Reviewerly/exHarmony huggingface dataset:
Compreensão do Supervisor de Enfermagem sobre o Exercício Docente no Campo de Estágio em um Hospital da Amazônia Legal

In the practical field, the Nursing professor must be aware of the theoretical and practical limitations of their students, observing all aspects of the learning field, including the diversities in the relationship between theory and practice, the patient and the needs of their profession. Only the nursing graduation, in the baccalaureate modality, does not guarantee the full qualification of the teaching professional regarding the pedagogical approaches to teaching, making internship supervision a great challenge. This essay is a descriptive, cross-sectional study, with a qualitative data approach, in order to understand the understanding of nursing professors who work in the NN [eliminated for the purposes of peer review] on the teaching exercise in internship supervision. The analysis of the results resulted in a predominance of the traditional teaching model with teaching practice conceived as a second option or as a salary supplement; inadequate internship field, with overcrowding of students; little commitment of the team to teaching and insufficient teaching-service relationship. It is concluded that there is a need for more in-depth studies on salary valuation policies for the category and improvement of teachers' pedagogical skills, taking into account the interdisciplinarity and the teaching-service relationship. Key-words: Education. Professor. Nursing. Clinical Internship.
Probabilistic Constrained Optimization for Predictive Video Streaming by Deep Learning

This paper optimizes predictive power allocation to minimize the average transmit power for video streaming subject to the constraint on stalling time, one of the most important factors affecting the experience of users requesting video-on-demand service. Different from the widely used first-predict-then-optimize strategy that regards the predicted channels as the real future channels, we integrate the prediction of large-scale channels into the optimization of power allocation, such that the quality of service constraint can be controlled. Due to the channel prediction errors, stalling is unavoidable and the stalling duration is random. This motivates us to consider an average stalling fraction constraint conditioned on the observed large-scale channel gains, which can be transformed into a conditional probabilistic constraint. The resultant optimization problem is difficult to solve since the probabilistic constraint lacks a closed-form expression. We resort to end-to-end deep learning to optimize the future powers from the past channels. In particular, we propose a method to learn the conditional probabilities in multiple steps with a single neural network. Simulation results show the advantages of the proposed method in reducing average power consumption and in ensuring the probabilistic constraint.
Implicit identification of biometric gait pattern with multilayered back propagation

Smartphone authentication is a widely discussed topic of research in the recent time because of the wide range of security sensitive personal applications and the associated private data. Traditional authentication methods like PIN, passwords, graphical pattern can’t identify the actual user , since the one who enters the credentials correctly will get access to the smartphone application and its data. Physiological biometric security mechanisms like fingerprint and face recognition can provide moderate security to sensitive applications and data but are vulnerable to impersonation, spoofing and presentation attacks. Behavioral biometrics on the other hand, where authentication is done using the behavioral pattern, are proved to be an effective authentication mechanism and can be deployed as continuous and transparent which improve the usability and effectiveness of the authentication system. The proposed work in this paper is based on behavioral biometrics and the dataset is collected from 30 subjects, keeping the smartphone in 4 different positions, performing 5 different activities with each activity contributing to 63000 samples. The data collected are applied to a random forest classifier and the results are compared with different number of folds for cross validation, varying maximum depth for the tree and the number of generated sub trees.
A Novel Hybrid Reconfigurable Architecture for Prediction of Side Channel Attacks with Its Countermeasure Mechanism

The Internet of Things (IoT) has pushed everyone‘s normal life zone to their comfort zone by making them use embedded IoT devices for controlling and monitoring their daily gadgets. IoT devices find their applications in health care, agriculture, industrial automation, and even vehicles. Since IoT involves numerous devices’ data sharing, which causes network traffic and makes them vulnerable to security breaches, especially Side-Channel Attacks (SCA), it creates demand for an intelligent framework. As of now, many secured cryptoengines are integrated into embedded chips, but still, SCAs play a very vital role in IoT networks. As a result, the paper proposes a novel reconfigurable architecture as Field Programmable Gate Arrays (FPGA) that integrates deep learning models for effective SCA prediction as well as a countermeasure mechanism based on chaotic maps. Finally, the experimentation results prove the excellence of the proposed framework in terms of prediction accuracy (99%), sensitivity (98.9%), and specificity (98.9%) in comparison with LSTM, ELM SVM, RF, NB, and ANN. The results also demonstrated that the proposed framework requires only 50% of the total energy for network operation.
Understanding Authorship Attributions of Kazakh Texts via Distance Measures

Author identification of a given text is an important issue to be solved or improved in Natural Language Processing field (NLP) and has many useful applications like plagiarism detection and forensic analysis. However, there is little to no information about effectiveness of authorship identification methods in application to Kazakh language. This paper is then dedicated to implementing and reviewing accuracy of several empirical language-based authorship identification techniques. For purposes of this paper, a new dataset was assembled, which consisted of texts from 11 different authors, including, but not limited to, Abai Qunanbaiuly, Muhtar Ayezov, and Saken Seifyllin. All documents in the corpus were pre-processed, tokenized, and split into samples containing 5,000 n-grams of size 1. Reviewed methods have included Chi-Squared Measure by Kilgariff and Delta Method by Burrows, as well as its derivatives and variants. Experimentally, we show that methods which are based on Burrows' Delta measure produce some promising results when applied to texts written in Kazakh language.
Impact of Daily Irradiance Profiles on Intra-Day Solar Forecasting

The analysis of solar energy integration requires capturing the temporal variability of solar irradiance, which can highly increase the errors of solar forecasting models. In this paper, a classification of day types for solar energy applications is investigated and the impact on intra-day solar generation forecasting is assessed. The proposed classification approach uses unsupervised learning based on a combination of self-organized maps and mean-shift clustering with six location-independent metrics related to irradiance variability and energy yield. Two types of forecasts (a deterministic forecast and a probabilistic one) are used as a basis to illustrate the impact of the resulting daily irradiance profiles. The forecasts are emulated by adding white noise to historic hourly irradiance observations and their performance is compared to state-of-the-art benchmarking models. The results illustrate the magnitude of dispersion between intra-hour observations and hourly forecasts, where deterministic forecasts observed 16% to 64% of deviation and probabilistic forecasts observed prediction intervals from 11% to 36%, depending on the daily typology. The findings of this study provide useful information on the impact of intra-day irradiance variability on the performance of forecasting models and how these can be adapted based on each day type to support the integration of solar generation in electricity systems.
Quality Assessment of Virtual Reality Images

With the rapid development of multimedia technology and computer graphics, virtual reality (VR) technology, as a new type of media, has attracted widespread attention and research interest. The VR technology can render 360-degree panoramic images to viewers as spherical images via VR devices like the head-mounted display, in order to achieve an immersive feeling. However, the omnidirectional view characteristics lead to high resolution and massive data of 360-degree images, which in turn make images so sensitive to external interference that their quality deteriorates. For the QA of VR images, this chapter first introduces the databases including projection format, stitching, and double fisheye images to fill the gap of scarce VR image database. Second, to effectively evaluate the VR image quality without reference VR images, it presents the no-reference QA method based on 3D convolutional neural network. Third, to measure the full range of compression distortion in video coding technology, it illustrates the no-reference QA method based on multi-channel neural network. In the end, the future research trends of VR image QA are discussed, and the necessity of constructing accurate and efficient objective QA models of VR images is indicated.
Improvement of the Teacher Training Curriculum to Strengthen the Job Competency of Chinese Assistant Teachers: Focusing on the CPIK Pre-training Cases

This study clearly set the purpose of teacher training in which Chinese assistant teachers participate and proposed a curriculum improvement plan to improve job competency. The teacher training operation method was proposed to operate flexibly through an online and offline parallel method, and the educational course reflects the characteristics and operation order of the training institution so that the contents can be linked without overlapping courses. In addition, two measures were proposed to enhance the expertise of Korean teachers in charge of Chinese education and Chinese assistant teachers. First, it runs online cooperative classes and allows Korean teachers to participate. Through this process, the two teachers should be able to share and discuss processes such as planning, guidance, and evaluation of cooperative class management. Second, education continuously necessary for Chinese assistant teachers should be provided through online maintenance education. Conservative education should be an opportunity to share cooperative classes with Korean teachers or examples of Chinese education for Korean learners. In addition, it is necessary to take charge of the educational feedback function that analyzes and spreads the results of education through conservative education.BR As described above, this study proposed an improvement plan for the teacher training curriculum as a way to increase the educational effect by using Chinese assistant teachers. It is expected that detailed educational program development and discussion on educational content will continue through follow-up research.
PARTICIPATION OF THE WORKING GROUPS «QUANTUM TECHNOLOGIES» AND «QUANTUM COMMUNICATIONS» OF THE INTERNATIONAL ACADEMY OF COMMUNICATIONS IN THE DEVELOPMENT OF QUANTUM APPLICATIONS USING IMPORT-SUBSTITUTING TECHNOLOGIES OF COMPUTER STEGANOGRAPHY FOR THE ANALYSIS OF ECONOMIC ASPECTS OF BIOLOGICAL TRANSFORMATION OF POLLUTION IN THE ENVIRONMENT AND ECOLOGICAL MONITORING OF INDUSTRIAL FACILITIES

The developments of the working groups «Quantum Technologies» and «Quantum Communications» of the International Academy of Communications in the development of quantum applications for the analysis of economic aspects of biological transformation of pollution in the environment and ecological monitoring of industrial facilities are presented. Special attention is paid to the use of import-substituting developments based on innovative computer steganography technologies protected by a patent for an invention.
Second Lecture on the Bulgarian Literary Critic’s Digital Library Project

The scientific report presents the lecture given to 11th grade students at the National Aprilov High School – Gabrovo, which compared the thematic accents and structural features of two literary-historical portraits of Vasil Aprilov, created by two critics from two literary generations – Boyan Penev and Georgi Konstantinov. The students were shown the intentions and techniques of the literary-historical portrait, the influence of temporal distance on its structure and language, the artistic value of the authentic document.
Implementing reverse up growth tracking approach under distributed data mining

Data mining is the methodology which discovers useful and hidden information from large databases. Many researchers have proposed innumerable algorithms in the field of data mining. In this system improvised UP-growth is considered for mining high utility itemset from potential high utility itemset and improvised under different constraints. The node utility and reorganised transaction utility are the aspects considered as the key term in the proposed system which are manipulated using the technique as in UP-growth. However, mining potential high utility itemset from RTU using UP-growth needs number of tree traversals. This is reduced in the proposed system by introducing bottom-up approach and merging certain manipulations. However, working the system as a sequential process will be time consuming. Distributed environment is considered in the proposed system to overwhelm the problem in existing methodology.
Development of Gamification Design on Heart Anatomy Learning Media

Cadaver as a learning media for manual anatomy is very important for students because it is believed to give a different impression from learning with other media. However, we still need learning support media considering the limitations of the use of corpses. It is necessary to have the role of digital media that students can use wherever they are without reducing the value or content of the material they usually learn manually. The author proposes a learning model with a mobile app-based gamification approach that can attract user's attention, increase learning motivation, and increase student interaction in the learning process. We build a Heart mobile application that is equipped with materials and quizzes. This paper presents the gamification design for the Heart application using a tetrad approach. The anatomy learning media uses 3D visualization and Augmented Reality, and the design of the learning flow uses the gamification method. The Heart program offers a simple and fun learning path. The System Usability Scale (SUS) results show a score of 72.25 and are included in the Good Usability category. Finally, gamification strategies are expected to improve users' efficacy and learning outcomes.
Analysis and positioning of geographic tourism resources based on image processing method with Ra-CGAN modeling

&lt;abstract&gt; &lt;p&gt;People's diversified tourism needs provide a broad development space and atmosphere for various tourism forms. The geographic resource information of the tourism unit can vividly highlight the unit's geographic spatial location and reflect the individual's spatial and attribute characteristics. It is not only the main goal of researching the information base of tourism resources, but it is also the difficulty that needs to be solved at present. This paper describes the use of image processing technology to realize the analysis and positioning of geographic tourism resources. Specifically, we propose a conditional generative adversarial network (CGAN) model, Ra-CGAN, with a multi-level channel attention mechanism. First, we built a generative model G with a multi-level channel attention mechanism. By fusing deep semantic and shallow detail information containing the attention mechanism, the network can extract rich contextual information. Second, we constructed a discriminative network D. We improved the segmentation results by correcting the difference between the ground-truth label map and the segmentation map generated by the generative model. Finally, through adversarial training between G and D with conditional constraints, we enabled high-order data distribution features learning to improve the boundary accuracy and smoothness of the segmentation results. In this study, the proposed method was validated on the large-scale remote sensing image object detection datasets DIOR and DOTA. Compared with the existing work, the method proposed in this paper achieves very good performance.&lt;/p&gt; &lt;/abstract&gt;
Strategi Meningkatkan Hasil Belajar Ekonomi dengan Aplikasi Google Classroom pada Siswa Kelas XI IPS 1 SMAN 3 Kerinci

This study was an Action Research conducted because the student’s achievement on Mathematics was still lower than The minimum Achievement Criteria (KKM). Therefore, the researcher conducted a research to improve the student’s learning achievement by using Google classroom platform. This study aimed at figuring out whether the use of Google classroom could improve the student’s learning achievement on economics at grade XI IPS 1 SMAN 3 Kerinci in this pandemic Covid-19 era. This study had two cycles. Each cycle consists of planing, action, obsevation, and reflection. The analysis result showed that there was significant improvement on the student’s achievement. The student’s average score in pre cycle was 47,5 (28%) Then, in cycle 1 the score was 69 (64%), in cycle 2 it improved significantly to 80 ( 84%). Based on the data it concluded that the implementation of Google classroom platform improved the student’s achievement on economics subject significantly. It was suggested to economics teachers to apply the Google classroom platform as the alternative strategies in teaching.
Studi Literatur Penggunaan Metode BIM Pada Software Autodesk Revit Structure

Building Information Modeling (BIM) can be a useful platform for engineers in designing or modeling a building. Autodesk Revit is a BIM based architectural and structural design program. The purpose of this research is to deepen the Autodesk Revit Structure application program in terms of features, facilities provided by revit, the basis for working on the revit program, as well as detailed design steps for columns, beams and foundations. The method that will be used in this research is to collect data from several appropriate sources with the title of the study, the reference sources include books, the internet and reviews related to case studies conducted on this application. The results of this study explain the facilities needed to run the revit structure, such as buying a license and downloading the trial for students, the steps for modeling a project using autodesk revit, such as understanding the initial display of the autodex revit software, project browser,box properties, ribown menu, introducing the display initial (user interface), as well as modeling structure. The conclusion of this research is that soft drowing on autodex revit can be made in 3D, making it easier for engineers to design detailed parts and check for errors that occur. So that in certain cases if there is an error in the modeling, the engineers can correct the error without changing all parts, because with this revit application, if one part is changed, it appears that it will automatically change.
The Logical Function Analysis on the Thread about Vaccines in Social Media Twitter

In logical function analysis, the analysis deals with clause complexes form which is the meaning relation of each clause in the clause complex. This thesis is about the logical function analysis used in the thread about vaccines in social media twitter. The objective of this research is to find out the types of logical function analysis in the threads about vaccines which is in clause complexes form. This research is conducted by qualitative descriptive approach which means this research makes the description of the situation. The data of this research is from the thread 1 about vaccine by Prof. Christine Pagel on her twitter account. The thread consists of 10 tweets that are 35 clause complexes. And the other data is from the thread 2 by Nida Qadir, MD on her twitter account. The thread consists of 5 tweets that are 21 clause complexes. The finding of this research is indicated that there are 2 main types of logical function; expansion and projection. In analyzing thread 1, there were 19 elaboration, 3 extension, 12 enhancement, and 1 types of idea. There is no locution type in thread 1. In analyzing the thread 2, there are 9 elaboration, 3 extension, 7, enhancement, 1 locution, and 1 idea.
Disclosure Risk From Homogeneity Attack in Differentially Privately Sanitized Frequency Distribution

Differential privacy (DP) provides a robust model to achieve privacy guarantees for released information. We examine the protection potency of sanitized multi-dimensional frequency distributions (FDs) via DP mechanisms against homogeneity attack (HA). Adversaries can obtain the exact values on sensitive attributes of their targets through HA without having to identify them from released data. We propose measures for disclosure risk (DR) from HA and derive closed-form relations between the privacy loss parameters and DR from HA. The availability of the closed-form relations will assist practitioners in understanding the abstract concepts of DP and privacy loss parameters by putting them in the context of a concrete privacy attack and offer a perspective for choosing privacy loss parameters when employing DP mechanisms. We apply the derived mathematical relations in real data to demonstrate the assessment of DR from HA on differentially privately sanitized FDs at various privacy loss parameters. The results suggest that relations between DR from HA and privacy loss are S-shaped; the former may not disappear even when privacy loss approaches 0.
Harnessing Unsupervised Word Translation to Address Resource Inequality for Peace and Health

Research geared toward human well-being in developing nations often concentrates on web content written in a world language (e.g., English) and ignores a significant chunk of content written in a poorly resourced yet highly prevalent first language of the region in concern (e.g., Hindi). Such omissions are common due to the sheer mismatch between linguistic resources offered in a world language and its low-resource counterpart. However, during a global pandemic or an imminent war, demand for linguistic resources might get recalibrated. In this work, we focus on the high-resource and low-resource language pair $$\langle en , hi _e \rangle $$ (English, and Romanized Hindi) and present a cross-lingual sampling method that takes example documents in English, and retrieves similar content written in Romanized Hindi, the most popular form of Hindi observed in social media. At the core of our technique is a novel finding that a surprisingly simple constrained nearest-neighbor sampling in polyglot Skip-gram word embedding space can retrieve substantial bilingual lexicons, even from harsh social media data sets. Our cross-lingual sampling method obtains substantial performance improvement in the important domains of detecting peace-seeking, hostility-diffusing hope speech in the context of the 2019 India-Pakistan conflict, and in detecting comments encouraging compliance with COVID-19 guidelines.