Reviewerly/exHarmony huggingface dataset:
Religion and the modern education

The purpose of the research is to solve the paradox of religion integration in education, by the new balance between religion, philosophy and science, during the post communism transition. In the field of thinking, the process is the transition from ideology to integral thinking. It is realized through the re-evaluation of the topics of the integration of religion, transitology and integral though, education, inclusiveness, solidarity, new laicity and new secularity. In the philosophical sense, integration is the objective process of being developed. This is understood as a return to identity towards a universal being. In the context of the social being, the process realizes the opening and cooperative development of all mental, spiritual-religious, scientific, creative-artistic, economic, cultural, material and non-material political fields. It includes the individual, the community, and all institutions of social life. The path of integration development is the transitive movement in a spiral form. In Albania, with the fall of communism, freedom of religion was legalized according to the standards of European democracy. The rehabilitation of religious figures that had been condemned and persecuted by the totalitarian regime began. The post-communist transition brought profound changes in the field of faith and religion such as the new dimension in the relationship of society with religion, new and unfamiliar attitudes of believers to religion, new relations between the state and religious institutions, new relations between education and religion in public institutions, opening of religious schools and increasing the influence of religion through the media and religious literature. What is considered tolerance in Europe, in the Albanian case is respect. Albanians are the best model for religious tolerance (respect). There has never been a religious clash in Albania for any reason. Respect for the religious affiliation and religious belief of the other in the Albanian case is modeled as the guiding value of their identity and appears in everyday life as the acceptance of the other. For this reason, they are the best model of respect and acceptance of the other, regardless of religious affiliation. This is an ontological value, built over the centuries and continues to this day. Albanians have not converted, but have adapted to a religious belief for economic and survival reasons. Marriages with different religions and keeping two names (Christian and Muslim) are natural phenomena among Albanians. In Albania, there are in the family and tribe people with Christian and Muslim religions individuals with two names, Christian and Muslim: Kristo and Muhamed. Albanians have lived in peaceful symbiosis with the Slavs in the centuries of the latter’s influx into Albanian lands. They have also lived peacefully with other neighbors, Greeks or Romans. This is even though the neighbors have not always been peaceful with the Albanians.
Metrics for Evaluating Cyber Security Data Visualizations in Virtual Reality

Abstract Cyber security analysts use data visualizations to speed up ingestion of security data. These visualizations typically take the form of 2D graphics displayed on computer monitors. Virtual reality has the potential to improve these visualizations with immersive 3D environments and unique interaction mechanics. However, research into this newly synergized area lacks evaluation, leading to unfounded claims of effectiveness. A potential cause for these missing evaluations was identified as a lack of guidance detailing how evaluations should be conducted in this area. Additionally, the small amount of research that does include evaluation incorrectly relies on subjective participant opinions to objectively measure system effectiveness. An example of this misuse is asking participants which system they thought was quicker, rather than timing them. The objective of this article is to propose a solution to these issues in the form of a surveyed, categorized, and analyzed set of evaluation metrics. A total of 49 metrics was identified from 41 papers. The presented metrics detail which dependent variables should be considered when evaluating works in the combined fields of cyber security, data visualization, and virtual reality. These metrics can be used to produce more accurate evaluations in future works in this area.
Influencing Factors of Employee Turnover Based on the Random Forest and DEMATEL Method: Evidence from the Qinghai Electric Power Company in China

Under the background of the deepening reform of China’s electric power industry, how to make use of existing resources, keep existing employees, and identify and distinguish the influencing factors of turnover have become major problems faced by companies. At the same time, the identification of influencing factors also provides a reference for relevant enterprises to do a good job in talent reserve and recruitment. The influencing factors of employee turnover are numerous and interrelated. Identifying and distinguishing the influencing factors are the focus of academic research. Meanwhile, it provides a reference for relevant enterprises to do an excellent job in personnel reserve and recruitment. Aiming at the limitation of constructing a direct matrix based on experts’ scoring method in the traditional DEMATEL model, a model combining random forest and DEMATEL is proposed, applied to identify the influencing factors of employee turnover in electric power enterprises in Qinghai Province. Based on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, the empirical analysis is conducted to analyze the influencing factors of employee turnover from two perspectives of the degree of centrality and the degree of causal. The research results show that marital status, household registration, political status, type of job, and the location of the work unit are the key influencing factors, providing a theoretical basis for the early warning of employee turnover and confirming the feasibility of the random forest-DEMATEL method proposed by us.
French error type annotation for dictation: A platform with automatic error type annotation for French dictation exercises

Dictation is considered an efficient exercise for testing the language proficiency of learners of French as a Foreign Language (FFL). However, the traditional teaching approach to dictation reduces the instructional feedback efficiency. To remedy this, this study adopts a design-based research approach and builds an automatic error type annotation platform for dictation practice named FRETA-D (French error type annotation for dictation) to pursue intelligent pedagogical feedback for both FFL teachers and students. FRETA-D can automatically identify error boundaries as well as classify the errors into fine-grained error types in learners' dictation texts. FRETA-D features a dataset-independent classifier based on a framework with 25 main error types, which is generalized from French grammar rules and characteristics of frequent learner dictation errors. Five French teachers are invited to evaluate the appropriateness of automatically predicted error types of 147 randomly selected samples, and the acceptance rate reaches more than 85%. Automatic evaluation on 1,009 sentences by comparing with manually labeled references also shows promising results, reaching more than 85% consistency with human judgments. The accessibility of FRETA-D has also been confirmed by 50 Chinese undergraduate FFL learners with different professional backgrounds. FRETA-D facilitates conducting dynamic statistical analysis of learners'error types. And we share the same findings with previous studies that there exist causal links between the dictation errors and learners' mastery of French phoneme and grapheme.
Design of Type 2 Fuzzy Logic Controller for FESTO Process Workstation

Fuzzy logic is the most effective mathematical solution that has been presented in the last few years to deal with the problems in which imprecise and uncertain data exist. It can be defined as a generalization of classical binary logic, which admits only two logical states, true or false, by adding degrees of truth between the extreme values. Its basics were initiated by Lotfi Zadeh in the mid-1960s. However, in the last few years, a question was raised by different authors: yes, by using a fuzzy set or membership functions we can solve the problem of some kinds of uncertainties, but what is the situation in cases where the uncertainties exist in the membership function itself? To handle this situation, researchers introduce type 2 fuzzy logic. In our paper, we will introduce the design of a type 2 fuzzy logic controller to control the level in a FESTO process workstation. The mathematical model is first deduced, and after that the controller is designed. The simulation results show that the obtained controller gives very good transient characteristics for the system response work.
Facial image inpainting for big data using an effective attention mechanism and a convolutional neural network

Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output image artifacts or fuzzy textures. Therefore, it is of practical significance to study how to effectively restore an incomplete facial image. In this study, we proposed a facial image inpainting method using a multistage generative adversarial network (GAN) and the global attention mechanism (GAM). For the overall network structure, we used the GAN as the main body, then we established skip connections to optimize the network structure, and used the encoder-decoder structure to better capture the semantic information of the missing part of a facial image. A local refinement network has been proposed to enhance the local restoration effect and to weaken the influence of unsatisfactory results. Moreover, GAM is added to the network to magnify the interactive features of the global dimension while reducing information dispersion, which is more suitable for restoring human facial information. Comparative experiments on CelebA and CelebA-HQ big datasets show that the proposed method generates realistic inpainting results in both regular and irregular masks and achieves peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as well as other evaluation indicators that illustrate the performance and efficiency of the proposed model.
Ethereum-Based User Authentication Model Using EOG Data for a Service Provider

Unlike general passwords, user authentication technology using biometric data cannot be lost or forgotten, and it has the advantage of being impossible to forge or falsify by attackers.Since this biometric data contains sensitive information, a safe storage method is needed.However, if biometric data is managed on a central server, it is limited by being vulnerable to integrity infringement attacks by system attackers and persistent infringement attacks on the authentication service.To solve this problem, a model using blockchain-based information security technology is proposed in this paper.The aim is to safely manage the user biometric data by dispersing the storage of the biometric data feature information for user authentication in smart contracts and IPFS using Ethereum.We have verified that it takes an average of 1,472.733ms and 217.829 ms, to register and authenticate a user in the proposed model and we expect that it will provide a reliable authentication service to the users compared to the existing authentication method in which the biometric data is managed by a single server.
The whole story: Rumors and science communication in the aftermath of 2012 Emilia seismic sequence

Controversies that stir the public debate on geological matters usually revolve around a few specific aspects, including the actual trigger of geological phenomena (i.e., natural vs. anthropogenic), their predictability, and the trustworthiness of the experts who provide information and advice on the phenomena. A typical example of such difficulties is the case of the 2012 Emilia, Italy, seismic sequence which struck an area of relatively moderate seismic hazard. In that period, geophysical prospecting was planned to assess the potential of a reservoir for gas storage, near the town of Rivara. The low frequency of important seismic events in the area, associated with the ongoing industrial planning prompted widespread rumors of an anthropogenic origin of the 2012 earthquakes. Controversy also arose about the actual size of the seismic events: earthquakes magnitude can be computed with different methods, and its value depends on the type, number, and geographical distribution of the available seismic stations. As a result, different institutions commonly release different estimates of the earthquake magnitude, casting doubts on the reliability of each estimate. Since 2012, public concern has also been caused by the repeated occurrence of unusual phenomena in the area, such as ground heating or bubbling well waters. Popular belief tends to establish a causal link between particular phenomena and seismic activity, reinforcing the false conviction that seismicity could be predicted. In this work we present and discuss some of the activities that INGV pursued through the years to contrast rumors and disseminate correct scientific information. In the aftermath of the 2012 seismic sequence, INGV worked in collaboration with the National Department of Civil Protection, the local administrations, the University Network of seismic engineering, the Regional Healthcare System and local volunteer organizations. The organization of public meetings, the collection and analysis of widespread rumors and the creation of ad hoc outreach materials all contributed to reinforce the mutual trust between our research institute and the local population.
The Increasing Threat to Digital Assets Due to the Development of Quantum Algorithms

The development in this digital era is fast pacing up to the future where machines will be able to perform tasks more efficiently and rapidly than even today’s supercomputers aren’t able to perform. Increasing technology and exponential development in the domain of Quantum Computing are leading humanity to the future where Computers will be able to solve unsolvable or exponential time-consuming problems in a span of short time. This will be proved advantageous in numerous ways but as every state of development has other aspects, this development will also be able to break down every possible cryptographic algorithm implemented in the classical computing era as they all are based on complex mathematical equations and calculations. A perfectly implemented Quantum Computer will be able to compute the mathematical calculations in parallel due to the phenomenon of Superposition and entanglement. All possible progress in Quantum Algorithms is discussed in the context of digital security in this paper which can be a possible threat to every executed cryptographic algorithm and securities instigated through them.
Explainable argumentation as a service

Gorgias Cloud offers an integrated application development environment that facilitates the development of argumentation-based systems over the internet. Argumentation is offered as a service in a way that this allows application systems to remotely access the argumentation service and utilize the results of the argumentative computation. Moreover, the service results include the explanation of the decision in both human and machine-readable formats. The first is useful for allowing the application validation to be done by experts, while the second is useful for development. It appears that this is the first case where argumentation is offered to developers in such an open and distributed way.
Subspace screening rule for multi-label estimator with sparsity-inducing regularization

Multi-label learning has drawn wide attention for the last decade. To exploit the correlation between labels, a multi-label classifier based on the nuclear norm has been proposed recently, which joints Ranking support vector machine (RankSVM) and Binary Relevance (BR) with robust Low-rank learning (RBRL). Therefore, it has satisfactory classification outcomes in the application. Nonetheless, tackling the large-scale problem still remains a challenge for RBRL. Motivated by this, a Subspace Screening Rule (SSR) for RBRL is proposed to accelerate the solving process. Its primary strategy is to reduce the size of the matrix variable to be estimated, based on the fact that a low-rank matrix can be represented by a few subspaces. Specifically, at each iteration we delete a majority of subspaces with zero coefficients in the optimal solution by matrix decomposition and optimality condition. Then, we solve the small-scale reduced problem rather than the initial large-scale matrix problem. An excellent acceleration effect is obtained. To further improve the solving speed, Approximate Singular Value Decomposition (ASVD) and Accelerated Proximal Gradient (APG) are employed in the different stages. Extensive experiments on seven benchmark datasets as well as an artificial dataset demonstrate the efficiency of SSR.
Prediction of English Major Training Model in Higher Vocational Education Based on Fuzzy Neural Network Algorithm

The prediction of English major training model in Higher Vocational Education Based on fuzzy neural network algorithm is the process of using a neural network to predict the future. The main function of this method is to analyze and understand the human brain and its characteristics. This method can be used as an effective tool for predicting or analyzing any kind of data, such as finance, economics, psychology, medicine etc. In addition, it can also be used for understanding people’s behavior and their reactions towards various situations. Higher Vocational and technical education, the importance of talent planning in Higher Vocational and technical education has become increasingly prominent, The premise of each higher vocational and technical talent planning is to make a reasonable prediction of each higher vocational and technical talent and professional training. Fuzzy neural network can effectively deal with nonlinear and fuzzy problems, and plays a great role in intelligent information processing. Therefore, this paper studies the prediction of English professional training model in Higher Vocational Education in fuzzy neural network algorithm.
Application of Network Course Platform Based on Cloud Computing in Physical Education

On the basis of traditional teaching, through the combination and application of current network technology, we can finally break through the restrictions of space and time in the traditional classroom. This is the so-called network course teaching, which can meet the needs of modern education more effectively. With the continuous development of network course teaching, it has attracted the attention of many scholars, and the research on network course teaching has become more and more extensive and in-depth. Nevertheless, there are some problems in the process of the continuous development of network course teaching, and the introduction of cloud computing in network course teaching can effectively improve these problems. This paper introduces the application of network course platform based on cloud computing in physical education teaching for your reference.
Optimization Models and Educational Teaching Research in Agricultural Logistics System

Logistics science is a new comprehensive discipline. It plays a very important role in the development of national economy and the improvement of production and management level. The task of cultivating new agricultural business entities is an important measure to build a strong agricultural industry. This paper studies and expounds the significance of the connection between the new agricultural management subject and the agricultural logistics system; Analyze the problems of coordination between the logistics system and the fresh characteristics of agricultural products, the docking between the main bodies of agricultural circulation channels and the control of production, supply and marketing costs in the docking process between new agricultural business entities and agricultural logistics; How to effectively optimize and allocate logistics operation and reduce logistics cost is of great significance not only to enhance the international competitiveness of enterprises, but also to promote the rapid development of the whole national economy.
Research and Design of Education Management System Based on Cloud Storage

Information construction in Colleges and universities is the key factor to realize modern education. In the process of information construction in Colleges and universities, due to the similarity of teaching data, there are a lot of duplicate data between the education management systems independently built by colleges and universities, resulting in a great waste of storage resources, and the teaching data is growing exponentially, which brings great storage pressure to the traditional data center. All users share a resource pool, resulting in low resource search efficiency. Moreover, due to the unclear role and authority of users, teaching resources can not be safely shared among college users.
Research and Design of Information Teaching Management System Integrating Association Rule Mining Algorithm

The main purpose of this research is to design and implement an information teaching management system integrating association rule mining algorithm. The process is divided into two parts: the first part is about the design model, and the second part is about implementing it. Research and design of information teaching management system integrating association rule mining algorithm In this research, we are trying to develop an information teaching management system, which can be applied to college students studying in the field of computer science. The purpose is to provide teachers with a tool that can help them work. We also hope to use it as a learning tool for students so that they can acquire more knowledge about how computers work and what they need to learn when using computers. Combine the research and design of information teaching management system with association rule mining algorithm. This algorithm can be used to find the most important rules in a given dataset, which are related to each other. It can then be used to select the best rule from the rules found using this method. It will help teachers choose the best teaching methods or skills to improve their skills and knowledge.
Design of Education Management System Based on Cloud Storage

Information construction in Colleges and universities is the key factor to realize modern education. In the process of information construction in Colleges and universities, due to the similarity of teaching data, there are a lot of duplicate data between the education management systems independently built by colleges and universities, resulting in a great waste of storage resources, and the teaching data is growing exponentially, which brings great storage pressure to the traditional data center. All users share a resource pool, resulting in low resource search efficiency. Moreover, due to the unclear role and authority of users, teaching resources can not be safely shared among college users.