Reviewerly/exHarmony huggingface dataset:
A TECHNOLOGY FOR DEVELOPING PROFESSIONAL COMPETENCÅ OF VOCATIONAL TRAINING TEACHERS

The relevance of constant professional development for teaching staff in professional (vocational) education schools (P(V)E schools) is conditional upon the innovative changes in education, the modernization of educational and industrial technologies and the elaboration of effective models and mechanisms of training highly qualified personnel. The professional development of masters of vocational training is correlated with the development of their professional competency, integrating the so-called systematic build-up of new knowledge and experience.&#x0D; The article aims to justify a particular technology for developing professional competency in masters of vocational training from P(V)E schools.&#x0D; Methods include a theoretical analysis of scientific works, a study of practical experience, systematization, generalization and a pedagogical experiment.&#x0D; Results. The results of the survey of teaching staff (including masters of vocational training) regarding the final level of professional motives confirm the following: a high level – 29%, a sufficient level – 56%, an average level – 15%. The results of these specialists’ self-assessment of their readiness for professional development are as follows: a high level – 48%, a sufficient level – 47%, an average level – 5%. The paper proves that there appears to be a need to discover some optimal technologies for developing professional competency in masters of vocational training, given the conditions for elaborating modern models of teacher education. An algorithm for designing the technology for developing professional competency in masters of vocational training from P(V)E schools includes a scientific aspect (defining and adhering to targets, objectives, methodological and other principles of professional education), a procedural aspect (modelling the content, forms and ...&#x0D; Conclusions. The paper presents the technology for developing professional competency in masters of vocational training from P(V)E schools as a psycho-pedagogical process organized according to an appropriate algorithm, whose implementation should result in the professional development of masters of vocational training.
Healthcare Data Storage Based on HBase

The strongly expanding field of big data has started to play a crucial role in the evolution of healthcare research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of structured, and unstructured data produced by current healthcare systems. An efficient storage method for reading and writing big healthcare data is still needed. In this paper, we propose a healthcare data storage method based on the HBase and MapReduce. We design a structure of HBase tables to efficiently store and manage healthcare data.
Android Based Math & Trash Educational Game Using Scirra Construct 2 and Adobe Phonegap

Educational games can be used as an effective learning method, especially for children, because children can gain knowledge in a fun way. Here the game is designed for young children to practice arithmetic and knowledge to separate organic and inorganic wastes. This game is based on Android using Construct 2 software, design using UML and Game Development Life Cycle (GDLC). At the end of the manufacturing process, software tests or tests consist of unit tests, integration tests and system tests. From the test results, the game "Math &amp; Trash" can be run on several versions of Android with a variety of different screen sizes. The educational game "Math &amp; Trash" is expected to have a positive impact on children on the importance of math subjects and the importance of protecting the surrounding environment.
Cardiac Motion Tracking in Short-axis MRI using Siamese Convolution Network

Analysis of mechanical properties of left ventricle (LV) myocardium for short-axis MRI is essential to diagnosis and treatment of cardiovascular diseases. However, the LV myocardium appears highly homogeneous, and there are few features inside the myocardium in cine MRI, which makes it difficult to track motion of the myocardium. In this paper, we propose a novel approach for motion tracking of the myocardium in MR image sequences. We constructed a Siamese convolution network with dense connections for feature extraction of points located on the myocardium contour. Based on a $L_{2}$ cost function, the matched patch pairs (positive pairs) and unmatched patch pairs (negative pairs) are used to train our network. Next, given a patch centered at the point located on the myocardium contour, the trained network is employed to track corresponding patches in the image at other time points. The robust point matching is performed between the given contour and the tracking contour of the myocardium to ensure the tracking contours smooth. Our proposed approach is evaluated on the public dataset, and evaluation results show that our approach achieves more accurate tracking accuracy compared with the state-of-the-art methods.
TT&C Ship Tracking and Control Simulation System Design and Implementation Based on Domestic OS

In order to respond to the domestic requirements for safety and availability of space TTC and then focuses on the implementation of service bus technology; finally verifies the system application. Application proves that the design of the system is reasonable, flexible and versatile, and it can meet the requirements of overall verification of the TT&C ship tracking and control system.
THEORETICAL BASIS OF PROBLEM OF SHAPING CREATIVE COMPETENCE IN FUTURE SOCIAL WORKERS FOR WORK WITH GIFTED CHILDREN

For effective forming of creative competence in future social work experts, we suggested using the creative approach to organization of educational process in a higher educational establishment, using the potential of different disciplines in the process of professional training. We clarified the main aspects of shaping of creative competence in future social workers in classroom and outside classroom, as well as main forms of the methodology of such training. The use of the mentioned approaches will give an opportunity to shape creative competence in future social workers for work with gifted children.
CRT-Based Robust Data Hiding Method by Extracting Features in DCT Domain

In this paper a robust data hiding method is presented by applying Discrete Cosine Transform (DCT) to the host signal and performing feature extraction, based on Chinese Remainder Theorem (CRT). Improving the robustness reduces the imperceptibility in watermarking systems due to the tradeoff between imperceptibility, robustness and capacity in data hiding schemes. The proposed method offers high imperceptibility alongside robustness. To embed the hidden information CRT based feature extraction process is carried out in DCT domain. Extracted features are distances of CRT remainders. These distances are not varied much in noisy situations thus it is possible to extract the hidden bits with high accuracy. To analyze the robustness of the proposed method common signal processing manipulations are applied to the watermarked images. Bit Correct Rate (BCR) values are calculated for extracted watermarks to evaluate the robustness. Results are compared to other state of the art methods and confirm the superiority of the proposed method.
A Prediction based Cloud Resource Provisioning using SVM

Aim: To develop a prediction model grounded on Machine Learning using Support Vector Machine (SVM). Background: Prediction of workload in a Cloud Environment is one of the primary task in provisioning resources. Forecasting the requirements of future workload lies in the competency of predicting technique which could maximize the usage of resources in a cloud computing environment. Objective: To reduce the training time of SVM model. Methods: K-Means clustering is applied on the training dataset to form ‘n’ clusters firstly. Then, for every tuple in the cluster, the tuple’s class label is compared with the tuple’s cluster label. If the two labels are identical then the tuple is rightly classified and such a tuple would not contribute much during the SVM training process that formulates the separating hyperplane with lowest generalization error. Otherwise the tuple is added to the reduced training dataset. This selective addition of tuples to train SVM is carried for all clusters. The support vectors are a few among the samples in reduced training dataset that determines the optimal separating hyperplane. Results: On Google Cluster Trace dataset, the proposed model incurred a reduction in the training time, Root Mean Square Error and a marginal increase in the R2 Score than the traditional SVM. The model has also been tested on Los Alamos National Laboratory’s Mustang and Trinity cluster traces. Conclusion: The Cloudsim’s CPU utilization (VM and Cloudlet utilization) was measured and it was found to increase upon running the same set of tasks through our proposed model.
STUDENTS GRAMMATICAL ERROR IN USINGSIMPLE PRESENT TENSE

Students error analysis is very important for helping English as aForeign Learners (EFL) teachers to develop their teaching materials,assessments and methods. The study aims to describe and analyze the studentsgrammatical error in the use of simple present tense, and the cause of errors bythe eleventh grade students of Senior High School (SMAN) 4 Bandar Lampungin the school year of 2018/2019. This is a kind of descriptive qualitative study, in which the data consisted of students’ sentences containing errors in the useof simple present in the text of analytical exposition, viewed from LinguisticCategory Taxonomy (LCT). The study discovers that there are five types oferrors based on LCT. They are errors of noun phrase verb phrase, verb-andverbconstruction, word order and transformation. Moreover, the causes oferror most widely performed are subsequently performance fault,overgeneralization, markers of transitional competence, teacher-induced error,scheme of communication and assimilation, interference.
Comparison of Super-resolution Reconstruction Algorithms Based on Texture Feature Classification

Single image super-resolution (SISR) is a heated research topic in recent years. This paper innovatively proposes a network selection method based on texture feature and test index of test image set. We select the universal data set Set5, Set14, B100, and DIV2K as our test data set. We propose a method of extracting texture features of test image set by using gray-level co-occurrence matrix and then classify test image sets based on four elements (Contrast, Correlation, Energy, Inverse Differential Moment). By calculating the perceptual index (PI) and root-mean-square error (RMSE) parameters of image sets under different reconstruction models, it can be concluded that enhanced super-resolution generative adversarial network (ESRGAN) has a significant effect on improving the perceived quality of images, while enhanced deep residual networks (EDSR) has the optimal algorithm accuracy. There is an extent negative correlation between PI and RMSE. This research results are of great significance for the selection of super-resolution reconstruction networks according to different indexes.
IMPACT OF CONTINUOUS IMPROVEMENT PROGRAM (PROJECT READERS) IN THE READING LEVEL OF GRADE 7 STUDENTS UNDER FRUSTRATION LEVEL IN SAN ISIDRO NATIONAL HIGH SCHOOL, ANTIPOLO CITY, PHILIPPINES

12Jan 2020 IMPACT OF CONTINUOUS IMPROVEMENT PROGRAM (PROJECT READERS) IN THE READING LEVEL OF GRADE 7 STUDENTS UNDER FRUSTRATION LEVEL IN SAN ISIDRO NATIONAL HIGH SCHOOL, ANTIPOLO CITY, PHILIPPINES Ronaldo I. Ata and Samson V. Edillo Principal III, Bagong Nayon II National High School Antipolo City, Philippines Head Teacher I, San Isidro National High School Antipolo City, Philippines
PERANCANGAN APLIKASI PENJAMINAN MUTU SEBAGAI DATA PENDUKUNG BENCHMARKING PERGURUAN TINGGI

One of information system management that can ease and support information and decision making that needed by medium and upper level management is to provide the easy of acces towards information within or outside that relates with the organization’s goal. This study was conducted in the higher education that has organization structure includes lower, medium and upper level management. The method that used was software delevoping method, i.e. waterfall. The result of this study is this information system can be used to support data menagement of quality assurance for support benchmarking of the higher education, obtain information of the higher education activities that agree with target or visi and mission, and also compare the progress of each unit in the higher education. Therefore, from the exsist studies, it can be easy for the executive boards to make policy and decision for running the higher education.
LibGuides: COMM 4326: Health Communication: Websites

A course guide to library research resources for Health Communication students. Websites and how to evaluate them
Analisa Perbandingan Kinerja Cross Platform Mobile Framework React Native Dan Flutter

Abstrak Dalam membuat aplikasi mobile, developer aplikasi harus membuat aplikasi untuk android dan ios, akan tetapi aplikasi yang dibuat untuk kedua sistem operasi tersebut memiliki bahasa dan logika pemrograman yang berbeda, sehingga developer memerlukan waktu, usaha, dan biaya dua kali untuk mengembangkan aplikasi yang sama. Kemudian, pada tahun 2015, facebook menyelesaikan permasalahan ini dengan mengembangkan suatu framework bernama react native, framework react native ini sehingga memungkinkan developer untuk mengembangkan aplikasi dalam single code base yang kemudian dapat berjalan di ios dan android, hingga kini framework react native merupakan mobile framework yang paling sering digunakan berdasarkan data survei dari website stackoverflow. Kemudian pada tahun 2018, google mengembangkan framework bernama flutter, sama halnya dengan react native, aplikasi yang dibuat menggunakan framework ini dapat berjalan pada ios dan android, perbedaannya adalah flutter memiliki engine buatan mereka sendiri bernama skia sementara react native menggunakan bridge untuk mengcompile code yang telah dibuat menggunakan bahasa pemrograman javascript menjadi Bahasa pemrograman swift dan c# untuk ios dan java/ kotlin untuk android. Penelitian ini menguji kinerja kedua framework pada 3 aplikasi yang telah dibuat dengan tampilan, dan fungsi yang sama lalu melihat penggunaan cpu, dan memori. Hasil akhir yang diperoleh react native unggul dalam penggunaan memori yang lebih rendah, sementara flutter unggul dalam penggunaan cpu yang lebih rendah. Kata kunci : cross platform mobile framework, react native, flutter, android, mobile programming. Abstract In making mobile application, developers often have to make applications for Android and iOS, even though the applications made for the two operating systems have different programming languages and logic, so developers need time, effort and cost twice to develop the application. Then in 2015, Facebook developed a framework called react native to overcome this problem, react native enabled developers to develop applications in single code base which can run on iOS and Android, until now framework react native is the most frequent mobile framework used based on survey data from stackoverflow. Furthermore, in 2018, Google developed a framework called flutter, same as react native, applications made using this framework can run on iOS and Android, the difference is that Flutter has their own engine called Skia while react native uses bridges to compile code that has been made using javascript programming language into swift / c # language for ios and java / kotlin for android. This study examines the performance of the two frameworks in 3 applications that have been made with the same appearance, and functions then monitors CPU usage, and memory usage. The final results obtained by react native has lower memory usage than flutter, while flutter has lower cpu usage than react native. Keywords: cross platform mobile framework, react native, flutter, android, mobile programming
Theoretical Confirmation of Seasonal and Solar Radiation Impacts on Outdoor Atmospheric Aerosols (PM2.5, SO2 and CO) in FCT Abuja, Nigeria

Aerosols PM2.5, SO2 and CO were studied within the Federal Capital Territory of Nigeria (F.C.T Abuja), the area comprises of 6 (six) councils “AMAC, Abaji, Bwari, Kuje, Kwali and Gwagwalada”. The study covered a period of one year (2017-2018), irrespective of the seasonal variation of the study area, the impacts of aerosols on incident solar energy for the period was observed. The National Space Research and Development Agency (NASRDA) uses the atmospheric satellites (AS) data of within an altitude 6 Km from the ground level. The data came in NETCDF format, which was extracted by a specialized software called the Arc Map 10.4.1, converted and exported in DBF format which can be read by Microsoft excel. The study shows that the human population in F.C.T increased with (r= 2.6 ±1) months per year which negatively affect the aerosol concentrations and the seasonal impact analysis conform to adiabatic process with respect to the atmospheric variables, as the concentrations were found to be higher in dry than in wet season. Also the Solar radiation impact study reveals a change within solar insolation range of 5.5-6.5 Kwh/m2/day(CO), 2.8-4.5 Kwh/m2/day (SO2) and 4.0-6.5 Kwh/m2/day (PM2.5) and a percentage decrease of 8.42 %, 29.50 %, and 2.87 % was recorded respectively. Which implies a relative impact of solar energy on aerosol (i.e. higher intensity solar energy also reduces a small fraction of the atmospheric aerosol) and vice-versa.
Toward a New Performing Recommendation System

Recommendation systems are a part of the personalization of access to information, to give user items that correspond to his expectations, to simplify the navigation, make it fast and to guide the customer to the most relevant elements. In This article we highlight on the different types of approaches already used in recommendation systems, like collaborative filtering, content-based approach, demographic and hybrid recommendation. We discuss the conception of our recommendation system that combine many approaches such as demographic filtering, sentiment analysis in social media navigation traces to upgrade the performance of our system.
Investigation of Money Turnover in the Computer Agent-Based Model

There are three main simulation paradigms. They are as follows: discrete-event modeling, system dynamics and agent-based modeling. In this set, the agent-based paradigm is the one geared towards economic and social system modelling, and, thus, called ‘the right mathematics for the social sciences’. When it was decided to explore communications in an economic system, it has become evident that the most suitable technology for economic system model engineering is the agent-based technology, whose concepts and software toolkits have been developed greatly during several past decades. This paper discusses the process of toolkit choice for communication model engineering, reveals some problems that can be solved with this model and delivers some specific results which were obtained via simulations. Some interesting phenomena were revealed while experimenting with this model.
Archiving and Managing Research Data: data services to the domains of the humanities and social sciences and beyond: DANS in the Netherlands

Data sharing has become a default requirement made by an increasing number of research funding and research performing organizations. Data should be findable, accessible, interoperable and reusable, in an as open as possible way, is the adagio of today. The idea is that the research system will be more efficient if data sharing will be part of the dominant research culture. This should lead both to a greater transparency of research, because FAIR data can be checked and will contribute to replicability of research. And for researchers it will make it possible to stand on the shoulders of predecessors, opening possibilities for comparative research or answering new questions on the basis of existing data. This is the theory. But in how far does the above serve the needs of the users? In how far is data that is offered for sharing actually being reused? And in how far do “old” data contribute to new knowledge creation? Actually, not very much is known about the reuse of data, and even less about how this reuse leads to new scientific insights. Although recommendations for citing data abound, it even appears to be very hard to trace back reused data in the literature. The core of this paper is on the use of a national data service, taking the EASY repository of Data Archiving and Networked Services (DANS) in the Netherlands as a case study, and presenting a quantitative overview for the period 2007-2019.
PENGARUH TINGKAT PEMAHAMAN SISTEM PENGENDALIAN INTERNAL TERHADAP KUALITAS AUDIT (Studi Empiris Pada Kantor Akuntan Publik Di Makassar)

YULIANA.2018. Essay. The Influence Of The Understanding Level Of The Internal Control System On Audit Quality (Empirical Study In Public Accountants In Makassar) supervised by Dr. Muhtar Sapiri,SE.,MM.,M.Kes and Indrayani Nur, S.Pd.,SE.,M.SiThe purpose of this study was to analyze and determine the effect of the level of understanding of the internal control system on audit quality. The object of the research is the auditor who works in Makassar KAP. The primary data collection method used is by distributing questionnaires directly to respondents. The sample in this study were 30 respondents. The analytical tool used is descriptive analysis and multiple linear regression analysisThe results of testing the hypothesis is based on statistical calculations with SPSS 20  it can be seen that the internal control system is divided into five independent variables (x) namely the control environment (x1), risk assessment(x2), information system(x3), control activities(x4) and monitoring(x5) of audit quality variables. Of the five variables, only two variables, namely x1,x2 which showed a significant positive result, and x3,x5 which showed a non-significant positive result, whereas x4 showed negative insignificant results.
High-Speed Rail Operating Environment Recognition Based on Neural Network and Adversarial Training

Neural network is one of the key technologies for deep learning. Experiments on some standard test datasets show that their recognition ability has reached the level of human beings. However, they are extremely vulnerable to adversarial examples, that is, adding some subtle perturbations to the input example can cause the model to give a wrong output with high confidence. In this paper, we propose a non-contact approach based on neural network and adversarial training to recognize the high-speed rail operating environment. We first built the environment dataset and trained neural network models to do the recognition. We found that our model had high prediction accuracy, but with poor security since it was easy to attack our model using Basic Iterative Methods (BIM). To improve its security, we performed adversarial training based on the adversarial training dataset we built. The evaluation experiments indicated that this approach could improve the security of our model at the same time ensuring the prediction accuracy on the original test dataset.
Understanding the Fundamentals of Blockchains: In Economics and Business Perspective

BLOCKCHAIN HAS ATTRACTED a ton of consideration. Many are amped up for this new innovation dependent on an open, authorization less, conveyed record that cryptographically guarantees permanence without a requirement for a confided in outsider and takes into account savvy contracts. Extensive and little organizations need to jump aboard, as they expect this innovation will bring down their expenses by making exchanges faster, more secure, straightforward, and decentralized. In any case, the innovation behind the blockchain is generally not surely knew there is no agreement on what benefits it might truly bring, or on how it might fizzle.