Reviewerly/exHarmony huggingface dataset:
A SAÚDE MENTAL DOS PROFISSIONAIS DE ENFERMAGEM QUE ESTÃO NA LINHA DE FRENTE DA COVID-19 - UMA REVISÃO DE LITERATURA

Introduction: The COVID-19 pandemic has become a major problem for the world and a major challenge for the global health system. With a high number of infected people due to its high transmissibility, it has been causing significant illnesses worldwide and overwhelming health systems and health professionals. Objective: Identify the damage to the health professionals’ mental health in the face of the COVID-19 pandemic, as evidenced in the literature, showing the main psychological effects and emotional stress of these professionals. Method: A bibliographic review, using the Health Sciences Descriptors (DeCS), was carried out to analyze the health professionals’ mental health in the face of COVID-19. As a criterion for establishing the sample, the databases Latin American and Caribbean Health Sciences Literature (LILACS) and Scientific Electronic Library Online (SciELO) were consulted, and publications until August 2020 were selected. Results: The sample of publications resulted in the selection of six articles from the databases in May 2020, with five articles indexed in LILACS and one in SciELO. The content analysis revealed three categories: nurses’ mental health, working conditions, and multiple working hours. Final Considerations: It became evident the need for strategies to help the professionals who are in this struggle, reducing the workload and offering psycho-emotional support to the professionals, in addition to offering them training to help prepare for a calamity situation such as the COVID-19 pandemic.
Study on the Development and Application of Computer Integrated Management System of School Physical Education based on Network Construction

Abstract With the Internet development, integrated management system has been gradually applied to the education industry, especially the sports industry, which not only improves the work efficiency, but also reduces the management cost. In sports management activities, decision-making, planning, control and organization are inseparable from information management, which is an important basis of sports management process. Through the integrated information system, we can effectively play the role of sports information resources, which can be studied for sports information management. Therefore, this paper constructs the comprehensive management of school physical education system (hereinafter referred to as TBMSOSPE), which can better improve the physical education (hereinafter referred to as PE) and management. With the new requirements of network construction in Higher Education in China, colleges can develop TBMSOSPE based on network construction.
Fidelity and concurrence of entangled qudits between a trapped ion and two laser beams

A quantum scheme is presented by which a three-level trapped ion interacts with a two laser beams in the absence and presence of the e®ect of classical field. We analyze the impact of the classical ¯eld and the Lamb-Dicke parameter (LDP) on the dynamical behavior of entanglement quanti¯er, population probabilities and the geometric phase. Based on four different variations of these two effects, LDP = 0.1, LDP=0.01 and ¯ = 0.0, ¯ = 0.49, the time dependence of geometric phase and populations probabilities are shown. The ¯nding emphasizes that both the time-dependent and LDP play an important role in the development of the entanglement, the geometric phase, ¯delity, and populations probabilities. This in-sight may be very useful in various applications in quantum optics and information processing.
Estimation of Software Security Risks through CVSS: A Design Phase Perspective

In today's world, the software makes work more straightforward and more manageable for users, employees, and organizations. It makes our working environment more comfortable, but on the other hand, it has security issues. Over the last two decades, software security has become a prime focus for security organizations as well as security practitioners. As the growth of software increases, the level of security risks compromised with software also increases. In most cases, 'compromising in design’ is one of the critical security risks.  Software security risks are the weakness in the software that accidentally allow hazardous operations. To give prime concern to the security mechanism, the valuable assets must be protected. Hence, prompt detection and remediation of software security risks is a crucial issue in software security. In this paper, the researchers have laid stressed on Software security risk at the design phase and listed some common software security risks from CWE, i.e., Common Weakness Enumeration. In addition, researchers have also mentioned the CVSS 3.1 vulnerability scoring mechanism and calculated the scores of listed security risks to prioritize the exploitability impact and its base score.
Problems Faced by Elementary School Students in the Online Learning Process during the COVID-19 Pandemic

The COVID-19 pandemic requires the education system which was originally face-to-face in schools to be switched to distance learning or also known as online learning (in the network). The purpose of this research is to find out what are the problems faced by students during the online learning process. In this study, we used a descriptive method, with primary data. The results obtained indicate that there are problems faced in the distance learning process, limited learning tools, limited internet access and internet quota. It is hoped that after this research, the problems can be immediately addressed and become an evaluation for various parties to continue to improve the quality of distance learning to be more effective. It is hoped that after this research, the problems can be immediately addressed and become an evaluation for various parties to continue to improve the quality of distance learning to be more effective. It is hoped that after this research, the problems can be immediately addressed and become an evaluation for various parties to continue to improve the quality of distance learning to be more effective.
Applying Psychometric Modeling to aid Feature Engineering in Predictive Log-Data Analytics: The NAEP EDM Competition

The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an n-gram feature approach for event sequences. Furthermore, instead of using the provided binary target label, we distinguished inefficient test takers who were going too fast and those who were going too slow for training a multi-label classifier. Our best-performing ensemble classifier comprised three sets of low-dimensional classifiers, dominated by test-taker speed. While our classifier reached moderate performance, relative to the competition leaderboard, our approach makes two important contributions. First, we show how classifiers that contain features engineered through literature-derived domain knowledge can provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the models. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label's validity needs to be questioned. Beyond competition-related findings, we furthermore report a state sequence analysis for demonstrating the viability of the employed tools. The latter yielded four different test-taking types that described distinctive differences between test takers, providing relevant implications for assessment practice.
Training Tesseract OCR for Typewritten Hindi Documents

Optical Character Recognition (OCR) is a prominent area of research in relevance with Indian script. One such popular open-source software is Tesseract. Tesseract is proficient in recognizing over hundred languages comprising Hindi. Tesseract has proven good in handling Hindi documents that are machine printed, but when it comes to typewriter typed Hindi documents, the effectiveness gets reduced and the obtained results are not too good. There is no OCR available that is capable of recognizing text from typewriter typed Hindi documents. Several academic documents are available in the form of a thesis, research papers, books, etc. The functionality of the existing OCR needs to be extended for recognizing typewriter typed documents. The research paper elaborates on the working of Tesseract OCR and highlights the effectiveness achieved in obtaining more precise results.
Didáctica del teorema de Pitágoras mediada por las TIC: el caso de una clase de Matemáticas (Teaching and Learning the Pythagorean Theorem through ICTs: A Case Study of a Mathematics Class)

Spanish Abstract: De acuerdo con los resultados historicos del programa PISA de la Organizacion de Cooperacion y Desarrollo Economico, los jovenes latinoamericanos tienen deficiencias en la resolucion de problemas matematicos que exigen habilidades y competencias de alto nivel. En este contexto, aunque el promedio de desempeno de Colombia ha mejorado considerablemente desde 2006, todavia se evidencian dificultades en dicha area. En tal sentido, este trabajo, por medio de la investigacion-accion participativa (IAP), hace una propuesta de implementacion de herramientas TIC para facilitar la ensenanza y aprendizaje del teorema de Pitagoras en la clase de Matematicas del grado octavo (8) del colegio San Vicente de Paul de Barranquilla. Se emplea un enfoque cualitativo para analizar la informacion recabada por medio de observaciones, entrevistas y encuestas. Los hallazgos indican que la mediacion de herramientas TIC, especificamente de software matematico, puede mejorar la competencia de profesor e influye en la mejora del aprendizaje de los alumnos porque potencia el interes y la comprension de los temas.English Abstract: According to the historical results of the Organization for Economic Cooperation and Development's PISA program, Latin American youth have deficiencies in solving mathematical problems involving high-level skills and competencies. In this context, although Colombia's average performance has improved considerably since 2006, there are still difficulties in this area. In this sense, using a participatory action research (PAR) method, this work proposes the implementation of ICT tools to facilitate the teaching and learning of Pythagoras' theorem in the eighth grade Mathematics class of the San Vicente de Paul school in Barranquilla. A qualitative approach is used to analyze information collected through observations, interviews and surveys. The findings show that the mediation of ICT tools, specifically mathematical software, provides the teacher with teaching keys and influences the improvement of student learning because it enhances interest and understanding of the topics.
Will PayPal make bitcoin more mainstream?

The company now allows cryptocurrency sales in the UK, but there are still hurdles to its everyday use, reports Matthew Sparkes
Analisis kesiapan penerapan paradigma organisasi pembelajar di dalam Program Studi Manajemen Informasi Kesehatan IIK Bali

The learning organization paradigm is a paradigm in the organization of an organization where everyone in the organizational component is expected to be able to improve/expand their abilities continuously. This study aims to determine the initial description of the Health Information Management Study Program at the IIK Bali in applying the learning organization paradigm and determine the focus of managerial development of the Health Information Management Study Program at the IIK Bali based on the paradigm of the learning organization. The type and design of this research is a descriptive exploratory study with a survey observational method with the aim of only looking for an initial description of the application of learning organization components in the IIK-Bali health information management study program personnel, without looking for cause and effect relationships or others. The subjects of this study were all educators and educators in the health information management study program in the IIK Bali. The research instrument used was a data collection instrument in the form of a questionnaire which is primary data that is data obtained directly at the first source. The results of this study indicate that dimension 4 (Embedded Systems) gets the lowest score from respondents i.e. 3,1 out of 6 (maximum score), so this dimension shows potential for improvement and enhancement. While the dimension that gets the highest score from respondents is dimension 3 (Team Learning and Collaboration) with a value of 4,4.Â The conclusion is that the Information Management Study Program still needs improvement before starting to run the learning organization paradigm. The dimensions of embedded systems 4 are the focus of improving the management of the study program.
Analysis on Devanagari Text Generation by Using Machine Learning Techniques

Many Authors are developed different methods for recognition of the Devanagari script. The existing method is processing to recognize the Devanagari script which is discussed with notable performances. Generally, the recognition process mainly consists of three steps pre-processing, extraction of feature and finally classification. For character recognition different algorithm were being developed with different advances which include neural network algorithm (NNA), pattern matching algorithm (PMA), structural algorithm (SA), support vector machine algorithm (SVMA), statistical algorithm, hidden Markov model (HMM) and template matching algorithm(TMA). In template matching algorithm only the typewritten characters can be recognized. But the other algorithm like neural network algorithm (NNA), structural algorithm (SA), and support vector machine (SVM) can recognize both  handwritten and typewritten Every algorithm contains both advantages and disadvantages. The main motivation of this thesis work is to overcome the above mention the drawbacks.
Distributed Denial Of Service (DDOS) Attack Detection On Zigbee Protocol Using Naive Bayes Algoritm

Distributed Denial of Service or better known as DDoS is an attempted attack from several computer systems that target a server so that the amount of traffic becomes too high so that the server cannot handle the request. DDoS is usually done by using several computer systems that are used as sources of attacks. So they attack one server through several computers so that the amount of traffic can also be higher. A DDoS attack is like a traffic jam that prevents a driver from reaching their desired destination on time. According to data, 33% of businesses in the world have fallen victim to DDoS attacks. DDoS is hard to trace. Some types of DDoS attacks can be very powerful and even reach speeds of 1.35 Tbps. Additionally, DDoS attacks can cause losses of $ 40,000 per hour if they occur. ZigBee is a standard from IEEE 802.15.4 for data communication on personal consumer devices as well as for business scale. ZigBee is designed with low power consumption and works for low level personal networks. ZigBee devices are commonly used to control another device or as a wireless sensor. ZigBee has a feature which is able to manage its own network, or manage data exchange on the network [1]. Another advantage of ZigBee is that it requires low power, so it can be used as a wireless control device which only needs to be installed once, because only one battery can make ZigBee last up to a year. In addition, ZigBee also has a "mesh" network topology so that it can form a wider network and more reliable data. In the previous research of Muhammad Aziz, Rusydi Umar, Faizin Ridho (2019) based on the results of the analysis carried out that the attack information that has been detected by the IDS based on signatures needs to be reviewed for accuracy using classification with statistical calculations. Based on the analysis and testing carried out with the artificial neural network method, it was found that the accuracy was 95.2381%. The neural network method can be applied in the field of network forensics in determining accurate results and helping to strengthen evidence at trial. The Naïve Bayes model performed relatively poor overall and produced the lowest accuracy score of this study (45%) when trained with the CICDDoS2019 dataset [47]. For the same model, precision was 66% and recall was 54%, meaning that almost half the time, the model misses to identify threats.
THE SYSTEM OF PROTECTION OF STUDENTS FROM INTERNAL THREATS

The article focuses on the pedagogical system of protecting students from internal threats. Objective information about the social health of students, as well as the need for a qualitatively new approach to solving problems in the field of spiritual support for its comprehensive development and the "cleansing" of the spiritual environment of young people. Today, students are shown the skills, knowledge and competencies to prepare for professional activities, to develop immunity to internal threats. The components of the structure of protection of students from internal threats: purposefulness, initiative, patriotism, conviction, willpower, responsibility and conscientiousness are claimed. The lexical meanings of these components are also covered.
Information Retrieval Bug Localization with Wavelets Analysis

Nowadays, maintaining an oversized and evolving computer code involves longer and value for the project team. In software maintenance, a bug report is employed to seek out a fault location. Once a bug report is received, it is suiTable.to automatically denote out the files that developers should change to repair the bug. This work develops a brand-new information retrieval (IR) system that allows representing textual data by signals. This new way of implementing gives us the chance to use various mathematical tools from the signal theory like Wavelets Transforms, unused nowadays within the field of IR. This paper proposes Wavelets Transforms for Bug Localization (WTBugLoc), a mathematical approach of IR-based bug localization using wavelet techniques. The results of the conducted experiments on the SWT (Standard Widget Toolkit) Eclipse project confirm the effectiveness of the proposed approach. The experiments also show that WTBugLoc outperforms method using the Vector Space Model (VSM).
Towards Narrowing the Search in Bounded-Suboptimal Safe Interval Path Planning

Path planning in the presence of dynamic obstacles is challenging as the time dimension has to be considered. A prominent approach to tackle this problem known to be complete and optimal is the A*-based Safe-interval Path Planning (SIPP). Bounded-suboptimal variants of SIPP employing the ideas of Weighted A* (WSIPP) and Focal Search (FocalSIPP) have been introduced recently, trading-off optimality for decreased planning time. In this paper, we revisit FocalSIPP and design several secondary heuristics for Focal Search with the intention to narrow the search in the direction of a preplanned optimal single-agent path not considering dynamic obstacles. The experimental results on various maps show that the designed heuristics generally outperform the hops-to-the-goal heuristic used in the original FocalSIPP and successfully compete with WSIPP as well.
Robonomics: The rise of the automated economy

The technological advances in the last decades are transforming the global economy and society paving the way for the automated economic system, aka robonomics. This editorial provides a brief overview of automation technologies, their application in various sectors of the economy and society, and elaborates on robonomics as an economic system and a scientific field. Robonomics is introduced as an economic system that relies almost entirely upon automation as a production factor rather than human labour. As a new scientific field, robonomics goes beyond the economic aspects of the automated economy and focuses on the social, cultural, demographic, political, environmental, legal, geographic, psychological and other issues raised by automation technologies as well. Finally, the editorial introduces ROBONOMICS: The Journal of the Automated Economy, and the publications in the inaugural issue.
Short-Term Power Forecasting for Renewable Energy Sources Using Genetics-Based Harmony Search Algorithm

Greenhouse gases are the cause of global warming. These problems can be overcome if renewable energy becomes easily adaptable and is utilized on a larger scale. Solar PV power generation is growing day by day due to technological advancements. The variability in solar PV power generation can be forecasted using an artificial intelligence-based technique, which can help in proper load scheduling and make the system less dependent on grid power. In this paper, solar PV power forecasting is done using a hybrid of harmony search algorithm and genetic algorithm. The major focus is on short-term hourly forecasting and reducing the time of execution. The ANN is developed using Bayesian regulation and the weights and bias are tuned using the hybrid algorithm to reduce the error between actual and forecasted value. The ANN tuned using the hybrid algorithm is executed and results are verified using MATLAB.
A General Model for Medical Stimuli Synthesis

Medical image perception research is clearly important, but it is difficult for researchers to use authentic medical images as stimuli in a controlled manner. On the one hand, public medical image datasets are relatively uncommon, often incomplete, and the data processing and labeling required for real images can be prohibitively time-consuming. On the other hand, it is hard to find medical images which have the desired experimental attributes (e.g., lesion types, locations, etc.). Therefore, the stimuli that are used for medical perception experiments are often highly artificial. While these stimuli are easily generated and manipulated, they are routinely critiqued for being obviously unrealistic. Thus, generating authentic looking (i.e., metameric) medical stimuli is important for medical image perception research. Here, we used the Generative Adversarial Network (GAN) to create perceptually authentic medical images. For different image modalities (e.g., MRI, CT, etc), the generator of the GAN was trained to approximate the realistic image manifold, given modality-specific training data. We used a variety of publicly available medical image datasets for training, including DDSM, DeepLesion, and fastMRI. Novel (fake) radiographs were synthesized by sampling from the learned image manifold. Our method was capable of manipulating the stimuli to match desired experimental attributes, such as texture and shape. We generated desired radiographs that included torso, limbs, and chest. Untrained observers and expert radiologists then completed a psychophysical experiment which required them to distinguish the real from fake (generated) radiographs. The resulting ROC analysis revealed consistent but near chance performance, indicating that observers attended to the task but could not reliably distinguish the real radiographs from our generated ones. The method, therefore, provides a means of creating realistic stimuli for medical image perception experiments.