Reviewerly/exHarmony huggingface dataset:
The Image of Modern University Teacher: Studying the Student Perceptions

The article analyses and describes the results of studying how the students perceive the image of a modern university teacher.Tertiary education in the 21 st century requires changes in teacher attitudes and behaviours.The perception of the teacher's image by students is one of the most important factors affecting their attitude to the academic process.Several criteria assessed by the students anonymously are singled out: professional, personality-related and social ones.It seems reasonable to look for the peculiarities of teacher image perception by the students of different universities, namely the College of Asian and African Studies (CAAS of MSU) and the Russian Economic University (REU).The aim of the study was to identify and analyse the specific features of the image of teacher perceived by various groups of students.The empirical part of the study was conducted at the chair of West European languages of CAAS.The participants were: 45 students of CAAS, majoring in one of the four specialties: philology, history, economics or political science, and 31 students of the REU, studying economics or IT.The students of CAAS learn oriental or African languages and European languages, while REU students learn only English.The methodology of the study was to design a questionnaire in order to reveal the teacher personality traits, social and professional qualities.The analysis showed that the students' perceptions of the teacher image undergo some transformation over years of study, though not very dramatic.The professional component proves to be the most crucial for undergraduate students of both universities.Most students agree that the content knowledge is more important than general erudition or command of contemporary teaching methods.The most important personality traits turned out to be communication skills -the ability to listen and hear and respect for students.As for the teacher type, there is a distinct mismatch between expectations and reality.
Research on Deep Learning Algorithm and Application Based on Convolutional Neural Network

The field of artificial intelligence has developed rapidly this year, and new high-tech companies with their main business have sprung up. After years of theoretical knowledge accumulation and computer hardware equipment upgrade, deep learning began to show its talents in the field of artificial intelligence such as computer vision and voice processing. Deep learning is mainly based on multi-layer neural network to imitate, learn, and apply images, texts, and voices. As one of the deep learning algorithms, convolutional neural networks are favored by researchers in related fields because of their local connections and the advantages of weight sharing. On the basis of expounding the deep learning algorithm of convolutional neural networks at home and abroad, this paper analyzes and summarizes the application of deep learning algorithm by convolutional neural networks and prospects its development trend, hoping to engage related convolutional neural networks. Provided a reference for researchers.
The impact of sustainability development reporting on the environmental performance in Jordanian government

This research explores the significance of sustainability development through the role of environmental disclosure in Jordanian ministries and government departments by focusing on ministries and government departments that are related with the environment.This research presents the relationship between the environmental disclosure in annual reports and its impact on the overall environmental performance in Jordan, with the use of a mixed approach that will provide better quality of research results.The quantitative approach was used to examine the budgets of 22 ministries and government departments.The study was conducted using the time series from 2006 to 2015.The qualitative approach was conducted to raise the issues related to the subject that are not looked at in the quantitative study and to understand the perception of respondents in the ministries and government departments on the factors and items included in annual reports affecting of environmental disclosure and environmental performance in Jordanian governments.The results show a significant relationship between environmental disclosure and environmental performance in Jordanian government.The findings of this paper present new implications for theorists and practitioners of sustainability development by presenting the importance of the impact of environmental disclosure in the ministries and departments and its relationship on environmental performance of the Jordanian government.
Applications of Machine Learning and Image Processing Techniques in the Detection of Leukemia

The cancer of blood and bone marrow is called Leukemia.It is a result of the uncontrolled reproduction of immature white blood cells.It hampers the ability of the body to fight infection.In Leukemia, the white blood cells (WBC) are generally affected.There are different types of Leukemia namely: Acute myelogenous Leukemia (AML), Acute lymphocytic Leukemia (ALL), Chronic myelogenous Leukemia (CML), Chronic lymphocytic Leukemia (CLL).This paper proposes to automate the Leukemia detection process using machine learning and different techniques of image processing.The dataset consists of images of blood smear which is of both Leukemia and non-Leukemia patients.K-means Clustering, Marker-Controlled Watershed segmentation, and HSV colour-based segmentation are the image segmentation algorithms that have been used.Various features from the segmented lymphocyte images are extracted since the structural components of normal and Leukemic lymphocytes differ remarkably.The SVM classifier, which is a machine learning technique, is used to further classify Leukemia into its different types.This paper aims at identifying Leukemia and determine its types whether it is AML, ALL, CML or CLL which takes the classification process one step further as a majority of the previous works have been restricted to just detection of Leukemia or classifying into few of the main subtypes.The proposed system is successfully implemented using MATLAB.
Virtual Trainer for Mobile Augmented Reality Based Electrical Lighting Installation

&lt;p&gt;Virtual Trainer of Mobile Augmented Reality (AR) -based Electrical Lighting Installation is a technology that can be utilized as a substitute for conventional trainers to modern virtual-based trainers. The use of virtual trainers Electric lighting installation can be used anywhere and anytime with the help of an Android-based smartphone device. The use of AR technology is a technological development that helps users be able to interact directly with lighting installation components in a virtual form that has been designed to resemble the actual form. This study applied AR technology to learning aids called virtual trainers in the field of electrical lighting installations specifically residential. In running a virtual trainer application, several procedures must have been performed before it became an application that was ready to use. The results of this study were virtual trainers as an aid in understanding the components of residential electric lighting installations with AR mobile technology. This application system simulated 3D of 1 phase electrical lighting installation components in residential homes.&lt;/p&gt;
Sentiment Analysis of Impact of Technology on Employment from Text on Twitter

Many different studies are in progress to analyze the content created by the users on social media due to its influence and social ripple effect. Various content created on social media has pieces of information and user’s sentiments about social issues. This study aims to analyze people’s sentiments about the impact of technology on employment and advancements in technologies and build a machine learning classifier to classify the sentiments. People are getting nervous, depressed and even doing suicides due to unemployment; hence, it is essential to explore this relatively new area of research. The study has two main objectives 1) to preprocess text collected from Twitter concerning the impact of technology on employment and analyze its sentiment, 2) to evaluate the performance of machine learning Naïve Bayes (NB) classifier on the text. To achieve this, a methodology is proposed that includes 1) data collection and preprocessing 2) analyze sentiment, 3) building machine learning classifier and 4) compare the performance of NB and support vector machine (SVM). NB and SVM achieved 87.18% and 82.05% accuracy respectively. The study found that 65% of the people hold negative sentiment regarding the impact of technology on employment and technological advancements; hence people must acquire new skills to minimize the effect of structural unemployment.
Dynamic degree balanced with CPU based VM allocation policy for load balancing

In cloud computing environment, Load balancing is key challenge. To address above challenge, we have proposed Dynamic Degree Balance with CPU based VM allocation policy. The proposed algorithm includes both VM allocation and task allocation. This algorithm is compared with the two static algorithms viz. Shortest Job First (SJF) and First Come First Serve (FCFS). CloudSim simulator is used to perform experiment. Degree of imbalance and waiting time is considered as evaluation parameter to check the results of experiment. The experimental result shows that proposed algorithm reduces degree of imbalance and waiting time of task.
A vision based method for automated measurement of circular fiber cross-sections

There are many synthetic fibers with circular cross-sections, and characteristics of the fiber cross-sections have a major influence on the fiber properties. Direct measurements made with microscopic techniques on the fiber cross-sections can output maximum information on transverse dimensions. However, measuring parameters of the fiber sections in the microscopic images is a laborious task. In this paper, a hybrid method based on machine vision is proposed to measure the diameter and ellipticity of the circular fiber cross-sections. The hybrid method consists of modified Circle Hough Transform (CHT) and edge tracing algorithm, and the two sub-algorithms are performed successively. The modified CHT is used for detection and initial measurement of the fiber sections. Then the results are refined by the edge tracing algorithm, outputting elliptical results along with the ellipticity information of each fiber section. Experimental results show that the proposed method outperforms existing methods in circular fiber cross-section measurement.
Text Document Clustering using Hashing Deep Learning Method

Web mining is the method of analyzing an grouping of behavioral, statistic, way of life, value-based, web and geographic data for the personalization of offers to online shoppers in genuine time.The goal of this study is to build an effective model for the use of hybrid data clustering and classification technology to evaluate online news data.Assess the best way to use site news information algorithms and assess the reliability of the online news databases use tools and techniques for data mining.A well-known platform to share information among online users is a web-based application.However, nowadays, it is the most challenge to handle gigantic data or enormous information such as web news or web-based promoting by users.On the other side, web applications are the most readily available medium for consumers to access up-to-date information.Such apps also need tremendous space, time and drain the battery power of the mobile devices of the users.One solution to mitigate these challenges is therefore, to extract or extract certain information on the basis of certain characteristics.In contrast, the attributes are the actions or the information collected from different sources by the consumer.This essay attempts to design and implement a web app to extract information on geolocation and space and provides a comparative study on three specific mining techniques.
Dynamic Harmonic State Estimation of Power System Based on Sage-Husa Square-Root Unscented Kalman Filter

The limitations and shortcomings of traditional Kalman filter (KF) and its derivative algorithms with the application of harmonic state estimation (HSE) are analyzed. With the consideration that the power system is nonlinear and the measurement noise is an unknown random noise which is difficult to estimate by experience or hypothesis, an improved Sage-Husa square-root unscented KF (SH-SRUKF) algorithm based on SRUKF is introduced and derived, the method and flowchart for dynamic HSE under SH-SRUKF algorithm is proposed in this paper. Taking an IEEE14 test system as an example, the results of dynamic HSE in three scenarios are discussed, including the measurement noise is uncertain random noise, it might contain bad data and feasible fluctuations in load power. The simulation results indicate that SH-SRUKF performs better than SRUKF in the robustness and accuracy for dynamic HSE.
A Contrastive Analysis of Classroom-Based Language Assessments

Classroom-based language assessments mainly include formative assessment and summative assessment, which are the most commonly used evaluation methods. The present study adopts a contrastive method to analyze the two types of assessments. Results of the study show that: 1) the characteristics of formative assessment contain teachers&amp;rsquo; adaptation to classes and immediate feedback provided for teachers, while summative assessment, as a high-risk one, needs a high standard control and safety for dependability and effectiveness; 2) formative assessment is suitable for any places with multiple standards for the judgement of learners&amp;rsquo; achievements while summative assessment evaluates the educational effect or the whole process of special education at a certain time point, and both assessments can always be conducted on network platforms nowadays; 3) evidence and interpretation are taken into consideration in formative assessment and summative assessment respectively while the two types of assessment complement each other for the related teaching goals; 4) more attention may be paid to both assessment for learning (AfL) and assessment as learning (AaL) in the future development of assessment. The contrastive study is expected to provide a reference for classroom-based language teaching and learning.
Authentication using Robust Primary PIN (Personal Identification Number), Multifactor Authentication for Credit Card Swipe and Online Transactions Security

In view of effectiveness, ease of access and profitability, the advancement in e-Commerce is an immense step in forward.The development is went with further unusual vulnerabilities to worry.The significant issue all through the world in credit card management is credit card fraud.Because of extortion and fraudsters persistently look for better approaches to confer unlawful activities the organizations, users and establishment finds tremendous loss yearly.In the common Credit Card extortion process, fraudulent transaction will be distinguished only after the transaction is finished.In recent Studies, the security of Credit Card Transaction from unauthorized admittance or usage are addressed by diverse access control methods.This paper illustrates a new scheme of Authentication using Primary PIN and Multifactor authentication to secure credit card transactions.
Test suites effectiveness evolution in open source systems: empirical study

&lt;span&gt;Test suite code coverage is usually used to indicate the capability of a test suite in detecting faults. Earlier research studies, which explored the relationship among test suite effectiveness and code coverage, have not addressed this relationship evolutionally. Moreover, some of these works were studied small or identical domain systems, which make the result generalization process unclear for other systems. Finally, some of these studies were conducted with automatically generated test suites, which might not present the real situation for studied systems, so the results cannot be generalized to real test suites. In this paper, the authors empirically explore three open-source software systems along with their 11 versions. These versions are evolved over time and might have more sources of code and test suites. This work objective is to study the correlation between test suite effectiveness, the size of the test suite, and coverage for three Java programs during their evolution. In this work, the code coverage, test suite LOC and mutation testing coverage are measured to assess the correlation between the effectiveness of fault detection, code coverage, and test suite size. Based on the result we cannot generalize the assumption that test size is always revealing a positive correlation with its eﬀectiveness, but still weak to the high correlation between test effectiveness, test size, and coverage.&lt;/span&gt;&lt;p&gt; &lt;/p&gt;
Finite-key analysis for round-robin-differential-phase-shift quantum key distribution

Since the round-robin-differential-phase-shift (RRDPS) quantum key distribution (QKD) protocol was proposed, it has attracted much attention due to its unique characteristic i.e., it can bind the amount of information leakage without monitoring signal disturbance. Recently, Yin et al . have developed a novel theory to estimate its information leakage tightly. However, the finite-sized key effects are not taken into account. Here, we fill this gap and extend the security proof of the RRDPS protocol to the finite-sized regime using post-selection technique. As a consequence, it’s predicted that the key rate of RRDPS in a finite-sized key scenario can be comparable to the asymptotic one, which is meaningful for the real-life applications.
Professional orientation of students as the basis of future labour motivation

The paper offers a look at the professional orientation of students in schools and universities as the main factor of motivation of young professionals. The results of the study among high school students (64 votes) and university students (47 votes) are presented. Identified key sources of information that help them make a decision to choose a profession, determinate assessments of understanding of their future profession, and changes in preferences over time. A half of students have problems with career orientation.
PERFORMANCE EVALUATION OF ALGORITHMS FOR GENDER CLASSIFICATION

Gender Classification based on the acoustic properties of voice sample is an important task for various applications.Gender of the person can be identified by the acoustic properties of voice.For classification choosing one of the best matching learning is another important task.In this proposed study we conducted various experiment study of various machine learning algorithms for Gender Classification.We analysed the performance of algorithms using voice dataset.From this we concluded that Random Forest and Artificial Neural Networks giving best results.
Knowledge on Stroke among Middle Aged Adult in a Community at Ratuwamai, Morang

Stroke is a medical emergency also known as Cerebrovascular Accident (CVA) or "brain attack". Nowadays, this health problem is increasing tremendously day by day in developing country like Nepal.This study follows with an objective to assess the knowledge on stroke among middle aged adult. A descriptive cross-sectional study was used to assess the level of knowledge on stroke among (40-65) years of aged group in a community of Ratuwamai, Morang. Non probability purposive sampling method was used to select respondents where sample size was 117. Semi-structured interview questionnaire was used for data collection and data was analyzed with Statistical Package for Social Science (SPSS) version 16.The study showed that among 117 respondents, 62.1% were above 50 years (Mean±SD = 53.53±7.169), 32.9% had achieved secondary level of education, 97.4% gave correct response as stroke is a brain problem, similarly 100% of respondents reported high risk is above 45years of age, 91.5% identified hypertension leading to stroke, 96.6% thought that sudden onset of dizziness as warning signs of stroke. This study found that overall knowledge regarding stroke was average (65.8%). Relationship of ethnicity (p=0.045) and level of education(p=0.000)were found to be associated with level of knowledge. A widespread awareness program is required to improve the knowledge of community people regarding stroke, which in turn might improve the control of risk factors, early treatment and better outcomes.
Vocabulary Alignment in Openly Specified Interactions

The problem of achieving common understanding between agents that use different vocabularies has been mainly addressed by techniques that assume the existence of shared external elements, such as a meta-language or a physical environment. In this article, we consider agents that use different vocabularies and only share knowledge of how to perform a task, given by the specification of an interaction protocol. We present a framework that lets agents learn a vocabulary alignment from the experience of interacting. Unlike previous work in this direction, we use open protocols that constrain possible actions instead of defining procedures, making our approach more general. We present two techniques that can be used either to learn an alignment from scratch or to repair an existent one, and we evaluate their performance experimentally.
Proposed universal framework for more user-friendly author instructions

When preparing a scientific manuscript for submission to a journal, it is often time-consuming to find the journal's specific preferences, which can influence acceptance. We propose that journals include a simple table at the start of their instructions for authors, clearly displaying the essential information, e.g. word count, number of keywords, format of tables and figures. Such a table could be also easily updated as journal preferences change. Thanks to this, the submitted articles would be more likely to meet the basic requirements. We hope this initiative will save time for everyone involved in scientific publishing.
The Digital Literacy of The First Semester Students in Physics Education

Technology continuously facing the development requires a significant change for the community to master several competencies in using and utilizing various facilities in digital technology, including teachers as a component that plays a important role in education must be equipped with digital literacy. The purpose of this study was to obtain a digital literacy description of physics education students as preservice physics teacher in the first semester. This research was conducted using a descriptive method involving 80 first-semester physics education students at a university in Tasikmalaya. Data is collected by using tests and interviews. The instrument used was a valid and reliable digital literacy test as well as an interview guide. Data obtained is processed by determining the student score means and correlated with the results of the interview. The results showed that the mean of student’s digital literacy was 50 with a low category. Three digital literacy competency areas owned by students show that the mean literacy competency of information and digital data is 36 (low category), the mean of communication and collaboration competency is 68 (medium category), and the mean of digital content creation is 47 (low category).