Reviewerly/exHarmony huggingface dataset:
An Opposition-Based Beluga Whale Optimization

The population-based evolutionary algorithm simulates the behavior of different organisms. Randomness, diversity, exploration, and exploitation make the population quickly converge to the optimal solution in the evolution process. For the swarm intelligence algorithm, it is essential to balance the exploration and exploitation ability of the algorithm. This paper proposes an opposition-based beluga whale optimization (OBWO) and uses an adaptive strategy to improve the algorithm’s performance. To prove the capability of the OBWO, we verified the new algorithm on CEC_2013 and compared it with other excellent algorithms. The results show that the performance of the OBWO is first-rate and far exceeds that of the original algorithm.
The importance of nurses in improving the quality of life of hemodialysis patients

Chronic Kidney Disease is an important Public Health problem due to its high rates of morbidity and mortality, it is a disease characterized by the slow and continuous loss of kidney function, a fact that causes, among other changes, the progressive accumulation of toxins and metabolism waste in the blood. . Thus, with the intention of removing impure and toxic substances from the blood and excess water in the body, hemodialysis becomes the most assertive method, although there are some restrictions due to this therapeutic treatment, such as dietary changes in basic and social daily activities. Objective: It aims to analyze the aspects that influence the good quality of life of the hemodialysis patient, and describe the importance of nursing care to the patient on hemodialysis, as well as the particularities of the disease and the role of the nurse. Methodology: bibliographic review with search in books, magazines, articles, publications by agencies and institutions such as the Ministry of Health. The search for information was based on data available online found on platforms such as: Scielo and Google Scholar. Conclusion: It is in nursing that many patients find solace and motivation to continue with the treatment. It was identified that the nurse and the nursing team have a fundamental role in helping the patient to have an improvement in the quality of life, guiding the patient to live with his limits and accompanying the evolution of the treatment, focusing on acting in a preventive way to possible complications with patients before, during and after hemodialysis, in addition to helping with emotional and family issues.
Retinex-Based Improvisation of Low Contrast Images with MATLAB

The advancement in technology has increased the use for digital images in fields of medicine, surveillance, entertainment, etc. However, the quality of digital images may be affected by the factors such as noise, blur, and low contrast. Image enhancement techniques aim for improving the optical conditions of images by adjusting its intensity levels. This study will provide an overview of current findings in the field of image enhancement and will also identify areas for future research. The objective of the research furnishes a thorough understanding of image enhancement techniques with their applications. Deep learning helps to solve problems, which were unmanageable and difficult to be achieved by humans. Deep learning methods overcome the dominance of conventional, custom made machine learning algorithmic methods. In this research study, an experiment is proposed for improving low-light, hazy, and blurry images for gaining better clarity with minimal efforts. The thrust of this study is to process the images for generating a perfect image to a greater extent.
A Fast Procedure for Image Lossy Compression by ADCTC Using Prediction of Distortions’ MSE

Green computing is a popular trend nowadays. In this paper, we show a way to incorporate this tendency in lossy image compression by an efficient coder based on discrete cosine transform. We study the case when it is desired to provide distortions characterized by mean square error not worse than a given threshold. In opposite to two-step and iterative methods that require, at least, two compression and one decompression of an image subject to processing, the proposed approach is based on quite accurate prediction of errors due to lossy compression in each block and, thus, for the entire image. Prediction employs information about quantization step and local activity in each block characterized by local standard deviation that can be calculated very quickly. Using test images, the dependences of introduce errors’ statistics on local standard deviation of information component are obtained by curve fitting into scatter-plots. This is done in advance and the obtained dependences can be then used for any image subject to lossy compression. The accuracy of the proposed approach is analyzed and shown appropriate for practical applications.
The Reform of College Physical Education Teaching Methods from the Perspective of Cognitive Psychology

This paper examines the transition between the media in the pedagogical process and the knowledge process in a physics college from the perspective of psychological theory.Physical education is an important part of higher education and training and plays a significant role in student ability and physical fitness.The development of physical activity can increase students' interest in life.Now, teaching and learning changes are further supported, and the general ability of students has a higher need in the history of good education.Psychology-based physical education in colleges and universities is a new teaching method that is replacing old teaching methods and is being used to improve teaching texts from the perspective of mental health students.However, if we want to highlight China's long-term spiritual perspective and its positive and tangible role in educational reform, we must start with a series of observations, inquiries, and data to discuss it.The purpose and importance of developing students' abilities to improve student learning.
News classification using text data generators and convolutional neural network (CNN)

Everyday news channels and newspaper publications receive a huge number of news updates. It is nearly impossible to read all this enormous amount of data and categorise the news accordingly. Automated News classification is a growing interest in the research of text mining and machine learning. Correctly identifying the news into particular category is still presenting challenge because of large and vast number of features in the dataset. In addition to that news updates sometimes don't have a clear distinction about their categories. Also, almost all the news updates consist of date, time and place and numbers like information about the happening which may seem important to the viewer but completely useless and instead a trouble to the classifier. In our project, we aim to overcome all these challenges to build a better classification system for the news. There are many classification algorithms that can be used to classify text.
Detection of Cyberbullying and Abusive Language on Social Media Using Supervised ML &amp; NLP Techniques

The introduction of the internet marked the beginning of the modern era of social networking.Nobody could have dreamed that the internet would eventually become home to such a large number of amazing services, including social networking, yet that is exactly what has happened.At this point in time, we are in a position to say that internet apps and social media platforms have become ingrained in people's everyday lives.Users of all ages spend significant amounts of time on these websites on a regular basis.Even if social media platforms make it possible for people to form emotional connections with one another, they also expose users to serious dangers, such as the threat of cyberattacks and the potential for online abuse.Cyberbullying is increasing in frequency alongside the proliferation of online social networking platforms.It is feasible to construct a machine learning (ML) model that can naturally recognise occurrences of social media bullying in order to find word associations in the tweets sent out by bullies.This can be accomplished by utilising machine learning.Nevertheless, there are many ways to identify abuse on social media, although most of them relied heavily on text.With this context and goal, developing appropriate methods to spot cyber abuse via social media can aid to prevent its occurrence.It is suggested that abuse on Twitter be identified and stopped using machine learning.The content of cyberbullying is trained and tested using naive Bayes.
<i>“Is it Even Giving the Correct Reading or Not?”</i> : How Trust and Relationships Mediate Blood Pressure Management in India

While chronic disease afflicts a large Indian population, the technologies used to manage chronic diseases have largely been informed by studies conducted in other sociocultural contexts. To address this gap, we conducted qualitative interviews with 21 patients clinically diagnosed with abnormal blood pressure (BP) living in low-resourced communities of Haryana, Uttarakhand and Uttar Pradesh in India. We found that patients’ trust in the BP ecosystem and social ties plays a significant role in shaping their perceptions of technology and chronic care. Trust in one actor of the ecosystem fosters trust in another, e.g., trust in BP reading depended on the type of device and the person measuring the BP. We also observed nuanced sharing and intermediation of BP devices. Based on our findings, we recommend designs to boost patients’ trust, familiarity, and access to technologies used in BP management and improve their experience of care in low-resource settings in India.
Analytical evaluation of the coefficients of the Hu-Paz-Zhang master equation: Ohmic spectral density, zero temperature, and consistency check

We investigate the exact master equation of Hu, Paz, and Zhang for a quantum harmonic oscillator at zero temperature with a Lorentz-Drude type Ohmic spectral density. This master equation plays an important role in the study of quantum Brownian motion and in various applications. In this paper, we give an analytical evaluation of the coefficients of this non-Markovian master equation without Lindblad form, which allows us to investigate consistencies of the solutions, the positivity of the stationary density operator, and the boundaries of the model's parameters.
K-means Nonhierarchical Cluster and Dbscan Outlier Detection In the Grouping of Stock Issuers

Group analysis aims to group objects based on similar characteristics so that they are in one group homogeneous and between groups heterogeneous. Study this aim group issuer share in Indonesia based on volatility, liquidity, and market capital. This study uses the non-hierarchical K-Means Clustering method, because the number of samples is big and the number of groups are known. The K-Means Clustering grouping method produces as many as 6 groups with different characteristics. 2. Group 1 consists of stock issuers with quite high volatility and liquidity. The characteristic of group 2 is that it consists of stock issuers with the lowest volatility. Big Capital is the nickname for group 3 because it has market capital or the asset value is very large among all groups and the volatility is very small, and liquid. In Group 4, stock issuers have the highest volatility and the lowest liquidity. Results of profile interpretation in group 5, issuer’s stocks have the highest liquidity and market capital is quite low. Share issuers in group 6 have the volatility highest. Group 3 is recommended as an option for investing. Because, having market capital or large asset values, liquid, and volatility is low enough to minimize risk. The originality of this research is that there is no combination of methods between grouping in fields with k-means clustering and detection of sales with DBSCAN, especially in the field of issuer share in Indonesia.
Correction of evanescent wave influence on the flexural wave velocity and wavelength estimation based on a mode shape function

The aim of this research is to use a simple acoustic method of a very near field recording, which enables measurement and display of oscillation modes, to estimate the velocity of flexural waves, based on the wavelengths of standing waves measured on the sample.The paper analyses cases of 1D geometry, flexural waves that occur on a beam excited by an impulse.Measurements were conducted on two different samples: steel and a wooden beam of the same length.Due to the appearance of evanescent waves at the boundary regions, the distance between the nodes of standing waves that occur deviates from half the wavelength, which can be compensated using a correction factor.Cases of fixed and free boundary conditions were analysed.By quantifying how much the boundary conditions change the mode shape function, it can be predicted how the mode of oscillation changes if the boundary conditions change, which can also find application in musical acoustics and sound radiation analysis.
The Use of the Dictogloss Technique to Improve Students Listening Ability in the Era of COVID-19 at the University of Muhammadiyah Bone

The objective of the research was to find out whether or not the use of Dictogloss technique can improve students’ listening ability. Besides, it was to find out the activeness learning of short conversation or narrative text is implemented in listening ability. The method of this research was Classroom Action Research by using Kurt Lewin’s model. The researcher used 2 cycles. Subject of the research in the third semester English Department of Muhammadiyah University of Bone. The instruments of the research were observing, questionnaire, and English test which was divided into three parts, they were diagnostis test, cycle test 1 and cycle test 2. This research revealed that there were 37,95% improvements of the student’ mean score from D-test to cycle test 1 in the first cycle and 61,22% improvements of the students’ mean score from the cycle test 1 in first cycle to the cycle test 2 in the second cycle. The observation result showed that the method could also increase the student team work through discussion in group, add their new vocabularies, increase their pronunciation and student’ involvement, it could be concluded that Dictogloss technique could improve the students ability in listening at the third semester English Department students of Muhammadiyah University of Bone and this Classroom Action Research was success because it had gained the target.
Single-camera system for measuring paper deformations based on image analysis

The article presents a new technique for measuring paper deformation in unidirectional tensile tests, based on recording and analysis of a series of specimen images.The proposed technique differs from the DIC-based deformation measurement in that the cross-correlation of image data has been replaced with linear filtering.For this purpose, a regular grid of markers is printed on the sample.Filtering the image creates local maxima in the places where markers occur.The developed algorithm finds their location with sub-pixel accuracy.Printing a grid of markers on tested paper and use of reference objects visible in the same image as the paper sample, freed from the need to mechanically connect the camera and the universal testing machine and from the necessity to electronically synchronize their work.The obtained deformation distributions and Poisson's ratios are in accordance with the literature data which confirms the correctness of the developed measurement technique.
Unifying Token- and Span-level Supervisions for Few-shot Sequence Labeling

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token-level or span-level) and have some weaknesses of the corresponding granularity. In this article, we first unify token- and span-level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token- and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets. All the code and data of this work will be released at https://github.com/zifengcheng/CDAP .
O IMPACTO NA SAÚDE MENTAL DO IDOSO NO ISOLAMENTO E NA QUARENTENA GERADOS PELA PANDEMIA DO COVID 19: TRATAMENTO MEDICAMENTOSO E PSICOTERÁPICO

The emergence of COVID-19 and the associated pandemic outbreak demonstrated the need to educate and mobilize the public to adopt behaviors for community mitigation.In a context of rapid epidemiological worsening, the use of digital media to access information benefits the population and can contribute to the control of the impact of the COVID-19 pandemic.This study aimed to evaluate the most viewed Brazilian videos on YouTube as a source of information about COVID-19.The quantitative, observational, and crosssectional study was conducted on YouTube on 04/30/2020, with videos uploaded between 01/01/2020 and 04/ 30/2020.The descriptor "coronavirus" was used and the first 100 most viewed videos that were recorded in Brazilian Portuguese, related to the topic, unduplicated, and being less than 30 minutes long were selected.Two judges recorded the Uniform Resource Locator (URL) of the videos downloaded from YouTube for data analysis.The 100 videos analyzed showed the following results: 140,027,282 views; 7,201,187 "likes"; 165,624 "dislikes"; 352,541 comments; and a total duration of 12 hours, 28 minutes, and 12 seconds.In terms of content, death was mentioned in 71 videos, and the top Temas em Saúde Coletiva Covid-19 -Vol. 3 Capítulo 1 2prevention behaviors listed by the U.S. Centers for Disease Control and Prevention were addressed by less than one-third of the videos.Most YouTube videos could do a greater job of disseminating information about COVID-19 prevention behaviors, promoting population adherence.Educating, mobilizing, and engaging the public in adopting and practicing behaviors for community mitigation are differential aspects of successfully addressing the COVID-19 pandemic.Given the need for social distancing, YouTube could play an important role in interaction and communication about COVID-19 and be a significant complementary information strategy to combat the pandemic.
Evaluation of the Results of Support for Schools by Manufacturing Enterprises – a Competence Management Aiding Model

Employers signal difficulties in sourcing technically-educated staff.They often engage, though to a limited degree, in cooperation with vocational schools to mitigate this difficulty.One of the reasons for the limited involvement of enterprises in cooperation with schools is the difficulty in assessing the benefits that it may bring.The aim of the study in the article was to develop and initially verify a model for evaluating the results of supporting secondary technical schools by manufacturing enterprises.The article features a multiple case study using several types of interviews, a distributed questionnaire and an analysis of secondary sources.The study was conducted in cooperation with four large manufacturing enterprises.The result of the research is a more thorough understanding of the possibilities and limitations in evaluating the results of support for schools.This support should translate in enterprises into more effective and efficient management of the competences of the future.
Digital transforma tion of the process of "Monitoring the assimila tion of the content of the standard curriculum of preschool education and training"

The article considers new methods and technologies for improving the process of centralized monitoring of the quality of education and training on the example of preschool educational institutions in Kazakhstan. General principles and approaches to the digital transformation of educational processes are proposed. The concept of "managing the process of monitoring the level of formation of competencies of preschool children" has been formalized in the context of digital transformation, taking into account the specifics of the Kazakhstan model of education. A model and algorithm for modifying the scale for assessing the level of competency development is proposed. A prototype of the monitoring process management system for mastering the content of a typical curriculum for preschool education and training, implemented using the technologies of production expert systems, the functionality of which includes the automated generation of a digital individual map of the child's development and recommendations for individual corrective work with the child, is presented.
Evolutionary-Edge Bundling with Concatenation Process of Control Points

Edge bundling is one of the information visualization techniques, which bundle the edges of a network diagram based on certain rules to increase the visibility of the network diagram and facilitate the analysis of key relationships among nodes. As one of evolutionary-based edge bundling, genetic algorithm-based edge bundling (called GABEB) is proposed which uses a genetic algorithm to optimize the placement of edges based on aesthetic criteria. However, it does not sufficiently consider the bundling between neighboring edges, and thus visual clutter issues still remain. Based on the above background, we propose an improved bundling method that considers the concatenating of control points at each edge using GABEB.
Robust Multi-Objective Optimization Model for the Integration of Blood Production and Distribution Planning

Blood is a very important element for humans. Currently, The Blood Transfusion Unit of The Indonesian Red Cross in City “X” determines the amount and type of blood to be processed based on stock availability. This method tends to be subjective so that the possibility of error in production decision is fairly high. This research intends to manage that the blood processing process and allocation can be carried out optimally and on target. The research objectives are to minimize the number of blood shortages, expired blood, and the total costs incurred, by applying a robust optimization method that considers the uncertainty of blood demand and the disturbances in the blood production process. Pass data of demands will be used for forecasting demand in the planning period. The forecast results can be adjusted to current conditions using the adjustment ratios. The robust optimization method can produce decisions that tend to be stable even when there are changes in blood demand. The results obtained in this study were 24% decrease in the number of shortages of blood stock, 88% decrease in the amount of expired blood, and 96% decrease in the overproduction cost.
An Inventory Control System to Minimize the Cost of Disposing of Expired Goods in Multi Indah Store

Multi Indah store is a small business engaged in the sale of shoe raw materials. The customer is a shoemaker. The problem faced by stores today is the process of sorting the shelf life which takes quite a long time so that it has an impact on the slow delivery of goods to customers. The development of an inventory control system using the RAD methodology is a solution offered to stores. The results of the system test found that the sorting process is faster and can help control the amount of inventory, service life, and items that are nearing expiration can be known quickly by shop owners through reports presented by the system.