Reviewerly/exHarmony huggingface dataset:
Challenges of digitalization for higher education in Russia

The modern trend of education development is digitalization, transition from conventional classroom offline model to distance online education. Formation of global digital society was accelerated due to the COVID–19 pandemic, when significant portion of Earth population, including students, was forced to work, to study distantly. In this regard, this article discusses the responses of Russian system of higher education to current challenges of digitalization. This article analyzes interactive educational technologies of online, digital, e-learning, visualization and gamification of education, implementation of network structures. On the basis of analysis of the modern state of Russian higher education, the unsolved key issues are demonstrated as well as the main trends of the required changes. This work describes the urgent issues of development of regulatory framework of digital education, intensification of development of digital infrastructure of universities, digital training and further training of teachers and students, analysis and actualization of existing specialties and fields. Solution of the mentioned issues requires for legislative, organizational, administrative, technological, teaching and learning efforts of the country, universities, and society. A response of the Russian system of higher education to the challenges of digitalization can be comprised of formation of unified information and education portal of national universities, designing a model of digital university.
Methodology for Assessing the Impact of the Diffusion of blockchain Technologies on the Development of the National Economic system (illustrated by the example of the Russian Economy)

Distributed data storage technologies are becoming an integral part of the modern economy. In this regard, today it is very relevant to a search for formalized approaches to assessing the impact of blockchain technologies on key parameters of macroeconomic generations. The subject of the research is the system of relations between economic entities of the national economy, associated with the perception of blockchain technologies that permeate economic processes. The aim of the study is to develop a methodological toolkit for scenario forecasting of possible consequences for the national economy of the introduction of blockchain technologies into the economic sector. The authors apply methods of cointegration analysis, scenario modeling, substantiation of the studied patterns by methods of regression analysis, etc. The authors use works of foreign and Russian scientists, official data of the Federal State Statistics Service of the Russian Federation as an information and statistical database. The authors systematize positive and negative externalities; propose an algorithm for studying the influence of blockchain technologies on the dynamics of GDP through the transformation of the key parameters of the functioning of the financial and real sectors of the economy; build a model and assess the possible impact on GDP of the integration of blockchain technologies into the economy. As a result, the authors make the following conclusions : to the greatest extent, the integration of blockchain technologies into the business processes of the national economy affects the change in the financial results of credit institutions, an increase in capital liquidity of economic agents, as well as the acceleration of the processes of socialization of channels of access of business entities to financial markets (expanding access of economic agents to exchanges). Scenario modeling of changes in these factors made it possible to establish that the potential for additional GDP growth in the Russian economy can reach about 1% per year as part of the integration of distributed data storage technologies into the system of economic relations. The developed and approved methods for the formalized assessment of the impact of blockchain technologies on the dynamics of economic growth create the basis for clarifying methodological approaches to the study of the problem posed, open up new opportunities for holding discussion platforms on this topic.
Elicitation of the Parameters of Multiple Linear Models

Estimating the parameters of a multiple linear model is a common task in all areas of sciences. In order to obtain conjugate distributions, the Bayesian estimation of these parameters is usually carried out using noninformative priors. When informative priors are considered in the Bayesian estimation an important problem arises because techniques arerequired to extract information from experts and represent it in an informative prior distribution. Elicitation techniques can be used for suchpurpose even though they are more complex than the traditional methods. In this paper, we propose a technique to construct an informative prior distribution from expert knowledge using hypothetical samples. Our proposal involves building a mental picture of the population of responses at several specific points of the explanatory variables of a given model andindirectly eliciting the mean and the variance at each of these points. In addition, this proposal consists of two steps: the first step describes the elicitation process and the second step shows a simulation process to estimate the model parameters.
Texture Analysis using wavelet Transform

In this research application of wavelet based multiscale image analysis methods for texture analysis has been highlighted. These methods are based on multiresolution properties of the two-dimensional wavelet transform, which is used to extract the features needed to discriminate and differentiate various textures more accurately then existing methods, we also took into account the texture model, the noise distribution, and the inter-dependence of the texture features which further help in discriminating factor. Multiresolution approach is nothing but a modified wavelet transform called the tree-structured wavelet transform or wavelet packets for texture analysis and classification. This approach is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, we are able to zoom into any desired frequency channels for further decomposition and thus we could extract more texture features as compared to other methods.
Rare-earth elements in Cu-Ni ores of the Norilsk region

The deposits of the Norilsk region are confined to the marginal northwestern part of the Siberian platform and are associated with the manifestation of the Early Mesozoic trap volcanism. Sulfide mineralization is concentrated in the intrusive facies of the volcano-intrusive complex. Four main types of Cu-Ni ores are distinguished according to textural features: massive, vein-disseminated, brecciated, and disseminated. The main types of Cu-Ni ores differ significantly from each other in the chemical composition and content of rock- and ore-forming components. The enrichment ratios of ore trace elements in the Oktyabrskaya and Talnakhskaya vein-disseminated ore mainly reach hundreds (S, Pt, Au, Se,Te, Pb, As, Sn, Mo) and thousands (Ni, Cu, Ag, Pd) times. A rather high enrichment of all types of Bi, Re and Cd ores has been revealed, which are potentially industrially significant as high-tech metals, and can be promising for associated mining. The amount of rare-earth elements (REE) in ores is several times less than in the upper crust and more than in the primitive mantle. The concentration of rare earths in the main types of ores, with the exception of massive ones, exceeds the level of the chondrite standard, and REE fractionation is weak. The distribution spectra of chondrite-normalized REEs have a slight negative slope: the total content of light REEs is higher than that of heavy ones. The similarity of the geometry of the REE spectra of massive, vein-disseminated, brecciated and disseminated types of ores containing a significant admixture of host rocks indicates the inheritance of the REE composition in ores from the host rocks. In the disseminated ores of common taxite gabbro-dolerites, the Eu anomaly is present in the Kharaelakhsky (Oktyabrskoye Deposit) and Talnakhsky (Talnakhskoye Deposit) intrusions, it is positive, and in the Norilsk I Intrusive (in the same rocks), it is negative. Higher concentrations of REEs and significant fractionation of light and heavy rare elements are typical for host contact-metamorphic and metasomatic rocks with vein-disseminated (exocontact) ores. For olivine-free hornfels and skarns in brecciated ores, as well as for vein-disseminated ores, significant fractionation of light REEs with relatively heavy ones and a pronounced Eu minimum are characteristic. In massive ores, the distribution of REEs is determined by the composition of xenoliths.
Poor University Students Management System Based on Web

Abstract Combining with the actual work of the poverty aid management in F University, this paper analyzes the work flow of the poverty aid management in University, designs and develops the Poor University Students Management System. The system uses web architecture mode, JSP programming technology and SQL Server database management technology. The system is composed of four modules: system management module, institute management module, class management module and student management module. It realizes the functions of poor students’ information collection, poor students’ application, students’ information query and poverty level audit. It improves the efficiency of poor students’ management and realizes the networking, automation and informatization of poor students’ management.
Prediction of Bitcoin Price Using Bi-LSTM Network

Machine Learning and Artificial Intelligence based money exchanging have p ulled in enthusiasm in the recent years with the introduction of Bitcoins. The cost of Bitcoins has increased in a large scale and it is fairly difficult to predict the future cost per Bitcoin. In this study, we utilize a machine learning and deep learning model to analyze the digital currency market to predict the cost of Bitcoin per day. We dissect everyday information for 1,691 cryptographic forms of money for the period between November 2017 and April 2019. The study shows that straightforward exchanging procedures assisted by best in class AI algorithms have met the standard benchmarks. Our outcomes also show that non-inconsequential, basic algorithmic instruments can help in envision of momentary development of the cryptographic money. The proposed system uses a Bi- directional LSTM for forecasting the bitcoin prices. The proposed model was able to trace the test dataset with Mean Absolute Percentage Error of 13%. The model is helpful for the user to take decision on investing in Bitcoins.
Genetic Algorithm Based Optimal Trajectories Planning for Robot Manipulators on Assigned Paths

This research work focuses on finding an optimal trajectory for a robot manipulator taking into consideration the dynamics constraints of the manipulator.A 3-DOF planar robot was modeled using MATLAB/SIMULINK toolbox.The simulation of the robot manipulator was carried out using the Genetic Algorithm to find the optimal trajectory both in the workspace with and without obstacles.In both cases, the Genetic Algorithm (GA) generated optimal trajectories.The results of both environments were also compared.The increase in the simulation result in an obstacle existence environment made it possible for optimal trajectory devoid of collision with any obstacle in the working area.Thereafter, result comparison was done with a similar work and the GA method produced a more desired result in terms of execution time.
A Design of a Hybrid Algorithm for Optical Character Recognition of Online Hand-Written Arabic Alphabets

The growing relevance of printed and digitalized hand-written characters has necessitated the need for convalescent automatic recognition of characters in Optical Character Recognition (OCR). Among the handwritten characters, Arabic is one of those with special attention due to its distinctive nature, and the inherent challenges in its recognition systems. This distinctiveness of Arabic characters, with the difference in personal writing styles and proficiency, are complicating the effectiveness of its online handwritten recognition systems. This research, based on limitations and scope of previous related studies, studied the recognition of Arabic isolated characters through the identification of its features and dots in view of producing an efficient online Arabic handwriting isolated character recognition system. It proposes a hybrid of decision tree and Artificial Neural Network (ANN), as against being combined with other algorithms as found in previous studies. The proposed recognition process has four main steps with associated sub-steps. The results showed that the proposed method achieved the highest performance at 96.7%, whereas the benchmark methods which are EDMS and Naeimizaghiani had 68.88% and 78.5 % respectively. Based on this, ANN has the best performance recognition rate at 98.8%, while the best rate for decision tree was obtained at 97.2%.
Experimental Analysis of the Relevance of Features and Effects on Gender Classification Models for Social Media Author Profiling

Automatic user profiling from social networks has become a popular task due to its commercial applications (targeted advertising, market studies...).Automatic profiling models infer demographic characteristics of social network users from their generated content or interactions.Users' demographic information is also precious for more social worrying tasks such as automatic early detection of mental disorders.For this type of users' analysis tasks, it has been shown that the way how they use language is an important indicator which contributes to the effectiveness of the models.Therefore, we also consider that for identifying aspects such as gender, age or user's origin, it is interesting to consider the use of the language both from psycho-linguistic and semantic features.A good selection of features will be vital for the performance of retrieval, classification, and decision-making software systems.In this paper, we will address gender classification as a part of the automatic profiling task.We show an experimental analysis of the performance of existing gender classification models based on external corpus and baselines for automatic profiling.We analyse in-depth the influence of the linguistic features in the classification accuracy of the model.After that analysis, we have put together a feature set for gender classification models in social networks with an accuracy performance above existing baselines. INTRODUCTIONAutomatic author profiling is a research area that has gained some relevance in recent years focused on inferring social-demographic information about the author or user of a certain application or software service ( Álvarez-Carmona et al., 2016).The growing number of studies in author profiling is mainly explained by the large number of possible applications in strategic sectors such as security, marketing, forensic, e-commerce, fake profiles identification, etc. (Rangel et al., 2014).Recent author profiling efforts include the development of shared tasks and corpora for evaluating author profiling, especially taking written texts by the user as relevant information for the demographic profile construction.Resultant author profiling models show that the language used in social network publications is a very relevant demographic indicator, identifying aspects such as gender, age or user's origin from psycho-linguistic and semantic features.
Analysis of Cloud Forensics : Review and Impact on Digital Forensics Aspects

Digital forensics is the science of finding evidence to digital crimes and attacks. Cloud Forensics is a part of Digital Forensics that watches over the crime that has taken place over the cloud and carries out an investigation on it. Cloud computing is an evolutionary technology based on a huge network, which spreads globally. Hence, Cloud Forensics is a part of Network Forensics, which in turn is a part of Digital Forensics. Cloud organizations along with the providers of cloud service and customers that uses cloud service, are still awaiting the establishment of an explicit forensic revolution. Without the much-needed forensic capability, they will not be able to safeguard the robustness of their system and suitability of their services that assist criminal and cybercrime investigations. In this paper, we review the forensic process, challenges in cloud forensics, and its impact on digital forensics.
State of the Art of Human-Computer Interaction (HCI) Master’s Programs 2020

This paper presents the wide review of the state of the university masters or graduate programs in the discipline of Human-Computer Interaction (HCI) and related areas up to 2020. This work has been done as part of the design and development process of a new interuniversity and international master's program graduate developed between the Universidad Nacional Abierta y a Distancia (UNAD) of Colombia and the Universidad de Lleida (UdL) in Spain. The review was necessary to know what other institutions did, the needs of the market and organizations, contrast with our owns experiences and ideas to propose of graduate program. It was done by searching for information using educational databases, listings that group masters or graduate program at the level of master in these areas, search engines for master's degrees, articles, documents of organizations that work on HCI. The objective of achieving the state of the masters in HCI is to know what universities are teaching worldwide compared to the needs of companies or organizations and if there is a congruence between what is being taught at the level world in the academy and the requirements of organizations.
Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.
FTIR Analysis of MgO/TiO2 Nanocomposite on Adsorption of Remazol Turquoise Blue Dye

A magnesium oxide and titanium (II) oxide nanocomposite, MgO/TiO2 was synthesized to improve its surface area thus its adsorption capability may be risen. The synthesis was applying sol-gel method, combining MgO and TiO2 nanoparticles with NaOH becomes a nanocomposite with higher surface area. Characterization using FTIR were carried out. The expectation of using MgO/TiO2 compared with TiO2 and MgO alone is it may react faster on an adsorption trial with remazol turquoise blue dye, and the TiO2 may give its photocatalysis ability to the synthesized material. The FTIR analysis results show that the MgO and TiO2 are successfully combined become MgO/TiO2 nanocomposite. On adsorbing remazol blue, MgO/TiO2’s surface is chemically influenced by the dye’s C=O and amides group.
Penerapan Analytical Network Process Dalam Menentukan Ras Ayam Serama Simple Additive Weighting (SAW)

Serama chicken is the result of dwarf chicken crossing and is a new type of ornamental poultry currently popular in Indonesia. So it becomes a consideration for lovers of serama to get information about quality of serama chickens that need to get accurate information about quality of serama chickens that still needs to be discussed for the assessment of serama chickens, therefore the author tries to implement it in the form of a Decision Support System in determining chicken breeds as well as by applying the Simple Additive Weighting method to assist in optimal decision making. So that the expected results of the quality of the serama chicken can help in determining the standard of serama chicken assessment.
A STRATEGY DEVELOPMENT FOR A SMOOTH TRANSITION INTO THE REMOTE STUDIES IN LATVIAN EDUCATION INSTITUTIONS IN A PANDEMIC

The aim of the abstract is to investigate challenges the secondary and higher education institutions face during the pandemic times in Latvia and to discuss strategy to be developed in an order to ensure smooth transition into the remote study process with the help of a technological inventory appropriate to be used for remote study process. Challenges both secondary and higher education institutions and their staff experience have to be analysed as well, altogether with the outcomes. A combination of the remote and in-class education, issues and benefits are overviewed in the paper as well. ISMA, the University of Applied Sciences and its transition approach were examined thus establishing strong practical benefit of the research. The research problem is to analyse existing challenges education establishments in Latvia used to face with during the period of pandemic and to examine solution strategy and tools to be applied in an order to establish smooth transition from in-class to a remote study process. The conclusion of the research: no doubts the establishment of the transition process from in present studies into the remote mode was challenging and complicated, however it was realised in a consonance of the frameworks and recommendations designed both by the European and Latvian public bodies. The process of remote studies is challenging, still many advantages were discovered and this let to conclude the approach of combined education has a right to be applied even after the pandemic is over. The outcomes of the research are also beneficial for other educational establishments of Latvia and abroad, thus ensuring known approach when rapid and clear transition strategies must be taken. The developed strategy might be applied in case if there is a necessity to ensure establishment of remote studies within short period of time.
MAANA: An Automated Tool for DoMAin-Specific HANdling of Ambiguity

MAANA (in Arabic: "meaning") is a tool for performing domain-specific handling of ambiguity in requirements. Given a requirements document as input, MAANA detects the requirements that are potentially ambiguous. The focus ofMAANA is on coordination ambiguity and prepositional-phrase attachment ambiguity; these are two common ambiguity types that have been studied in the requirements engineering literature. To detect ambiguity, MAANA utilizes structural patterns and a set of heuristics derived from a domain-specific corpus. The generated analysis file after running the tool can be reviewed by requirements analysts. Through combining different knowledge sources, MAANA highlights also the requirements that might contain unacknowledged ambiguity. That is when the analysts understand different interpretations for the same requirement, without explicitly discussing it with the other analysts due to time constraints. This artifact paper presents the details of MAANA. MAANA is associated with the ICSE 2021 technical paper titled "Using Domain-specific Corpora for Improved Handlingof Ambiguity in Requirements". The tool is publicly available onGitHub and Zenodo.