Reviewerly/exHarmony huggingface dataset:
Hierarchical Parallel Processing for Data Clustering in GPU Using Deep Nearest Neighbor Searching

Today growing number of corporations and research groups can rely on this new tool for are hitching up artificial intelligence horsepower. Insurance, Banking, Retail, Telecom and many other such sectors can find it fruitful for optimizing their options. artificial intelligence applications are becoming more prevalent: the improving tax collections and detecting tax fraud; improving its health care for employees while reducing the corporation’s costs; We are beginning to figure out how to mine these growing mountains of artificial intelligence, data, and parallelism makes the mining operations possible. We need to provide efficient solution to solve data clustering in GPU. Hierarchical parallel processing method is applied to find the data clusters in GPU. Deep nearest neighbor searching algorithm is used to create deep belief network and predict the accuracy. The efficiency is determined in the training set using the mean square error rate. The obtained results are compared with the traditional techniques. The result is tested by using TensorFlow using different GPU time slots.
Design of Robot Plotter Software for Making Pattern with Turtle Graphics Algorithm

Robot plotter can be more practical with a turtle graphics algorithm-designed interface application. Here is a literature review to develop a plotter robot control model. An experiment constructed software to transmit interface and robot plotter commands. Robot plotter uses selective compliance articulated arm (SCARA) on two x and y joints. A Turtle Graphics application commands the plotter robot to make batik design. A plotter robot can create a simple batik pattern on paper. Combining the writer's electrical engineering and art knowledge created a distinctive interface. This is new research. Research improves education and industry. The study helps automate batik pattern making. For those with disabilities, research can help them make patterns.
Mandatory Disclosures and Market Reaction: Evidence from Qatar Stock Exchange

Information disclosure, inter alia, has a tremendously increasing impact on the stock market. This study aims to investigate the reaction of stock market to the mandatory disclosures in Qatar Stock Exchange (QSE) for the period 2020-2022. To be precise, the paper investigates the market reaction as indicated by the trading volume and stock price change to disclosure of periodic financial reports (quarterly, semi-annual, and annual) and company news and, subordinately, the relationship between trade volume and stock price change of the 47 (&amp;plusmn;1) traded listed companies on the QSE for the period spans from January 2020 to October 2022. Trading volume, all market share index value and stock price change were descriptively analyzed, then investigated using a one-to-one period approach, pre and post disclosure. In consistency with a priori predictions, the result shows significant market reaction as indicated by the volatility of both trade volume and stock price change. Moderate evidence is given to support the market reaction to the periodic disclosure other than annual financial reports. Additionally, findings show a weak positive relationship between trade volume and stock price change. Interestingly, the investigation provides substantial support to the weak form market efficiency, at least for the widely traded stocks. This indicates that insiders are not being better informed about the company&amp;rsquo;s true value than outsiders in the QSE. Finally, results support to the widely held belief, but heretofore undocumented evidence from the region, that permanent disclosures provide information benefits to investors and, hence, greater benefit.
IMPLEMENTASI PENGUJIAN LEARNING MANAGEMENT SYSTEM APLIKASI PEMBELAJARAN JARAK JAUH BERBASIS MOODLE DI UNIVERSITAS XYZ

Increasing learning by innovating internet-based information technology and e-learning, providing convenience in increasing learning flexibly without space and time limits, implementing distance learning that is officially licensed by the government, providing fresh air for universities or institutions in developing Innovative applications using the Moodle LMS are more interactive and customized. Writing this article uses an experimental method by designing a moodle LMS with domain hosting in Informatics study program, implementing Moodle LMS to find out how far the comparison is between blended learning which is currently used at XYZ University, based on test results by distributing questionnaires to informatics students, the average usability value is obtained -average 92.1%.
Design of a Web-Based Expert System Using Certainty Factors for Internal Disease Diagnosis

Diseases are classified into different types based on the symptoms, causes, and disturbances they cause to the human body. Among the disease categories that have a wide range of sub-specialties and branches is internal medicine. Early disease detection is difficult because many diseases exhibit similar symptoms. To diagnose the disease, medical specialists will conduct a series of physical and laboratory tests. However, before going to the doctor, the patient should learn about the disease and begin treatment so that it does not worsen and become dangerous. As a result, we require a system that can assist patients in receiving information and detecting their disease early. An expert system was developed in this study to help people obtain complete and precise information about their disease. It is hoped that by using this system, people will be able to obtain preliminary information before visiting a doctor, thereby reducing the risk. An expert system is a type of artificial intelligence application that is a computer program designed on knowledge and rules that could act similarly to or as well as an expert. Expert systems must be able to function in the presence of uncertainty. A number of theories have been developed to deal with uncertainties, one of which is the certainty factor method, which is ideal for analyzing uncertain data. This technique provides a solution by calculating the values of certainty and uncertainty. This study concentrated on three types of internal medicine and 27 symptoms to be diagnosed; the end result was a diagnosis of the type of internal medicine encountered and the percentage of confidence, along with treatment recommendations.
Neighborhood Graph Convolutional Networks for Recommender Systems

Recommendation system based on collaborative filtering has attracted much attention because it can make personalized recommendations according to the different needs of different users. However, a training recommender system based on collaborative filtering needs a large amount of user-item interaction information. In practical applications, the collaborative filtering recommender system has problems of cold start and sparsity due to the lack of information in the data. The researchers noticed that the knowledge graph contained a lot of attribute relations and structured information. In order to alleviate and solve the problems of cold start and sparsity in the existing recommendation system, the researchers tried to introduce the knowledge graph into collaborative filtering. However, the existing recommendation systems based on knowledge graphs usually only pay attention to the project information and use the project information to model user preferences, but seldom pay attention to the user information. In this paper, we discuss the necessity of using user information and propose a new convolutional neural network framework, which is an end-to-end framework, which explores the correlation attributes in the knowledge graph to capture the correlation, and then to model high-order user information and high-order project information. It is worth mentioning that we use 9 different ways to aggregate high-order user neighborhood information and high-order project neighborhood information based on 3 different aggregators. The proposed model is applied to two data sets about books and music, and the results show that the accuracy is significantly improved, which proves the superiority of our method.
Review on School Management System

Our team created the School Management Application specially for students to make managing the everyday work of the institute and the smooth operation of the school administration. Software for school management is used for management, administration, and learning activities in colleges and schools. managing the timetable, exams, class tests, attendance, money collecting, and finances for the students and the staff. Your broad phrases must be any phrase that can be used to categories the given material properly, such as algorithms, security, or pattern recognition. It is difficult to keep information about students, faculty, and parents on paper in the present school management system. When someone tries to get any of the stored information, it becomes a complex and time-consuming operation due to the large amount of information. While parents and teachers now have more responsibilities than only looking after students, it may be challenging for them to keep an eye on them. While the task of storing and retrieving information is challenging, it also calls for a large number of extra workers.[2] Keywords Admission, Library, Attendance, Bus Tracking, Fees report, Live classroom, Dynamic report, education system etc. [3]
Surveillance video synopsis based on spatio-temporal offset

With the wide popularity of surveillance cameras, video synopsis technology has become a research hotspot. The existing methods of surveillance video synopsis usually summarize the input video by shifting the object tube in the video on the time axis, which ignore the serious collision artifacts and chronological disorder between moving objects. To solve these problems, we propose a surveillance video synopsis methodology called “surveillance video synopsis based on spatio-temporal offset (STO)” that can simultaneously shift the moving object in the temporal domain and spatial domain. First, object detection and tracking algorithms are used to extract the object tube from the input video. Two collision relations are proposed by analyzing relationship between tubes to classify collision artifacts. Then, we present two spatial offset cases to find the optimal spatial offset of the object tube. Finally, an adaptive optimization frame density model is proposed to analyze the optimal temporal offset of the object tube. Simultaneously, the object tube and the background are stitched according to the STO to generate the synopsis video. Extensive experimental results demonstrate the effectiveness of the proposed method in improving frame compression rate and alleviating collision artifacts.
THE WRITING STYLE IN TRAVEL BLOGS

This paper focuses on the writing style in travel blogs. Its aim is to research stylistic forms typical of travel blog entries and their contribution to the functions of the travel blog as a whole. In the theoretical part of the paper, we present the characteristic features of a travel blog and the language trends in the digital age. Travel blogs have been defined as digital media settings. Blogs were once defined as websites that included bloggers’ entries and were regularly updated. They were often compared to personal journals. Today blogs have a rather complex structure and they fulfil several functions: bloggers can use their writing to stand out from the crowd, to share their experiences, opinions, once-private thoughts, and even to create a source of income. As a result, blogs are becoming increasingly sophisticated. Furthermore, travel blogs fulfil a variety of functions: the self-portraying, informative, entertaining, evaluative, advertising, instructive, and communicative function. Bloggers want to inform their followers as well as they can, but because of todays flood of information on the web, they must be careful not to let their followers drown in information. Blogs are thus characterized by syntactic compression. In the travel blogs we have analyzed, this is achieved through various linguistic means, such as nominalizations, the usage of infinitival constructions, elisions, and the usage of passive structures. These means fulfil several functions at the same time: they both convey information and help the bloggers maintain the contact with their followers. Finally, it should be noted that the present stylistic analysis has been carried out on a sample of only three travel blogs; its results should therefore not be used as a basis for generalizations. To comprehensively describe and systematize the communicative and functional aspects of the travel blog format, a larger data set consisting of several travel blogs should be analysed. In the upcoming doctoral dissertation, we are going to focus on aspects of this media format which have been only briefly mentioned in the present paper. Among them are multimodal resources, mediality, the interaction between bloggers and readers, the effect of bloggers' network on the individual text modules, and the role of embedding other semiotic resources such as videos.
Research on Course Sharing Technology Based on IPFS and Blockchain

This paper studies the course sharing project by using IPFS and Ethereum technology.Designed a course sharing project based on IPFS and Ethereum.This project solves the problem of huge course data and single point of failure.First of all, using IPFS as the access medium can effectively reduce storage costs and improve storage efficiency.Second, use Ethereum technology to solve the single point of failure problem.Finally, the Ethereum transaction mechanism is adopted to enhance the enthusiasm of course users to share and improve the liquidity of the course.
Energy Blockchain Business Model Considering Physical-Information-Value

This paper proposes a research perspective of energy blockchain based on the three-dimensional attributes of physics-information-value: analyzes the coupling between blockchain technology and energy Internet in the dimensions of physics, information, and value under complex network systems, and proposes a physical-information-value-based research perspective.The basic framework of energy blockchain from a three-dimensional perspective of information-value, and in-depth research on the main content of energy blockchain from the perspectives of physics, information and value, and finally designed an energy blockchain from a multi-dimensional comprehensive perspective with the energy microgrid as the scene.cooperative autonomous mode and its operation process.
SOME INFERENCES ON OFFSET NORMAL DISTRIBUTION: PROPERTIES AND CHARACTERIZATIONS

The offset normal distribution, defined as the induced shape parameter of a Gaussian random configuration in the plane, is one of the most important probability distributions with tremendous applications both in pure and applied mathematics. In this paper some basic properties of the offset normal distribution are discussed. Based on the basic properties, several characterizations are presented by the left and right truncated moments. The first characterization result is based on a relation between left truncated moment and failure rate function. The second characterization result is based on a relation between right truncated moment and reversed failure rate function.
Predicting Accounting Fraud in Publicly Traded Chinese Firms via A PCA-RF Method

Financial fraud occurs from time to time and gradually becomes a worldwide problem with the expansion of the international capital markets and the rise of the information industry economy under the Internet eco-system.This paper provides a methodology for predicting financial fraud using basic financial data.The methodology is based on PCA-RF.Different from traditional methods such as logistic regression and support vector machine, we creatively proposed a PCA-RF model, first using principal component analysis to reduce the dimensionality of the data, then using grid search to optimize the random forest model, and finally directly selecting the raw financial data from the financial statements for direct analysis.We compare the analysis results with random forest and neural network methods, and the study finds that the PCA-RF model is superior for predicting domestic financial fraud in China.In this paper, we use an ensemble learning approach to introduce the PCA-RF method into the field of prediction of financial fraud for listed companies.
Risk Identification Method of Enterprise AccountingInformation Fraud Based on Weighted Association Rule Algorithm

In order to reduce the financial risks existing in enterprises, this paper studies the risk identification of enterprise accounting information fraud with weighted key algorithm.Based on the study of fuzzy association rules, this study analyzes the financial risks of enterprises, puts forward an analysis model of enterprise financial risks based on fuzzy association rules mining algorithm, and makes a qualitative analysis of enterprise financial crises, so as to predict enterprise financial risks and provide reference for enterprises to avoid risks.Comparing the correlation algorithm with the linear regression model, the result shows that the prediction error rate of the linear regression model is higher than that of the correlation algorithm model.Class I misjudgment rate is 40% higher, class II misjudgment rate is flat, overall misjudgment rate is 20% higher, and accuracy linear regression is 20% lower than correlation algorithm.It can be seen that the misjudgment rate of the correlation algorithm model is low, the prediction accuracy is high, and the discrimination and prediction ability of the linear regression model is poor, which proves the effectiveness of the application of the correlation algorithm in this paper.
Privacy-Preserving Attribute-Based Educational Service Recommendation in Online Education System

Educational service recommendation has attracted considerable attention since it can solve complicated educational tasks by gathering the wisdom of a crowd of teachers in recent years.In the education service recommendation system, parent (student) can send requirements to the education platform, and get a suitable teacher recommended.In the existing education service recommendation schemes, although parent (student) can send the basic requirements to get education service recommendations, they cannot set personalized requirements to obtain personalized education services.In addition, teacher' ability or credibility has not been concerned, and the privacy-preserving of tasks and task recipients have also been ignored.To address the above problems, this article proposes a privacy-preserving attribute-based education service recommendation scheme, which realizes fine-grained access control and keywords search for education services by using attribute-based searchable encryption (ABKS).Then, the anonymous key generation method is adopted, in which the attribute authority and the teacher interact to generate the key to ensure the security of the teacher's key.Besides, education platform can choose the best teacher to accept the task by evaluation mechanism.The security proof and performance analysis show that the scheme has strong security and practicality in the online education system.
Peningkatan Hasil Belajar IPS pada Materi Pemanfaatan Sumber Daya Alam Melalui Penerapan Problem Based Learning di Kelas IV SDN Baddurih Kecamatan Pademawu Kabupaten Pamekasan Tahun Pelajaran 2019/2020

Based on the results of observations and interviews conducted with teachers and fourth grade students at Baddurih Elementary School, Pademawu District, the following data were obtained: from the observations made, the researcher obtained data that during social studies learning the teacher only explained material through the lecture method which was then followed by giving assignments to work on the questions in the LKS. After finishing working on the LKS, the teacher and students discuss the results of student work. During learning students tend to be passive. The formulation of the problem in this study is as follows: 1) How is the application of the Problem Based Learning model to improve social studies learning outcomes for class IV?, 2) Can the application of problem-based learning improve social studies learning outcomes for class IV? using the Kemmis &amp; MC model research design. Taggart where each cycle consists of 4 stages, namely planning (planning), acting and observing (implementation and observation), reflecting (reflection), revise plan (improving plans). Application of the Problem Based Learning model can improve student learning outcomes in class IV. This is evidenced by an increase in the average value of student learning outcomes at each meeting. In cycle I, the average value of new student learning outcomes reached 63.69. The percentage of success classically only reached 43.5%. In cycle II, the learning outcomes obtained by students were very satisfying. reached 76.21. The percentage of success classically only reached 91.7%. Has succeeded in achieving the KKM value that has been determined
The Effects Of Hate Speech On Social Media: The Case Of Syrian Refugees In Türkiye

Making hate speech on social media has started to reach great dimensions with the increase in internet usage and therefore social media users. Especially in recent years, the use of social media in a bureaucratic dimension has integrated the concept of social media diplomacy into our lives. Syrian refugees, who have been on Turkish territory for more than 10 years and most of them are still under temporary protection, have made Türkiye the country hosting the highest number of refugees in the world. After the civil war that started in their country, millions of Syrians came to Türkiye and settled in Türkiye. The population is constantly increasing with new births and there is an intense Syrian population presence, especially in some cities and regions. Therefore, anti-immigrant and anti-refugee discourses have started to increase in Türkiye, and social media has become an important channel for the dissemination of these discourses and the organization of these ideas. This study, in a descriptive way, defines hate speech and offers such discourse examples while explaining how Syrian immigrants, as the majority immigrant group, are subjected to hate speech on social media.
Current research methods in mathematical and computer modelling of motivation management

Hands-on introduction to basics of data science with Python language and standard machine learning libraries are presented. The reader (student or researcher) will get a smooth and concise introduction to the state-of-the-art tools of data analysis. This is an introductory chapter containing basics of programming in Python-how to start and how to create a simple program that reads data and draws basic plots summarising statistical descriptions of a sample/data. The tutorial will be continued in the next chapter.