Reviewerly/exHarmony huggingface dataset:
The Bees Algorithm Approach to Determining SMC Controller Parameters for the Position Control of a SCARA Robot Manipulator

In this study, position control of a SCARA robot manipulator is investigated using the sliding mode control (SMC) method based on parameter optimization using The Bees Algorithm.The modeling the SCARA manipulator is conducted in MSC Adams and the control implementation is carried out in MATLAB software.The numerical model of the SCARA manipulator is acquired by setting up a virtual prototype on MSC Adams software.In addition, the inverse kinematic equations of the SCARA manipulator are formed using Matlab/Simulink software in order to check the accuracy of the created virtual prototype.In addition, the SMC controller parameters are optimized with The Bees Algorithm to get better results.Then, the control performance of the system is examined on the virtual prototype using MSC Adams-MATLAB co-simulation.Moreover, Genetic Algorithm, another meta-heuristic method, is used for parameter optimization and the performance of The Bees Algorithm is compared with the results obtained.As a result, it has been observed that The Bees Algorithm can be used in studies related to the control of robotic systems.
Nano-Influencers Edutubers: Perspective of Centennial Generation Families in Spain

In recent decades, the incipient technological development has generated a radical change in the way people access and transmit knowledge. Educational institutions must have a teaching staff adapted to new forms of consuming information. The purpose of this research is to know the media competence of Spanish teachers, from the perspective of families of schoolchildren in primary education. This analysis is based on the investigation published by 50 renowned international experts in media competence, which revolves around six major dimensions. In our analysis, we focused on the processes of interaction, production, and dissemination of content by teachers on YouTube. A questionnaire has been devised with a sample formed by 1228 families, a personal interview with a sample formed by 20 families, and a comparative analysis of the productions and interaction of amateur teachers on YouTube platform and of recognized “edutubers,” as well as the use given to the dissemination of content on social networks. In the results obtained, the profile of a teaching staff that is increasingly disseminating and producing on social networks stands out, becoming content creators through their own YouTube channels, which also proposes tools for an interaction adapted to the centennial generation, using different digital communication tools. Differences were found comparing the three dimensions involved in this analysis, with teachers obtaining more positive evaluations as producers and as interactors in private schools than in subsidized and public schools. Likewise, differences were found between nano-influencers and macro-influencers in the use of aesthetic elements that make up the videos analyzed among the “edutubers.”
Data Mining Prediksi Minat Calon Mahasiswa Memilih Perguruan Tinggi Menggunakan Algoritma C4.5

The city of Cirebon has many diverse private universities and offers a variety of majors. Due to a large number of existing private universities, the competition between universities to attract prospective new students is very tight. So to be able to compete with these universities, the campus needs to predict the interest of prospective new students by knowing what factors are driving prospective new students to choose colleges. The purpose of this study was to predict the interest of prospective students in choosing a college. In this study, the data processed are the results of a survey conducted on prospective new students in the D3-Informatics Management Study Program. There are 25 sample data as training datasets with 5 assessment data. The data will be processed using the Data Mining process with the Classification Method. Furthermore, with the C4.5 algorithm in the Classification method, iterations are obtained up to node 4 with 3 assessment data which are factors in determining the interest of prospective students in the study program. Furthermore, the results of data processing using the classification method with the C4.5 algorithm were tested using Rapid Miner Software, which obtained a 100% accuracy rate with 120 testing datasets. The final result of using Data Mining with the C4.5 algorithm has been able to predict the interest of prospective new students based on assessment factors in choosing universities.
Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity

Interactive Data Visualizations (IDV) can be useful for cybersecurity subject matter experts (CSMEs) while they are exploring new data or investigating familiar datasets for anomalies, correlating events, etc. For an IDV to be useful to a CSME, interaction with that visualization should be simple and intuitive (free of additional mental tasks) and the visualization’s layout must map to a CSME's understanding. While CSMEs may learn to interpret visualizations created by others, they should be encouraged to visualize their datasets in ways that best reflect their own ways of thinking. Developing their own visual schemes makes optimal use of both the data analysis tools and human visual cognition. In this article, we focus on a currently available interactive stereoscopically perceivable multidimensional data visualization solution, as such tools could provide CSMEs with better perception of their data compared to interpreting IDV on flat media (whether visualized as 2D or 3D structures).
Modeling the civil servant discipline in Indonesia: partial least square-structural equation modeling approach

Purpose – This paper seeks to discover the factors that influence the supervisor to give the punishment level to civil servant staff—the data being used is a questionnaire to several civil servants in public academic institutions.&#x0D; Methodology/approach – This research used computational tools to classify transgressions into punishment categories (light, medium, or severe) with the model using the data science technique based on the partial least square-structural equation modeling (PLS-SEM) approach.&#x0D; Findings – It was found that the model of civil servant discipline in Indonesia is based on 14 hypotheses from bootstrapping technique and by using data science technique to support the result analysis of PLS-SEM.&#x0D; Novelty/value – This research contributed to providing civil servant supervisors to understand factors that influence the discipline of their staff, so it can be used to determine the punishment categorization.
Formation of Teenagers’ Value Orientations through Creolized Texts

The importance of the adolescence in the value orientations formation as a stable personality trait associated with the shaping of worldview has always been a matter of special research attention. This article seeks to investigate the formation of teenagers’ value orientations through creolized texts. The creolized text is a text in which verbal and nonverbal components form a visual, structural, semantic and functional wholeness, aimed at a complex impact on the recipient. The authors cleared out cognitive, emotional, and behavioural criteria for the formation of students’ value orientations. To determine students’ instrumental and terminal values, M. Rokeach’s method of value orientation diagnostics was used. Educational workshops aimed at altering teenagers’ values with the use of creolized texts were arranged. Examples of motivational posters and comics created by students in the process of these extracurricular activities are provided. The reflexive component is presented through students’ impressions of working with creolized texts.
The Effect of STREAM-Based Teaching Materials Using Smart Apps Creator 3 on Students’ Scientific Literacy

This study aimed to develop STREAM-based water teaching using Smart Apps Creator 3 on students’ scientific literacy. The method carried out by Research and Development uses the 4D design (Define, Design, Develop and Disseminate). The research data were collected and analyzed in the form of quantitative descriptive from the students’ pre-test and post-test results. This research used a Google form by distributing a questionnaire given to students with 56 respondents and 11 science teachers who had used STREAM-based water teaching materials using SAC 3. The results of the study, the average percentage of scientific literacy with N-Gain was 76% with a tall category, which means that the application of learning materials based on STREAM using Smart Apps Creator 3 is proven to improve student learning. Conclusion in this study is STREAM-based teaching materials using SAC 3 are statistically proven to improve students’ scientific literacy to water material in online learning during the current pandemic.
Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

Code-mixing is a phenomenon where at least two languages are combined in a hybrid manner in the context of a single conversation. The use of mixed language is widespread in multilingual and multicultural countries and poses significant challenges for the development of automated language processing tools. In Taiwan’s electronic health record (EHR) systems, unstructured EHR texts are usually represented in a mixture of English and Chinese which increases the difficulty for de-identification and synthetization of protected health information (PHI). We explored this problem by applying several state-of-the-art pre-trained mono- and multilingual language models and propose to exploit the principle-based approach (PBA) for the tasks of PHI recognition and resynthesis on a code-mixed EHR corpus annotated with 6 main categories and 25 subcategories of PHIs. A hierarchical principle slot schema is defined in the PBA to encode knowledge of code-mixed PHIs and utilize slots to learn from the training set to assemble principles for recognizing PHI mentions and synthesizing surrogates simultaneously. In addition, a semantic disambiguation process is implemented to disambiguate ambiguous PHI categories in the de-identification process and to dynamically extend the knowledge encoded in PBA during the knowledge augmentation process. The experiment results demonstrate that the proposed method can achieve the best micro- and macro-F-scores in comparison to the other mono- and multilingual language models fine-tuned on our code-mixed corpus.
Reducing Defocused-Information Crosstalk to Multi-View Holography by Using Multichannel Encryption of Random Phase Distribution

A new optical encryption algorithm, called the random-phase encryption algorithm, is proposed in this study. When this algorithm was applied in constructing computer-generated holograms, the out-of-focus image crosstalk was effectively reduced, and the image quality was greatly enhanced. In this study, the researchers encrypted each multi-depth or multi-view random phase sub-image with the phase-locked key to generate multi-channel encryption phase information. During the reconstruction, the switch of the phase-locked key of the sub-image was found to achieve different image reconstruction effects with different views or depths. This algorithm proved to substantially reduce the out-of-focus image crosstalk and to enhance the reconstruction quality of the original computer holography without concerning the mutual interference among the information of each view for multi-view and multi-depth holograms.
A Comparison of Classification Models in Predicting Graduate Admission Decision

While the decision process of graduate admissions remains elusive, specific criteria are decidedly significant in determining admission outcome. Prospective students applying to graduate programs experience a real predicament of selecting the right schools to invest limited resources for the application. This paper presents comparisons of various machine learning classification models, including Naïve Bayes, Logistic Regression, Multilayer Perceptron and Decisions Tree models, in predicting the admission outcome of candidates with a set of known parameters using a dataset of 400 applicant records. By comparing the performance metrics of these methods, the study finds Naïve Bayes to be the most accurate model for this type of dataset. Predictive models such as the ones discussed in this paper can be a valuable tool for prospective students in shortlisting universities in their application process. The study also proposes a framework that incorporates machine learning-based classification into the admissions decision process. Implementing such methods may help support graduate admissions committees in streamlining large pools of applications or observing and understanding trends in their past admission decisions.
Genetic Algorithm-Based Parametrization of a PI Controller for DC Motor Control

This paper analyses the application of a genetic algorithm (GA) for the purpose of designing the control system with separately excited DC motor controlled according to the rotor angle. The presented research is based on the utilization of a mathematical model designed with separate electrical and mechanical sub-systems. Such an approach allows fine-tuning of PID controllers by using an evolutionary procedure, mainly GA. For purpose of PID tuning, the new fitness function which combines several step response parameters with the aim of forming a unique surface which is then minimized with a genetic algorithm. From the results, it can be seen that the elitism-based algorithm achieved better results compared to the eligibility-based selection. Such an algorithm achieved a fitness value of 0.999982 resulting in a steady-state error of 0.000584 rad. The obtained results indicate the possibility of applying a GA in the parameterization of the PID controller for DC motor control.
A k,n-Threshold Secret Image Sharing Scheme Based on a Non-Full Rank Linear Model

Secret image sharing is a hot issue in the research field of data hiding schemes for digital images. This paper proposes a general k,n threshold secret image sharing scheme, which distributes secret data into n meaningful image shadows based on a non-full rank linear model. The image shadows are indistinguishable from their corresponding distinct cover images. Any k combination of the n shares can perfectly restore the secret data. In the proposed scheme, the integer parameters k,n, with k≤n, can be set arbitrarily to meet the application requirement. The experimental results demonstrate the applicability of the proposed general scheme. The embedding capacity, the visual quality of image shadows, and the security level are satisfactory.
E-Banking Security Study—10 Years Later

ICT security in the banking area is going through rapid changes. It is ten years since we covered the state of e-banking security, and both authentication schemes and legislation has evolved. With the Payment Services Directive (PSD2) for European Union coming into force, we believe it is a good time to update our findings. PSD2 brings new requirements for multi-factor authentication, thus it is necessary to revise compliance of currently used schemes. This work’s main contribution is an overview of current authentication methods, their properties with respect to international standards, and their resistance against attacks. We further discuss the multi-factor authentication schemes composed of those methods and their compliance with the PSD2 requirements. In order to present the overview, we introduced the e-banking attacks taxonomy, which is compatible with authenticator threats from NIST Digital Identity Guidelines but has an increased level of detail with respect to the e-banking area. The available sources in this area are usually either very broad, targeted on the business executive, or focus on one particular issue or attack in greater detail. We believe our article can bridge such diverse sources by providing a comprehensive and complex tool to help with orientation in the area.
Strategic Human Resource Practices and Employee’s Engagement: Evidence from Jordanian Commercial Banks

The study investigates the impact of strategic human resource practices on employee engagement and the mediating role of transformational leadership in the context of Jordanian 13 commercial banks. This study will be a pioneer study in Jordan that will analyze the same topic up to the researcher’s boundaries of knowledge. The research population consists of all the 13 Jordanian commercial banks, as per the website for the Association of Banks in Jordan (Association of Banks in Jordan, 2020). The number of workers in the banks is estimated at 15,553. The researcher will use the proportional stratified random sample technique. According to the population size (15,553) divided on the selected banks and depending on Sekaran`s table (Sekaran &amp; Bougie, 2016, 263), the sample size was determined based on the employees` number in each selected bank with an acceptable margin of error 5%. The sample size is 375. The unit of analysis in this study will be the employee working in Jordanian commercial banks. The study results confirm the impact of HRM on employee’s commitment and the partial mediating role of transformational leadership.
Text to Speech Synthesizer for Tigrigna Linguistic using Concatenative Based approach with LSTM model

Objectives:The purpose of this study is to describe text-to-speech system for the Tigrigna language, using dialog fusion architecture and developing a prototype text-to-speech synthesizer for Tigrigna Language.Methods : The direct observation and review of articles are applied in this research paper to identify the whole strings which are represented the language.Tools used in this work are Mathlab, LPC, and python.In this paper LSTM deep learning model was applied to find out accuracy, precision, recall, and Fscore.Findings: The overall performance of the system in the word level which is evaluated by NeoSpeech tool is found to be 78% which is fruitful.When it comes to the intelligibility and naturalness of the synthesized speech in the sentence level, it is measured in MOS scale and the overall intelligibility and naturalness of the system are found to be 3.28 and 3.27 respectively.Based on the experiment LSTM Deep learning model provides an accuracy of 91.05%, the precision of 78.05%, recall of 86.59 %, and F-score of 83.05% respectively.The values of performance, intelligibility, and naturalness are inspiring and show that diphone speech units are good candidates to develop a fully functional speech synthesizer.Novelty: The researchers come up with the first text to speech LSTM deep learning model for the Tigrigna language which is critical and will be a baseline for other related research to be done for Tigrigna and other languages.
Quantum Search Algorithm for Weighted Solutions

This paper presents a quantum search algorithm (QSA) for weighted solutions. In a QSA, the final quantum state has an equal probability of solutions. However, solutions should have different probabilities in specific problems, such as dynamic spectrum management (DSM). We propose a method to make differences with probability in accordance with rewards for each solution, and present an application to DSM. The proposed algorithm has some error terms for the final quantum state, and we provide the error terms in accordance with the number of iterations. In addition, we present the simulation results of the proposed algorithm compared with a classical exact algorithm and QSA. The proposed algorithm has a lower complexity and relative difference than the classical exact algorithm. Furthermore, the proposed algorithm has a higher performance than the QSA in specific cases.
A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

Among the many issues facing the world, the issue of traffic accidents, many security facilities have developed research on fingerprints, palmistry biometrics and other biometrics. In modern technologies, facial and eye detection algorithms have been developed and identified. This paper includes comparisons of face and eye tracking methods, which are extremely useful for identifying individuals in a variety of situations. Facial biometrics are more stable due to their distinct features, which gives them an advantage over other biometrics such as palmistry and fingerprint. This work dealt with 74 studies of face and eye detection and a comparison of algorithms that were used in previous researches, where the Viola-jones algorithm was identified as the best facial algorithm in terms of accuracy up to 98% for different types of face databases because this algorithm depends on geometry of face and also able to detect all faces in the same image, Furthermore, it has been determined that SVM is the best eye detection algorithm due to the accuracy of its characteristics and the speed of execution in real time so, the highest accuracy achieved was 98% accurate .
Modeling Iteration’s Perspectives in Software Engineering

Iteration is ubiquitous during software development and particularly notable in complex system development. It has both positive and negative effects; the positives of iteration include improving quality and understandability, reducing complexity and maintenance, leading to innovation, and being cost-effective in the long run; Negatives of iteration include; time, cost, and effort overrun. Its management is a challenging task and becomes more complex due to the non-uniformity of the terminology used at various places. Although Software Development Life Cycles (SDLC) are highly iterative, not much work related to them has been reported in the literature. Insights into iteration are explained in this paper by defining different perspectives (Exploration, Refinement, Rework, and Negotiation) on iteration through literature review, modeling each perspective, and simulating the effect of each iterative perspective on project completion time. An attempt has been made to create awareness about efficient use of iteration during software development by informing which perspective of iteration has what kind of impact on project completion time to avoid delays.
From the abstract to the concrete and beyond:

Methodological procedures are frequently described using ideal-typical sequences and relatively linear phases, often covering the whole research process. We aim to uncover the long-term process of construction of a conceptual framework of a study as constituted by cycles using the method of ascending from the abstract to the concrete (AAC) as a starting point. Empirical research often requires the introduction of intermediate concepts between general theory and data. In this paper we follow how a specific intermediate concept – the intermediary object – was constructed and interpreted in two research cases: 1. the use of building information modelling (BIM) in collaboration between designers, architects and engineers in construction projects; 2. the craft of historical wooden boat building in Russia and Finland. Using ‘manuscript archaeology’ we follow the construction of a conceptual framework by analysing the development of the key texts and documents in these cases. In both cases the process contained multiple ‘episodes’– relatively independent and complete parts of research process, constructed around specific tangible products (like a conference paper or a draft of an article), but forming a sequence when taken together. Each episode contained own cycle of movement from the abstract to the concrete, and the whole research process constituted a relatively autonomous but interconnected network of methodological cycles, where concepts emerged based on conceptualizations from previous episodes. This shows how transmethodological processes of constructing a conceptual framework for a specific study through multiple methodological cycles often develop across a long period, with both vertical movements between theory and data, and horizontal movements between theoretical conceptualizations.