Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPLEMENTATION OF VIRTUAL AND AUGMENTED REALITY TECHNOLOGIES, GAME SIMULATORS FOR FUTURE SPECIALISTS’ TRAINING

The article presents the outcomes of the research of virtual and augmented reality technologies percularities and the possibility of their implementation in the educational process. The main reasons and technologies of virtual reality in the field of information technologies are determined. The analysis of virtual reality programs has been carried out. To determine the relevance of the implementation of virtual, augmented reality and game simulator technologies into the educational process the survey was created by the authors and conducted remotely among the population of Khmelnytskyi region on the Google Forms. The results of the conducted survey suggest the answers to a number of questions, including whether our society is ready to implement virtual, augmented reality and game simulators into the educational process of future specialists’ training, as the field of virtual reality research continues to expand relentlessly, the integration of VR (Virtual Reality) with various areas of human life continues. In addition to the survey in Google Forms, a survey was also conducted to collect information about currently existing virtual environments, programs and game simulators that can be added to educational components or integrated into other educational systems not only in the field of education (Minecraft, Cooking Simulator, PC Building Simulator 1-2, Google Arts & Culture). It is quite possible to integrate these simulators under the educational components of certain specialized subject areas. The conducted survey makes it possible to assume that users are already ready not only for the integration of virtual environments in the game sphere, but also for the full involvement of virtual technologies and game environments in the application of educational components. VR (Virtual Reality) and AR (Augmented Reality) technologies are gaining popularity in cultural, educational, gaming, business environments and more. The disturbed issues are studied in education, which encourages a deeper insight into the essence of the processes that take place under the influence of the latest information and communication developments.
The impact of business intelligence on the performance indicators of the strategic map (extracted research)

The study dealt with important variables for the present business organizations represented in business intelligence and indicators of the strategic map. Where business intelligence is one of the modern topics that have been widely popular in many organizations, as it makes the organization in continuous communica-tion with customers, knowing their requirements, producing and providing appropriate goods and services to them, which increases the organization’s opportunities for competition. On the other hand, the strategic map is a model for clarifying the organization’s strategy as it aims to facilitate the translation of the strategy into a practical reality and achieve linkage between the work of employees and the overall objectives of the organization and identifies performance indicators in four areas represented in the financial, marketing, internal operations, and of learning and growth. The study aimed to determine the nature of the relationship between business intelligence and its reflection on the extent of adoption of the strategic map indicators. The study was built on a number of main hypotheses that reflect the relationship between intelligence and its impact on the extent of adoption of the strategic map indicators. To test the validity of these hypotheses, a number of retail stores belonging to international business organizations operating within the city of Dohuk and Erbil were chosen to conduct the field side of the study. The final study sample included (100) workers in the researched organizations, and the data were analyzed using a set of statistical methods using the program ) Spss-18-For ( windows. The most important results of the study were the existence of a significant positive correlation between the variables of the study and a positive significant relationship between them. The study concluded that some recommendations were presented to enhance the levels of business intelligence and indicators of the strategic map in the surveyed organizations.
Score and Feature Level Fusion Approaches for Evaluation of Multi-Features of Fingerprint Modality for Person Recognition System

In the biometrics, the technologies grow day by day and the security also increased related to that technologies. The fingerprint was the most intensively researched in the field of biometrics system due to permanence and uniqueness features which made varies of different peoples. The paper addressing many stages, in addition to the primary stages of any biometrics system the fusion of unimodal system was used in order to improve the performance of the system. The double enhancement techniques were used to make the images very clear by Histogram Equalization and Fast Fourier Transformation (FFT). The feature extraction was conducted using three techniques which called Zernike Moment (ZM), Hu-Moments (Hu) and Gray-Level Co-occurrence Matrix (GLCM) that categorized to statistical and texture features. The matching between these features was performed using the Euclidean distance to find the scores matrix. Additionally, the fusion as the most modern technique was used to improve the performance of the biometrics system which performed in this work by feature level and score level fusions. The feature level fusion by using concatenation and score level fusion by using Weight sum rule strategy led to improve the performance of the system. The system was evaluated by False Accepted Rate (FAR), False Rejected Rate (FRR), Equal Error Rate (EER) and Genuine Accept Rate (GAR). The results show that, the fusion gave the most efficiency results compared with individual system. The work was tested on four datasets such as Fingerprint Verification Competition (FVC2000), (FVC2002), (FVC2004) and our department datasets which called KVK dataset. The best results were achieved by FVC2002 with maximum GAR reached to 98.45% and minimum EER of 1.54% as compared with other datasets and existing works.
A Research on the Relationship between Teachers' Support and Students' Academic Achievement in Higher Vocational Colleges

This paper aims to explore teachers' support and students' academic achievements. Because students' academic achievements are related to the quality of higher education, however, not all students' academic achievements are invested in learning activities independently, and a big factor in learning gains is students' social support. With reference to previous studies and existing literature, This study selects teacher support as the independent variable, vocational students' academic achievements as the dependent variable, students' academic self-efficacy and learning engagement as the mediation variables to build a research model to explore the chain mediation effect of teacher support on academic achievement through academic self-efficacy and learning engagement. In this study, 414 vocational college students in Chongqing were investigated by questionnaire. The reliability and validity of the questionnaire were consistent with the academic level, and were statistically tested by SPSS 25.0; The results show that: 1. Teacher support positively predicts vocational students' academic achievements; 2.Teacher support can indirectly predict the academic achievements of higher vocational students through academic self-efficacy; 3. Teacher support can indirectly predict the academic achievements of higher vocational students through learning engagement; 4. Academic self-efficacy - learning engagement plays a chain mediation role between teacher support and academic achievement. Finally, based on the conclusions of the study, this study puts forward specific suggestions for future research.
Using a Machine Learning Approach to Model a Chatbot for Ceylon Electricity Board Website

Customer support is one of the main aspects of the user experience for online services. However, the rise of natural language processing techniques, the industry is looking at automated chatbot solutions to provide quality services to an ever-growing user base. In Sri Lanka, Ceylon Electricity Board website is one of the largest websites that customers use always to get information about electricity services. Hence, a chatbot system is very essential in CEB website. This paper presents a study about implementing and evaluating of a chatbot model for CEB website. This study implements virtual conversation agent based on deep learning algorithm which is multilayer perceptron neural network and a special text dataset for conversations about CEB services. The conversation agent model is made by utilizing the natural language processing techniques to facilitate the processing of user messages. The output of this research is the response from the chatbot and identify the best testing method to get highest accuracy for chatbot model. The chatbot model achieves the highest accuracy with the number of epochs set to 2000 and the learning rate value of 0.01 on response context data training so that it gets 78.8% accuracy. Keywords: Natural language processing, chatbot, deep learning, multilayer perceptron neural network, Monte Carlo cross validation, k-fold cross validation
The Dynamic Lives of Supermassive Black Holes in Merging Galaxies

Ample evidence suggests that the evolution of supermassive black holes and their host galaxies is connected, but the physical processes driving this link remain unclear. The role of galaxy mergers in triggering black hole growth and in creating the empirical black hole/galaxy bulge correlations is a matter of particularly active debate. Galaxy mergers are of additional interest as the precursors of supermassive black hole binaries, which may merge and produce powerful gravitational waves. This gravitational wave emission can also eject the merged black hole from its host nucleus, leaving the galaxy without a central black hole and producing an offset (“recoiling") quasar. There has been rapid recent progress from both theory and observations in constraining the merger/AGN connection and identifying signatures of active black hole pairs. I review these findings and discuss ongoing work to address remaining open questions. I also describe the handful of candidate recoiling black holes discovered to date, as well as results from numerical models that indicate promising avenues for identifying a population of such objects in current and future wide-field surveys.
Survey Study: Monument Recognition using Artificial Intelligence

— Monuments are man-made structures that are designed to imbue a location with important meanings. They serve both a political and artistic function. There is an overwhelming amount of information and features to look into monuments. Monument work is a unique industry. It would help the economies and growth of many countries. Every culture generally has its own set of distinct characteristics, like monuments, writing, and music. Considering the history and stories associated with monuments, it is difficult to evaluate the techniques used to detect these monuments. Within the domain of object recognition and classification, monument recognition is an arduous task. Numerous difficulties must be overcome, as multiple variables can alter the recognition method. This paper has been written to study the recent research on this point from 2017 to 2023. In this study, monuments from a variety of civilizations and countries, including Egypt, India, Turkey, Paris, Greece, and Singapore, have been compared to the algorithms or methodology utilized.
Migraine and plant antioxidant: Review article

— The pathophysiology of migraine has been the subject of many works, the precise molecular mechanism regarding the disturbances that underlie migraine remains unknown. Additionally, it was found that oxidative stress contributes significantly to migraine development. For many years, the idea that migraine sufferers experience oxidative stress was addressed. In the last few years nutraceutics, includings antioxidants, have received a lot of attention among the substances which may be utilized to cure migraines. Antioxidants that are supplied with the food prevent the oxidative stress through suppressing the propagation, initiation, and actual oxidative chain reaction. The agents now being utilized to prevent migraines do in fact have a certain antioxidative activity. Antioxidants that have been addressed in this research are growing more regularly utilized through the migraine sufferers not just because of low or even lack of side effects, yet as well due to their efficiency (shortening of an episode duration or reduced frequency of migraine episodes).
ENSEMBLE-LEARNING FRAMEWORK AS A SECURITY MODEL FOR HARDENING THE SECURITY POSTURE OF SDN IN PERSPECTIVE OF CYBER LAW

Information is considered the most important resource for any organization and to share this resource requires network usage and management. Network operators are attempting to adapt to the coordination of distinctive sorts of networks while meeting the difficulties of expanding traffic. The customary network tends to be unbending. After the forwarding strategy has been defined, the best method to alter it is to rearrange all the impacted devices. It is lengthy and focuses on adaptation and overcoming the challenges of portability and huge data. System administrators have greater versatility to configure their networks using Software Defined Networking (SDN) in the sight of cyber law. With SDN, network management moves from classifying usefulness, lawfulness as far as low-level device arrangements to building programming that encourages system administration and troubleshooting. However, with the rise in network technology, great concerns of security emerged so high that, nowadays, security is considered the major issue to address in any organization. For this reason, in this paper, we have proposed an ensemble learning framework for hardening the security posture of SDN in prospective of cyber law. The proposed framework is evaluated on two different datasets, and it has exhibited great results as using different techniques together improves the performance.
Generalized linear mixed modeling of signal detection theory

Signal Detection Theory (SDT; Green & Swets, 1966) is a well-established technique to analyze accuracy data in a number of experimental paradigms in psychology, most notably memory and perception, by separating a response bias/criterion from the theoretically bias-free discriminability/sensitivity. As SDT has traditionally been applied, the researcher may be confronted with loss in statistical power and erroneous inferences. A generalized linear mixed-effects modeling (GLMM) approach is presented and advantages with regard to power and precision are demonstrated with an example analysis. Using this approach, a correlation of response bias and sensitivity was detected in the dataset, especially prevalent at the item level, though a correlation between these measures is usually not found to be reported in the memory literature. Directions for future extensions of the method as well as a brief discussion of the correlation between response bias and sensitivity are enclosed.
T01P10 / Systems Theory and Modelling for Public Policy: System Dynamics, Agent-based Models, and Other Approaches

The exploration of complexity is an intriguing contemporary development in the field of public policy. While undoubtedly still a specialist interest, there have been several notable recent contributions to the literature, including the publication of a handbook devoted to the topic of complexity and public policy. Yet, the concept of “complexity” enters the literature in different ways and in rather different guises. Some take their cue directly from complexity science and the modelling of complex adaptive systems. This carries with it significant ontological commitments. Indeed it poses some challenging questions regarding the aspirations for the control and direction of social systems that are typically seen as underpinning public policy. Others invoke the idea of complexity either metaphorically or in loose analogy to thinking about complexity in natural systems
IN- SERVICE PRINCIPALS’ DEVELOPMENT IN BRAZIL: DISCONNECTION AND INSULATION FROM INTERNATIONAL CONTEMPORARY KNOWLEDGE IN EDUCATION ADMINISTRATION

This study explores the public policy implemented by the Brazilian federal government for in-service school leaders’ development, in cooperation with state and local education systems, in order to improve school learning outcomes. It aims to investigate the content included in the post-graduate Diploma in School Management (DSM), related to international contemporary knowledge of education administration as presented in the literature. Public documents on the DSM programme, such as courses syllabi, content of instruction and references were analysed. The programme did not refer to any of the most quoted authors or the most quoted articles in the field. A displacement between the policy formulation and its implementation was observed. Disputes over programme conception was evidenced, prevailing a more insulated and local view on school management. Results show that the policy for school principals’ development in Brazil took very limited advantage of the contribution of research on educational leadership worldwide, despite research evidences showing that leadership practices share much more similarities than differences considering contexts.
Tick-borne viral encephalitis: are they far from the Americas?

ISSNL: 0122-0268 ©The Author(s) 2021. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons. org/licenses/by-nc-sa/4.0/), lets others remix, tweak, and build upon your work non-commercially, as long as they credit you and license their new creations under the identical terms. Tick-borne viral encephalitis: are they far from the Americas?
Protein Inter-Residue Distance Prediction Using Residual and Capsule Networks

The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein’s inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms used in the state-of-the-art methods. Using a standard dataset of 3420 training proteins and an independent dataset of 150 test proteins, we show that our method can predict distances 51.06% more accurately than a standard residual network method, when accuracy of all longrange distances are evaluated using mean absolute error. To further validate our results, we demonstrate that three-dimensional models built using the distances predicted by our method are more accurate than models built using the distances predicted by residual networks. Overall, our results, for the first time, highlight the potential of capsule-residual hybrid networks for solving the protein inter-residue distance prediction problem.
An Economic Mathematical Fuzzy Model for Data Envelopment Analysis

Performance assessment is a central to the management process in any type of organization. In addition, making rational economical decisions to improve organizational performance is a daunting task, as any organization is typically a multi-faceted entity which rely on complex systems that use uncertain information. Data envelopment analysis (DEA) is a powerful quantitative tool that makes use of multiple inputs and outputs to obtain useful information about the performance and efficiency of an organization. In many real-life applications, observations are usually fuzzy in nature. Therefore, DEA efficiency measurement may be sensitive to such variations. The purpose of this study is to develop a unified economical fuzzy DEA model that handles variables of different natures (vague and deterministic) independently and can be adapted to both input- and output-oriented problems, whether it is constant/variable return to scale. To handle fuzzy variables specially the economic variables in the model, the -cut approach was adopted. The model implementation is demonstrated through an illustrative case study. Managers will be able to use this model to identify and remedy underperformance, as well as to design regulations that aim to encourage efficiency and ensure that consumers benefit from the resulting efficiency gains.
Automatic Vacant Parking Places Management System Using Multicamera Vehicle Detection

A multicamera system for detecting cars and mapping them into parking spaces in a parking lot is described in this study. A limited number of sequences and without more difficult reality conditions (illumination changes and various weather conditions) were used to evaluate approaches from a cutting-edge system that function correctly under controlled conditions. However, the majority of them only provide parts of systems, typically detectors, rather than the entire system. The proposed system was made to work in real-world situations that take into account a variety of occlusion scenarios, changes in lighting, and weather. For the purpose of design and validation, a brand-new multicamera data set was recorded. Two of the system’s object detector results are shown, and several provided postprocessing stages are used to build the system. The findings clearly indicate that the suggested method works appropriately in demanding settings such as near entire occlusions, lighting variations, and varying weather conditions.
CONSTRUCTING KNOTS WITH SPECIFIED GEOMETRIC LIMITS

It is known that any tame hyperbolic 3-manifold with infinite volume and a single end is the geometric limit of a sequence of finite volume hyperbolic knot complements. Purcell and Souto showed that if the original manifold embeds in the 3-sphere, then such knots can be taken to lie in the 3-sphere. However, their proof was nonconstructive; no examples were produced. In this paper, we give a constructive proof in the geometrically finite case. That is, given a geometrically finite, tame hyperbolic 3-manifold with one end, we build an explicit family of knots whose complements converge to it geometrically. Our knots lie in the (topological) double of the original manifold. The construction generalises the class of fully augmented links to a Kleinian groups setting.
Exploration and exploitation of knowledge in the business response capacity of México

The strategic orientation of a company is determined by the degree of its capacity to absorb new knowledge from abroad. Degree to which they are able to acquire and assimilate information from the context, to transform and exploit within the organization. The objective was to analyze the effects of the potential absorption capacity (PACAP) on the realized absorption capacity (RACAP) and its mediating effect on the response capacity (CR), in 4 economic sectors of Mexico: commerce, construction, maquila and services. This research tests, with a structural equation model, samples of more than 12,0000 companies, collected in the Monthly Business Opinion Survey (EMOE). The findings suggest that PACAP influences CR only if RACAP positively moderates its relationship, in the 4 business sectors. This allows providing empirical evidence on how PACAP is related to RACAP and can achieve CR.