Reviewerly/exHarmony huggingface dataset:
EFFECT OF GDP AND INFLATION ON UNEMPLOYMENT, CASE STUDY OF SUDAN

The purpose of this paper is to find out the effect of GDP and inflation on unemployment in Sudan from 1990 to 2020.Unemployment is taken as a dependent variable in this paper, GDP, and inflation as an explanatory variable.The augmented Dicky-Fuller test and Ordinary Least Square (OLS) method have been applied in this paper.The main findings of the paper depict that there is no unit root problem as all the variables are stationary at the level.The results of multiple linear regression models indicate that GDP has a negative and significant effect on unemployment, while inflation has a significant and positive effect on unemployment in Sudan.It also the diagnostic result of the paper shows that the model is free from heteroscedasticity.The R-squared value is 74% for the model; therefore, the model delivers a comparatively good fit of the data.This paper will help policymakers to modify the policy to reduce the unemployment rate in Sudan.The government should also embark on the creation of more job opportunities for A Study On The Effects Of Gdp And Inflatıon On Unemployment, Case Study Of Sudan
Semiotic Touch in Interpreting Poetry

Poetry is a poetic work of art. The poetic word already contains a unique beauty for poetry. Poetry is a medium for people who want to express their feelings. The language used by poetry is trimmer and more intensive than the language used for daily conversation. This intensive disclosure is what makes poetry valuable in the eyes of humans. Semiotics is a scientific discipline and analytical method that can examine the signs contained in an object to find out the meaning contained in the object. This study uses a literature review method where the technique used is to collect reading materials and literature specifically and then analyze the content of the study based on the focus of the problems discussed. After finding the matrix or keywords in a poem, the next step in studying poetry based on the approach is determining the theme. The theme is determined to get the main essence in a poem.
Mapping &amp; Visualization of Education Systems Using Blockchain Technology

Abstract: The blockchain technology, proposed by Satoshi Nakamoto, is being regarded as a possibility by businesses, including the financial training, and medical services industries, due to its decentralization and non-altering features. The current admission process of students involves hard copies of students and those documents are provided after their graduation, which can be eliminated by using blockchain technology for the storage of student data on IPFS. While many colleges are migrating to cloud storage, there are various threats associated with the clouds. Most schools and colleges store their student's data locally with no backup and some even don't use backup storage. COVID*19 has recently caused DDOS attacks and ransomware attacks to various colleges. Besides the fact that these traditional database practices often create a mix-up of the students' data, there is also the problem that the data can be misplaced. The purpose of this project will be to implement a model for using blockchains to implement Mapping &amp; Visualization of Education System that maintains students’ privacy and confidentiality. As a result of using hashes and decentralized data storage, the proposed model provides greater security. Additionally, the use of a blockchain based technology to maintain Student Management System’s data a highly trusted and reliable model.
Comparative Analysis of Mammography Image Segmentation Strategies

Breast cancer is a serious medical problem that affects women all over the world, and it is one of the most well-known tumors that kill women. The specialists of Breast cancer Prefer to use imaging methods such as a mammography to speed up recovery and reduce the risk of breast cancer. An ROI describe the tumor will be retrieved from the image that is entered to detect a malignant tumor. One of the basic techniques used to classify breast cancer is segmentation. Segmentation may be difficult in the presence of noise, blurring or low contrast. Pre-processing aids in the removal of extraneous data from a picture or the enhancement of image contrast in the early stages. Classification is greatly influenced by segmentation. Recent research have presented automatic and semi-automated segmentation algorithms for extracting the region of interest (ROI), lesions, and masses to check for breast cancer. In this study provides high-level overview of approaches of segmentation, with a focus on mammography images from current research. The datasets that were available were discussed as well as the problems encountered during the segmentation operation for the identification of breast cancer.
The system for assessing the quality of education in the universities of the leading countries of the world

The main purpose of the article is to study the most successful experience in the implementation of the education quality assessment system in the leading countries of the world. The world is constantly changing and developing. Together with it, we are changing, our ideas about the natural and anthropogenic environment, about our capabilities and needs are changing. We strive to know more, be able to do better, learn faster. And on this path, education is a powerful tool capable of changing the world. As a socio-cultural institution, education is acquiring new properties and characteristics. But the desire of people to form or receive education of the highest quality remains unchanged, especially in the context of systemic transformations. Universities have always been the main producers and providers of knowledge in society, but today they are recognized as the main generators of knowledge. In the context of global competition, a complex of interrelationships between high-quality higher education, scientific research both in the academic environment and outside it is becoming more important for ensuring the innovative and productive development of the state. As a result of the study, the most effective mechanisms for the implementation of the education quality assessment system in the leading countries of the world, as well as the most optimal methods for their adaptation, were analyzed.
Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

&lt;p&gt;&lt;span&gt;The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.&lt;/span&gt;&lt;/p&gt;
Entanglement of two superconducting qubits induced by a thermal noise of a cavity with Kerr medium taking into account the atomic coherence

The system consisting of two identical artificial atoms (qubits), resonantly interacting with the mode of quantum field of an ideal microwave cavity in the presence of Kerr nonlinearity, is considered. For the considered model, an exact solution of the quantum Liouville equation for the full density matrix of the system two qubits + resonator field mode is obtained. To solve the quantum equation of evolution, the representation of dressed states, that is, the eigenfunctions of the Hamiltonian, was used. A complete set of dressed states of the considered model is found. With its help, the solution of the evolution equation was initially found for coherent initial states of qubits and Fock states of the field, that is, states with a certain number of photons in the resonator mode. Then, the above solution was generalized to the case of the thermal state of the resonator field. A reduced density matrix of two qubits is found by averaging over the field variables. The two-qubit density matrix is used to calculate the parameter of qubit entanglement in the analytical form. Concurrence was chosen as a quantitative criterion for qubit entanglement. A numerical simulation of the time dependence of the consistency of qubits for various parameters of the model and the initial states of qubits was carried out. The most interesting result seems to be that taking into account the initial coherence of qubits in the model with Kerr nonlinearity leads to a significant increase in the maximum degree of entanglement of qubits induced by the thermal field, even in the case of high intensities of the resonator field.
The Show must go on: How Students’ Perception on Online Learning in Bojonegoro

Pandemic covid 19 spreads rapidly. Due to its quick breaks, a lot of countries decided to change some daily routine activies. One of affected aspects is education. Face to face learning has to be turned out into online learning in any level of schools. This present reseach aims to investigate and survey the students’ perception on online learning and what online fatlform mostly used by the students. This research is classified as qualitative research. This research took 74 participants. The data was obtained by distributing questionnaire which consist of some aspects namely method in delivering material, accessibility, beneft and challenges in onine learning and the most common used flatform during online learning. The result reveals that students prefer to get explanation in online learning. Internet connection and accessibility are complicated problems foun during online learning. Most of students also have lack of proper devices. They mostly use their smartphone with less memory devices. As a result, they have problems during online learning. In short, online learning is challenging for students. Microsoft teams is the most common online flatform used by the students in online learning
Design and Implementation of Electrical Parameter Monitoring System Based on ZigBee Wireless Communication

In order to solve the problems of many lines, troublesome movement and redundant management of traditional electrical parameter monitoring system, a design and implementation of electrical parameter monitoring system based on ZigBee wireless communication is proposed. Taking the monitor as an example, this paper briefly introduces the application status of the monitor monitoring system and electrical safety detection. According to the comparison of wireless communication technology and the analysis of actual demand, this paper expounds the advantages of ZigBee wireless technology. Based on the combination of received signal strength indication (RSSI) ranging algorithm and trilateral positioning algorithm, the wireless positioning of mobile targets is realized. Then, the sparse correlation matrix of signal attenuation coefficient between base stations is established in the upper computer to optimize the positioning by using the look-up table method. The relevant test results show that through the serial port test software 2000 times of data transmission and reception, 1866 times of communication are successful, and the success rate is 93.3%, which basically meets the requirements of data transmission stability; After data transmission time detection, it takes about 1.4 seconds to send data from the monitor to the client for reception. The client sends the module query command, and the time when the command reaches the monitor is 1.6 seconds. The data transmission speed can basically meet the requirements of real-time monitoring. The design and implementation of the monitoring system can not only help to reduce the workload of medical staff in hospital nurse stations and improve their work efficiency, but also provide the possibility of practical significance for developing into an application tool for patient family members or personal health management in the future.
Pedagogical Communication Promoting Regulation Of Adult Learners’ Foreign Language Educational Activity

The orientation of an adult's personality in the process of education is expressed in his desire for self-actualization, disclosure of his capabilities and achievement of positive results based on the obtained knowledge in the areas relevant to him. In this regard, the reserves for improving educational process are laid down in the very mechanism of pedagogical communication – humanistic in its essence, aimed at stimulating the internal motivation of the student, his need for self-realization and self-expression in learning. However, the adult is often far from being immediately able to organize his educational activities and participate successfully with other people in the process of foreign language communication, which can cause psychological barriers. The subject of our research is pedagogical communication which contributes to overcoming psychological barriers in mastering a foreign language and stimulates practically directed and self-educational activities of adults in the language sphere. This research confirms that the emergence of psychological barriers manifested as an internal obstacle to learning is due to a number of socio-psychological factors and the nature of learning itself. The main psycho-pedagogical parameters that characterize the features of pedagogical communication as a factor contributing to self-realization of the adult’s personality and reducing psychological barriers in learning were determined. Pedagogical principles for optimizing adult learners’ self-educational activity were developed. The methods used in the research are theoretical analysis of the problem; questionnaires, interviewing and semantic differential. The obtained data were processed by correlation and factor analysis.
A Purposed Multilayered Framework for Security and Privacy in Big Data

In this day and age, Big Data is extensively used among numerous organizations.From gathering and storing sensitive data to performing analytics, Big Data plays a fundamental role in any organizational growth in various social and economic areas.With data secrecy, a huge volume of data creates different security and privacy concerns that lead to several threats and vulnerabilities.Conventional methodologies for secured data are no longer applicable when applied in the perspective of Big Data.This paper presents a detailed overview of Big Data characteristics regarding V's and provides an insight into the analytics.It also addresses the security and privacy risks and proposed a multilayered framework to address and mitigate these challenges.
Pelatihan Online Metode Kuantitatif: Model Runtun Waktu Terapan

Abstract Purpose: the aim of this training is to improve the skill and ability of participants to apply quantitative method of time series model in their researchers by using zoom application. Method: the descriptive analysis is used to analyze this activity based on the responses of the participants in this training. Results: based on the results of the training on the quantitative method of applied time series model, it can be concluded that this activity is very important so that participants can understand data and be able to enter data correctly so as not to cause problems when estimating and processing other statistical data. Contribution: in general, the participants felt that they were very good at implementing this activity and they hoped that there would be additional activities that were more focused on practice so that participants could apply various time series models in research and teaching at their respective institutions. İt is recommended that participants need further training related to the application of the theories that have been presented in this training in order participants are able to publish their papers in international indexed journals.
Internet of Musical Things Environments and Pure Data: A Perfect Match?

The Internet of Musical Things (IoMusT) is an emerging area based on concepts from the Internet of Things, new interfaces for musical expression, human-computer interaction, ubiquitous music, and many others. Despite being a novelty, it already has some ecosystem models that use their abstractions to allow musical practice. However, they are not standardized and tend to consider only the preferences of their creators, relegating Pure Data, when present, to a secondary role. In this context, this article presents the Sunflower environment, a tool created by the authors of this text and which uses Pure Data in all its development stages, highlighting the importance of such a tool.
Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

There exists an enormous volume of data in the database system, which is accountable for the storage of data and organization of data. The intruders can breach the security system of database and steal the important information. Therefore, it is of great significance to carry out the cumulative anomaly identification of the security database. In view of the shortcomings of traditional anomaly detection methods in detection performance and poor effect of anomaly recognition, this paper proposes a cumulative anomaly recognition method based on discrete Markov chain for security database. First, the sniffer is used to read the user access behaviour data, and then, it is processed, that is, standardized processing. Then, the segmentation method is used to extract the user behaviour features, and the normal feature data and abnormal feature data are obtained. Finally, the state sequence generated by the discrete Markov chain is used to calculate the state probability, which is used to evaluate the abnormal process behaviour. The proposed method in this paper is based on the Markov chain and can be used for better anomaly recognition. The results are obtained in terms of sensitivity score, precision score, and F1-score. The results are also compared with the results obtained by using some of the state-of-the-art traditional techniques. The comparison clearly indicated that the proposed method is more effective as compared to the tradition methods. The proposed method has the highest F1-score of 0.8586, and then the traditional methods have F1-scores of 0.7233, 0.8236, and 0.7562 for methods 1, 2, and 3, respectively.
Braking Intention Recognition Method Based on the Fuzzy Neural Network

This paper focuses on a braking intention recognition method based on the adaptive network-based fuzzy inference system (ANFIS) and the data of braking tests. The displacement of brake pedal and its change rate were selected as the parameters of braking intention recognition; the braking conditions were divided into light braking, medium braking, and emergency braking. The test scheme of braking intention identification was designed, the braking test platform was built based on a vehicle, and the sample data of multiple groups of braking conditions were obtained. The parameters of braking intention recognition were fuzzed, the model of braking intention recognition was constructed based on ANFIS, and the recognition model was trained and tested. For the above three typical braking conditions, the constructed model of braking intention recognition was verified offline by using the data of the braking test. The results show that the proposed braking intention recognition method has high accuracy of braking intention recognition, which provides a theoretical basis for further application research.
Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques

Abstract: Individuals and businesses are increasingly using opinionated social media, such as product evaluations, to make decisions. People, however, try to game the system for profit or fame by opinion spamming (e.g., creating bogus reviews) to promote or demote certain specific items. Such bogus reviews should be identified in order for reviews to reflect real user experiences and opinions. Most of the consumers are influenced by the online reviews on the product and it plays a crucial role in finalizing purchase decisions in the market. But fake reviewers or spammers misused and take advantage by writing fake reviews, positive fake reviews to promote the product, or negative fake reviews to demote the product. There has been huge research in this domain for more than a decade for detecting fake reviews or fake reviewers. Howsoever many fake reviewers work together by creating groups to target any product and writing fake reviews on product in bulk, reviewers create multiple fake IDs and write fake reviews. Detecting false reviews and specific fraudulent reviewers was the subject of previous work on opinion spam. The primary thing of this study is to give a strong and comprehensive relative study for detecting fake reviews and reviewers using machine learning. Keywords: Fake review, Fake reviewers Spam opinion, Opinion mining, FIM, Reviewer-centric spam, feature engineering
Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.