Reviewerly/exHarmony huggingface dataset:
Analysis of the competencies and learning outcomes concepts of virtual environments

The present article focuses on the presentation of a proposal for the definition of the concepts used in our learning model in virtual environments referred as competencies and learning outcomes, based on an analysis of the existing bibliography in English, Spanish and Russian and the experience accumulated during 7 years of work in this modality.
Research on Zombie Enterprise

With the decreasing marginal effect of macro-policy, the structural problems accumulated in the process of rapid development of China's economy since the reform and opening-up 40-odd years ago have begun to emerge, such as high debt, overcapacity of enterprises, declining return on investment, operational difficulties and even continuous losses.These "blood-sucking zombie enterprises" are gradually exposed to the public's eyes.These zombie enterprises have adverse effects on the future development of China, such as wasting social resources, disturbing market order, disengagement from asset supervision, harming workers' rights and interests, and forming financial risks.Based on logic analysis, this article analyzes the formation of zombie enterprises, the necessity of dealing with zombie enterprises and the solutions.The result shows that Zombie companies lead to misallocation of resources, occupying social capital, making it difficult for emerging companies to obtain funds, and hindering innovation and development.Bankruptcy disposal of zombie companies must pass strict legislation, law enforcement and legal supervision to effectively solve financial risks and protect the rights and interests of employees.
Design and Manufacturing Motorized Trike Drift Using 110 cc Suzuki Jet Cooled Gasoline Engine

Motorized trike drift is a unique means of transportation that is adapted from a bicycle with 3 wheels, 2 wheels at the rear and 1 wheel at the front for steering control. The purpose of the study was to design and manufacture of motorized trike drift using gasoline engine Suzuki Jet Cooled 110 cc as a power source. From the process of designing and manufacturing, has produced a motorized trike drift using gasoline engine Suzuki Jet Cooled 110 cc as a power source. The transmission system used is a chain and sprocket. From the test results, the speed of motorized trike drift can reach 40 km /hour, but for convenience, the speed limit is 20-25 km / hour. For fuel use, 1 litre of fuel can be used within 30 minutes or within a distance of 15-20 km.
Recognition System Of The Al Qur’an Surah Al-Falaq Verse 1-5 Through Voice Using Ada-Boost

In everyday life, it is not uncommon when we hear the sound of chanting the holy verses of the Al Al Qur’an which are read in mosques before prayer time or in other conditions we seem interested in knowing what Surah and which verse is being recited. This is due to the love of Muslims themselves for the Al Qur’an but not all Muslims memorize the entire contents of the Al Qur’an . Based on the limitations and the magnitude of curiosity about Surah and Verse information, the writer is interested in developing a computer system that can recognize and provide information on the recited Surah and Verse. Advances in computer technology not only make it easier for humans to carry out activities. One of the human intelligences that are planted into computer technology is to recognize the verses of the Al Al Qur’an Surah Al-Falaq through voice. Ada-Boost method is one method to identify or recognize voice classification, and by using this method the success rate in recognizing verse numbers reaches 72%. This system can only recognize the number of verses of the Al Al Qur’an Surah Al-Falaq, recorded sound files with the .wav file extension and built using the Delphi programming language.
COHABITATION OF FIBONACCI AND GALOIS MODES IN ONE LINEAR FEEDBACK SHIFT REGISTER

A linear feedback shift register (LFSR) is the basic element of the pseudo-random generators used to generate a sequence of pseudo-random values for a stream cipher. It consists of several cells; each cell is a flip-flop and a feedback function. The feedback function is a linear polynomial function; this function has a degree equal to the number of cells in the register. The basic elements of the register are connected to each other in two different ways, either in Fibonacci mode or in Galois mode. In this work, we propose the realization of a specific register which is cohabitation of these two modes (Fibonacci and Galois) in the same register and for the same feedback function, and which will be controlled by a random function for the selection of mode, which will be based on the chaotic logistics map. This specific register gave better results compared to registers with separate modes.
Co-authorship Prediction Based on Temporal Graph Attention

The social network analysis has received significant interests and concerns of researchers recently, and co-authorship prediction is an important link prediction problem. Traditional models inefficiently use multi-relational information to enhance topological features. In this paper, we focus on the co-authorship prediction in the co-authorship knowledge graph (KGs) to show that multi-relation graphs can enhance feature expression ability and improve prediction performance. Currently, the main models for link prediction in KGs are based on KG embedding learning, such as several models using convolutional neural networks and graph neural networks. These models capture rich and expressive embeddings of entities and relations, and obtain good results. However, the co-authorship KGs have much temporal information in reality, which cannot be integrated by these models since they are aimed at static KGs. Therefore, we propose a temporal graph attention network to model the temporal interactions between the neighbors and encapsulate the spatiotemporal context information of the entities. In addition, we also capture the semantic information and multi-hop neighborhood information of the entities to enrich the expression ability of the embeddings. Finally, our experimental evaluations on all dataset verify the effectiveness of our approach based on temporal graph attention mechanism, which outperforms the state-of-the-art models.
Sentiment Analysis of Portuguese Economic News

This paper proposes a rule-based method for automatic polarity detection over economic news texts, which proved suitable for detecting the sentiment in Portuguese economic news. The data used in our experiments consists of 400 manually annotated sentences extracted from economic news, used for evaluation, and about 90 thousand Portuguese economic news, extracted from two well-known Portuguese newspapers, covering the period from 2010 to 2020, that have been used for training our systems. In order to perform sentiment analysis of economic news, we have also tested the adaptation of existing pre-trained modules, and also performed experiments with a set of Machine Learning approaches, and self-training. Experimental results show that our rule-based approach, that uses manually written rules related to the economic context, achieves the best results for automatically detecting the polarity of economic news, largely surpassing the other approaches.
Improving Class III Learning Outcomes in Indonesian Language Learning Elaborating Directional Texts at SDN Krasak 01

&lt;p&gt;&lt;em&gt;The author's experience in learning Bahasa Indonesia theme 6 sub-theme 2 in Indonesian language learning on basic competencies outlines the text of instructions on animal and plant care, in grade 3 of Sdn Krasak 01, Brebes district, lesson year 2019/2020. Showing that students' mastery of Bahasa Indonesia subject matter theme 6 sub-theme 2 is still low.of 16 students, 6 (43.75%)experienced completion of learning and the rest have not completed the study of 10 students (56.25%). Based on the description above, the authors carry out the research. By making efforts to improve learning by focusing on discussion methods with contextual approaches to improve students' achievements and activities in indonesian language learning. The result of improvement in cycle 1 of learning completion rose to 62.5%. In cycle II 79.37%. From these results it appears that students' learning outcomes each cycle have improved.&lt;/em&gt;&lt;/p&gt;
Realtime Face-Mask Detection on Raspberry Kit

Abstract: Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. Still, difficulties created by masks are usually disregarded. Face recognition is a promising area of applied computer vision . This technique is used to recognize a face or identify a person automatically from given images. In our daily life activates like, in a passport checking, smart door, access control, voter verification, criminal investigation, and many other purposes face recognition is widely used to authenticate a person correctly and automatically. Face recognition has gained much attention as a unique, reliable biometric recognition technology that makes it most popular than any other biometric technique likes password, pin, fingerprint, etc. Many of the governments across the world also interested in the face recognition system to secure public places such as parks, airports, bus stations, and railway stations, etc. Face recognition is one of the well-studied real-life problems. Excellent progress has been done against face recognition technology throughout the last years. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. The occluded face detection problem has been approached using Cascaded Convolutional Neural Network (CNN). Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Finally, a correlative study also made here for a better understanding.
Symbolic-Numeric Algorithms for Computing Orthonormal Bases of $$\text {SU(3)}$$ Group for Orbital Angular Momentum

We have developed symbolic-numeric algorithms implemented in the Wolfram Mathematica to compute the orthonormal canonical Gel’fand–Tseitlin (G-T), non-canonical Bargmann-Moshinsky (B-M) and Elliott (E) bases of irreducible representations $$\text {SU(3)}\supset \text {SO(3)} \supset \text {SO(2)}$$ group for a given orbital of angular momentum. The algorithms resolve the missing label problem by solving eigenvalue problem for the “labeling” B-M operator $$\boldsymbol{X}^{(3)}$$ . The effective numeric algorithm for construction of the G-T basis provides a unique capability to perform large scale calculations even with 8 byte real numbers. The algorithms for the construction of B-M and E bases implemented very fast modified Gramm–Schmidt orthonormalization procedure. In B-M basis, a very effective formula for calculation of the matrix $$\boldsymbol{X}^{(3)}$$ is derived by graphical method. The implemented algorithm for construction of the B-M basis makes it possible to perform large scale exact as well as arbitrary precision calculations. The algorithm for the construction of the E basis resolves the missing label problem by calculation of the matrix $$\boldsymbol{X}^{(3)}$$ in an orthogonal basis from this matrix previously built in non-orthogonal basis. The implementation of this algorithm provides large scale calculations with arbitrary precision.
Klasifikasi Citra Histopatologi Kanker Payudara menggunakan Data Resampling Random dan Residual Network

Data imbalance between classes is one of the problems on image classification. The data in each class is not equal and has a relatively large difference generated in less than optimal classification results. Ideally, the data in each class is equal or have a slight difference. This article discusses the classification of the histopathology breast cancer image. The data consist of 8 classes with unbalanced data. The method for balancing the data in each class uses random resampling which is applied to training data only. The data used from BreakHist with magnifications of 40x, 100x, 200x, and 400x . The classification uses Residual Network (ResNet) 18 and 50. The best results are obtained on images with a magnification of 400x. Classification results using ResNet18 has an average accuracy of 79.82%, an average precision of 71.39%, and an average recall of 82.37%. Meanwhile using ResNet50 showed an average accuracy of 81.67%, average precision of 78.41%, and an average recall of 82.91%.
Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM Tree

Web Image Context Extraction (WICE) consists in obtaining the textual information describing an image using the content of the surrounding webpage. A common preprocessing step before performing WICE is to render the content of the webpage. When done at a large scale (e.g., for search engine indexation), it may become very computationally costly (up to several seconds per page). To avoid this cost, we introduce a novel WICE approach that combines Graph Neural Networks (GNNs) and Natural Language Processing models. Our method relies on a graph model containing both node types and text as features. The model is fed through several blocks of GNNs to extract the textual context. Since no labeled WICE dataset with ground truth exists, we train and evaluate the GNNs on a proxy task that consists in finding the semantically closest text to the image caption. We then interpret importance weights to find the most relevant text nodes and define them as the image context. Thanks to GNNs, our model is able to encode both structural and semantic information from the webpage. We show that our approach gives promising results to help address the large-scale WICE problem using only HTML data.
ASSESSMENT OF THE ROLE OF ACCOUNTING INFORMATION ON EFFECTIVE INVESTMENT DECISION - THE CASE OF BANQUE POPULAIRE DU RWANDA ATLASMARA

The purpose of this study is to assess the role of accounting information on effective investment decisions at Banque Populaire du Rwanda Atlasmara. The target population was 50 staff members. The study used a primary method that involved questionnaires. Secondary methods of data collection involved a desk review of relevant materials. Data collection was then analyzed by using SPSS software. The study indicated a significant correlation between accounting information and investment decisions and all rely on information for an investment decision. It was seen from the analysis of responses, 83% argued always use accounting information for investment. It was revealed that the quality of accounting information in terms of its accuracy, adequacy, reliability, and mode of disclosure is a pertinent element of efficiency of investment decision making. The study recommends that commercial banks should use accounting always to increase the accuracy of their investment decision-making. The study recommends that Banque Populaire du Rwanda should consult the accounting information before making investment decisions and all interested parties to accounting information should use necessary financial ratios analysis for an investment decision. The study concludes that there is a significant correlation between accounting information and investment decision.&#x0D; &#x0D; JEL: M10; M41; R42&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0845/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Self-Diagnosis with Advanced Hospital Management

In the case of an epidemic, it is impossible to consult a doctor about minor ailments. This program helps to get recommendations from doctors. So that people can be treated for minor ailments at home. The main purpose of self-examination in a high-quality hospital management system is to provide self-examination to patients, if hospitals are not available in nearby areas. This program will be developed using one of the heuristic search methods in artificial intelligence which means greedy local search. The program provides additional space, which means that the patient can communicate with the appropriate physician by chatting online. The patient can receive appropriate suggestions for his or her problem. In fact, in self-examination this app makes a report about the patient's health status. That report may include the patient's disease name, level of symptoms and strength and the recommendation provided by the program. This proposal can be in two ways. The first suggestion would be a suitable drug based on the level of Weight Loss; the second proposal would be a meeting of specialist doctors which means that his illness may be in serious condition. It offers suggestions on some of the preventable diseases that mean minor problems with some pills. This can be helpful for patients who are far away from the hospital and for doctors and for working with patients inside or outside the hospital. The administrator can update medications in the database regularly. We can create a suggestion box for patients to improve the app
FBSC: An Analyzing Sentiments Using Fuzzy Based Bayesian Classification

The thriving Micro blog service, Twitter, attracts more people to post their feelings and opinions on various topics. Millions of users share opinions on totally different aspects of life on a daily basis. It observing the user’s sentiment options topics in the twitter network. The sentiment classification is comparable to the user’s opinions that are based on dynamic manner. An optimal Fuzzy based Bayesian classification is a capable way that has been proposed to improve the classification accuracy, unless the large amount of information on these platforms make them viable for use as data sources, in applications based on sentiment analysis. The research work developed a Fuzzy based Bayesian sentiment classification (FBSC) based dynamic online twitter search data architecture that ensures truthful positive, negative and neutral results.
An Analysis of Educational Value in the Novel “Never Let Me Go” by Kazuo Ishiguro

This research was aimed to analyzed the educational values in the novel entitled “Never Let Me Go” by Kazuo Ishiguro. The educational values that were expected to found were believing and being faithful to god almighty, being noble moral, healthy, knowledge, competence, creativity, independent, becoming democratic citizen and responsible. The researcher use descriptive qualitative approach to analyzed the novel. Specifically, the researcher used content analysis in gathering the data. The data were gathered by reading the novel thoroughly to identify and classify the educational value that can be found. The results of the study show that there were only seven educational values that can be found in the novel. Sorted from the most frequent encounters to the least are the value of responsibility, creativity, being noble morals, competence, healthy, knowledge and independent. Meanwhile, the other educational values, namely being faithful to god almighty and becoming democratic citizen, were not exist in this novel.Key Words: Novel, Educational Value
Storytelling and speech act theory in knowledge management: the potential of synthesis

The problem of converting the implicit knowledge of employees into explicit knowledge of a business entity (a firm) is considered from the perspective of a combination of knowledge management theory in so far as it relate to storytelling and speech act theory.The purpose of the work is to determine the possibility and areas of application of the concept of performativity and speech act theory in knowledge management for its humanization and socialization.The object of the research is converting implicit non-formalized knowledge into explicit formalized one and explicit knowledge into implicit one as a sphere of application of speech act theory in knowledge management.The subject of the research is socio-economic and managerial relations in this area, transformed by performativity.The scientific novelty of the research lies in the identification and application of the relationship in the form of storytelling of knowledge management theory and speech act theory to develop measures for improving the efficiency of the knowledge-based firm's activities.The practical significance of the study lies in the fact that its results can be used by knowledge managers to improve the efficiency of the mechanism for converting implicit knowledge into explicit one and vice versa.
A Preparation for Listening Module in IELTS

This research paper is an attempt to improve the listening skill of the students. Listening is a receptive language skill which students usually find the most difficult. This research paper is aimed to improve student's exam performance in taking the IELTS (International English Language Testing System) and also to improve their listening skill in their academic life and in real life situation. In this paper, the listening skills tested in IELTS, types of questions and some of the strategies and approaches to answering the questions are described not only for teachers but also for students. Therefore, this research paper is hoped to be helpful to postgraduate or undergraduate students for achieving the high score in the International English Language Testing System.
NURSING INTERVENTIONS TO PATIENTS AFFECTED BY ACUTE MYOCARDIAL INFARCTION

Objective: To identify and describe in the current literature nursing interventions for patients affected by acute myocardial infarction. Methods: This is an integrative literature review conducted with materials indexed in the Virtual Health Library (VHL), in the databases: BDENF, LILACS and MEDLINE. Using as descriptors: Assistance, Myocardial Infarction and Nursing, mediated by the Boolean operator AND. The inclusion criteria were: full text, available, free of charge, in Portuguese, published in the chronicity of time from 2015 to 2020 and which responds to the research theme. Results: After crossing the descriptors, 1701 publications were identified, of which were included in review 10 that met the established criteria. 04 on the basis of LILACS, 01 on MEDLINE and 05 on BDENF, with the following percentages: 40%, 10%, 50%, respectively. Conclusion: It is necessary to continually update the team’s chest pain protocol and approaches to patients affected with acute myocardial infarction, as they present constant changes. Thus, aiming at improvements in interventions, quality assistance, minimization of adverse events and maximization of patient safety.