Reviewerly/exHarmony huggingface dataset:
Improved SOSK-Means Automatic Clustering Algorithm with a Three-Part Mutualism Phase and Random Weighted Reflection Coefficient for High-Dimensional Datasets

Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, metaheuristic algorithms are hybridized with K-means to extend the capacity of K-means in handling automatic clustering problems. In this study, we proposed an improved version of an existing hybridization of the classical symbiotic organisms search algorithm with the classical K-means algorithm to provide robust and optimum data clustering performance in automatic clustering problems. Moreover, the classical K-means algorithm is sensitive to noisy data and outliers; therefore, we proposed the exclusion of outliers from the centroid update’s procedure, using a global threshold of point-to-centroid distance distribution for automatic outlier detection, and subsequent exclusion, in the calculation of new centroids in the K-means phase. Furthermore, a self-adaptive benefit factor with a three-part mutualism phase is incorporated into the symbiotic organism search phase to enhance the performance of the hybrid algorithm. A population size of 40+2g was used for the symbiotic organism search (SOS) algorithm for a well distributed initial solution sample, based on the central limit theorem that the selection of the right sample size produces a sample mean that approximates the true centroid on Gaussian distribution. The effectiveness and robustness of the improved hybrid algorithm were evaluated on 42 datasets. The results were compared with the existing hybrid algorithm, the standard SOS and K-means algorithms, and other hybrid and non-hybrid metaheuristic algorithms. Finally, statistical and convergence analysis tests were conducted to measure the effectiveness of the improved algorithm. The results of the extensive computational experiments showed that the proposed improved hybrid algorithm outperformed the existing SOSK-means algorithm and demonstrated superior performance compared to some of the competing hybrid and non-hybrid metaheuristic algorithms.
Federated learning for 5G-enabled infrastructure inspection with UAVs

Abstract Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaffolding to spot damages or malfunctioning equipment. Scaffolding is time-consuming and incurs accident risks. To tackle this challenges, grid operators are gradually using Unmanned Aerial Vehicles (UAVs). UAV trajectories are observed by a centralized operation center engineers for identifying electrical assets. Moreover, asset identification can be further automated through the use of Artificial Intelligence (AI) models. However, centralized training of AI models with UAV images may cause inspection delays when the network is overloaded and requires Cloud environments with enough processing power for model training on the operation center. This imposes privacy concerns as sensitive data is stored and processed externally from the infrastructure facility. This article proposes a federated learning method for UAV-based inspection that leverages a Multi-access Edge Computing platform installed in edge nodes to train UAV data and improve the overall inspection autonomy. The method is applied for the inspection of the Public Power Corporation’s Innovation Hub. Experiments are performed with the proposed method as well as with a centralized AI inspection method and demonstrate the federated learning benefits in reliability, AI model processing time and privacy conservation.
Digital Drama Technique in English as a Foreign Language Teaching and Learning: From Viewers’ and Actors’ Perspectives

Purpose: Due to the emergence of COVID -19, the transition from offline to online teaching also changes how drama techniques work. Specifically, the digital drama technique was used to teach English, when regular classes were almost impossible. There seems to be very little, if any, research on digital drama techniques, especially in the Vietnamese context. Consequently, this study examines how the digital drama technique affects Vietnamese students learning English as a foreign language (EFL). Additionally, the differences and similarities between teachers’ and students’ perceptions of the technique’s impact are examined.&#x0D; Methodology: The study used a quantitative approach. A survey was used to collect data from 1207 students and 69 teachers.&#x0D; Findings: The survey revealed that the technique was helpful for EFL students’ learning. The participants mostly perceived the positive impacts of the technique. However, differences in perception between teacher and student were discovered. The students considered it beneficial for improving their writing and reading skills while teachers perceived this technique as helpful in exploring new creative ideas.&#x0D; Practical implications: The technique was approved as useful for language teaching and learning. English teachers are encouraged to use this technique in their teaching in practice.&#x0D; Contribution to literature: The study becomes a good source for further research to build a more profound theoretical framework. More researchable ideas related to this unique online teaching technique are suggested to enrich the literature in this field.
Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

In this paper, we propose an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. The traditional method mainly involves obtaining the speed of the control variable based on the Jacobian inverse or linearization of the robot’s kinematic model, which cannot avoid the singularity position and/or self-collision phenomena. To address these problems, we directly design an optimized differential evolution algorithm to solve the trajectory planning problem for mobile manipulator robots. First, we analyze various constraints on the actual movement and describe them specifically using various equations or inequalities, including non-holonomic constraints on the mobile platform, the physical limitations of the driving motors, self-collision avoidance restriction, and smoothly traversing the singularity position. Next, we re-define the trajectory tracking of a mobile manipulator robot as an optimization problem under multiple constraints, including the trajectory tracking task and various constraints simultaneously. Then, we propose a new differential evolution (DE) algorithm by optimizing some critical operations to solve the optimization problem, such as improving the population’s distribution, limiting the population distribution range, and adding a success index. Additionally, we design two simple trajectories and two complex trajectories to determine the performance of the optimized DE algorithm in solving the trajectory tracking problem. The results demonstrate that the optimized DE algorithm can effectively realize the high-precision trajectory tracking task of a differential wheeled mobile manipulator robot through the consideration of kinematic limitations and self-collision avoidance.
Enhancing Reading Comprehension Skills of Prospective Teachers Using Suitable Reading Strategies

This study focused on using appropriate reading strategies to improve the reading comprehension of prospective teachers in two teacher education colleges in the Vellore District of Tamil Nadu, India. The paper begins by introducing the importance of English language in the contemporary environment, and then highlights the significance of reading comprehension for academic purposes. Three reading strategies were employed in the experimental study. The sample size and the testing methods employed in the experiment are described in the methodology section. The statistical analysis to determine the reading comprehension skills was done with mean, paired sample t-test and co-efficient. The results confirm that the use of appropriate reading strategies enhances the reading comprehension skills of the samples.
Formation of Media and Information Literacy of Future Teachers by Means of Pedagogical Disciplines

The article highlights the scientific foundations of the formation of media and information literacy of future teachers by means of pedagogical disciplines. Based upon the analysis of scientific literature, it has been found that the concept of media and information literacy has been ambiguously interpreted by scientists. Considering various viewpoints of scientists, the author’s understanding of the essence of this phenomenon as well as the content of the structural components of media and information literacy of applicants for higher pedagogical education have been determined. It has been noted that experimental work on the formation of media and information literacy of future teachers was implemented on the basis of H. S. Skovoroda Kharkiv National Pedagogical University and covered their study of academic disciplines «Introduction to the Specialty», «General pedagogy», and «Pedagogy» as well as students’ extra-curricular work. It has been specified that the mastering of media and information literacy by the subjects of educational process took place during brainstorming sessions and discussions of the «buzz-group» type in such formats as: online, web-quests, webinars, working in the appropriate online courses, self-study of materials presented on the Internet on the problem outlined (cartoons, training videos and videos proper), performing special problem tasks and interactive exercises, participating in online trainings, quizzes, workshops, games, working with the multimedia online manual «Media driver», etc.
Creation of a Mobile Application for Navigation for a Potential Use of People with Visual Impairment Exercising the NTRIP Protocol

The global navigation satellite systems (GNSS) have become important in conjunction with the advancement of technology, in order to improve the accuracy of positioning and navigation on mobile devices. In the current project, a mobile application for navigation using the network transport of restricted test case modeling (RTCM) via internet protocol (NTRIP) was developed, and it has been focused on the autonomous mobility of people with visual disabilities. This occurred through a web viewer that stores the base cartography in a genome database (GDB). Such information is integrated into the application interface with Java Script language within the Android Studio platform, with a personalized design. This incorporates a screen reader for selection, navigation and direction of destinations, in addition to an early warning system for obstacles. Additionally, a differential position correction was implemented using the BKG Ntrip Client (BNC) software, for the adjustment of coordinates with the precise point positioning (PPP) method through streams in the format of RTCM with casters EPEC3, IGS03 and BCEP00BKG0. The evaluation of the application was performed using the National Standard for Spatial Data Accuracy (NSSDA), establishing 30 control points. These were obtained through the fast static method, in order to compare the horizontal accuracy of the observations in static and navigation modes between high-end and mid-range mobile devices.
A Comparison between Task Distribution Strategies for Load Balancing Using a Multiagent System

This work presents a comparison between several task distribution methods for load balancing with the help of an original implementation of a solution based on a multi-agent system. Among the original contributions, one can mention the design and implementation of the agent-based solution and the proposal of various scenarios, strategies and metrics that are further analyzed in the experimental case studies. The best strategy depends on the context. When the objective is to use the processors at their highest processing potential, the agents preferences strategy produces the best usage of the processing resources with an aggregated load per turn for all PAs up to four times higher than the rest of the strategies. When one needs to have a balance between the loads of the processing elements, the maximum availability strategy is better than the rest of the examined strategies, producing the lowest imbalance rate between PAs out of all the strategies in most scenarios. The random distribution strategy produces the lowest average load especially for tasks with higher required processing time, and thus, it should generally be avoided.
Digital competencies and skills as a determinant factor in Higher Education

This paper focuses on contributing to a better understanding of the study of Digital Competencies, Digital Skills in Higher Education. First, we have developed a descriptive analysis of journals and authors to understand the meaning of digital competencies and skills and their development in Higher Education. Secondly, we have analysed the conceptual knowledge using Web of Science; its use is growing rapidly in the research of articles or topics, being considered a topic to investigate and explore due to its importance and relevance.
IMPLEMENTATION OF THE CONCEPT OF A SMART UNIVERSITY IN TERMS OF DISTANCE EDUCATION

A theoretical analysis of the requirements of modern society for distance education was carried out, which emphasizes the importance of introducing Smart Education. The main functions of Smart Education, which meets the society’s need for open, high-quality distance education, are substantiated. It is noted that Smart Education implements not only basic didactic principles, but also relatively new ones: interactivity, adaptability of learning, flexibility of the educational process. For their implementation, the structure of a Smart course is defined, the development of the components of which is ensured by the use of smart technologies. The needs of the development of the concept of a Smart University are substantiated and its characteristic features are determined. It is established that the implementation of the concept of a Smart University should include scientific research, staffing, educational process, management and marketing of educational institutions, use of modern information technology and digitalization. A strategy has been developed to implement the concept of a Smart University, which is based on the combination of four components "4T": technologies (digital technologies, educational content in free access for students, providing feedback to teachers and students, automation of administrative tasks), tactics (student-centered model, which is based on the appropriate facilities), talents (competent teachers who have a complex of professional competencies, and smart students who are able to develop and improve themselves in the future professional field), traditions (traditions that are supported by modern requirements with prospects for the future). The use of Smart technologies ensures the implementation of the tactics of organizing the activities of a modern university, which is aimed at developing talented students and teachers’ professionalism, taking into account the experience and traditions of classical education. The implementation of the Smart University strategy will help create a modern educational environment and provide access to open distance education.
FEEDBACK PRACTICES IN THE CONTEXT OF SYNCHRONOUS ESP DISTANCE LEARNING IN A NON-LANGUAGE UNIVERSITY UNDER MARTIAL LAW IN UKRAINE

The facts presented in the article indicate that the only possible form of mass education in Ukraine in war conditions is synchronous distance learning. An analysis of relevant modern foreign and domestic studies has shown that researchers consider feedback in the learning process as one of the central concepts in education, paying considerable attention to the characteristics of feedback forms, including the formative one, but the ways of its implementation in the process of synchronous distance learning have not been sufficiently studied. The authors' study proves that in synchronous distance learning of ESP on the ZOOM platform, formative feedback is possible under certain technical and didactic conditions. Formative feedback is possible and should occur at all stages of the ESP session. The article describes the practices of organizing effective formative feedback used by the authors of the study while teaching all types of speech activity: listening, speaking, reading and writing. Compared to offline learning, synchronous distance learning significantly increases the share of formative peer feedback, which contributes not only to the activation of the educational process, but also to the socialization of students. The study focuses on the fact that feedback is a two-way process that affects the performance of both the recipient of feedback and the one who gives it. The results of the study can be useful for both university foreign language teachers and school teachers.
Comparative Review of Machine Learning and Deep Learning Techniques for Texture Classification

In this paper, we present a review and analysis of different methods and resources for texture classification, presenting the most popular techniques that have been used in the past decade.The paper covers some of the most traditional approaches involving texture descriptors like Gray Level Co-occurrence Matrix and Gabor Filter Banks, to some of more recent approaches such as Convolutional Neural Network (CNN) and ScatNets.These methods are tested and their performance is evaluated on six standard datasets.
An Improved Spanning Tree-Based Algorithm for Coverage of Large Areas Using Multi-UAV Systems

In this work, we propose an improved artificially weighted spanning tree coverage (IAWSTC) algorithm for distributed coverage path planning of multiple flying robots. The proposed approach is suitable for environment exploration in cluttered regions, where unexpected obstacles can appear. In addition, we present an online re-planner smoothing algorithm with unexpected detected obstacles. To validate our approach, we performed simulations and real robot experiments. The results showed that our proposed approach produces sub-regions with less redundancy than its previous version.
Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

The problem of lack of anonymity and confidentiality can be experienced by those who collect statistical data online as well as those who provide the data. One end may be secure, for example, the one providing data, and yet the other end, for example, the one collecting data, may not be secure. In another scenario, both the data provider and collector may seek anonymity. Preventing the decryption of data provided while providing aggregated results is the best solution for such scenarios. To achieve this, this paper proposes a protocol that puts into application. Homomorphic Encryption and a Distributed Ring algorithm, to ensure data anonymity of both parties involved in a spatial search that is a data provider and a searcher. Firstly, we identify a Homomorphic Encryption technique that can work best for a spatial search by reviewing literature on Homomorphic Encryption techniques. Among the Homomorphic Encryption techniques reviewed were Rivest, Shamir and Adleman (RSA), El Gamal cryptosystem, Goldwasser-Micali cryptosystem, Benaloh cryptosystem, Paillier cryptosystem and Fully Homomorphic Encryption (FHE). After a comprehensive study, Paillier Homomorphic Encryption technique was identified as the best approach to be employed in securing a spatial search. Secondly, we propose a protocol for distributed spatial searching using Paillier cryptosystem and distributed ring algorithm principles. Finally, a proof of concept prototype using the proposed approach was implemented. From initial experiments conducted using the proposed approach, it is evident that the bigger cost comes from the communication over the network and less from the encryption algorithm and protocol itself. A 39.7% overhead when compared to the usefulness of the approach, is outweighed making the solution highly practical and useful.
Hierarchies of resources for measurement-based quantum computation

Abstract For certain restricted computational tasks, quantum mechanics provides a provable advantage over any possible classical implementation. Several of these results have been proven using the framework of measurement-based quantum computation (MBQC), where nonlocality and more generally contextuality have been identified as necessary resources for certain quantum computations. Here, we consider the computational power of MBQC in more detail by refining its resource requirements, both on the allowed operations and the number of accessible qubits. More precisely, we identify which Boolean functions can be computed in non-adaptive MBQC, with local operations contained within a finite level in the Clifford hierarchy. Moreover, for non-adaptive MBQC restricted to certain subtheories such as stabiliser MBQC, we compute the minimal number of qubits required to compute a given Boolean function. Our results point towards hierarchies of resources that more sharply characterise the power of MBQC beyond the binary of contextuality vs non-contextuality.
A comparative analysis of different recommender systems for university major and career domain guidance

Abstract Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.
Ecological Analogy for Generative Adversarial Networks and Diversity Control

Abstract Generative adversarial networks are popular deep neural networks for generative modeling in the field of artificial intelligence. In the generative modeling, we want to output a sample with some random numbers as an input. We train the artificial neural network with a training data set for the purpose. The network is known with astonishingly fruitful demonstrations, but we know the difficulty in the training because of the complex training dynamics. Here, we introduce an ecological analogy for the training dynamics. With the simple ecological model, we can understand the dynamics. Furthermore, a controller for the training can be designed based on the understanding. We then demonstrate how the network and the controller work with an ideal case, MNIST.