Reviewerly/exHarmony huggingface dataset:
Analyzing the Type and Experience of Leisure Participation According to Stress Level in Korean Men during COVID-19 Pandemic

Background: With the coronavirus disease 2019 (COVID-19) pandemic, Korean men are under immense stress as it has become increasingly difficult to support their families due to economic difficulties following the loss of employment, along with the absence of social activities due to lockdowns and restrictions caused by the pandemic.The experience of sociocultural leisure activities, particularly, can have a socially and psychologically positive effect as a means to overcome a difficult situation such as the pandemic scenario.Thus, this study aims to examine the extent the level of stress interferes with leisure activities during the COVID-19 pandemic and to identify how participating in leisure activities can reduce stress.Material and Methods: In this study, results were analyzed using descriptive statistics (frequency and mean) and inferential statistics (ANOVA and Chi-square test) using SPSS (Version 25.0, IBM, Armonk, NY, USA) on 942 Korean men.Results: When differences in leisure experiences were analyzed according to psychosocial stress, it was found that there was a significant difference in all factors: fun, information, new experience, and art.Furthermore, when the differences in the type of leisure activities and leisure experience were analyzed according to psychosocial stress, variations were found in leisure experience depending on leisure activity type in all groups including the healthy group, potential stress group, and the high-risk group.Conclusions: It seems necessary to increase accessibility for participating in leisure activities for men.Increasing accessibility to leisure activities may provide leisure that can be enjoyed throughout life beyond a one-time experience and reduce all aspects of stress and increase psychological and emotional stability during pandemic.Furthermore, the government needs to actively improve youth welfare in such a way that it becomes more future-oriented rather than present-oriented by reducing the level of psychological stress by considering the circumstances specific to Korean men.
IMPACT OF FINANCIAL TECHNOLOGIES ADOPTION ON THE PERFORMANCE OF DOMESTIC SYSTEMICALLY IMPORTANT BANKS IN NIGERIA

The purpose of this study is to investigate the impact of financial technologies adoption on the performance of domestic systemically important banks in Nigeria from 2015-2019. Data was extracted from the audited annual financial statements of domestic systemically important banks in Nigeria. The author adopted the panel regression to analyse data collected from the audited annual financial statements of domestic systemically important banks in Nigeria from 2015-2019. Findings shows that Automated Teller Machine, Point of Sale and mobile transfer/web increased the performance of domestic systemically important banks in Nigeria from 2015-2019. Furthermore, investments in financial technologies possessed a positive impact on performance of domestic systemically important banks in Nigeria for the period under review. Policy options and limitations of the study are proffered in the main text.
An Artificial Intelligence-Based Approach to Social Data-Aware Optimization for Enterprise Management

Enterprise management has always been a hot issue in society. In today’s society, enterprises are no longer individuals cut off from society and no longer have profit as their sole purpose, but need to exist and develop in combination with social information. With the increasing competition among enterprises, the original enterprise management methods can no longer meet the needs of sustainable development of enterprises, nor can they effectively utilize social data. To better understand the daily emotions of corporate workers, we use a multimodal emotion recognition method in this paper. Multimodal emotion recognition refers to the recognition of human emotional states through different modal information such as speech, visual, and text related to human emotional expressions, which has important research significance in the fields of human-computer interaction, artificial intelligence, and emotional computing and has received much attention from researchers. Given the great success of deep learning methods developed in recent years for various tasks, various deep neural networks are now used to learn high-level representations of emotional features for multimodal emotion recognition. The analysis of employee sentiment is complemented by traditional management methods that make full use of social data. In this paper, based on the study of a single enterprise management model, the proposed model contains five substructure modules, starting with feature inputs, extracting features at different levels through three convolutional modules and outputting recognition results through a softmax classifier. The focus is on how to utilize social data, while combining deep learning with traditional enterprise management methods to fill the research gap in this area in academia.
Improving the Effect of English Classroom Teaching Based on the Real-Time Supervision of College Students

This paper proposes a method to improve the effect of English classroom teaching based on the real-time monitoring of college students’ status. Education informatization reinsert teaching evaluation and teaching management methods, use the new generation of information technology to track the entire process of monitoring and teaching, and carry out academic analysis and learning diagnosis, to pay attention to knowledge teaching to more emphasis on the cultivation of ability quality. Behavior analysis can identify students’ classroom behavior in real time to track changes in the state of students. We herein count the classroom status of students and formulate quantitative guidelines and analyze the sequence diagram of each classmate’s class behavior state and statistics from the time and duration of various classroom behaviors in classmates. Each student’s listening quality is scored, and the visual methods such as sequential maps and distributed cake maps are used in class behavior to enable students to understand their own quality of the lesson. By analyzing the laws of student class behavior status distribution, we provide a data-driven mechanism for teacher classroom teaching quality evaluation and teaching method improvement.
On the Construction of Self-Complementary Codes and their Application in the Problem of Information Hiding

Line codes are widely used to protect against errors in data transmission and storage systems, to ensure the stability of various cryptographic algorithms and protocols, to protect hidden information from errors in a stegocontainer. One of the classes of codes that find application in a number of the listed areas is the class of linear self-complementary codes over a binary field. Such codes contain a vector of all ones, and their weight enumerator is a symmetric polynomial. In applied problems, self-complementary [n, k]-codes are often required for a given length n and dimension k to have the maximum possible code distance d(k, n). For n < 13, the values of d(k, n) are already known. In this paper, for self-complementary codes of length n=13, 14, 15, the problem is to find lower bounds on d(k, n), as well as to find the values of d(k, n) themselves. The development of an efficient method for obtaining a lower estimate close to d(k, n) is an urgent task, since finding the values of d(k, n) in the general case is a difficult task. The paper proposes four methods for finding lower bounds: based on cyclic codes, based on residual codes, based on the (u-u+v)-construction, and based on the tensor product of codes. On the joint use of these methods for the considered lengths, it was possible to efficiently obtain lower bounds, either coinciding with the found values of d(k, n) or differing by one. The paper proposes a sequence of checks, which in some cases helps to prove the absence of a self-complementary [n, k]-code with code distance d. In the final part of the work, on the basis of self-complementary codes, a design for hiding information is proposed that is resistant to interference in the stegocontainer. The above calculations show the greater efficiency of the new design compared to the known designs.
Visualization System For Sentiment Analysis Using Textblob On Twitter

Sentiment analysis is the classification of texts in several categories, usually positive, negative, happy or sad. In recent years, this technique has been used to analyze online product reviews, online news, general elections, disasters, stock markets, and social media, especially twitter. The results of the sentiment analysis can be used as one of the decisions making considerations. In helping to analyze the results of sentiments, visual analytics are used that can help to navigate data, compare various data sets, and explore data distribution. This research aims to analyze sentiment in Bahasa Indonesia using Tweepy and TextBlob as a python library to access and classify Tweets and with the help of visual analytics to explore and observe the distribution of Tweets based on geographical location, especially in provinces in Indonesia. In addition, this research also provides a comparison of the results of the validation of TextBlob using the Naïve Bayes and SVM algorithms with the results of each accuracy of 62.26% and 69.18% which are higher accuracy compared to SentiWordNet.
Challenges of Load Balancing Techniques in Cloud Environment: A Review

To highlight issues and challenges of the Load Balancing Algorithm (LBA) in the context of cloud computing.The authors of this work discussed load balancing and the load balancing problems in cloud computing, how to solve them, the basic techniques used to maintain load over the cloud, and the benefits and drawbacks of these algorithms.Furthermore, the static and dynamic load balancing strategies are compared.Load balancing methods in the cloud for data centers must avoid VM overload/low load, which is a challenge in cloud computing.Load balancing mechanisms aid in the management of workloads.The primary goal of load balancing among machines is to spread workloads and computing resources across one or more servers while lowering energy consumption and maximizing resource usage.Cloud computing is a type of computing in which scalable resources are made available on-demand and are frequently virtual, as in an online service.For a variety of reasons, including cost saving, speed, efficiency, performance, and security, cloud computing has recently become a popular choice for individuals and enterprises.Cloud computing uses the Internet to deliver a variety of services to a company's computers and devices.These resources can include data storage, databases, servers, networks, and software, among other tools and applications.In this paper, developers and researchers must design and construct a load balancer e for parallel and distributed cloud systems.
Efficient Elliptic Curve Operators for Jacobian Coordinates

The speed up of group operations on elliptic curves is proposed using a new type of projective coordinate representation. These operations are the most common computations in key exchange and encryption for both current and postquantum technology. The boost this improvement brings to computational efficiency impacts not only encryption efforts but also attacks. For maintaining security, the community needs to take note of this development as it may need to operate changes in the key size of various algorithms. Our proposed projective representation can be viewed as a warp on the Jacobian projective coordinates, or as a new operation replacing the addition in a Jacobian projective representation, basically yielding a new group with the same algebra elements and homomorphic to it. Efficient algorithms are introduced for computing the expression Pk+Q where P and Q are points on the curve and k is an integer. They exploit optimized versions for particular k values. Measurements of the numbers of basic computer instructions needed for operations based on the new representation show clear improvements. The experiments are based on benchmarks selected using standard NIST elliptic curves.
Refinement and Teaching of Cases of Curriculum Ideology and Politics in the Course of Composite Materials

To promote the ideological and political construction of college courses comprehensively is an inevitable requirement for the implementation of the fundamental task of morality education, and an important task for the quality improvement of talent training comprehensively. It is important and challenging to excavate and refine ideological and political cases in the teaching content that should be imparted vividly to students and arouse resonance and thinking via classroom teaching, after-school exercises and course assessment. This article takes the professional course of composite materials of inorganic materials engineering major in Anhui university of technology as an example, and conducts preliminary exploration and analysis on key issues such as how to excavate and refine ideological and political cases, how to combine the content of the course to implement ideological and political education, and the methods of course construction.
Accelerating Extreme Search of Multidimensional Functions Based on Natural Gradient Descent with Dirichlet Distributions

The high accuracy attainment, using less complex architectures of neural networks, remains one of the most important problems in machine learning. In many studies, increasing the quality of recognition and prediction is obtained by extending neural networks with usual or special neurons, which significantly increases the time of training. However, engaging an optimization algorithm, which gives us a value of the loss function in the neighborhood of global minimum, can reduce the number of layers and epochs. In this work, we explore the extreme searching of multidimensional functions by proposed natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing a multidimensional surface with probability distributions, which allows us to reduce the change in the accuracy of gradient and step size. The proposed algorithm is equipped with step-size adaptation, which allows it to obtain higher accuracy, taking a small number of iterations in the process of minimization, compared with the usual gradient descent and adaptive moment estimate. We provide experiments on test functions in four- and three-dimensional spaces, where natural gradient descent proves its ability to converge in the neighborhood of global minimum. Such an approach can find its application in minimizing the loss function in various types of neural networks, such as convolution, recurrent, spiking and quantum networks.
Non-Stationary Stochastic Global Optimization Algorithms

Studying the theoretical properties of optimization algorithms such as genetic algorithms and evolutionary strategies allows us to determine when they are suitable for solving a particular type of optimization problem. Such a study consists of three main steps. The first step is considering such algorithms as Stochastic Global Optimization Algorithms (SGoals ), i.e., iterative algorithm that applies stochastic operations to a set of candidate solutions. The second step is to define a formal characterization of the iterative process in terms of measure theory and define some of such stochastic operations as stationary Markov kernels (defined in terms of transition probabilities that do not change over time). The third step is to characterize non-stationary SGoals, i.e., SGoals having stochastic operations with transition probabilities that may change over time. In this paper, we develop the third step of this study. First, we generalize the sufficient conditions convergence from stationary to non-stationary Markov processes. Second, we introduce the necessary theory to define kernels for arithmetic operations between measurable functions. Third, we develop Markov kernels for some selection and recombination schemes. Finally, we formalize the simulated annealing algorithm and evolutionary strategies using the systematic formal approach.
Performance Evaluation of E-Voting Based on Hyperledger Fabric Blockchain Platform

Permissioned blockchain platforms have become more prevalent in a wide range of applications. These, such as hyperledger fabric platforms, are sensitive to latency and throughput. In this work, the E-voting case study adopts a hyperledger fabric platform where performance evaluation has been studied in terms of scalability, latency, throughput, CPU usage, and memory allocation. Three scenarios were performed with varying transaction rates, block size, and organizations. Another two scenarios were performed, first with varying block timeout and second, measuring the impact of CPUs and memory allocation on the proposed fabric’s entities (peers, orderer, couchDB, chaincode, etc.). The result shows that an increase in block size will significantly affect metrics such as latency and throughput. Good results were obtained with high transaction send rates on large block size. Similarly, low performance is obtained using a small block size with increased send rates. Also, it was noticed that increasing the number of organizations will increase latency and decrease the throughput. Therefore, in applications with a large number of concurrent transactions, to maintain high throughput, block timeouts and block size should be large. On The other hand, the number of CPUs and amount of memory allocation would impact hyperledger fabric performance.
A Hybrid Model Integrating Adaboost Approach for Sentimental Analysis of Airline Tweets

Sentimental Analysis has grown as a significant opinion strategy in the field of online media due to quick information development and internet technologies. This research will play an important role for recommendation of best airline for Indian passengers to prefer the appropriate airline for their journey and also useful for the Indian ministry of aviation. In this study we have gathered different tiny texts called comments from different social media traveling websites using webharvy data fetcher scraping tool related to six top rated Indian airlines. The main problem with airline tweet SA (sentimental analysis) is determining the best sentiment classifier for appropriately classifying the tweets. VADER model has used sentiment ratings to connect lexical characteristics to emotion intensities. In this research, a Hybrid model integrated Adaboost approach (HMIAA) has proposed, which combines the basic learning classifier SVM with the forward-learning ensemble method Gradient Boosted Tree to form a single robust classifier or model, with the objective of improving SCT (sentimental classification technique) efficiency (performance) and accuracy. The findings reveal that the suggested hybrid approach integrating Adaboost technique outperforms other basic classifiers. After completion of sentimental analysis of all datasets we can recommend the passengers for the best airline.
Social learning dynamics influence performance and career self-efficacy in career-oriented educational virtual environments

Educational virtual environments (EVEs) are defined by their features of immersion (degree of sensory engagement) and fidelity (degree of realism). Increasingly, EVEs are being used for career development and training purposes, which we refer to as career-oriented EVEs. However, little research has examined the effects of immersion and fidelity on career-related outcomes, like self-efficacy and interests, and the learning dynamics that may influence these outcomes. We address these research needs across two studies using an inductive approach. Study 1 compares welding career exploration in EVEs to traditional career exploration and finds that individuals using EVEs report more positive career self-efficacy. Study 2 examines the influence of social learning dynamics, or how individuals learn from each other through behavioral modeling, on performance and career-related self-efficacy and interest. Groups were assigned to use either a high or low immersion and fidelity EVE. Findings indicate strong social learning dynamics in both EVEs, but the effects were stronger for groups using the higher immersion and fidelity EVE. Specifically, groups converged on two performance measures, and the performance of individuals who were situated as behavioral models significantly predicted the performance of other group members. Performance at the individual level, in turn, predicted career self-efficacy and interest for men but not women, and only for those using the higher immersion and fidelity EVE. Based on these findings, we conclude with practical recommendations for and implications of implementing career-oriented EVEs for career exploration and skills training.
Convolutional neural networks for automatic detection of breast pathologies

Breast cancer in women is a worldwide health problem that has a high mortality rate. A strategy to reduce breast cancer mortality in women is to implement preventive programs such as mammography screening for early breast cancer diagnosis. In this presentation, a method for automatic detection of breast pathologies using a deep convolutional neural network and a class activation map is proposed. The neural network is pretrained on the regions of interest in order to modify the output layers to have two output classes. The proposed method is compared with different CNN models and applied to classify the public dataset Curated Breast Imaging Subset of DDSM (CBIS-DDSM).
Using Smartphones in Teaching English to Secondary School Students in South Africa

This study aimed to explore teachers’ experiences of using smartphones in teaching and learning English in Capricorn South Circuit, Limpopo Province. We used a qualitative research approach and adopted a case study design to conduct the study. Purposive sampling strategy was used to select data from six teachers. Data were collected through three methods, namely; interviews, observation and a reflective journal. We adopted Strauss and Corbin’s Model to analyse data. Thus, the study generated three findings: (a) reluctance among some teachers to use smartphones in teaching and learning; (b) challenges experienced at schools such as contextual factors (e.g., poor network coverage) and disruptive learners; as well as (c) strategies used by teachers to plan and implement the use of smartphones in teaching and learning to determine its success. These findings have major implications for the use of smartphones in teaching English in schools. Therefore, the study recommends that there be training for teachers regarding the use of smartphones in teaching. Additionally, the study recommends that the school management should be supportive and actively involved in the implementation of using of smartphones for teaching and learning in schools. In conclusion, teachers should ensure that timeous and thorough planning is done to ensure success if the use of smartphones is to succeed. This study is significant because it emphasises the need for a shift in teaching from a teacher-centred approach to a learner-centred approach that incorporates the use of technology in classroom.