Reviewerly/exHarmony huggingface dataset:
Meta-analysis of affecting variables on organizational transparency and affected variables by it in conducted studies in Iran

Organizational transparency is one of the topics that have not been studied much, but it has attracted the attention of many politicians and researchers. This concept refers to the free flow of information and the ability to easily and easily access it for all stakeholders. The purpose of this study was to investigate the affecting variables on organizational transparency and affected variables by it in conducted studies in this field in Iran. This research was a kind of applied research and a descriptive branch that has been done by using the Meta-Analysis method and using the comprehensive software of Meta-Analysis (CMA2). In this study, were selected 21 studies and were identified 7 independent and 18 dependent different variables from these studies. The results of Meta-Analysis study showed that the variables of appropriate strategy, public accountability, genuine leadership, social capital, transformational leadership, innovative leadership had the most effect on the formation and exacerbation of Organizational Transparency in the organizations. Also, the results of this study showed that Organizational Transparency affects important organizational variables such as Empowerment, Organizational Communication, Organizational Spirituality, Organizational Health, Social Capital, Trauma Management, Organizational Culture, Organizational Performance, Organizational Silence of Employees, Customer Satisfaction, Job Adaptation, Organizational Conflict, Corruption, Employee Moral Behavior, Organizational Citizenship Behavior, Job Commitment, Organizational loyalty, organizational trust.
Design of Personalized Online Education System of Forestry Science Based on Big Data Technology

With the advent of the information age, the cycle of knowledge updating of forestry science isbecoming shorter and shorter. How to make teaching and training of forestry science meet theneeds of people in different places and learning time, the traditional face-to-face education offorestry science has been inadequate in this regard. In this case, modern distance educationcame into being. Its advantage is that students can interact with teachers and complete learningtasks when time and space are not unified. With the combination of computer aided instruction(CAI) and Internet technology, online education based on Web has become a new trend in thedevelopment of modern education. This paper takes the distance education system as anexample. In the work practice, according to the performance improvement needs of distanceeducation system. This paper uses web testing technology to obtain the mainstream networkequipment, network transmission bandwidth, system response, network load and otherinformation to comprehensively analyze and record the use of the system. In this paper, throughthe analysis of the network test report, according to the report to develop the best networkoptimization strategy. After the network is optimized, it is tested and optimized again toimprove the network performance. So as to provide a comprehensive, convenient, intuitive andaccurate test and optimization scheme for distance education network. The system can reducethe heavy performance guarantee work caused by the continuous complexity of online educationnetwork application, and provide convenience for online education application system.
KEEFEKTIFAN PENERAPAN E-LEARNING EDMODO UNTUK MENINGKATKAN HASIL BELAJAR AKUNTANSI SISWA

ABSTRACT The purpose of this research is to know the effectiveness of Edmodo's e-learning application to improve the accounting learning outcomes of class XI Accounting students at SMK. This research used the experimental research method. It was conducted at SMK. The population of this research was students of class XI Accounting at SMK. The samples of research were taken by using cluster rundom sampling. They consisted of 69 students, 36 students of XI AK 1 as the experimental class and 35 students of XI AK 4 as the control class. The data were collected throught of documentation, learning outcomes tests, and observation. The data analysis technique uses the t-test at the significance level of 5%. The results of the study concluded that the application of e-learning Edmodo was effective to improve the accounting learning outcomes of class XI Accounting students at SMK. Based on the average value of cognitive posttest, the experimental class of 88.89 and the control class of 84.69. the difference from both classes is 4.2. So is the second posttest of psychomotor domain, the experimental class had an average of 87.5 and the control class 82.86. the difference in value from the classes is 4.64. for the affective domain the experimental class obtained an average of 84.38 included in the good category, while the control class of 78.12 was included in the sufficient category. While t-test showed the value of Sig. (2 tailed) < 0.05 in both the cognitive and psychomotor domains. Keywords: Effectiveness, E-Learning, Edmodo, learning outcomes. ABSTRAK Tujuan penelitian ini adalah untuk mengetahui keefektifan penerapan e-learning Edmodo untuk meningkatkan hasil belajar akuntansi siswa kelas XI Akuntansi SMK. Penelitian ini merupakan penelitian eksperimen yang dilaksanakan di SMK. Populasi pada penelitian ini adalah siswa kelas XI Akuntansi SMK. Sampel penelitian berjumlah 69 siswa yang terdiri dari 36 siswa kelas XI AK 1 sebagai kelas eksperimen dan XI AK 4 berjumlah 35 siswa sebagai kelas kontrol. Teknik pengumpulan data berupa dokumentasi, tes hasil belajar, dan observasi. Teknik analisis data menggunakan uji t pada taraf signifikansi 5%. Hasil penelitian menyimpulkan bahwa penerapan e-learning Edmodo efektif untuk meningkatkan hasil belajar akuntansi siswa kelas XI Akuntansi SMK. Sesuai dengan nilai rata-rata posttest kognitif yaitu kelas eksperimen sebesar 88,89 dan kelas kontrol sebesar 84,69. Selisih nilai dari kedua kelas yaitu 4,2. Begitu juga dengan posttest kedua kelas pada ranah psikomotor, kelas eksperimen memiliki rata-rata 87,5 dan pada kelas kontrol 82,86. Selisih nilai dari kedua kelas sebesar 4,64. Uji-t menunjukkan nilai Sig. (2 tailed) < 0,05 baik pada ranah kognitif maupun psikomotor. Kata Kunci: Keefektifan, E-Learning, Edmodo, Hasil belajar
The Impact of Covid 19 On Education System

The COVID-19 pandemic has made the world suffer and its impacts can be seen in every domain of society such as education, health sector, finance, daily routine, etc. These impacts are so diverse which are proved to be unfavorable and constructive as well. In the current scenario where everyone is talking about the adverse impact of Covid-19, the present paper throws light on the impact of Covid-19 on the education sector in both, schools and universities level. As we know that the younger generation is the future of the world, their growth, safety, and holistic development should be a prime concern of the governments. This paper presents how during the Covid-19, the education world has got a boost through various online platforms. It also puts forth a critical analysis of both, traditional and online teaching.
El cáncer de mama y la visión por computador para su aplicación en la imagen para el diagnóstico.

espanolEntender como la vision por computador influye e influira decisivamente cada vez mas en una mayor prevencion, deteccion precoz y tratamiento del cancer de mama es entender el modo de funcionar de los sistemas sanitarios del siglo XXI. Por lo anterior, en este trabajo, nos centraremos en entender las implicaciones que la vision por computador tendra en la imagen para el diagnostico medico del cancer de mama. EnglishUnderstanding how computer vision increasingly influences and will decisively influence the prevention, early detection and treatment of breast cancer is to understand the way health systems work in the 21st century. Therefore, in this work, we will focus on understanding the implications that computer vision will have on imaging for the medical diagnosis of breast cancer.
A didactic approach to the machine learning application to weather forecast

We propose a didactic approach to use the Machine Learning protocol in order to perform weather forecast. This study is motivated by the possibility to apply this method to predict weather conditions in proximity of the Etna and Stromboli volcanic areas, located in Sicily (south Italy). Here the complex orography may significantly influence the weather conditions due to Stau and Foehn effects, with possible impact on the air traffic of the nearby Catania and Reggio Calabria airports. We first introduce a simple thermodynamic approach, suited to provide information on temperature and pressure when the Stau and Foehn effects take place. Then, in order to gain information also on the rainfall accumulation, the Machine Learning approach is presented: according to this protocol, the model is able to “learn” from a set of input data constituted by the meteorological conditions (in our case dry, light rain, moderate rain and heavy rain) associated to the rainfall, measured in mm. We observe that, since in the input dataset provided by the Salina weather station the dry condition was the most common, the algorithm is very accurate in predicting it. Further improvements can be obtained by increasing the number of considered weather stations and the time interval.
Comparative Study on Digitization of Prepaid Recharges Using IVR and M-Commerce

Interactive  Voice  Response (IVR) systems  play  an  important  role  in  collecting  and  disseminating  information  in  developing  regions. Recently,  researchers  have  used IVR technology to  build  voice  forums, in which  callers leave messages that  can be heard  over the Internet and  over the phone.  However, despite their appeal, voice forums remain difficult to set up, and difficult to scale due to the  overhead of moderating content and the cost of phone calls. Internet technology is regarded as the  third  wave  of  revolution  after  agricultural  and  industrial  revolution.  After  phone  technology  is  heralding the era of mobile in India. The growth of Mobile is phenomenal compared to previous deliver  channels. It took approximately more than twenty  years for Mobile to become popular for online  services. More so, with India all set to emerge as the second largest mobile subscriber base in the world  after China, the telecom operators are raring to use this medium to offer online services including fund  transfers to all sorts of people. Mobile can be categorized as the latest advancement in digitization. The  paper describes the rapid growth and development of payment systems and how there has been a slow  shift from IVR e-payment systems to m-payment systems. The key mobile payment systems described in  the paper includes but not limited to, the m-Paisa, Airtel money, ICICI pocket etc.  The business models  described  in the  paper  have  been  analyzed  by  considering  a few  key factors  and  analysis  results  depicted that the biggest challenge of deploying mobile payment systems is initiated by uncertainties in  the environment.
Improve Unsupervised Pretraining for Few-Label Transfer

Unsupervised pretraining has achieved great success and many recent works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance than supervised pretraining on downstream target datasets. But in this paper, we find this conclusion may not hold when the target dataset has very few labeled samples for finetuning, \ie, few-label transfer. We analyze the possible reason from the clustering perspective: 1) The clustering quality of target samples is of great importance to few-label transfer; 2) Though contrastive learning is essential to learn how to cluster, its clustering quality is still inferior to supervised pretraining due to lack of label supervision. Based on the analysis, we interestingly discover that only involving some unlabeled target domain into the unsupervised pretraining can improve the clustering quality, subsequently reducing the transfer performance gap with supervised pretraining. This finding also motivates us to propose a new progressive few-label transfer algorithm for real applications, which aims to maximize the transfer performance under a limited annotation budget. To support our analysis and proposed method, we conduct extensive experiments on nine different target datasets. Experimental results show our proposed method can significantly boost the few-label transfer performance of unsupervised pretraining.
Towards Rotation Invariance in Object Detection

Rotation augmentations generally improve a model's invariance/equivariance to rotation - except in object detection. In object detection the shape is not known, therefore rotation creates a label ambiguity. We show that the de-facto method for bounding box label rotation, the Largest Box Method, creates very large labels, leading to poor performance and in many cases worse performance than using no rotation at all. We propose a new method of rotation augmentation that can be implemented in a few lines of code. First, we create a differentiable approximation of label accuracy and show that axis-aligning the bounding box around an ellipse is optimal. We then introduce Rotation Uncertainty (RU) Loss, allowing the model to adapt to the uncertainty of the labels. On five different datasets (including COCO, PascalVOC, and Transparent Object Bin Picking), this approach improves the rotational invariance of both one-stage and two-stage architectures when measured with AP, AP50, and AP75. The code is available at this https URL.
IMPROVING STUDENT LEARNING OUTCOMES IN PKN SUBJECTS THROUGH ROLE PLAYING METHOD

The problem in this study is the low student learning outcomes. The formulation of the problem posed is whether using the role playing method can improve student learning outcomes. This type of research is classroom action research which is carried out in 2 cycles using the research design of the Kemis and Mc Taggart models. The stages in this study include the planning, implementation of actions, observation, and reflection. The data obtained in this study include the results of observations of teacher activities, and student activity observation sheets. The subjects of this study were students of class IV, totaling 23 people. The test results of the first cycle of action obtained the percentage of completeness classical is 56.52%, the percentage of classical absorption is 62.39%. In the second cycle, the results of the action test increased. Cycle II obtained the percentage of classical completeness of 91.30%, the percentage of classical absorption of 80.21%. Based on the test results, it can be concluded that using the role playing method can improve student learning outcomes.
Abnormal Situations and Eccentric Measurements

It is sometimes needful to exceptionalize certain parametric quantities in our calculations about risk. Some outcomes have to be viewed as totally unacceptable and others as altogether unrealistic. And to fit our assessment into the range of afforded by the standard machinery of risk calculation, two eccentric quantities must accordingly be introduced, catastrophic (−∞) outcome yields and virtually zero (∈) probabilities. Let us consider this more closely.
물류 스타트업 육성방안에 관한 연구 -인천광역시를 중심으로-

With the advent of the era of the 4th Industrial Revolution, various support policies and programs are being introduced as the promotion of startups related to the 4th industry is promoted as a core policy of the government. Based on major technologies such as Artificial Intelligence(AI), Big Data, Internet of Things(IoT), Blockchain, and Automation leading the 4th industrial revolution, logistics and distribution companies are expanding the range of markets and services provided. The purpose of this study is to examine the current status of startups in the logistics field based on major technologies of the 4th Industrial Revolution, which are rapidly growing at home and abroad, and suggest implications for revitalizing logistics startups through a policy demand survey. As a result of the study, in order to foster domestic logistics startups, we propose policy support for integration of logistics startups, integrated management of information, provision of physical space, network platform, and practical education and mentoring.
Factors Affecting Learners’ Satisfaction and Loyalty to the Vietnamese American Training Center, Can Tho City

The objective of the study is to determine factors affecting the satisfaction and loyalty of learners to the quality of training services at the Vietnamese American Training Center (VATC) in Can Tho City. The research data were collected from 200 learners studying at VATC Can Tho. Applying structural equation modeling (SEM), the study has demonstrated four factors affecting learner satisfaction, including textbooks and materials; managerial and administrative procedures; facilities, and training programs. Besides, the study has pointed out that satisfaction positively affects learners’ loyalty to VATC Can Tho. Some managerial implications to improve learners’ satisfaction and loyalty to VATC Can Tho were suggested such as Develop training programs, Upgrade learning materials, Improve administrative procedures, and Enhance the center's facilities. Keywords: satisfaction, loyalty, Vietnamese American Training Center.
POPULAR MUSIC COMPOSITION USING LSTM NEURAL NETWORKS

Komposisi Musik sudah berada selama ratusan tahun dan terus berevolusi. Begitu juga dengan teknologi yang sudah sangat maju di zaman ini. Teknologi memberi kemampuan untuk berinovasi, dan melalui inovasi ini pun komposisi atau pembuatan musik juga berevolusi. Kecerdasan buatan atau Intelligence adalah sebuah batu loncatan baru di era ini, dimana sebuah mesin atau teknologi pada dasarnya dapat mereplikasi kemampuan otak manusia, yang dinamakan atau Jaringan Syaraf. Penelitian ini menggunakan Long Short Term Memory untuk mengkomposisi musik Pop, dan LSTM sendiri adalah sebuah tipe jaringan syaraf buatan yang sesuai untuk mempelajari dependensi temporal khususnya dalam pembuatan dan komposisi musik. Dengan jaringan tidak terlalu dalam, dapat terbukti bahwa LSTM mampu memproduksi musik yang sebanding dengan musik yang dikomposisi oleh manusia, dan dibuktikan juga oleh survei yang dibuat dengan Turing Test sebagian.Music composition has been around for a very long time, and so has the evolution of technology. It allows humans to create new exciting things, some even in the ways that the old timers would not be able to comprehend. Parallel to the evolution of music comes the evolution of technology. With technology, we are able to create and marvel at the capacity of what the human mind can do, and even simulating how the human mind works. Right now, we are in the age of Artificial Intelligence, with Neural Networks being the centerpiece of the field. This research implements Long Short Term Memory, a type of Recurrent Neural Network that - simply put - is able to learn for a long time without forgetting its earliest memories on what it's learning. As it is a sequence-based algorithm, this type of network is very suitable for creating music. Despite the relatively shallow network used for this model, previous researches have proven that LSTM is able to perform very well, especially when creating sequences of music. Using a vanilla LSTM and at the end, this research tries to create music based on Pop music data. With this network, the music created from this model was surprisingly good. In fact, during the Partial Turing Test conducted for the evaluation of the output music of this research's model, 70% of the surveyed participants deemed the produced music as man made. Thus, it proves that LSTM really does a great job in creating music.
Using visible satellite images for model evaluation and data assimilation

&amp;lt;p&amp;gt;Satellite provide high-resolution information on the state of the atmosphere and thus represent observations are well-suited for data assimilation and model evaluation. So far mainly the thermal infrared channels have been utilized for these purposes. However, there is a rising interest to use also the channels in the solar part of the spectrum, which contain additional, complementary information. Visible channels can provide better information on the water and ice content of clouds than thermal infrared channels, have no problems to detect low clouds and are sensitive to cloud microphysics and the cloud top structure. Moreover, visible reflectances are strongly correlated with the solar irradiation at the surface and thus their assimilation has a clear potential to improve also radiation forecasts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;So far visible satellite images have not been assimilated directly for operational purposes, as multiple scattering dominates in the visible spectral range and makes radiative transfer (RT) computations with standard methods complex and slow. Only recently, we developed a sufficiently fast and accurate forward operator that relies on a compressed reflectance look-up table (LUT) computed with slow standard RT methods. Here we report on using feed-forward neural networks as an alternative to the look-up table and demonstrate that it is possible to achieve higher speed and accuracy. Moreover, both the amount of training data and the memory required by the operator can be reduced by three orders of magnitude. A further advantage is that tangent-linear and adjoint versions can easily be derived for arbitrary network structures and do not have to be changed when the network is trained with different data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We will also discuss two ways to use the forward operator to improve forecasts. First, we show that observed and synthetic visible &amp;amp;#160;Meteosat SEVIRI images can be used to detect systematic errors in the model clouds that can cause severe problems for data assimilation. Second, based on assimilation experiments using the ICON-D2 model and the local ensemble transformation Kalman filter implemented in DWDs data assimilation coding environment (DACE) we demonstrate for test periods of several weeks that errors in the cloud distribution and the surface radiation can be significantly reduced by assimilating visible SEVIRI images.&amp;lt;/p&amp;gt;
Comparison of methods to estimate a multiple linear regression model when the error is distributed Platykurtic

In this paper, we used three methods (Maximum Likelihood Method, Modified Maximum Likelihood Method, Bootstrap Method) to estimate the parameters of the multiple linear regression model when the random error is distributed (Platykurtic distribution) and the results show that the modified maximum likelihood method is the best after comparing between the methods by Using mean square error for the model and the parameters and four sample sizes (20,60,100,200) and by simulation.