, and the management staff of the institution. Based on interviews conducted with students, the results indicate a significant level of dissatisfaction with the educational dimension and the teaching methods of the professors. Additionally, in the administrative dimension, dissatisfaction is more concentrated in the areas of welfare services and university facilities. In the administrative dimension, failure to fulfil responsibilities correctly has led to student dissatisfaction.
Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Abstract This study addresses the classification of objects using their electromagnetic signatures with Convolutional Neural Networks (CNNs) trained on noiseless data. The singularity expansion method (SEM) was applied to establish a compact model that accurately represents the ultra-wideband scattered field of an object, independently of its orientation and observation angle. To perform the classification, we used a CNN associated with a noise-robust SEM technique to classify different objects based on their characteristic parameters. To validate this approach, we compared the performance of the classifier with and without SEM pre-processing of the scattered field for different noise levels and for object sizes not present in the training set. Moreover, we propose a procedure that determines the direction of the receiving antenna and orientation of an object based on the residues associated with each complex natural resonance. This classification procedure using pre-processed SEM data is promising, especially when generalizing to object sizes not included in the training set.
Penyuluhan Bahaya Narkoba Di Kalangan Remaja Khususnya Pelajar SMKN 1 Kisaran

The purpose of this community service activity is to educate students at SMK Negeri 1 Kisaran on how to avoid drug abuse. drug abuse. This community service activity used an counseling strategy; all activities were planned and conducted with 25 student representatives from each grade and level. The comparison results between pre-test and post-test showed that the PKM can improve the knowledge of participants about the dangers of drugs and drug abuse prevention efforts; the number of participants who were able to mention the types of drugs, the dangers of drugs, and efforts to prevent drug drug abuse prevention efforts increased from 30% to 95%, and the average score reached 87 out of the maximum score. reached 87 out of the maximum score.
Enhanced Multi-Class Newsgroup Document Classification Using N-Gram Approach

With the advancement of technology, consumption of data and information over web is increasing day by day. This has led to rising dependence of our lives on internet data at personal and professional fronts. Being a populous nation with high internet penetration, India is a huge market in digital media. The adaptability to online news, has created a large number of text news data which requires classification into categories for further applications. To address the work for efficient news document classification in the field of computational linguistics, we have proposed a novel approach for Newsgroup classification by combining tokens with a context window size of one, two and three known as unigram, bigram and trigram respectively. &#x0D; In our research work, we explored the relevance of trigram by experimenting over different collection of n-grams using three supervised classifiers namely Perceptron, Nearest Centroid, and Random Forest. In the experiment, we found that inclusion of trigram along with unigram and bigram achieves the best result in terms of F1-macro score and accuracy among all three classifiers. Perceptron shows the best results in comparison with Random Forest and Perceptron classifiers, with an accuracy of 0.846945 and an F1-macro score of 0.83055. Significant time was spent on the validation of our efficient approach with the existing literature.
The Role of Community Health Nurses in Promoting Occupational Health and Safety Awareness within Facilities by Embracing Modern Distance Learning Techniques

Overall, accessibility, engagement, and cost-effectiveness can all be significantly impacted by the use of remote learning for occupational health and safety training. In order to make significant progress toward raising occupational health and safety awareness within facilities, it is critical to deal with any potential roadblocks and implement plans that will optimize its effectiveness. Using remote learning for occupational health and safety training of facility staff presents both potential benefits and challenges with regard to raising safety awareness and improving occupational health. Community health nurses play a critical role in improving health and wellness in local communities (CHNs). Workplace health and safety is another area where this is pertinent, particularly for smaller businesses located in impoverished areas where access to occupational health professionals may be limited. By utilizing contemporary remote learning technology, CHNs can serve as effective advocates for and teachers of personnel on critical safety procedures. Thus, the purpose of this study was to examine the role of community health nurses in promoting occupational health and safety awareness within facilities by embracing modern distance learning techniques.