Reviewerly/exHarmony huggingface dataset:
Anonymization Techniques for Privacy Preservation in eLearning

Modern eLearning environments are characterized by tools to track learner behavior and collect large amount of data. This data is often used to perform various analyzes by the environment itself or may be provided to a third party to receive, for example, information about student performance. The processed data and obtained statistics can always be subject to attacks by malicious persons in order to reveal specific identities of certain learners or to find sensitive data. Therefore, various algorithms are applied to protect privacy, and they also use anonymization techniques. The purpose of the paper is to summarize possible techniques for data anonymization and to demonstrate one of their applications suitable for implementation in eLearning environments.
Trace Elements in Olivine of Volcanic Rocks: Application to the Study of Magmatic Systems

A quantitative local analytical method with the application of inductively coupled plasma mass spectrometry with laser ablation (LA-ICP-MS) was tested at Vernadsky Institute for the determination of contents of trace elements (Cu, Zn, Co, Ni, Mn, Cr, Sc, V, Ca, Ti, Al, Y, and REE) in olivine. Olivine phenocrysts from volcanic rocks of various geological settings have been studied: island-arc basalts, mid-ocean ridge (MOR) basalts, and high-alkaline continental volcanic rocks. The contents of some elements (Ni, Co, Mn, Cr, Sc, and Zn) systematically vary during the evolution of the composition of olivine, and the concentration fields of these elements in olivine from different settings overlap one another. At the same time, the contents of some other elements (Ca, Al, Ti, V, and Cu) fundamentally differ in olivine from different geological settings. Copper content in olivine from oceanic tholeiites and highly alkaline continental volcanics is 1–3 ppm, which is systematically lower than copper content in olivine from island-arc basalts (3–9 ppm). The concentrations of vanadium in olivine in MOR basalts are higher than in island-arc and alkaline continental ones, which may be due to relatively more reduced crystallization conditions as more favorable for the incorporation of V3+ into the olivine structure. Variations in the distribution coefficients of trace elements between olivine and silicate melt (DOl/Melement) were determined for volcanic rocks from Kamchatka, the Bouvet Triple Junction, and Gaussberg volcano. It has been demonstrated that the unusually high values DOl/MNi of DOl/MNi = 50–150 previously identified for the lamproites of Gaussberg volcano indicate a mismatch between the composition of the quenched glass and the composition of the equilibrium melt for olivine phenocrysts. When using the bulk compositions of Gaussberg rocks, values of DOl/MNi = 11–21 were obtained, which correspond to experimental estimates for high-potassium rocks. The redox crystallization conditions of the studied rocks were estimated using several oxybarometers based on the distribution of vanadium between coexisting olivine and melt. These values were: ΔQFM= +0.6 to +1.5 for oceanic tholeiites of the Bouvet Triple Junction area, South Atlantic, and ΔQFM = +1.5 to +2.4 for Mutnovsky volcano, Kamchatka. Estimates of the redox crystallization conditions of the highly alkaline rocks of Gaussberg volcano significantly vary depending on which model is chosen: ΔQFM= +0.2 to +4.8, which may be due to the strong effect of K2O content in the melt involved in one of the models. The newly acquired analytical data confirmed the possibility of using contents of trace elements in olivine to characterize igneous systems from different geological settings and highlighted the need for additional experimental studies on the distribution of these elements between olivine and melt, especially in highly alkaline systems.
The impact of Artificial Intelligence on the Socioeconomic factors in the UAE

Artificial Intelligence (AI) is rapidly transforming industries worldwide, including the United Arab Emirates (UAE). As the UAE aims to become a leader in AI adoption and deployment, it is crucial to understand its impact on the country's socioeconomic factors. This research proposal aims to investigate the impact of AI on employment, wage inequality, productivity, innovation, and ethical implications in the UAE.
MINERAL RESOURCES OF THE NUCLEAR INDUSTRY OF RUSSIA AND ISOLATION OF RADIOACTIVE WASTE

The tasks of ensuring the sustainable development of the nuclear energy industry in Russia are considered, which are aimed at solving the problems of the initial and final stages of the nuclear fuel cycle from the reproduction of the mineral resource base of uranium to the management of high-level liquid waste from the reprocessing of spent nuclear fuel. An analysis was made of the trends in the development of the mineral resource base of uranium in connection with the predicted growth in electricity generation at nuclear power plants. Based on the example of the territory of southeastern Transbaikalia, within the framework of topical mineralogical studies, new approaches to the allocation and justification of prospecting areas are proposed based on models of mineral-forming systems of uranium deposits using Earth remote sensing data, GIS modeling and neural network technologies. A detailed analysis of trends in solving the problems of the final stages of the nuclear fuel cycle has been carried out. For isolation and subsequent disposal of the REE-actinide fraction of high-level waste, it is proposed to use phosphate glass ceramics with monazite
Sb-As LOJAN DEPOSIT (REPUBLIC OF NORTH MACEDONIA): ORE TYPES, CONDITIONS OF ORE TYPES LOCALIZATION AND GEOCHEMICAL FEATURES

This article discusses the results of comprehensive studies of Sb-As ores of the Lojan deposit, located in the north-east of the Republic of North Macedonia (RSM), near the border with Serbia. 5 types of Sb-As ores have been identified: breccated realgar-auripigment ores, realgar breccias, breccated antimonite ores, massive, almost monomineral realgar ores, as well as realgar-antimonite nest-shaped ores. The ores are characterized not only by an unusual paragenesis of nickel, arsenic and antimony minerals, but also by a very close fusion of antimonite, realgar and collomorphic quartz. A wide range of elements in ores (As, Sb, Cr, Ti, Mn, Ni, Mo, Co, Ag, Tl, U, etc.) is apparently due to the combination of mineralization in ores of several different-time parageneses. As a result of thermometric studies of fluid inclusions in quartz, Thom vary from 180 OC to 220 OC (the average value is 201 OC). Studies of the isotopic composition of sulfur in antimonite and realgar have shown rather narrow ranges of δ34S values from -5.19 to -0.26 and from -4.80 to 1.92, respectively, which indicates an endogenous source of sulfur. The results obtained allow us to attribute the Lojan deposit to the epithermal class
A contemporary „re-Romanisation”. Overview of thirty years of Italian presence in Oradea

After the golden age of the city, due to the peninsular bishops and humanists and later through the military and civil architects, the Italians returned as protagonists after 1989, when in Oradea, the third spoken language, after Romanian and Hungarian, became Italian, not only through native speakers, but also through the corollary of speakers around the central native group, employees, collaborators, family, friends. Where there is an Italian, in a professional or private entourage, everyone present becomes an Italian speaker! The closest trusted collaborators of Italian entrepreneurs are always good Italian speakers. The Italians represent a force with significant vitality and impact on the urban demographic and cultural texture. Therefore a still discreet but increasingly evident phenomenon of „re-romanisation” is taking place through presence and language; a presence that is now visible, not only through the economic realities it brings, but which also produces linguistic contamination and transformation.
SOLAR TRACKER SYSTEM FOR MONITORING THE MAXIMUM SUN POTENTIAL THAT CAN BE PRODUCED IN A SPECIFIC PLACE

Power grids have been the mainstay of power systems for more than a century, supporting economic activity by bringing power to homes, industry and services. As the transition to green energy advances, the role of electricity will become more important, making power grids even more special to society and economies. The energy sector is going through essential changes such as decarbonization, digitalization, climate change etc. and as a solution, governments, industries and other stakeholders will need to establish new policies, regulations and updated market models for a new system in the future. Therefore, our purpose was to develop a system that can track the sun from sunrise to sunset, which can be placed in a specific location to record and store data about the maximum potential sun that can be produced. The solar tracker is guided by two step-by-step motors, creating horizontal and vertical movement so that the rising sun will fall perpendicular to the solar panel. The two motors are controlled with the mobile phone which stores data about the amount of energy that is produced. A prototype was made using parts from reused materials such as bicycle wheel, concrete mixer crown, computer elements, parts for chairs, etc. The system is independent of electricity, so it can be placed anywhere without being connected to an outlet. The motors and the data controller are powered by the energy produced by the solar panel in the form of a closed circuit. The data is used to estimate whether investments in solar panel systems in a specific area are profitable and can provide information about the energy produced daily, weekly, monthly or annually. The advantage of the project is that we can control it with our mobile phone and all data is automatically stored without losing any information due to its own power supply.
Framework for advance persistent threats resilience with blockchain-enhanced intrusion detection response

Due to the vast development of Advanced Persistent Threats (APTs), that aims on the infiltration of highly prospects organizations and governmental sectors. It was researched that using Blockchain technology will increase the possibility of an appropriate approach behavior to add with IDR (Intrusion Detection Response) Blockchain-Enhanceed technology maintain phenomenal features allowing for easier forensic analysis, as well as a real-time response and many others. Threats will be shared within the infrastructure in a centralized fashion to appoint each intrusion with an appropriate response. The ideology of having an enhance technology, negates the possibility of tampering, along with persistency in a comprehensive reaction against current and future threats. This research will create a higher aim force with a guarantee of a zero fail theoretically, as the usage of blockchain will get involved in the various type of intruders once found. The occupation of threat aim to get into the DMZ while maintaining a full hideout, defending itself from IDR by intercepting traffic from it. Therefore, a proper constructed information block (blockchain) shall maintain this data to avoid its break-ability due to the prestine behaviour of Blockchain-technology within transfers. The features that Ethereum holds as a blockchain technology will allow Intrusion Detectors traffic forwarded without an affect on Confidentiality, Integrity and Availability. The details within Cryptocurrencies such as bitcoin and Ethereum. In the case of this research Ethereum shall be in our use case as its functionality with smart contract to be used as a response shall play a valid role with respect to IDR. The paper will provide extensive theoretical details that shall be implemented using ``Solidity'' the programming language used by Ethereum creating a solid detailed if-then situations to be created by an AI method and set of proportionate responses. The responses will be accumulated continuously within the blockchain being transfer from and within the blocks to ensure CIA (confidentiality, integrity and availability). This shall solve the issues of communication from and to a non-comprehensive yet continuously patched IDR with the set of responses as all will happen without human interference at the network.
Image Manipulation Detection Using Augmentation and Convolutional Neural Networks

Image tampering is now simpler than ever, thanks to the explosion of digital photos and the creation of easy image modification tools. As a result, if the situation is not handled properly, the major problems may arise. Many computer vision and deep learning strategies have been put out over the years to address the problem. Having said that, people can easily recognize the photographs that were used in that research. This begs the key question of how CNNs might do on more difficult samples. In this chapter, we build a complex CNN network and use various machine learning algorithms to classify the images and compare the accuracies obtained by them. Its performance is also compared on two different datasets. Additionally, we assess the impact of various hyperparameters and a data augmentation strategy on classification performance. This leads to a conclusion that performance can be considerably impacted by dataset difficulty.
Image and video steganography using least significant bit technique

The significant advancement of advanced video and Images over the internet, Image and Video Steganography techniques have become crucial in numerous social networking applications. Steganography in intelligence aids operations for espionage, counterespionage, and operational security and enables to communicate incognito and monitor counter terrorism. This paper aims to propose a symmetric key based hiding technique known as Least Significant Bit (LSB) to hide information by using Image or Video as a cover. This has been implemented with the help of MATLAB.
AutoML frameworks for disease prediction using medical claims

In healthcare, accurate illness prediction is essential for early discovery, prompt treatment, and better patient outcomes. The quantity of data from medical claims offers a chance to use machine learning methods to anticipate diseases. The complicated and unstructured nature of medical claims data makes it difficult to create precise prediction models. The emergence of AutoML frameworks as potent tools for tackling the difficulties of illness prediction using medical claims is presented in this research. We examine and evaluate several cuttingedge AutoML frameworks and their use in illness prediction tasks with data from medical claims. These frameworks use cutting-edge algorithms and approaches, including feature engineering, ensemble learning, and model stacking, to improve illness prediction models' predictive accuracy and interpretability. A broader spectrum of medical professionals and researchers may use AutoML because of its automated nature, which facilitates the effective use of computing resources and lessens the load of domain expertise. This study uses data from medical claims to demonstrate the potential of AutoML frameworks for precise illness prediction. In order to fully realise the promise of medical claims data for better patient care and health outcomes, it emphasises the significance of automated techniques in healthcare analytics and the necessity of more research and development in this field.
A STUDY ON THE IMPACT OF E-COMMERCE ADOPTION FOR ENHANCING SUPPLY CHAIN EFFICIENCY IN BANGLADESH SMES

This study examines the impact of e-commerce adoption on supply chain efficiency in small and medium-sized enterprises (SMEs) in Bangladesh. The research aims to identify the factors influencing the adoption of e-commerce in SMEs’ supply chain operations, understand the challenges and barriers faced by SMEs in adopting e-commerce, and analyze the impact of e-commerce adoption on supply chain efficiency. A qualitative research design is employed, utilizing semi-structured interviews with SME owners and managers who have adopted e-commerce for their supply chain operations. The study highlights the importance of SMEs in Bangladesh’s economy and the growing significance of e-commerce in supply chain operations. Despite the rapid growth of Bangladesh’s e-commerce industry, the integration of e-commerce into SMEs’ supply chain operations is still in its early stages. Factors such as technological readiness, infrastructure availability, organizational culture, and external influences affect the pace of e-commerce adoption. The findings reveal that increased efficiency, cost savings, and improved customer experience are the primary drivers for e-commerce adoption. However, resistance to change, lack of technical skills, and security concerns pose challenges to adoption. Participants recommend training programs to enhance technical skills and foster a culture of change. Additionally, organizations should conduct thorough market research, seek customer feedback, and engage in discussions with supply chain partners to make informed decisions regarding e-commerce adoption. Investing in cybersecurity measures is crucial to address security risks. The study contributes to the existing literature on e-commerce adoption in supply chain operations, particularly in the context of developing nations like Bangladesh. It provides valuable insights for SMEs, policymakers, and researchers, emphasizing the need to carefully evaluate the benefits, costs, and risks associated with e-commerce adoption. Enhancing supply chain efficiency through e-commerce adoption can lead to improved competitiveness, cost reduction, and increased profitability for SMEs in Bangladesh.
Selection of the Importance of Optimum Utilization Technology for Natural Gas Power Plants Using AHP Technique

Recently, the Act on the Integrated Management of Environmental Pollution Facilities was implemented in Korea. Under the integrated management, the license period of the workplace will be renewed by five to eight years through the assessment of the level of environmental management of the workplace in accordance with the integrated law implemented in 2017. In this process, the application rate of the optimal use technique is evaluated, which is essential to obtain a high evaluation score. However, there is a problem in assessing optimal use techniques without considering the situation at the workplace. An AHP analysis was conducted on experts according to the situation of the workplace by examining the applicable optimal use techniques and the EU standards, and the priority was prepared by selecting the techniques to be applied first.
role of discursive structure in the genre specification of online discourses

The paper starts with the assumption that each time people engage in discourses, they perform social actions in order to adaptively satisfy their communicative purposes and needs (cf. Verschueren–Brisard 2009; Steen 2011; Giltrow 2013). Moreover, these social actions form types in accordance with the typical co-occurrences of typical discourse situations involving typical roles, typical themes and typical communicative goals tar- geted by discourse participants (cf. Bakhtin 1986). These typical co-occurrences serve as ground for the functioning of genre knowledge and result in typical patterns of construal.&#x0D; In this paper, I present a two-pronged empirical study that reveals the role of discursive structure in the creation of the genre-specific character of online discourses. By discursive structural units, I mean discourse segments (usually larger than one sentence but smaller than the entire discourse) used by the speaker to accomplish a communicative strategy aimed at achieving a more specific communicative purpose. During the research, I used two types of data. On the one hand, I compiled a research corpus consisting of 50 online recipes and 50 online book reviews in Hungarian. On the other hand, I conducted an experiment in which one group of Hungarian native speaker informants (25 persons) had to create a recipe for a good novel, while another group of informants (25 persons) had to write a review of a given recipe. The research thus focused on the genres of recipe and (book) review. I performed corpus-based qualitative analyses on both datasets using the MAXQDA software. After carefully developing a data-driven annotation system on a smaller pilot material, I systematically identified the communicative strategic units of both the non-elicited and the elicited dataset [cf. Swales’s (1990) move structure analysis]. The research findings are as follows: (i) Discursive structure formed by the speaker’s communicative strategies is a genre-marking quality of the online discourses under study. (ii) Recipes written by laypersons and professionals do not have a different discursive structure but the identified discursive structure is very clearly manifested. (iii) Book reviews written by lay readers differ significantly from those written by professional critics in terms of discursive structure and this sharp differentiation reveals a separation between two more specific genres, i.e. amateur book review and professional critique.
Modeling Epidemics: Neural Network Based on Data and SIR-Model

Earlier, a method for constructing an initial approximation for solving the inverse problem of acoustics by a gradient method based on a convolutional neural network trained to predict the distribution of velocities in a medium from wave response was proposed [9]. It was shown that the neural network trained on responses from simple layered media can be successfully used for solving the inverse problem for a significantly more complex model. In this paper, we present algorithms for processing data about epidemics and an example of applying a neural network for modeling the propagation of COVID-19 in Novosibirsk region (Russia) based only on data. A neural network NN-COVID-19 that uses data about the epidemics is constructed. It is shown that this neural network predicts the propagation of COVID-19 for five days by an order of magnitude better than SEIR-HCD. When a new variant (Omicron) appeared, this neural network was able to predict (after retraining) the propagation of the epidemics more accurately. Note that the proposed neural network uses not only epidemiological data but also social ones (such as holidays, restrictive measures, etc.). The proposed approach makes it possible to refine mathematical models. A comparison of the curves constructed by SEIR-HCD model and by the neural network shows that the plots of solutions of the direct problem almost coincide with the plots constructed by the neural network. This helps refine coefficients of the differential model.
Gradient-free Federated Learning Methods with l1 and l2-randomization for Non-smooth Convex Stochastic Optimization Problems

This paper studies non-smooth problems of convex stochastic optimization. Using the smoothing technique based on the replacement of the function value at the considered point by the averaged function value over a ball (in l1-norm or l2-norm) of a small radius centered at this point, and then the original problem is reduced to a smooth problem (whose Lipschitz constant of the gradient is inversely proportional to the radius of the ball). An essential property of the smoothing used is the possibility of calculating an unbiased estimation of the gradient of a smoothed function based only on realizations of the original function. The obtained smooth stochastic optimization problem is proposed to be solved in a distributed federated learning architecture (the problem is solved in parallel: nodes make local steps, e.g. stochastic gradient descent, then communicate—all with all, then all this is repeated). The goal of the article is to build on the basis of modern achievements in the field of gradient–free non-smooth optimization and in the field of federated learning gradient-free methods for solving problems of non-smooth stochastic optimization in the architecture of federated learning.