Reviewerly/exHarmony huggingface dataset:
Implementation of Logging Feature in Android Payment SDK using Scrum Method

Electronic money or e-money is a technological innovation that allows payment transaction processing to be easier and more practical. PT Aino Indonesia is a payment gateway company that serves payment processes using this technology for various business segments. To satisfy the requirements of each business segment, Aino needs to develop a wide range of applications. These applications require a standard for the payment system to make it faster to integrate and maintain. The payment team at Aino has developed a module or SDK that provides a standard payment system for Android-based applications. Two new features will be added to this SDK, the logging feature and log file upload. The logging feature will record the data generated by each transaction process into a log file. Next, the SDK will upload the log files to the server to help retrieve transaction data uploaded from the device. Both features will help the process of tracking issues and problems that may arise during the transaction process. The development of the two features in this SDK uses the Scrum method to ensure it stays on track.
The Collection development and Student’s Awareness of Library Resources for Research: Utilization of Print and Electronic Collection in KSAU-HS Riyadh, Saudi Arabia

The study purpose of the Health Sciences Libraries has an essential part of the library's resources print and digital version (like textbooks, Ebooks, E-journals, and databases).Medical Libraries depend on medical literature.The Study aim at investigating the information resources available in college libraries which is under the umbrella of KSAU-HS University usage for studying and research.Universities Libraries are looking for to essential part in the study and development process.An important component in the research process is the availability of information resources and services provided by libraries.Students are important producers of research in universities, and an important part in their research assignments is access to information.This study was opinions from students about the part of the academic library services in facilitating their study and research satisfaction with the sources and services currently provided in KSAU-HS Libraries.Data was collected from 362 randomly selected students in three College libraries King Saud bin Abdulaziz University Riyadh.The results show that the satisfied (76.5 %) of respondents recognized the important role of the library in facilitating research, and 78.5 % of the respondent's rates of study literature fulfill in libraries are accessible and show was satisfied with the current facilities by the libraries.However, certain areas needed to be improved such as increased physical and online resources for student's researchers, and doctor visits, in malevolence of the environment for remote access assistance for supporting students and research in university libraries.The findings will be useful too especially for the Health Sciences libraries in providing better services to the students for their research and then Libraries more improved and better play role if researchers knew that support was available, and if their involvement was more formalized.Libraries have a chance to use their skills to help researchers improve the quality of information resources their funding requests, and to develop to the organization's success in winning research support.
Stavovi studenata o kvalitetu nastave na daljinu

COVID-19 pandemic brought sudden and significant changes in planning and organizing teaching activities in the system of university education all around the world. Switching to online or blended teaching continues to be a challenge for both teachers and students. Rapid development of teachers’ digital competencies and students’ skills, develop- ment of different teaching models and application of virtual tools in teaching, during the pandemic, imposed the question of the quality of such teaching in the context of student achievement and potential development, as well as recognized professional competencies of new age teachers. The aim of our research was to determine students’ attitudes about the quality of online teaching during the pandemic, in the context of both their learning and development, and preparedness of teaching staff for shifting to online teaching. The sample consisted of students of bachelor and master academic studies, at the Faculty of Education, University of Kragujevac, Jagodina (N = 63). The survey technique was used in research and an instrument, which contained 10 five-point Likert-type items, was created to examine students’ attitudes. The results of the research confirm good professional competencies of teachers in the time of intensive digitalization of education, but also the need to strengthen their digital competencies, as well as systematic work on improving online teaching, in relation to its organization and organization of exam activities.
On the Origin of Queries: Where Chat Reference Questions Begin

Academic libraries providing chat reference services have a range of options for where and how library users can engage with chat providers. These entry points can be static or proactively invite users into the service. The study compares the URLs from which users entered chat reference at three large public research libraries in the United States. The URLs show where users were when they sought assistance with library tasks. This exploratory investigation is important for extending knowledge of user help-seeking behavior and how chat service configuration may impact use. Data are variables related to chat interactions (n = 19,839) at Bowling Green State University (BGSU), University of Illinois Chicago (UIC), and University of New Mexico (UNM) in three semesters 2019–20. Findings show users originated most chats from either the library website or discovery layer; chat initiations from other resources are minimal; chat traffic increased at all three during the COVID-19 pandemic.
Quality control of blood components: selection of laboratory testing methods

The blood components transfusion is a significant component of therapy for blood loss, anemia, and surgical operations. One of the main tasks of blood service institutions is the procurement of high-quality transfusion media, the effectiveness of which directly depends on the functioning of the safety system. The implementation of this direction of work is impossible without laboratory testing of the donor biomaterial, a prerequisite for which is the standardization of procedures to ensure the reliability and reproducibility of the results. The aim of this study is to analyze the existing methods for determining hematological parameters and assess their suitability for quality control of blood components. Material and methods. The samples of erythrocyte-containing blood components, fresh frozen plasma, platelet concentrate, as well as reference buffer solutions for pH measurement have been tested. To determine the number of blood cells, conductometry with hydrodynamic focusing, counting in the Goryaev chamber and laser flow cytofluorimetry, were used. The pH indicator was measured by potentiometry. The content of general and free hemoglobin was assessed by photometric, hemiglobincyanide and SLS method. The hematocrit value was determined on a hematological analyzer and by centrifugation in hematocrit capillaries. The activity of factor VIII was checked by the clotting method. Results and discussion. The generalized results of the metrological assessment and a comparative analysis of the listed laboratory methods, taking into account their sensitivity and precision have been presented. Conclusions. A list of recommended methods for quality control of blood components is formed, for each of which an assigned convergence characteristic is established.
Model of digital competencies of teachers: terminological and content aspects

Problem and goal . The article substantiates the structure of the model of digital competencies of teachers, discusses the principles of organizing personalized methodological support to ensure the processes of digital transformation of the school. Methodology. The construction of the conceptual and terminological apparatus of the research is carried out on the basis of theoretical analysis and synthesis of scientific positions of sources, a variety of definitions of concepts and terms. A comparative analysis of Russian and foreign models of digital competencies allowed to form a construct of a model of digital competencies of a teacher. The modeling of management activities for the development of digital competencies of teachers is carried out. Results. The structure of the model of digital competencies of teachers, level profiles and descriptors of their digital competencies is proposed. The algorithm of personalization of the educational trajectory and a model of management of the development of digital competencies of teachers that meets the requirements of continuity, individualization, and consistency is shown. Conclusion. The results of the study can be applied by digital transformation project teams, heads of educational organizations to improve the management of digital transformation processes.
Transformer-Based Rating-Aware Sequential Recommendation

As a significant application of Big Data, recommender system can effectively solve information overload. The user's behavior sequence forms massive data and has excellent mining value. Sequential recommendation is to extract user's features in massive sequential data and predict the next interaction based on the user's recent temporal behavior. Currently, recurrent neural networks (RNN) and Graph Neural Networks (GNN) take on the role of item embedding in sequential recommendation and have shown adequate performance. However, such RNN based model and GNN based model cannot deeply mine the complex behavior sequence and neglect user preference like rating information. Inspired by the popular Transformer, we adopt the Transformer encoder layer to process sequence and represent item embedding by multi-head attention. Meanwhile, rating information is integrated into weight calculation when we represent the user preference with self-attention. Weight with rating not only retains the structural information of sequence but also combines the user's preferences. What's more, we consider global and local preferences to formulate hybrid performance and make recommendations in Top-N. For persuasiveness, we conduct experiments on large real-world datasets, and our model performs better in most cases on two datasets compared to state-of-the-art methods.
News-Based Sentiment Indicators

We construct sentiment indices for 20 countries from 1980 to 2019. Relying on computational text analysis, we capture specific language like 'fear', 'risk', 'hedging', 'opinion', and, 'crisis', as well as 'positive' and 'negative' sentiments, in news articles from the Financial Times. We assess the performance of our sentiment indices as 'news-based' early warning indicators (EWIs) for financial crises. We find that sentiment indices spike and/or trend up ahead of financial crises.
A Comprehensive Analysis on Multi-class Imbalanced Big Data Classification

In real life, most of the big dataset is skewed in nature. Domains like medical diagnosis, bioinformatics, banking theft, natural disaster, network intrusion, oil-spill detection, instrument failure and army crimes have dataset which are not always balanced; rather, imbalanced dataset is the common occurrence. This will lead to biased classification if the positive class instances are very few when compared with negative class instances. Multi-class imbalanced big data learning is a challenging research topic in big data analytics. In this review paper, we analyse the conventional balancing techniques of data-level, algorithm-level approaches and ensemble techniques which balance multi-class imbalanced dataset. Though these existing ensemble techniques balance the dataset, it cannot be applied to streaming data and has scalability issues. This paper is intended to analyse different techniques to develop a novel ensemble technique to learn, balance and pre-process the big data stream for classification by analysing different techniques and its accuracy levels.
Predictive Model for Taking Decision to Prevent University Dropout

Dropout is an educational phenomenon studied for decades due to the diversity of its causes, whose effects fall on society's development.This document presents an experimental study to obtain a predictive model that allows anticipating a university dropout.The study uses 51,497 instances with 26 attributes obtained from social sciences, administrative sciences, and engineering collected from 2010 to 2019.Artificial neural networks and decision trees were implemented as classification algorithms, and also, algorithms of attribute selection and resampling methods were used to balance the main class.The results show that the best performing model was that of Random Forest with a Matthew correlation coefficient of 87.43% against 53.39% obtained by artificial neural networks and 94.34% accuracy by Random Forest.The model has allowed predicting an approximate number of possible dropouts per period, contributing to the involved instances in preventing or reducing dropout in higher education.
An Analysis of Competitiveness to Hold International Conferences by Regions in South Korea using Entropy-TOPSIS

The purpose of this study is to analyze the competitiveness of holding international conferences by regions in South Korea, based on its statistical data, using Entropy and TOPSIS. Analysis factors are infrastructure (meeting facilities, accommodate facilities, professionals, accessibility to city), experiences (frequency of international conferences, participants per a event, sales figures per a event), regional attraction (tourism resources, city safety), service quality (satisfaction scores with facility environment and operational capability), and administrative support (related regulations, CVB). As a result, the most important factor is professionals with weight of 0.260079, followed by frequency of 0.212207, and accessibility of 0.186700. Considering weights of factors and using statistical data by regions, the most competitive region is Seoul. And the following areas were ranked in order: Gyeonggi, Incheon, Busan, Jeju, Gyeongnam, Daegu, Jeonbuk, Daejeon, Gangwon, and Gyeongbuk. Metropolitan areas including Seoul, Gyeonggi, Incheon, Southeast area, Busan, Jeju, and Gyeongnam all had competitive advantage to hold and attract international conferences in South Korea. Each region should establish differentiated strategies considering the currant status, such as regional statistical data.
Construction of English Pronunciation Judgment and Detection Model Based on Deep Learning Neural Networks Data Stream Fusion

Aiming at the defects of pronunciation errors and limited collection of pronunciation data resources in traditional artificial neural networks, an English pronunciation judgment and detection model based on deep learning neural networks data stream fusion is proposed. Taking Chinese English pronunciation as the research object, three groups of phonetic data were selected as experimental auxiliary data, based on the convolutional neural network, through the preset reset of the pronunciation detection system of the model, the sampling and recognition extraction of the speech system, the wrong speech detection and the feature analysis of the multi-level data stream tandem, the experiments are carried out with CU-CHLOE language learning database, WSJ1 database and 863 Mandarin database. The experimental results show that the recognition accuracy of this model is higher than that of the traditional neural network model, the accuracy of error type diagnosis is significantly improved, and its noise robustness is the best.
Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images

Objective image quality metrics (IQMs) are widely developed and utilized, considering that they can lead to optimal radiation doses in computed tomography (CT) imaging. However, how well these IQMs relate to a radiologist’s perception of subjective image quality, which is the gold standard for assessing diagnostic image quality, has not been fully explored. Therefore, in this study, we aim to analyze the relationship between subjective and objective quality metrics. We compared 13 full-reference and no-reference IQMs, including root mean square error, peak signal-to-noise ratio, structural similarity index (SSIM), multi-scale SSIM, information content weighted (IW)-SSIM, gradient magnitude similarity deviation, feature similarity index, noise quality metric, visual information fidelity, natural image quality evaluator, blind/referenceless image spatial quality evaluator, perception-based image quality evaluator, and the model observer non pre- whitening with eye filter (NPWE). The data used in this study were CT images under seven noise levels. The scores obtained from these data with the objective IQMs were then compared with the three radiologists’ scores by using Pearson linear correlation coefficient (PLCC) and Spearman’s rank order correlation coefficient (SROCC). The results show that SSIM performs best in terms of PLCC and SROCC but lacked some characteristics of the radiologists’ assessment. Full reference IQMs, except for IW-SSIM, generally outperform no-reference IQMs. No-reference IQMs show poor PLCC and SROCC scores, and the model observer NPWE shows the worst performance among them. These results may contribute to evaluating and developing IQMs with the preferences of radiologists.
Parallel Approximation of Multivariate Tensors using GPUs

When solving many applied and research problems, it becomes necessary to work with multidimensional arrays (tensors). In practice, an efficient and compact representation of these objects is used in the form of so-called ten­sor trains. The paper considers a parallel implementation of the TT-cross algorithm, which allows one to obtain a decomposition of a multidimensional array into a tensor train using a graphics processor of the CUDA architecture. The main aspects and features of parallelization and implementation of the algorithm are presented. The obtained parallel implementation was tested on a representative number of examples. A significant reduction in computational time is demonstrated in comparison with a similar sequential implementation of the algorithm, which indicates the effectiveness of the proposed approaches to parallelization.
Concept of TF-IDF, Common Bag of Word and Word Embedding for Effective Sentiment Classification

Sentiment Classification is one of the well-known and most popular domain of machine learning and natural language processing. An algorithm is developed to understand the opinion of an entity similar to human beings. This research fining article presents a similar to the mention above. Concept of natural language processing is considered for text representation. Later novel word embedding model is proposed for effective classification of the data. Tf-IDF and Common BoW representation models were considered for representation of text data. Importance of these models are discussed in the respective sections. The proposed is testing using IMDB datasets. 50% training and 50% testing with three random shuffling of the datasets are used for evaluation of the model.
PredictV: A Vehicle Prediction Scheme to Circumvent Occluded Frames

There are many methods to overcome traffic congestion on the roads, but occlusion is still there in most methods, so it is a dire need of the time that researchers have to look into this matter. In the rural and urban areas, heavy congestion on the roads has become the leading cause of occlusion. The PredictV method works on the prediction principle based on existing values and is proved one of the naval approaches for this problem. This scheme uses blob detection for the first frame detection and predicts other vehicles based on percentage increment in the different parameters to identify the vehicles. For improving the quality of the sample, data cleaning has been included in this work. With the help of this approach, congestion has been avoided, and this method prefers to use predicted points to detect vehicles on the frame. The suggested approach is implemented via a MATLAB simulator. It is tested on a large dataset which includes 7152 frames of 6 different videos from the Urban Tracker and KoPer dataset. In total, there are 46876 vehicles present on the frame at first, and the existing methods have detected only 58&#x0025; and 73&#x0025; of vehicles, whereas the detection rate is 82&#x0025; with this suggested approach. The occlusion rate is only 17&#x0025; on average, which has been reduced via this proposed approach which was 24&#x0025; and 43&#x0025; earlier than the existing one. The performance of this suggested method has been tested based on performance and results, which are pretty impressive that is almost 99.74&#x0025;. In short, the outcome of this prediction technique has been improved now, and the effectiveness has been observed too.
Introduction to MRI

A Straospheric, Warp-Speed Sketch of NMR and MRI; Uniquesness of MRI; A Real MRI Case Study;
Applicability of Blockchain Technology to The Normal Accounting Cycle

Blockchain technologyis a distributed, unchangeable ledger that makes recording transactions and managing assets in a business network much easier and nowa type of accountingsoftwareconcernedwith the transfer of assetownership and the maintenanceof anaccuratefinancial ledger. Despitethenumerousbenefits ofblockchaintechnology,there is no study on theapplicability of blockchain technologytothenormalaccountingcycle in emerging economies in Africa.Thus,thispaperprovidesgeneralinsightsonhowblockchaintechnologymaybeusedinthenormalaccountingcycle in West Africa.Thestudyadoptedaqualitativeresearchmethodandcontentanalysisresearchdesigntounderstand the extent to which business leaders in West Africa are aware, understand, and utilize blockchain technology in the processing of accounting transactions to the preparation of financial statements.Results indicatethat West African business leaders are well aware, understand and applyblockchaintechnologyapplicationsinthenormalaccountingcycle,anditprovidescostsavings,digitalidentity,andsecurity.Thestudyrecommendsfurtherinvestigationsintohowtoaddressscalabilitywhen dealingwith recurrent and large transactions.