Reviewerly/exHarmony huggingface dataset:
Training, professional development and competence assessment of vocational and professional education teachers: An international review

Introduction. Training, professional development and evaluation of professional competence of vocational education teachers is considered to be the basis of providing any national economy with qualified workers and specialists. It has been increasingly recognized that more attention needs to be paid to this area. The vocational training systems are currently undergoing significant changes worldwide and require new strategies for administrative decisions. The purpose of this article is to analyze and systematize the international experience of training, professional development and competence assessment of vocational education teachers with a focus on Germany, Finland, and China in order to take it into account in modern Russian education. Materials and Methods. The theoretical and methodological basis of the research includes theories of vocational training and vocational teacher education, theories focusing on the content of vocational and teacher education and professional development of teaching staff. The authors used the following research methods: reviewing and comparative analysis of scholarly literature, reports of state institutions, regulations, and websites of educational settings in Germany, Finland and China. Results. The authors have identified trends in the convergence of qualifications and positions of teachers of professional disciplines and industrial instructors. Significant differences in the qualifications and skills of teachers and instructors in the workplace have been identified. Among the forms of professional development for vocational teachers, the authors have highlighted in-service training and activities focused in teaching methods and curriculum development. It is emphasized that the development of teachers' competence is gradual and level-based, the set of skills being determined by vocational education positions, duties and responsibilities In the countries analyzed, internships and professional self-assessment play a particularly important role in the professional development of vocational school teachers. Conclusions. A comparative international analysis of training, professional development and competence assessment of vocational teachers has enabled the authors to determine the main vectors for updating the system of professional training and vocational teacher education in Russia.
Geological, geophysical and surface geochemical guides for gold exploration in the Falémé Volcanic Belt, West African Craton, Senegal

The Paleoproterozoic sequences of the Kédougou-Kéniéba Inlier, West African Craton, contain major orogenic gold deposits mainly located in the vicinity of the Senegal-Mali Shear Zone in the Malian territory. Up to now few gold deposits has been discovered in the Senegalese side. Our new geological, geophysical and surface multi-element geochemistry data allow us to improve our understanding of the gold distribution within the Senegalese side (including the Boboti intrusion and its vicinity in the Falémé Volcanic Belt (FVB) and the Dialé-Daléma host rocks). On the one hand, the geological study shows variability of lithologies hosting gold mineralization in the FVB, including intermediate to felsic intrusions, andesite, quartz veins and sedimentary rocks. On the other hand, geophysical and surface multi-element geochemistry study, handled with regionalized Principal Component Analysis with kriging of gold factor unveil a change of the gold metal association from the Boboti intrusion (association of Au, Ni, Co, Cu and Pb) toward the FVB (association of Au and Zn), and the Dialé-Daléma sedimentary host rocks, where Au is only associated with As. Such variability of metal association can be used as an exploration guide to characterize gold-rich zones, whether located in the Dialé-Daléma Series, within the Boboti metaluminous pluton, or in the FVB areas. Furthermore, a comparison between soil and termite sampling shows that the latter is a good and more effective and suitable exploration guide in areas of high weathering profile and with intense surface mining activities, such as the FVB area. The continuity of gold anomalies from surface to the subsurface levels is confirmed by drilling data. This study has allowed us to better constrain the gold geochemical anomalies in high weathering environment such as the southeastern part of Senegal and produce a practical guide for exploration companies.
Inspection of the Most Suitable Approach and Information Projection Method for Interactions in the Night Flight of a Projector-Mounted Drone

Security is considered heavy labor work owing to night shifts and long working hours. In recent years, the number of security guards has been increasing, and the labor shortage for security guards has become a problem in Japan. To overcome these problems, robots have been used for security purposes. However, most are unable to interact with users or guards at night. In this study, a drone called aerial ubiquitous display (AUD) is proposed to improve the problems of existing security methods using robots and to provide night security. The AUD enables human-drone interaction and night security. Moreover, when an AUD interacts with humans, the drone must come close to the human and project information on the ground. Therefore, this study investigated the optimal parameters for a projector-equipped drone to approach a human at night. In addition, by comparing these results with those of the day approach, we verified whether there would be a change in perception between daytime and nighttime. Furthermore, an experiment was conducted to investigate the types of projections that are most likely to capture a user’s attention.
Decision Making via Game Theory for Autonomous Vehicles in the Presence of a Moving Obstacle

We consider an emergency maneuver scenario involving two autonomous vehicles interacting with a road obstacle characterized by a random behavior. We employ game theory to solve the resulting problems, first framing a static game of compete information, and further adding incomplete information about the obstacle so as to transform it into a Bayesian game. Depending on the considered scenario, the autonomous vehicles can have multiple available actions, such as to stay at the same lane and swerve and move to another one. These actions can lead to different outcomes, such as keep driving on an empty lane, hit the obstacle, or hit another car. We analyse the Nash equilibria of the game and test the hypothesis that the knowledge of one vehicle about an obstacle can be advantageous to other road participants, which is key in the context of connected vehicles.
The impact of knowledge hiding and toxic leadership on knowledge worker productivity – Evidence from IT sector of Pakistan

This study intends to identify the impact of Knowledge Hiding and Toxic Leadership on Knowledge Management Process at Information Technology (IT) companies of Pakistan and explore the overall impact on Knowledge Worker Productivity by keeping knowledge worker ambidexterity as mediator. Any factor that could negatively influence the knowledge management process would hamper employees’ productivity in that setup. The knowledge-based view is used as base theory which characterizes knowledge as the most significant strategic utensil for performance, however, the IT sector being a highly knowledge-intensive sector provides rationale for conducting study in this area. The study is quantitative and the sample size consists of 405 respondents selected based on convenience sampling. However, Analysis was performed using Structural Equation Modelling Software Smart Partial Least-Square (SEM-PLS). The findings show that evasive hiding, playing dumb and toxic leadership does not have any significant impact on knowledge management process, however, rationalized hiding was positively related to knowledge management process and knowledge worker productivity. Also, partial mediation of knowledge worker ambidexterity was observed. Furthermore, this study is a significant addition to the existing literature as to the best of our knowledge, it is one of the earlier contributions to explore knowledge hiding as a barrier to the knowledge management process along with toxic leadership introducing mediating role of knowledge worker ambidexterity. Theoretical contributions and practical implications are also discussed at the end.
Path Planning of Mobile Robots Based on Improved Bi-RRT Algorithm

Bidirectional Fast Expanding Random Tree (Bi-RRT) algorithm has the problems of long search time and low sampling efficiency in path planning in complex environment due to the randomness of sampling points. Therefore, an improved Bi-RRT path planning algorithm for mobile robots was proposed;The algorithm introduces a heuristic search strategy, takes the starting point and the ending point of the robot as the center, constructs a two-dimensional Gaussian distribution function, and uses this probability density function to constrain the generation of sampling points, so that the spatial sampling points closer to the target point have a higher probability of occurrence, while retaining some uniformly distributed sampling points. In this way, the sampling process can not only make use of the location information of the target point but also ensure the probabilistic completeness of the algorithm; With the guidance of heuristic sampling points designed by the algorithm, two random trees can grow rapidly toward the target area, which reduces the blindness of the search and improves the efficiency of the search; Simulation results: Compared with the basic BI-RRT algorithm, the planning time of the improved algorithm is shortened by 43.9% in complex environment, the number of extended nodes is reduced by 41.4%, and the path length is optimized by 8.1%. The influence of the ratio of Gaussian distribution sampling points to the total number of sampling points on the performance of the algorithm is analyzed.
When Machine Learning Meets Social Science: A Comparative Study of Ordinary Least Square, Stochastic Gradient Descent, and Support Vector Regression for Exploring the Determinants of Behavioral Intentions to Tuberculosis Screening

Regression analysis is one of the most widely utilized methods because of its adaptability and simplicity. Recently, the machine learning (ML) approach, which is one aspect of regression methods, has been gaining attention from researchers, including social science, but there are only a few studies that compared the traditional approaches with the ML approach. This study was conducted to explore the usefulness of the ML approach by comparing the ordinary least square estimate (OLS), the stochastic gradient descent algorithm (SGD), and the support vector regression (SVR) with a model predicting and explaining the tuberculosis screening intention. The optimized models were evaluated by four aspects: computational speed, effect and importance of individual predictor, and model performance. The result demonstrated that each model yielded a similar direction of effect and importance in each predictor, and the SVR with the radial kernel had the finest model performance compared to its computational speed. Finally, this study discussed the usefulness and attentive points of the ML approach when a researcher utilizes it in the field of communication.
Contemporary technologies of presenting Scientific Library's electronic digital resources in the global information medium

The experience of the V. Stefanyk National Scientific Library of Ukraine in Lviv in the formation of electronic resources of the book collection is disclosed. Project solutions and features of creating collections for a digital library are presented. A set of measures, which are planned to be carried out within the library in order to effectively operate the digital library on its web portal, as well as to establish effective user access to the electronic documents posted there, are outlined. Attention is focused on already implemented projects with the library's partners, which are currently available for free and authorized access: “Zasoby cyfrowe Zakladu Narodowego im. Ossolińskich”; “Lvivski Arkhyieparkhialni vidomosti”, “Zbirnyk matematychno-pryrodopysno-likarskoi sektsii NTSh”, in test access “Arkhiv NShT NB in Warsaw” ta “Arkhiv NTSh ZNiO in Wroclaw”. Particular attention was paid to the need to create a digital insurance fund of document resources of the Vasyl Stefanyk NSLU in Lviv. The process of digital processing and metadata creation, as well as the subsequent storage of digital copies of documents, is described in detail. It was emphasized that the implementation of the project will provide an opportunity to effectively preserve the most valuable and rare publications of the book collection and will contribute to their further inclusion in the international scientific information medium.
Creating an Edge-to-Cloud Computing Continuum: Status and Perspective

The creation of a computing (and storage) continuum that spans across multiple infrastructures has been recently proposed with the aim at breaking the barriers of current cloud computing silos. While the potential of this vision is clear, such as possible energy efficiency improvements, application transparency, better quality of service, the problems in realizing this vision are challenging. In fact, the necessity (a) to consider WAN latencies, network bottlenecks and possible network outages, (b) to support different administrative boundaries (hence, security), (c) to define novel orchestration algorithms are only a first portion of the ingredients required to create this continuum. Even more important are the necessity to achieve an economic sustainability and the necessity to migrate applications to the new paradigm, which are often not considered by the current research efforts. Starting from the experience achieved in the open-source liqo.io project, this talk will introduce the potentials and the challenges in the creation of the edge-to-cloud computing continuum, and it will present what are the major blocking points along our way.
Conceptual Linked Data Model for South African Municipalities Public Services Domain

In recent years, the expansive growth of data communication within governmental organizations has led to the initiative of electronic government (eGov). Technology providers have understood the importance of standardization in the local administration system to foster the necessary reuse and interlink of information. We review the literature to identify and select the oeGOV datasets to conceptualize the South African municipalities’ ontologies for the public services domain. Unified Modeling Language (UML) is used to capture the requirement models while ontology development methodology is used during the modeling phase. Finally, we present a conceptually linked municipality data ontology in Web Ontology Language (OWL), supported by the y Protégé ontology management tool, that models the public services domain, thus allowing the standardization and interoperability of respective municipalities’ departments.
Novel Meta-Learning Techniques for the Multiclass Image Classification Problem

Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially simpler problems, allowing the application of numerous well-established learning algorithms that may not apply directly to the original task. This work focuses on the efficiency of decomposition-based methods and proposes several improvements to the meta-learning level. In this paper, four methods for optimizing the ensemble phase of multiclass classification are introduced. The first demonstrates that employing a mixture of experts scheme can drastically reduce the number of operations in the training phase by eliminating redundant learning processes in decomposition-based techniques for multiclass problems. The second technique for combining learner-based outcomes relies on Bayes’ theorem. Combining the Bayes rule with arbitrary decompositions reduces training complexity relative to the number of classifiers even further. Two additional methods are also proposed for increasing the final classification accuracy by decomposing the initial task into smaller ones and ensembling the output of the base learners along with that of a multiclass classifier. Finally, the proposed novel meta-learning techniques are evaluated on four distinct datasets of varying classification difficulty. In every case, the proposed methods present a substantial accuracy improvement over existing traditional image classification techniques.
Assessment-based correct rates in learning spaces

The basic local independence model (BLIM) is the standard probabilistic model in knowledge structure theory. It assumes that the probability of a correct response to a problem is constant for all individuals that master the problem, and accordingly, for all individuals that do not master it, irrespective of the mastering of other problems. Recently published data on the problem correct rate as inferred from a response-based assessment of the mastering of the problem seem to contradict this assumption. The analysis presented in this paper, however, reveals that deviations from constancy in the observed direction are to be expected under the BLIM. They are mainly due to the inaccuracy inherent in any response-based assessment. The implications of these results for the empirical validation of the BLIM are discussed.
Deep Learning-Based Suppression of Speckle-Noise in Synthetic Aperture Radar (SAR) Images: A Comprehensive Review

In synthetic aperture radar (SAR) images, the speckle noise is inherent which is in granular form and oftenly defined as a multiplicative nature. Due to multiplicative nature not only the qualities of the SAR images get degraded but also all coherent images. In order to deals with granular form of noise, there are various despeckling techniques has been developed from the few decades. The primary objective of this paper is to majorly focus on the existing despeckling model with its emerging trends and greatly transform in mechanism. The nature cum characteristics of the speckle noise has been discussed in detail. Furthermore, the cons of the homomorphic filtering mechanisms are highlighted. The classification of various despeckling methods based on spatial and transform domain which can play the most significant role in the designing of the robust techniques has addressed. The efficiency of the despeckling methods gets analyzed using various performance metrics such as SNR, PSNR, SSIM, and UIQI. Also, the visual appearance of the any despeckling method can be analyzed using confusion matrix and Cohen’s kappa method.
Research on Remote Sensing Image Object Detection Based on Deep Learning

Remote sensing image has the characteristics of large and complex data volume, which makes the traditional remote sensing image object detection technology difficult to meet the current demand. As artificial intelligence achieves more and more achievements, the application of object detection technology based on deep learning in remote sensing image is becoming more and more widely applied. In this paper, based on YOLOv3 object detection algorithm, the collected data is effectively expanded according to the remote sensing image objective feature, and multiple training and verification tests are carried out. According to experimental results, the proposed remote sensing image object detection model can effectively eliminate the impurity pixel amount with high accuracy, and can improve the quality of object detection.
A Novel Approach for High Authentication in Digital Watermarking Technique

Digital image watermarking is a fascinating area of research as they protect multimedia information from unauthorized access. It has become very important in our Internet community. Digital watermarking techniques are used to protect electronic media against illegal flow in the form of bitmaps, sound and movies or any other network, watermark into the server information with copyright protection, access management, broadcast tracking, owner identification, etc. A watermark can be almost any form, label, tag or electronic signal. A host can be multimedia elements such as pictures, video or audio. Since images are the building blocks of multimedia, developing a productive watermark will be critical method for graphics. The most obvious way to embed data in multimedia information is through electronic watermarking. The digital watermark is a powerful alternative and plays an important role in copyright security.
Voice and Text-Based Virtual Assistant for Academic Advising Using Knowledge-Based Intelligent Decision Support Expert System

Academic advising in universities and reputed institutions serve as a roadmap in guiding the students for framing their career plan. One of the major roles and responsibilities of an academic staff is to guide the students properly in selecting the course as per the rules stipulated in bylaws. The staff members face many challenges while performing the role as an advisor. The promising challenge for an advisor is balancing the academic workload and carrying out the advisory role. One of the major constraint for the staff members is that they were not able to provide immediate answers to the queries raised by the stakeholders. The main objective of the research was intended to address this problem. Hence, the voice and text-based virtual assistant for academic advising using knowledge-based intelligent decision support expert system is used. It is a computerized program that is implemented as a conversational chatbot that is designed to handle the queries raised by the students related to academic advisement, clarification on college bylaws, progression, moving to next level, and in choosing the specialization.
Combining the Knowledge Graph and T5 in Question Answering in NLP

The NLP relies heavily on question answering. It provides an automated method for retrieving responses from a context. Finding context from several documents, studies, and testimonials is becoming increasingly important. Because NLP models for question answering are quite sophisticated and resource intensive, the purpose of this study is to provide a novel lightweight method to question answering in NLP by introducing the knowledge graph as input to the transfer learning model of T5. This study provides an overall holistic perspective of the suggested model’s various parameters.
Prediction and Analysis of Liver Disease Using Extreme Learning Machine

Hepar, also known as the liver, is a vital organ of the digestive system which performs hepatic blood flow, helps to clot, etc. Poor eating habits and a sedentary lifestyle, unfortunately, have an impact on the liver. Developing a fully automatic computer-assisted disease prediction model would be extremely beneficial to medical professionals as manual analysis is a tedious and time-taking task. To accomplish this, we provide an automated liver disease prediction model based on the extreme learning machine classifier based on his/her biological parameters. ELM is a fast single-layer feed-forward neural network with high generalization capabilities that have previously been used to generate a variety of classification models. The proposed model is evaluated using the ILPD dataset. The performance of the proposed model is assessed using a range of activation functions and hidden neuron counts. According to the calculations, the proposed model beats other models currently in use.