Reviewerly/exHarmony huggingface dataset:
The Influence of ERP Usage on Organizational Learning: An Empirical Investigation

A number of different hotels have been seen to direct significant investment towards Enterprise Recourse Planning (ERP) systems with the aim of securing sound levels of organizational learning. As a strategic instrument, organizational learning has been recommended in the modern management arena as potentially able to achieve a competitive edge and as stabilizing the success of businesses. Learning, as an aim, is not only able to improve the skillset and knowledge of employees, but also achieving organizational growth and development, whilst also helping to build a dynamic learning organization. Organizational learning is especially important in modern-day firms, when staff might choose to leave or change their role owing to the view that knowledge-sharing could be detrimental to their own success. The present work seeks to examine the impact of ERP usage on organizational learning. A new research model has been presented, this model has been empirically investigated in the Jordanian hotel industry. 350 questionnaires were distributed across a total of 350 hotels. 317 questionnaires were returned. Structural equation modeling (AMOS 18) was used to analyze the data. The findings from the empirical findings emphasize that ERP usage has significant impact on organizational learning. In line with the study findings, various aspects of organizational learning, such as continuous learning, system perspective, openness and experimentation and transfer and integration are recognized as able to best encourage the use of ERP. Suggestions for future work and discussion on research limitations are also discussed.
The effects of algorithm-based software education using micro:bit on elementary school students' creativity

This study analyzed the effects after developing and applying a software education program using microbits based on the ADDIE development model as an educational plan for improving the creativity of elementary school students.The educational program was developed based on the results of pre-requirement analysis conducted with 40 elementary school teachers.In order to verify the effectiveness of the developed education program, 42 participants of the educational donation program conducted by J University were held for 6 days, 7 sessions per day, for 24 students.As a result of conducting pre and post-tests using Torrance's TTCT test and analyzing educational effects, it was found that the software education program developed in this study had a positive effect on the creativity of elementary school students.
Information System of Achievement Scholarship Recipients (Case Study: SDN I Glp Minyeuk Sigli)

Achievement scholarship is one of the SDN I Glp Minyeuk Sigli programs which is managed by a school that has the aim of helping children who excel but are less well off financially in school, it is hoped that with this achievement scholarship program many children cannot afford the cost but have academic achievement can continue school without having to be confused or worry about cost issues. This achievement scholarship program is aimed at children of SDN I Glp Minyeuk Sigli in particular so that this program will manage a lot of children's data, in order to be able to manage large children's data quickly and accurately, an information system for receiving achievement scholarships is needed to help in performance so will be fast in processing, processing, and making reports. The purpose of this study was to determine the information system for the Achievement Scholarship Recipients of SDN I Glp Minyeuk Sigli, and to design a desktop-based Information System for the Achievement Scholarship Recipients of SDN I Glp Minyeuk Sigli. Based on the results of observations and the author can draw a conclusion which includes the following, namely; use of the information system for the data collection of outstanding scholarships at SDN 1 Glp. Miyeuk Sigli is expected to be able to help make Achievement Scholarship Information data that is accurate and effective, and the existence of an information system, it makes it easy for SDN 1 Glp. Miyeuk Sigli for speeding up general information and reducing errors.
Updating The Socio-Professional Competence Of The Digital Economy Specialist

The article considers the problem of professional training of digital economy specialists in the focus of a competent approach, which is fundamentally developed in domestic pedagogical science but has not lost its significance for the period of digital transformation.Based on the methodology of the post-classical paradigm of technoscience, the article justifies the request of the economy to form digital competencies for the conditions of digital transition, integration and convergence of technologies, understanding knowledge as the main resource of digital technologies, the variety of interactions and the speed of updating information.The article presents a comparative analysis of international projects for the development of digital competencies in Argentina, Poland, Russia and the Joint Research Center (JRC) of the European Union.The authors point to a steady tendency to view digital competencies in a wide context, not limited to the field of computer science, mathematics and technology, but interpreting them as end-to-end "transversal means" "intellectual imbibitions," which permeate all aspects of human life.At the same time, the article notes options for testing this approach in domestic practice of training engineering personnel.The authors explain the socio-professional orientation of the specialist's competence as a consequence of this trend and the increase in values and ethical standards by diversity, speed and safety of information movement.In Russian practice, the model of a new generation specialist integrates blocks of personal, social and professional development.
Multi-switch: A tool for finding potential edge-disjoint 1-factors

Let n be even, let π = ( d 1 , ... , d n ) be a graphic degree sequence, and let π - k = ( d 1 - k , ... , d n - k ) also be graphic. Kundu proved that π has a realization G containing a k -factor, or k -regular graph. Another way to state the conclusion of Kundu's theorem is that π potentially contains a k -factor. Busch, Ferrara, Hartke, Jacobsen, Kaul, and West conjectured that more was true: π potentially contains k edge-disjoint 1-factors. Along these lines, they proved π would potentially contain edge-disjoint copies of a ( k -2)-factor and two 1-factors. We follow the methods of Busch et al. but introduce a new tool which we call a multi-switch. Using this new idea, we prove that π potentially has edge-disjoint copies of a ( k -4)-factor and four 1-factors. We also prove that π potentially has (⌊k/2⌋+2) edge-disjoint 1-factors, but in this case cannot prove the existence of a large regular graph.
Pengaruh Perubahan Pitch dan Formant Terhadap Hasil Identifikasi Kepemilikan Suara Dengan Metode Audio Forensik

Audio forensics is a field of science that analyzes audio such as sound recordings. Voice recordings always have information in the form of frequency characteristics, the identities of these frequencies can be identified. Furthermore, an analysis of changes in pitch and formant will be carried out. This study used pitch analysis and analysis of variance on formants. With the correct procedure for handling recorded sound evidence which is then followed by procedural examination and analysis, it is hoped that the results of the voice recognition examination can scientifically show the ownership of the voice in the recording. Based on the results of the overall analysis of the sound recordings of evidence and comparison after carrying out various stages of analysis, the voice recordings are "not identical" from the same person. The thing that causes the inequality in voice identification is the difference in intonation or tone of the subject's speech when the voice is recorded.
Analysis of Students' Views of the Quality of Pedagogical Education in Russia

All participants in educational relations are worried about the problem how to improve the quality of education, with everyone representing it through the prism of their value system. In this connection there is a need to structure these representations. It is necessary to begin with bringing into a unified system the representations of education direct customers – students. The aim of our article is to analyze how pedagogical university students perceive the quality of their education. On the basis of the survey involving 117 respondents from Elabuga Institute of Kazan Federal University (Elabuga, Republic of Tatarstan) and the Northeast Federal University named after M.K. Ammosov (Yakutsk, Republic of Sakha (Yakutia)) we analyzed such criteria as quality of teaching, quality of learning, formation of universal competencies, personal qualities necessary for modern teachers and the creation of appropriate conditions by a university (digitalization of the educational environment, academic mobility) for students’ self-development. The t-Student criterion was used to determine the differences between the respondents’ competencies, knowledge, abilities, skills and qualities that they had possessed before entering the university and those that were formed during the university study. The obtained data indicate a high assessment of the quality of education by the students. The results also showed the educational process strong and weak points that students of the two federal universities face. Thus, focusing on one of the customers’ (students’) views will allow teachers to manage the educational process effectively and improve its quality.
Karakteristik Subsemiring Fuzzy

Dalam tulisan ini, didefinisikan subsemiring fuzzy dan menyelidiki sifat yang terkait. Selain itu, diperkenalkan konsep subsemiring yang diinduksi dari level subset. Akhirnya, karakteristik subsemiring fuzzy diperoleh.&#x0D; [In this paper, we define the notion of fuzzy subsemiring and investigate the related properties. Moreover, we introduce the idea of a subsemiring induced from the level subset. Finally, a characterization of a fuzzy subsemiring is obtained.]
Hand gesture recognition method based on dual-channel convolutional neural network

With the rapid development of security monitoring, assisted driving, remote health diagnosis and other fields in recent years, the recognition of human characteristics has attracted more and more attention. The wideband radar has a high range resolution compared to the narrow-band radar which make it able to extract fine features of human micro-movements. However, the micro-movement features of the human body are often in a complex background. Furthermore, the micro-movement features of the human body are weak compared to the main body. Therefore, the classification and recognition of human micro-motion based on wideband radar is still a difficult problem. Inspired by the successful application of convolutional neural network in image processing, this paper proposes a wideband hand gesture recognition method based on dual-channel convolutional neural network for wideband radar, which takes the range-Doppler map and high resolution range profile of human micro-motion as inputs. The effectiveness of this method is verified by experimental data, after the information is convolved, the features are fused, and finally the purpose of classification is achieved. The target recognition rate of this method is 95.67%, which is much higher than 89.87% of the High Resolution Range Profile(HRRP) and 88.61% of the Range Doppler(RD), which verifies the effectiveness of the method.
A Robust Morphological Deep Net Method for Image Segmentation Using Clustering (Retinal Image Segmentation Using Deep Net)

Introduction:The segmentation of retinal blood vessel now a day is one of the most important factors which decides the performance of a Computer-aided design (CAD) based system.Segmentation is the process of extracting the region of interest i.e. the disease in the image.The boundaries of retinal blood vessels need to be segmented accurately as an eye surgeon cannot be able to predict the area of disease in case segmentation not done accurately.Objective: This proposed method aims to segment retinal blood vessels using morphological operation which robustly extract the feature.The final image is obtained by using distance-based clustering. Results:The proposed method had shown an accuracy of more than 98.15% and the images are enhanced as the peak signal to noise ratio (PSNR) value is more than 50. Conclusion:The proposed method is efficient in contrast with various existing techniques.
Early Action Prediction using 3DCNN with LSTM and Bidirectional LSTM

Predicting and identifying suspicious activities before hand is highly beneficial because it results in increased protection in video surveillance cameras’. Detecting and predicting human's action before it is carried out has a variety of uses like autonomous robots, surveillance, and health care. The main focus of the paper is on automated recognition of human actions in surveillance videos. 3DCNN (3 Dimensional Convolutional Neural Network) is based on 3D convolutions, there by capturing the motion information encoded in multiple adjacent frames. The 3DCNN is combined with Long short team memory (LSTM) and Bidirectional LSTM for prediction of abnormal events from past observations of events in video stream. It is observed that 3DCNN with LSTM resulted in increased accuracy compared to 3DCNN with Bidirectional LSTM. The experiments were carried out on UCF crime Dataset.
Shared Augmented Reality Experience Between a Microsoft Flight Simulator User and a User in the Real World

Our demo consists of an application that allows a user with an augmented reality (AR) display to watch another user, flying an airplane in the Microsoft Flight Simulator 2020 (MSFS), at their respective location in the real world. To do that, we take the location data of a virtual 3D airplane model in a virtual representation of the world of a user playing MSFS, and stream it via a server to a mobile AR device. The mobile device user can then see the same 3D airplane model at exactly that real world location, that corresponds to the location of the virtual 3D airplane model in the virtual representation of the world. The mobile device user can also see the plane movement updated according to the 3D airplane movement in the virtual world. We implemented the application on both a cellphone and a see-through headset.
Implicit detection of user handedness in touchscreen devices through interaction analysis

Mobile devices now rival desktop computers as the most popular devices for web surfing and E-commerce. As screen sizes of mobile devices continue to get larger, operating smartphones with a single-hand becomes increasingly difficult. Automatic operating hand detection would enable E-commerce applications to adapt their interfaces to better suit their user’s handedness interaction requirements. This paper addresses the problem of identifying the operative hand by avoiding the use of mobile sensors that may pose a problem in terms of battery consumption or distortion due to different calibrations, improving the accuracy of user categorization through an evaluation of different classification strategies. A supervised classifier based on machine learning was constructed to label the operating hand as left or right. The classifier uses features extracted from touch traces such as scrolls and button clicks on a data-set of 174 users. The approach proposed by this paper is not platform-specific and does not rely on access to gyroscopes or accelerometers, widening its applicability to any device with a touchscreen.
DeepCompete : A deep learning approach to competing risks in continuous time domain.

An increasing number of people survive longer ages leading to a growing population of people 65 years of age or older. A large percentage of this population is afflicted with multiple acute diseases (multi-morbidity). Clinicians need new tools to quantify the relative risk of an adverse event due to each competing disease and prioritize treatment among various diseases affecting a patient. Currently available deep learning survival analysis models have limited ability to incorporate multiple risks. Also, deep learning survival analysis models in current literature work predominantly in the discrete-time domain, while all biochemical processes continuously happen in the body. In this work, we introduce a novel architecture for a continuous-time deep learning model to combat these two issues, DeepCompete, aimed at survival analysis for competing risks. Our model learns the risk of each disease in an entirely data-driven fashion without making strong assumptions about the underlying stochastic processes. Further, we demonstrate that our model has superior results compared to state of the art continuous-time statistical models for survival analysis.
Double Exponential Smoothing Berimputasi LOCF Dan Linear Interpolation Dalam Akurasi Peramalan Harga Harian Emas

Gold is another kind of investment that often experiences price change, mostly every day. Because of its price fluctuation, forecasting is needed to help the investor in investment decision making. But during this Coronavirus Disease 2019 (Covid-19), the gold price is fluctuating extremely than the past 4 years so a better forecasting method approached and analysis technique is needed due to this case. Double Exponential Smoothing method is chosen to forecast this daily gold price. On the other hand, there are so many missing values spreading around the main dataset so the imputation method is needed too, Last Observation Carried Forward (LOCF) and linear interpolation are chosen for imputing the missing values. In this research, the main dataset was split into 3 (three) datasets, which are Precovid-19 (before Covid-19, used only for visualizing the actual fluctuation condition during this pandemic), Incovid-19 (during Covid-19 based on the date where the first Covid-19 case occurred in Indonesia), and Combination (a binding dataset of Pracovid-19 and Incovid-19). Although Incovid-19’s MAPE value is higher than Pracovid-19 and Combination’s MAPE values, in evaluation session showed that Incovid-19’s MAPE of forecast results has the lowest value rather than Combination’s MAPE of forecast results, so the conclusion of this research is Incovid-19 dataset with LOCF imputation is the most adaptive with the actual condition and it is used to forecast the daily gold price until the last period of the main dataset then.
Digital transformation of Russian society as a condition of forming young teachers’ professional identity in a big city

Digitalization affects every sphere of life including education. However, for it to be beneficial, it needs transfomation in order to fit the current situation or changes that have occurred in human life over a time.The purpose of this research is to consider the impact of the digital transformation of Russian society on the formation of young teachers’ professional identity. The constructivist approach and the "soft concepts" of identity that have developed within its framework, serve as the methodological basis of the proposed research. The results of the applied authors’ sociological study conducted in 2020 serve as the empirical basis of this study. The method of collecting information was an in-depth interview. The reseach concluded that digitalization has a positive impact on formation of professional identity of young teachers. We also note that, despite the fact that the forced transition to online education has increased the prestige of teaching, digitalization cannot be considered as an independent factor that is able to change the professional space so that it becomes more attractive for young professionals.&#x0D; &#x0D; Keywords: mass digitalization, forced onlineization, digitalization of school education, online education, professional identity, young teachers, Russian megapolis.
Sentiment Analysis on the Performance of Engineering Students in University Examination: A Non-parametric Approach Using Two-way Analysis of Variance Model

Higher Education System in India has undergone a rapid change after the year 2000 in India. The Government of India has initiated to start more number of higher learning institutes for the growth of the education sector. In the early years of 2000 a number of engineering colleges / universities were opened for strengthening engineering education by the government and by private management. The engineering educational authorities in particular have formulated a number of guidelines for improving quality of engineering education. The improvements were made in respect of integration of different courses and different streams of engineering disciplines. In this article an attempt is made to study the effects of such integrated efforts based on Sentiment Analysis. The aspects of diversified course structures and the engineering streams are identified with respect to pass percentage of students in university examinations. Friedman Two-way analysis of variance model is used for the analysis of the sampled data. Inferences based on the hypotheses tested are presented.
An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied to solve hard real software engineering problems. However, to choose and design a MOEA is considered a difficult task, since there are several parameters and components to be configured. These aspects directly impact the generated solutions and the performance of MOEAs. In this sense, this paper proposes an approach for the automatic generation of MOEAs applied to the Integration and Test Order (ITO) problem. Such a problem refers to the generation of optimal sequences of units for integration testing. The approach includes a set of parameters and components of different MOEAs, and is implemented with two design algorithms: Grammatical Evolution (GE) and Iterated Racing (irace). Evaluation results are presented, comparing the MOEAs generated by both design algorithms. Furthermore, the generated MOEAs are compared to two well-known MOEAs used in the literature to solve the ITO problem. Results show that the MOEAs generated with GE and irace perform similarly, and both outperform traditional MOEAs. The approach can reduce efforts spent to design and configure MOEAs, and serves as basis for implementing solutions to other software engineering problems.
Service Outages Prediction through Logs and Tickets Analysis

Service outage or downtime is a growing challenge to the service providers and end users.The major cause for the unavailability firstly is failure of equipments and applications at various places and secondly failure for proactive diagnosis and rectification.The system activities that are logged and the response of customers and providers in the form of trouble tickets could be studied for minimizing network faults.The downtime can be reduced when the failures are predicted well in time and proactively corrected.Accurate prediction of faults helps in responding to downtime even before the customer tickets are raised or network trouble is encountered.Most of the research focuses on trouble shooting through forecasting the quantity of trouble tickets using the historical ones.If these tickets can be supported with the warning in the form of Syslogs and the technical support of network tickets the predictive models would be more efficient and accurate.Dynamic and truly adaptive machine learning algorithms are essentially required for processing the torrent of data and formulating predictions based on the trends and the patterns existing in it.The work refers to i) identifying number of trouble tickets that are related to the device a few days before the network component fails, ii) predicting fault will occur in broadband networks.Lasso and Ridge regression are used for the first and Bayesian structural time series analysis and prophet are used for the latter.