Reviewerly/exHarmony huggingface dataset:
The Moderating Role of Leadership in the Talent Management and Employee Retention of Abu Dhabi Department of Education and Knowledge

The purpose of this study to examine the moderating role of Leadership on the relationship between Talent Management (TM) and Employee Retention (ER) in Department of Education and Knowledge in Abu Dhabi. This research is deductive approach and quantitative methods. This quantitative study used 57 items structured questionnaires which were distributed to 354 teachers in Abu Dhabi schools using strata random sampling techniques. A total of 354 usable surveys were collected from 256 schools in Abu Dhabi. And analysis using (Smart-PLS-SEM 3.2.6) path modelling. The findings of this study positive relationship between (P-value 0.002) career development and employee retention, (P-value 0.026) competency mapping and employee retention, (P-value 0.007) employee engagement and employee retention, (P-value 0.015) performance management and employee retention. Furthermore, regarding the moderation effect of leadership between the variables, the bootstrapping procedure by using Smart-PLS was used and the findings revealed that, out of four hypotheses, two hypotheses were found the significant moderation effect between employee engagement and employee retention (β=0.213, t=2.445, p-0.015), performance management and employee retention (β= -0.242, t=3.178, p-0.002). While, the moderation effect of leadership with career development (p=0.408) and competency mapping (p=0.792) was found insignificant. Therefore, it can be concluded that the leadership moderating thus impeding the contribution of talent management towards employee retention. The result implied that the Department of Education and Knowledge in Abu Dhabi should adopt talent management to enhance their employee retention in future. Recommendations for further research were discussed at the end of the thesis.
A Novel Fuzzy Clustering Approach for Gene Classification

Automatic cluster detection is crucial for real-time gene expression data where the quantity of missing values and noise ratio is relatively high.In this paper, algorithms of dynamical determination of the number of cluster and clustering have been proposed without any pre and post clustering assumptions.Proposed fuzzy Meskat-Hasan (MH) clustering provides solutions for sophisticated datasets.MH clustering extracts the hidden information of the unknown datasets.Based on the findings, it determines the number of clusters and performs seed based clustering dynamically.MH Extended K-Means cluster algorithm which is a nonparametric extension of the traditional K-Means algorithm and provides the solution for automatic cluster detection including runtime cluster selection.To ensure the accuracy and optimum partitioning, seven validation techniques were used for cluster evaluation.Four well known datasets were used for validation purposes.In the end, MH clustering and MH Extended K-Means clustering algorithms were found as a triumph over traditional algorithms.
TrPM: A Linguistic Petri Nets module to describe the trends of a time series

Linguistic Petri Net (LPN) is a method to generate linguistic descriptions, which maintains the way that Petri Nets (PNs) work along with the mechanisms necessary to generate linguistic descriptions of systems. This paper presents a new LPN module, called the Trends Processing Module (TrPM), to generate linguistic descriptions of trends in a time series. This takes the series as an input and returns a sequence of places that represent in detail each one of the trends of the series, which allows us to generate the description. A public dataset has been used to test the presented module. The designed module can be used to design another more complex LPNs that need an approximate model of the trends of a time series.
The quality of an English summative test of a public junior high school, Kupang-NTT

This study aimed at examining the quality of an English summative test of grade VII in a public school located in Kupang. Particularly, this study examined content validity, reliability, and conducted item analysis including item validity, item difficulty, item discrimination, and distracter effectiveness. This study was descriptive evaluative research with documentation to collect data. The data was analyzed quantitatively except for content validity, which was done qualitatively. Content validity was analyzed by matching the test items with materials stated in the curriculum. The findings revealed that the English summative test had a high content validity. The reliability was estimated by applying the Kuder-Richardson’s formula (K-R20). The result showed that the test was reliable and very good for a classroom test. The item analysis was conducted by using ITEMAN 3.0. and it revealed that the the test was mostly constructed by easy items, most of the items could discriminate the students, most distracters were able to perform well, and the most of items were valid.
USING MOBILE LEARNING AND RESEARCH BASED LEARNING TO ATTRACT STUDENTS INTO QUANTUM INFORMATION RESEARCH

Research-Based Learning seeks a new valued perception of scientific research through its inclusion in educative spaces.Mobile Learning promotes the inclusion of different terrains of university education.Research is not an exception.This work presents an initiative to include undergraduate and graduate students in Quantum Information research assisted by a mobile site to learn and collaborate.We analyse the performance in terms of participants, impact factor and scientific collaborations based on analytics included in the site, tracking the participants in their different inclusion spaces.
SNS-CF: Siamese Network with Spatially Semantic Correlation Features for Object Tracking

Recent advances in object tracking based on deep Siamese networks shifted the attention away from correlation filters. However, the Siamese network alone does not have as high accuracy as state-of-the-art correlation filter-based trackers, whereas correlation filter-based trackers alone have a frame update problem. In this paper, we present a Siamese network with spatially semantic correlation features (SNS-CF) for accurate, robust object tracking. To deal with various types of features spread in many regions of the input image frame, the proposed SNS-CF consists of-(1) a Siamese feature extractor, (2) a spatially semantic feature extractor, and (3) an adaptive correlation filter. To the best of authors knowledge, the proposed SNS-CF is the first attempt to fuse the Siamese network and the correlation filter to provide high frame rate, real-time visual tracking with a favorable tracking performance to the state-of-the-art methods in multiple benchmarks.
Integration of Decision Trees Using Distance to Centroid and to Decision Boundary

Plethora of ensemble techniques have been implemented and studied in order to achieve better classification results than base classifiers. In this paper an algorithm for integration of decision trees is proposed, which means that homogeneous base classifiers will be used. The novelty of the presented approach is the usage of the simultaneous distance of the object from the decision boundary and the center of mass of objects belonging to one class label in order to determine the score functions of base classifiers. This means that the score function assigned to the class label by each classifier depends on the distance of the classified object from the decision boundary and from the centroid. The algorithm was evaluated using an open-source benchmarking dataset. The results indicate an improvement in the classification quality in comparison to the referential method - majority voting method.
On a Granular Approach for Fuzzy Color Modelling

In this paper, a new fuzzy granular approach for modeling color categories is proposed. Fuzzy granular colors where introduced by some of the authors as a way to model color categories with non-convex membership functions, particularly those categories that can be defined in terms of the union of a finite collection of sub-categories (which are very commonly found and applied in practice). To build a granular color, an aggregation of fuzzy colors having semantic relationships, the so-called "granules", is performed. In this paper we introduce the use of Voronoi tessellations for modelling the granules, comparing its behavior with respect to a sphere-based approach. We illustrate the advantages of our approach with respect to current state of the art on the basis of some experiments.
Digital Skills and Literacy among Prospective Teachers of Sukkur Pakistan: A Conceptual Framework

The importance of information and communication technologies in the teaching and learning process has been highlighted several times in different studies worldwide. Digital literacy is a serious concern to prospective teachers because they are the main source for students to learn about digital tools and technologies to compete with others and it is only possible when teachers are digitally literate and eager to accept this as a need for 21st-century education. According to National Education Policy 2017 (the main pillars of ICT policy are the use of information and communication technologies (ICT) to strengthen the quality of teaching and enhance student learning and develop complementary approaches to ICT in education) and in light of 4th goal (Quality Education) of the Sustainable Development Goals (SDGs) it is significant for the integration of ICT in teaching and learning process in Pakistan. Whereas, the objective and proposed hypothesis of this study are to assess whether the teachers are digitally literate or interested to accept this digital transformation in education. In the context of methodology, for assessing the digital literacy among the student-teachers the data will be collected through a quantitative research approach from approximately 200 students of the Education department at Sukkur IBA University. Whereas, the gathered data will be analyzed by using the SPSS software by using Mann-Whitney-U Test and Independent Sample T-Test. The expected outcome of this research will be helpful to ensure the quality education and integration of ICT in teaching and learning processes for the betterment of the Educational system in Sindh, Pakistan because our educational system is so backward in technology. In conclusion, this study will enhance the knowledge and technological skills of prospective teachers through ICT integration. It will also increase the interest of prospective teachers in digital literacy through ICT based training
Perbandingan Metode Sobel, Prewitt, Robert dan Canny pada Deteksi Tepi Objek Bergerak

Computer vision is one of field of image processing. To be able to recognize a shape, it requires the initial stages in image processing, namely as edge detection. The object used in tracking in computer vision is a moving object (video). Edge detection is used to recognize edges of objects and reduce existing noise. Edge detection algorithms used for this research are using Sobel, Prewitt, Robert and Canny. Tests were carried out on three videos taken from the Matlab library. Testing is done using Simulik Matlab tools. The edge and overlay test results show that the Prewitt algorithm has better edge detection results compared to other algorithms. The Prewitt algorithm produces edges whose level of accuracy is smoother and clearer like the original object. The Canny algorithm failed to produce an edge on the video object. The Sobel and Robert algorithm can detect edges, but it is not clear as Prewitt does, because there are some missing edges.
Robust Estimation of Breast Cancer Incidence Risk in Presence of Incomplete or Inaccurate Information

Purpose: To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information. Data and methods: Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.gov/). We conducted two ablation-type experiments to compare the robustness of different classifiers where we randomly switched known information to missing with a missing probability of pm in one experiment, and randomly corrupted the existing information with a probability of pc in another experiment. We considered three prominent machine-learning classifiers such as Logistic regression (LR), Random Forests (RF) and a custom Neural Network (NN) architecture and compared their degradation of discrimination performance as a function of increasing probability of missing or inaccurate data. Results: LR, RF and custom NN resulted in an Area Under Curve (AUC) of 0.645, 0.643 and 0.649, respectively, on a test set with 500,000 total observations. When we manipulated the data by varying probabilities pm and pc from 0 to 1, NN resulted in better performance in terms of AUC compared to RF and LR as long as less than half the data was missing/inaccurate (that is, for values of pm < 0.5 and pc < 0.5). However, for missing (pm) or corruption (pc) probabilities above 0.5, LR gave similar performance as the custom NN. RF resulted in overall poorer performance when the data had additional missing or incorrect entries. Conclusion: In cases where the input information is missing or inaccurate, our experiments show that the proposed custom NN provides reliable risk estimates in medical datasets like BCSC. These results are particularly important in health care applications where not every attribute of the individual participant might be available.
An Empirical Study on Integration of Experiential Learning and Mobile Learning

This paper discusses how experiential learning and mobile learning can be integrated to facilitate knowledge acquisition and knowledge sharing among students, and equip students with the abilities to solve real-world problems in a ubiquitous manner. Running in a cyclic manner, experiential learning guides students to interpret why something happened, plan what should be done in the future, collect and manipulate facts and data, and experience and reveal what they learnt. With the capabilities of current mobile devices (like virtual personal assistants), experiential learning can be implemented with a higher level of effectiveness. The results reveal that experiential learning is beneficial for learners who believe it can help them to solve practical problems and the usage of mobile devices can offer flexibility to study and address the diversity of learners’ needs.
Performance Comparison of Natural Language Understanding Engines in the Educational Domain

Recently, chatbots are having a great importance in different domains and are becoming more and more common in customer service.One possible cause is the wide variety of platforms that offer the natural language understanding as a service, for which no programming skills are required.Then, the problem is related to which platform to use to develop a chatbot in the educational domain.Therefore, the main objective of this paper is to compare the main natural language understanding (NLU) engines and determine which could perform better in the educational domain.In this way, researchers can make more justified decisions about which NLU engine to use to develop an educational chatbot.Besides, in this study, six NLU platforms were compared and performance was measured with the F1 score.Training data and input messages were extracted from Mariateguino Bot, which was the chatbot of the José Carlos Mariátegui University during 2018.The results of this comparison indicates that Watson Assistant has the best performance, with an average F1 score of 0.82, which means that it is able to answer correctly in most cases.Finally, other factors can condition the choice of a natural language understanding engine, so that ultimately the choice is left to the user.
PEMANFAATAN NATURAL LANGUAGE PROCESSING DAN PATTERN MATCHING DALAM PEMBELAJARAN MELALUI GURU VIRTUAL

Virtual teachers are devices or agents with the function of replacing the role of humans (teachers) with their teaching skills. The virtual teacher also has a function as task facilitator and is presented during training, especially when the teacher is actually not present. The virtual teacher's objectivity is carried out by explaining the subject matter in a virtual environment, for example, virtual independent learning. In the western world, virtual schools are quite widely applied, especially to students who are homeschooling. In a virtual school, all materials, assignments and grades are accessed via the internet. The virtual teacher concept refers to a device that provides guidance and practice. Teachers who are equipped with interactive agent functions that can provide direction and feedback in order to improve student abilities. By utilizing natural language processing (NLP) and pattern matching to help students learn alone without being accompanied by a teacher. NLP is used as a learning tool as outlined in the text of the sentence as part of the learning material which is done virtually. Meanwhile, pattern matching is used to group the questions asked so that the computer can gain an understanding of the questions posed by students. This grouping aims to solve problems with different word order which basically have different meanings. The teacher has the facility to make material, make questions, and answer student questions. A teacher can add knowledge about the database to enrich the source of knowledge for students.
Experience of Use of Kahoot! Digital Tool in the University Library Activities

The article examines the experience of introducing gamification in the activities of the Scientific Library of Kyiv National University of Culture and Arts A review of the annual Top Tools for Learning 2019 checklist outlines four key areas of research in higher education highlighted by J. Hart. The process of gamification is considered as one that helps to support the students’ attention, learn the learning material faster, promotes the critical thinking development. Experience of using Kahoot! digital tool to test students’ knowledge of the Philosophy course using the online quiz is highlighted. The creation of a quiz layout, parameter settings, testing and conducting the game were provided by library staff. The quiz “Do you know philosophy?” consisted of 17 questions. Fifteen of them were of the test type, two were “true or false”. 44 second-year students participated in the game at the same time. The winner correctly answered 13 questions, scoring 13 339 points. The average score was 6779 points. Kahoot! efficiency as a learning tool was analyzed on the basis of the results of 44 participants. Overall game performance was 42 %. Most likely, students answered questions like “true or false.” The time to answer the test questions ranged from 2.3 seconds to 10.7 seconds. 100 % of those polled said that playing questions helped them gain new knowledge; this type of training is eligible for existence and should be recommended for use in the educational process in the future. Information technologies have been found to offer ample opportunity to interpret their role as educational tools. The application of gamification allows for the formation of a comfortable learning environment for young people, increases the motivation to master the material independently. The quiz “Do you know philosophy?” is an example of a multidisciplinary approach to studying at the university and implementation of the principles of professionally oriented teaching of the humanities.
Development of Learning Media Using VideoScribe for the Subject of Present Continuous Tense

To master English, students must learn many things, including about grammar in order to use English properly and correctly. There are a number of ways lecturers can do to support teaching and learning process, one of which is by making use of technology to develop learning media. Using media is expected to make learning process about grammar enjoyable. This research aims to develop learning media using VideoScribe for the subject of Present Continuous Tense which is one of the materials studied by odd semester students at Universitas Muhammadiyah Riau. This research further designs, implements and tests the feasibility of the learning media. This research is a research and development (R&amp;D) model with ADDIE which consists of 5 stages, namely Analysis, Design, Development, Implementation and Evaluation. The subjects of this study were undergraduate students majoring in non-English Department which consisted of 35 students in odd semester. The results of alpha testing, expert material assessment showed that the media that were designed was in a very good category with an average score of 4.26. Meanwhile, for trials with students, the video animation media using VideoScribe was in a very good category with an average score of 4.25. Therefore, it can be concluded that the instructional media designed is suitable to be used to support teaching and learning activities.
ENGLISH BORROWINGS IN COVID-19 PANDEMIC

This study describes English borrowing or loan words during COVID-19 pandemic frequently used by public and absorped by Indonesian. Some words mostly from medical and healthcare terms, but many of them are from daily, practical, social life terms. People don’t realize that they use many unfamiliar words and phrases when they communicate in Indonesian. Those words are English borrowings. The aims of the study are ; (1) to investigate some English borrowing words or phrases, (2) to describe the borrowing process from English into Indonesian. The Qualitative Decriptive Analysis Method used in analysing English borrowings . Obtains 95 English words taken then classified into the process of borrowings. 56 words from 95 words or 58,95 % already available in Kamus Besar Bahasa Indonesia (KBBI), 39 words or 41,05% is not listed in KBBI or signed with n.a or not available. In the borrowing process obtains 34 words or 35,7% as adoption , 45 words or 47,3% as adaptation , 14 words or 14,7 % as translation, and the rest is 2 words or 2,1 % as creation process. The result shows that English borrowings absorped by Indonesian through both adoption and adaptation are quite significant, total both is 83%. It means that Indonesian is quite open language to foreign languages. This fact also shows that Indonesian is a dynamic, up to date, and modern language.
CHARACTERIZATION AND PERFORMANCE OF POLYMER COMPOSITE MEMBRANES FOR THE REMOVAL OF HUMIC SUBSTANCES FROM WATER

This present study, investigates the performance of activated sardine scale powder (ASSP)/ Cellulose acetate (CA)/Polyvinylpyrrolidone (PVP) and powdered activated carbon (PAC)/ Cellulose acetate (CA) / Polyvinylpyrrolidone (PVP) composites membranes prepared by phase inversion for the elimination of humic substances (HS) from aqueous solutions.The materials and composites membranes were characterized by FTIR, XRD, SEM analyses.Overall, the results exhibit the higher performance of ASSP /CA/ PVP composite membrane than PAC /CA/ PVP composite membrane for the elimination of humic substances from water.Adsorption isotherm non-linear studies indicated that the two composites membranes can be successfully modeled by the Langmuir, Freundlich, and Temkin.The maximum monolayer adsorption capacity was found to be 31.00mg/g and 23.32 mg/g for ASSP /CA/ PVP and PAC /CA/ PVP composites membranes, respectively.ASSP /CA/ PVP composite membrane seems to be a promising material for HS removal from aqueous solutions.