Reviewerly/exHarmony huggingface dataset:
Research on Internal Control of Enterprise Accounting Informatization Based on Cloud Computing

With the rapid development of network information technology, people begin to enter a new information age. With the deepening of global competition, the geographical advantages of enterprises will fade. Modern enterprises have to rely on data to drive growth, and data has obviously become the core competitiveness of enterprises. IT industry is changing every aspect of people's daily life, especially the emergence of cloud computing, which makes e-commerce develop rapidly in the network economy. Through the way of cloud computing, data calculation and analysis can be ensured to be more accurate and rapid, especially in the process of enterprise accounting informationization, the application of cloud computing technology has more important significance. This paper expounds the concept and characteristics of cloud computing, analyzes the influence of cloud computing on internal control and audit of enterprises under the information environment, and puts forward relevant countermeasures, providing reference for enterprises to implement internal control and audit under the cloud environment.
Product Evaluation using Sentiment Analysis

Sentiment analysis is a machine learning technique which helps machines to understand and read emotions in human and predicts the same using Artificial Intelligence. In this paper we are trying to evaluate the product reviews in platforms like Amazon, flipchart etc whether it is positive, negative or neutral using Natural language processing technique. Because evaluating product reviews helps companies to make changes in product if needed and keep themselves in the market Competition. To classify reviews, we use Naïve Bayes Classifier (NB) and NLTK library which is a open source platform in Natural Language Processing helps machines to convert the Text into Polarity Score using its Functions like Tokenization, Lemmatization etc. This library helps to calculate Subjectivity and Polarity. And also, we trained our model with Bag of words or lexicons dictionary and test it on the analysis status. More the accuracy score better will be the classification. And also, we have used the Term Frequency-Inverse Document Frequency for the extraction of frequency of words from the reviews of Product.
CLUSTERING PERFORMANCE VALIDATION OF KOHONEN SELF-ORGANIZING MAPSEXPONENTIAL DECAY AVERAGE RATE OF CHANGE (KSOM-EDARC) IMPLEMENTED ON FIREDISASTER TWEETS

Cluster validity assessment is one of the very important in the field of clustering analysis. The previous introduction of the Kohonen Self-organizingMaps  Exponential Decay Average Rate of Change (KSOM-EDARC) in the area of clustering paved the way for further exploration of its clusteringcapability. This method is one of the recent enhancement of the algorithm, which enhances its learning rate decay function of using the ExponentialDecay Average Rate of Change (EDARC). Previous studies show that the clustering performance of the improved algorithm is better than theconventional algorithm. The assessment was made using visual analysis and comparison on the learning rates of the improved algorithm against thetraditional KSOM. Hence, this paper provides a cluster validity assessment on the performance of the KSOM-EDARC and KSOM using three internalvalidity index to validate the performance of the two algorithms further. Three internal validity index is used to validate the clustering results of thealgorithm. The Fire Disaster Tweets Dataset was used for clustering by the two algorithms. The average index values and the percentage differencebetween the two algorithms were determined. Based on the results recorded by the indices of the two algorithms, the KSOM-EDARC produced wellseparated and denser cluster results compared with the KSOM. The study concluded that KSOM-EDARC has better and improved performance and abetter solution than the traditional algorithm.
Accountable Error Characterization

Customers of machine learning systems demand accountability from the companies employing these algorithms for various prediction tasks.Accountability requires understanding of system limit and condition of erroneous predictions, as customers are often interested in understanding the incorrect predictions, and model developers are absorbed in finding methods that can be used to get incremental improvements to an existing system.Therefore, we propose an accountable error characterization method, AEC, to understand when and where errors occur within the existing black-box models.AEC, as constructed with human-understandable linguistic features, allows the model developers to automatically identify the main sources of errors for a given classification system.It can also be used to sample for the set of most informative input points for a next round of training.We perform error detection for a sentiment analysis task using AEC as a case study.Our results on the sample sentiment task show that AEC is able to characterize erroneous predictions into human understandable categories and also achieves promising results on selecting erroneous samples when compared with the uncertainty-based sampling.
Active auditing Services for Storing and Securing the Data in the Cloud

Cloud computing paradigm is a variation on the long vision of computing as utilisation of resources, in which data owners can host all data tenuously in the cloud and access high-quality software products on demand from a mutual pool of configurable computing resources. We create a secure storage system to alleviate data owners' burdens and to remove their substantial control over storage reliability and security, we develop a secure data storage services with high service-level requirements. Our proposed one involves three favors: the cloud server, data owner and TPA. The pros and cons of cloud computing are examined in this paper and then we put a set of analytically and cryptographically desirable properties allowing large data storage security audit services to become a authenticity. Active auditing is required to check the dynamic data integrity. But it's not easy to do an audit effectively, since data is complex in the cloud. To fail the auditing process, server will implement replay attack and forge attack. Modification, insertion and deletion comprise the complex operations. The owner sends the update message to the auditor any time the complex operation is performed. Built on the techniquepart structure, random case and Dynamic-Hash Table(DHT), our audit service can support validated improvements to externalise the data, and timely abnormal detection.
DEVELOPING STUDENTS’ WORKBOOK OF READING SKIL BASED ON INQUIRY BASED LEARNING FOR THE TENTH GRADE STUDENTS SMA SWASTA BUDISATRYA MEDAN

ABSTRACT This study aims to: (1) find out the reading skill workbook used in tenth grade in SMA Swasta Budisatrya Medan, (2) determine the workbook needed by tenth grade students in reading skills, and (3) to develop valid workbooks for use in reading skills according to the needs of tenth grade students. The research was conducted by using the Research and Development method from Borg and Gall, Gall (2003). The research subject was X IPA 1 students of SMA Swasta Budisatrya. The research object is an inquiry based learning workbook. The instrument used was student questionnaire. The data was collected using questionnaires validation sheets. The validator consists of material expert, english teacher practitioner and the one class of tenth grade students. The data were analyzed using descriptive analysis. The results of the study show that: (1) the workbook that used does not support inquiry based learning processes, (2) inquiry-based learning based workbooks are workbooks needed by students, and (3) the developed inquiry based learning workbooks have been valid with a percentage of 91.2% from expert material and 92.8% from the English teacher and this workbook is appropriate with students' needs for reading skills. Keywords: Inquiry Based Learning, Reading, Students’ Workbook
A Study on a Structure Based Human Facial Age Estimation

Researchers have shown more interest in the soft biometrics area to fill the communication gaps between humans and machines with the growth of real-world application has increased day to day life. Soft-biometric consists of age, gender, ethnicity, height, facial measurements and etc. This paper contains a detailed discussion about the contribution of the researchers in the area of gender classification and age estimation using neural networking. Most of the work is done using Convolutional neural networks and auto encoders. Various elements related to neural network models such as dataset, findings, calculative metrics and results are embraced for effortless interpretation of tabular correlation research. Finally, the authors summarize germane tasks for future various research aspects. We plan a primary study on increasing the performance of pre-trained deep networks by applying post-processing strategies. The main advantage with respect to fine-tuning strategies consists of the simplicity and low computational cost of the post-processing step. To the best of depart our knowledge, this paper is the first study on age estimation that proposes these of post-processing strategies for features extracted using pre-trained deep networks. Our method exploits a set of pre-trained Convolutional Neural Networks (CNNs) to extract features from the input face image. The method then performs a feature level fusion, reduces the dimensional of the feature space, and estimates the age of the individual by using a Feed-Forward Neural Network (FFNN). We evaluated the performance of our method on a public dataset (Audience Benchmark of Unfiltered Faces for Gender and Age Classification) and on a dataset of non-ideal samples affected by controlled rotations, which we collected in our laboratory. Our age estimation method obtained better or comparable results with respect to state-of-the-art techniques and achieved satisfactory performance in non-ideal conditions. Results also showed that CNNs trained on general datasets can obtain satisfactory accuracy for different types of validation images, also without applying fine-tuning methods
Ключевые факторы успеха IT-проектов

Purpose of research : Explore the current critical success factor (CSF) literature of IT project implementations. Methodology: The journals were searched using key terms identified in the preliminary literature review. Originality /value of research : This study provides a literature review to identify the critical success factors in IT project management that must be adhered to in order to be successful. Literature review was carried out using primary journal articles not earlier than 2005. A comparative analysis of CSFs IT projects between countries was carried out. Findings : IT Project is a complex campaign that combines success and failure. It's a sophisticated implementation process, especially in developing countries, which often takes several years, huge amounts of funding and includes business process reengineering. To cope with IT project failure, organizations should use tools, techniques, and CSFs in managing their IT projects. This article overviewed the widespread factors of success in IT projects and analysis of articles on CSF detection in countries such as India, Pakistan, Malaysia, Saudi Arabia, Norway.
Research on Outlier Detection for High-Dimensional Data Based on PPCLOF

Aiming at the “dimension disaster” problem encountered in the outlier detection of high-dimensional data, this paper uses the projection pursuit algorithm to perform non-linear dimensionality reduction on high-dimensional data by calculating the phase relationship between dimensions. According to the sample points obtained by dimensionality reduction, the LOF (Local Outlier Factor) algorithm is applied to calculate the outlier factor to obtain the relevant outlier data. In order to improve the calculation accuracy and efficiency of the LOF algorithm, clustering method is used to cut the outlier calculation data to reduce the amount of calculation. Experiments on real-world and artificial datasets, compared with the existing algorithms, demonstrated the effectiveness and efficiency of the proposed algorithm.
Rearranged Descriptor Approach based on Radon Transform to Digits Recognition

In this paper, the Radon transform method is used to generate a set of rotation invariant characteristics. Experiments of our approach were carried out on a database of ten decimal digits (0 to 9) in 24 different orientations from 0° to 360 ° by a step of 15 °. A multilayer perceptron neural network is used in the classification phase to test the effectiveness of our approach. The proposed approach is noise-effective and leads to a classification rate equal to 100 % for images without noise and a classification rate equal to 95.2 for images with noise.
A Comprehensive Framework for Implementing DevOps in Cloud for Education

In the current scenario, the use of Information and Communication Technologies (ICT) in the field of education is limited due to many reasons. Traditionally, the education sector used to be a manual process with involvement of additional manpower to manage it. Because of which there exist many challenges related to usability of educational solutions. Due to the increasing importance of education, the institutes are looking to extend their services with the limited infrastructure to fulfil the upcoming requirements. The use of emerging technologies like cloud computing and DevOps can be used in limiting such challenges and are welcome options for the education sector. The cloud computing is an aggregation of compute, storage and network resources which are delivered over the network as per demand while DevOps (Development and Operations) is another technology, which is used for automating the applications and services using on different tools for continuous integration, continuous testing, continuous deployment and continuous monitoring tools. The DevOps tools are highly compatible with many private cloud platforms. Therefore, this strategy will address all of the current challenges by opening up new educational opportunities and provide benefits like cost savings, ease of implementation, automated process, reduces resource consumption and so on. The cloud integrated DevOps solutions can provide many colossal benefits for education which were never before. The aim of this research paper is to propose a framework that can automate the time-consuming manual process in education by using Cloud integrated DevOps tools. It provides the way to implement the propose framework using opensource private cloud platform like OpenStack and DevOps tools like Jenkins and Ansible. This paper also discusses the performance characteristics of OpenStack and DevOps tools used in proposed framework.
FACE RECOGNITION FOR AUTOMATED ATTENDANCE SYSTEM USING LBPH ALGORITHM

Day to day activities in an institution all over the world some or the other way involves in taking attendance of the students who are present in the classrooms. Traditionally the attendance is taken through a roll call, this takes a lot of time and may sometime lead to a false attendance. To overcome this tedious process this project aims to reduce the time and mistakes made by the traditional process by introducing facial recognition as a solution, this helps in many ways from keeping a large registers in short space to avoiding false attendance. This project showcases the ability of facial recognition using LBPH algorithm and Haar cascading algorithms combined, this produces a facial map of the individual which helps in improving the post image processing of the individual image taken during attendance. The result of this method showed a 92% efficiency compared to traditional methods, it also showed some drawbacks that can be easily addressed by improving the environment and using deep learning by machine computing using artificial intelligence.
PSYCHOLOGICAL AND PEDAGOGICAL COMPONENT IN THE PERSONALITY STRUCTURE OF A NON-PEDAGOGICAL UNIVERSITY TEACHER

The article actualizes the problem of training teachers of higher educational institutions of non-pedagogical profile. The authors substantiate the role and significance of the psychological and pedagogical component in the structure of their personality as an important condition for improving the quality of the educational process. The main idea implemented in this work: the transition of higher education in Kazakhstan from an informative system of education to a problem-based, creatively developing system without serious psychological and pedagogical training of higher education teachers, without mastering the mechanisms of personal and professional growth is impossible. As a result of the analysis of foreign experience and literature sources, the authors conclude that it is necessary to strengthen the psychological and pedagogical training of teachers of Kazakhstan higher school of non-pedagogical profile through updating the content of advanced training courses for University teachers, modernization of educational programs of postgraduate education. In this context, the authors consider the introduction of an additional specialty "higher education Teacher", an assistant to a University teacher, to be a promising direction.
ПЕДАГОГИЧЕСКИЕ И ПСИХОЛОГИЧЕСКИЕ ПРОБЛЕМЫ ПОНЯТИЯ «ПРОФЕССИЯ»

The profession enjoys the high social status and respect that society attaches to them. This article presents an analysis of the concept of «Profession». Particular attention is give to the pedagogical and psychological problems of this concept. The purpose of the article is to determine the role and place of the profession in society. In order to determine opinions in the research of foreign scholars on the search for the «Scopus», analyzed for documents using the keyword «profession». The research work the relevance and importance of a theoretical understanding of the study of pedagogical and psychological problems of the concept of «Profession».
L2b: Lexicon Boosted Bayesian Classification For Popularity Prediction Of Movies With Improved Accuracy Using Twitter Corpus

Social networking plays a vital role in the research phenomenon. It helps in acquiring the most recent news and information consistently with settling on ground-breaking choices and expectations. There are enormous expressions of people opinion in addition to what's circumventing the world. Twitter, is a well known blogging site where individuals place their perspectives and inclinations based on their interest. This paper presents a Lexicon Boosted Bayesian (L2B) classification for popularity prediction of movies based on twitter corpus. The proposed approach predicts the accomplishment of the film utilizing perspective individuals that might actually be accomplished by assessment examination with Naive Bayes classification and Lexicon approach. In addition, it focuses with the representation of information in R language which stands the best in speaking to the investigation in pictorial arrangement and a guide perception that depicts the area of the tweets where it has come from.  The result analysis proves the effectiveness of proposed technique in comparison with the existing approaches for predicting the success of the movies.
Optimizing High-Performance Weighted Extreme Learning Machine with DAPM for Imbalance Credit Card Fraud Detection

Fraudulent activities associated with financial transactions are observed in the present scenario, especially with the use of credit cards, at a fast rate. As banking services are rising in digitalization and mobile banking is on the increase in structured written requests, credit card payments rates rise all year, with billions of transactions detected as unfair. For financial institutions to maintain the goodwill of their customers a fraud detection system requiring different detection strategies is therefore extremely important. Researchers and practitioners, using different algorithms, have proposed many methods for fraud detection to find the pattern of fraud. Data mining (DM) algorithms were influential to detect fraudulent transactions by combating fraudsters' attacks on the classic frameworks for preventing fraud. The paper aims to classify fraudulent transactions by Weighted Extreme Learning Machine (WELM) classifiers of 2 Artificial Neural networks (ANN) and 3 separate data sets of Credit Card Fraud (CCF). We use a high-performance Weighted Extreme Learning Machine (HPWELM). The efficiency of the classifiers is calculated based on accuracy, precision, recall &amp; G-mean. The research work has been implemented in Python 3.6. The results are represented in form of tables &amp; snapshots. Results demonstrate that accuracy of the HPWELM classifier has achieved a remarkable improvement in the training and testing phases of the algorithm.
A Study on Professional Outlook of Human Resource Environment During the Post COVID-19 Scenario

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Robust PID Controller Design on Quantum Fuzzy Inference: Imperfect KB Quantum Self-Organization Effect-Quantum Supremacy Effect

Quantum PID controller design based on quantum fuzzy inference from two K-gains ( and ) of classical PID (with constant K-gains) controllers investigated. Computational intelligence toolkit as soft computing technology in learning situations applied. Quantum approach performance in design of robust conventional controllers as intractable classical task of control system theory demonstrated. Simulation of intelligent control Benchmark demonstrated.
DEVELOPMENT OF MUSICAL THINKING IN STUDENTS IN THE SYSTEM OF CONTINUING EDUCATION AS A PEDAGOGICAL PROBLEM

The article is devoted to the peculiarities of the implementation of pedagogical tasks in the development of students' musical thinking in the system of lifelong education.
The evaluation of enterprise resource planning application using information systems success model

SAP ERP application is an application designed to provide useful information to support the strategy, operations, and decision-making at PT XYZ. However, when researchers made observations at the head office of PT XYZ, the ERP applications at PT XYZ were very manynonstandard programs. So it becomes a question of whether the ERP application still providesNet Benefit to PT XYZ. This study used the Information System Success Model as the basis for the evaluation. As a result, the significant variable is System Quality towards User Satisfaction,User Satisfaction towards Net Benefits. Meanwhile, the variables that did not significantly affect Information Quality towards Intention to Use, Information Quality towards User Satisfaction, System Quality towards Intention to Use, Service Quality towards Intention to Use, Service Quality towards User Satisfaction, and Intention to Use towards Net Benefits.