Reviewerly/exHarmony huggingface dataset:
Industrial (technological in the system of inclusive education) practice: design of the evaluation fund

The purpose of the study is to develop funds of evaluation tools of industrial (technological in the system of inclusive education) practice, which will contribute to the formation of the readiness of future teachers for professional activity in the conditions of inclusion. The scientific novelty lies in the fact that the author's approach is presented in the design stages of the evaluation fund of the production (technological in the system of inclusive education) practice of the university; the developed evaluation funds themselves, in the specifics of their content, a detailed description of the evaluation procedures, the results of the student's activities, methodological products, evaluation tools, evaluation criteria. As a result, the funds of evaluation funds of a new type of industrial (technological in the system of inclusive education) practice for future teachers, undergraduate students are presented.
The non-consensus of the “metaverse” economy

&lt;p&gt;This paper attempts to clarify the main economic logic of the metaverse from an economic methodology, showing that the virtual digital economic activities of the metaverse cannot be separated from the real economy, exploring the boundary between the metaverse and the real world, and suggesting that the metaverse essentially needs to be constructed by relying on the real social structure and economic logic, and that there is no consensus with its completely decentralized concept. The increase in the realistic match or realisation of the metaverse is the result of long-term social evolution, and decentralisation is not a sufficient necessary condition for the development of the metaverse, which should be based on the central bank’s digital RMB to build a metaverse financial infrastructure. This paper also discusses the current status and development path of technology applications and industrial investments for realising the metaverse. In the primary stage of the metaverse economy, infrastructure hardware and software and underlying technologies and their related applications will be prioritised for development; games and business service experience applications have more room for development, while social and other content applications are subject to personal information protection and ideological constraints and will be steadily and orderly promoted.&lt;/p&gt;
<i>Data science in the library: tools and strategies for supporting data-driven research and instruction</i>

"Data science in the library: tools and strategies for supporting data-driven research and instruction." Journal of Electronic Resources Librarianship, 34(4), pp. 353–354
Phishing Site Detection Using Artificial Intelligence

Cyber security is becoming a crucial and indispensable component of the modern era wherein it can be estimated that there will be over 800 million utilizers of web services by the end of the year 2022. Therefore, a need for security solutions to safeguard the general population from phishing scams is of paramount importance as it not only has repercussions on monetary components but also has dire consequences on the psychological well-being of the general population, making them fearful of exploring and utilizing the World Wide Web which is the sole cause to get rid of the probable downsides by formulating reliably methodical frameworks. The sole intent of this endeavour is to target and scrutinize the recurrent idiosyncrasy displayed by several phishing web pages and develop a framework to ascertain such sites on the Internet. The proposed approach to solve the above-mentioned problem is the ensemble model of decision trees having max depth 18, and this ensemble model is created by using K-Fold cross-validation techniques where K models were created, and the max vote classifier made by an ensemble of all of them. 10 models were created, and accuracy for each model ranged from 93.12% to 98.28%. The above method was researched, and it was derived that it is implementable by deploying a web application wherein the utilizer can type the site URL and using the same, the end project would be able to generate figures for several criteria basis of which the developed model had been trained, which would be able to give an analysis to ascertain whether or not a website is fraudulent(Phishing) or legitimate.
Using feature films in the educational work with students in the process of teaching humanities

The article is devoted to perfecting moral and patriotic education of students in the process of teaching humanities. The authors present their methodology how to use feature films while studying philosophy and professional ethics. The great educational potential of such films is demonstrated by the example of analyzing “Anxious Sunday” (1983). The idea of possible usage of the method described to form the inclusive competence of students is proposed.
Coherent State

A coherent state, such as that from a laser, is not just a bunch of photons. How do we describe a state that is a coherent wave like that from a laser? We will see that the coherent state can be described as a superposition of Fock states. The properties of the coherent state are derived, including the Poisson distribution of photon number. The quadrature components of the coherent state are derived, showing that the coherent state is a minimum uncertainty state, leading to the shot noise limit. The phasor representation of the electric field is introduced, and the number–phase uncertainty relation is derived.
Hierarchical volumetric transformer with comprehensive attention for medical image segmentation

Transformer is widely used in medical image segmentation tasks due to its powerful ability to model global dependencies. However, most of the existing transformer-based methods are two-dimensional networks, which are only suitable for processing two-dimensional slices and ignore the linguistic association between different slices of the original volume image blocks. To solve this problem, we propose a novel segmentation framework by deeply exploring the respective characteristic of convolution, comprehensive attention mechanism, and transformer, and assembling them hierarchically to fully exploit their complementary advantages. Specifically, we first propose a novel volumetric transformer block to help extract features serially in the encoder and restore the feature map resolution to the original level in parallel in the decoder. It can not only obtain the information of the plane, but also make full use of the correlation information between different slices. Then the local multi-channel attention block is proposed to adaptively enhance the effective features of the encoder branch at the channel level, while suppressing the invalid features. Finally, the global multi-scale attention block with deep supervision is introduced to adaptively extract valid information at different scale levels while filtering out useless information. Extensive experiments demonstrate that our proposed method achieves promising performance on multi-organ CT and cardiac MR image segmentation.
Arousing Children's Motivation in Learning English during the Corona Virus Outbreak

Motivation is an important factor that determines a learner success in learning English. The purpose of this study is to find the teaching strategies to arouse young learner motivation in learning English during the corona virus outbreak in SDN 1 Singopuran. This is descriptive qualitative research in which the subject is an English teacher at SDN 1 Singopuran. The data were collected through observation, interview, and document analysis. They were then analyzed using descriptive interactive method. To maintain the data credibility, the research applied this theory to obtain data from various sources by different studies, having an interview with one of the English teacher and comparing the results of interviews with data obtained from observations. The results showed that the strategies to motivate young learners to learn English during the corona virus outbreak in SDN 1 Singopuran by using educational books, flashcards, songs, pictures, and giving rewards to increase their knowledge with quizzes related to materials and pictures.
Link Prediction via Fused Attribute Features Activation with Graph Convolutional Network

Link prediction is an effective method to guarantee the integrity of the knowledge graph, aiming to predict the missing part of the triple. So far most of the existing researches have been proposeed to embed entities and relations into a vector space or inferred the paths between entities in a knowledge graph. However, most of the previous works merely take account of the single path or first-order information, ignoring the relation between the entities and their attributes. Motivated by this, for a better representation of entities and relations, we in this article exploit the characteristics of the attribute to enrich the information of entities cooperated with a graph neural network. In our method the edges connected by a node are regarded as its contextual information, which will be extracted as an attribute feature. Then the message propagation network is utilized to generate the node and edge representions, after which an aggregation function is applied to integrate node attributes, node representation as well as edge representation to realize link prediction. Experiments on the same datasets show that our model outperforms the baselines in multiple metrics including MRR and Hits@N. At the same time, ablation experiments validate a strong expandability of the node attribute feature learning method we propose, which enables the model to accelerate the convergence of training and improve the performance on the link prediction task.
PROBLEMS OF COMMUNICATION AND ADAPTATION OF STUDENTS IN THE FIELD OF ECONOMICS AND SERVICE WHEN STUDYING AT A UNIVERSITY

It is well known that in the process of education and upbringing at the university, the socialization and formation of personal qualities of a young person takes place. Higher education promotes personal development in accordance with the cultural, social, economic and other requirements of modern society. It is education that allows young people to acquire an idea of universal values, gain professional knowledge and competencies necessary for their further successful personal development and existence in society. While studying at the university, self-determination and selfrealization of a young person takes place. However, not all students of the initial courses have an effective adaptation to new living conditions in the learning process, often students experience certain difficulties in communicating in a team of peers and teachers. Researchers are concerned about the increased level of anxiety and tension among first-year students who have communication problems leading to various psychosomatic disorders in the body and maladaptation to the educational process of the university [2]. The relevance of these studies is due to the importance of studying the adaptation process of students to the learning process at the university. We have made an attempt to conduct a comparative study of the characteristics of well-being and adaptation of students of the directions of Economics and Service of the Ufa State Petroleum Technical University of the 1st and 4th courses according to the indicators of the SAN questionnaire, the study of competence in communication by assessing the level of sociability (V.F. Ryakhovsky test), personal anxiety by the method of determining Ch.D. Spielberger – Yu.L. Khanin [6, 7].
CrGAN: Continuous Rendering of Image Style

There are many continuous changes in nature, such as season, day and night, and face aging. These image processing tasks are interesting and somewhat challenging. Therefore, it is necessary to explore the intermediate domains of the transformation. We propose a new image-to-image translation model, named CrGAN, which aims to achieve continuous image translation. This continuous process is usually based on the interpolation of the latent code. CrGAN is different from that. First, images are encoded into content and style, and CrGAN extracts the content code of the source domain images and the style code of the target domain images. Then, we use a new functional instance normalization layer (NEWIN) to control the rendering degree of content and style. NEWIN is linked with the discriminator to form an interpolated adversarial loss to guide the generator to generate the image gradient process. Compared with state-of-the-art, CrGAN shows better performance in face changes, seasonal changes and other work.
A data cleaning method based on spatiotemporal inverse distance weight interpolation for meteorological data in power system

The scale of modern power grids is huge and the structure is complex, and most of the power equipment is directly exposed to the external environment. In recent years, the problems of global warming and environmental damage have become increasingly prominent. The impact of disasters on the safe and stable operation of power grids has become increasingly prominent. In order to deal with the impact of meteorological disasters on the power grid and reduce the losses caused by meteorological disasters to the power system, it is necessary to build a micrometeorological monitoring station for the power grid, and use the meteorological monitoring data for disaster early warning analysis. However, existing meteorological monitoring stations have a series of data quality problems due to the particularity of their own spatial locations, and there are a lot of dirty data in the original data, which seriously affects the accuracy of subsequent data analysis. To this end, this paper proposes a power meteorological data cleaning method based on spatiotemporal inverse distance weight interpolation. Experiments show that this method can remove most of the dirty data in the original power meteorological data, thereby effectively improving the prediction accuracy of power grid meteorological disasters. Safe and stable operation is of great significance
The key challenges of the Russian Academy of Sciences Library in its transfer «from the printed to digital»

The results of the transfer from the card reference and bibliographic instrument to e-catalog are summarized. Options for using library e-catalogs for advanced user bibliographic services are discussed. The library’s book collection is partially conversed into full-text e-library; many books are digitized for the web-portal «Scientific heritage of Russia». The variations of targeted thematic full-text collections with bibliographic browsing and search formats are analyzed; options for graphic formats for full-text publications in e-libraries to provide fast access to the books directly from the catalog are examined.
Automatic Recognition System for Traffic Signs in Ecuador Based on Faster R-CNN with ZFNet

This research presents an application of the Deep Learning technology in the development of an automatic system detection of traffic signs of Ecuador. The development of this work has been divided into two parts, i) in first a database was built with regulatory and preventive traffic signs, taken in urban environments from several cities in Ecuador. The dataset consists of 52 classes, collected in the various lighting environments (dawn, day, sunset and cloudy) from 6 am to 7 pm, in various localities of Ecuador, ii) then, an object detector based on Faster-RCNN with ZF-Net was implemented as a detection/recognition module. The entire experimental part was developed on the ViiA technology platform, which consists of a vehicle for the implementation of driving assistance systems using Computer Vision and Artificial Intelligence, in real road driving conditions.
Adaptive statistical filtering of data in the phase images decoding under random additive noise of a photodetector

A method for adaptive statistical filtering of experimental data is proposed to reduce the measurement error of three-dimensional geometric parameters using phase triangulation methods. The method is especially promising for use under conditions of measuring surfaces with random light-scattering properties, a limited dynamic range of a photodetector, and random additive noise. The practical value and efficiency of the proposed approach in the processing of phase images is shown.
Graph Databases

Introduced in SQL Server 2017, the Graph database stores nodes and edges within SQL Server, allowing you to use T-SQL to query graph data. Graph data is specifically oriented around many-to-many relationships. Graph data doesn’t lend itself to traditional relational structures but instead is focused on hierarchies and relationships past those more traditional structures. Graph data is separated from either traditional analytical data, better served by columnstore indexes, or from OLTP systems, better served by rowstore indexes. However, when you have a data set that consists of complex hierarchical data, focused on the relationships between that data, the graph database comes into play.
Effect of U-Net Hyperparameter Optimisation in Polyp Segmentation from Colonoscopy Images

Colorectal cancer can be detected from the location of polyps present in the colon. Colonoscopy is the standard method for polyp detection from where the images are retrieved. Deep neural networks are preferred for the segmentation of polyps from colonoscopy images. The hyperparameters directly control the performance of the neural network model. In this work, the impacts of different neural network optimizers and various activation functions are analyzed for U-net architecture. The analysis shows that the N- Adam optimizer with Sigmoid activation function yields better accuracy of 94.06% for polyp segmentation from colonoscopy images.
PERANCANGAN SISTEM INFORMASI PERSEDIAAN BARANG BERBASIS WEB PADA PD.KOPI AYAM RAS

&#x0D; Kopi Ayam Ras is a company engaged in the storage and sale of goods (distributor). The process of data collection of incoming and outgoing goods, inventory stock, and reporting of goods is not optimal when searching for data due to the large number of data items and data reservoirs still using a manual system. The purpose of this inventory information system is to solve problems that arise in the system that is currently running at PD. Kopi Ayam Ras is also a good solution for companies to create a faster and more accurate inventory management system. Methods of data collection is done by means of interviews, observation, documentation and analysis of the system used by the company. The result of this research is to produce a web-based inventory information system with the FIFO method that provides convenience in providing information on inventory data through incoming and outgoing goods using data flow diagrams, DFD, ERD and MySQL for data processing.&#x0D;
CISQA: Corporate Smart Insights Question Answering System

The development of semantic information systems is a highly topical issue in various domains, e.g., financial news, which both ordinary users and experts deal with. Until now, queries in natural language on open vocabulary from, e.g., query.wikidata.org were hard to answer. The development of such software offers the possibility to make semantic information systems more effective, efficient, and user-friendly. This paper proposes a semantic knowledge-based question answering system called cisqa. It develops entity linking and relation linking approaches that have been experimentally proven to be superior to the state of the art. cisqa also handles question ambiguity, translates natural language questions into SPARQL-queries, and delivers answers to the user in an appropriate manner.
MIOCENE STRATIGRAPHY, VERTICAL AXIS ROTATIONS, AND TRANSTENSIONAL TECTONICS OF THE LAKE MEAD AREA, NEVADA: AN EXAMPLE OF PAUL UMHOEFER’S DEDICATION TO MENTORSHIP, TEACHING, AND RESEARCH

Dr. Paul Umhoefer of Northern Arizona University, was passionate about transtensional tectonics and basins. His contributions to unraveling the complicated stratigraphy and tectonics of the Lake Mead area spanned over a decade of research and student mentorship. Paul used the exceptional rock exposures of the area as a natural classroom and research area for over a dozen graduate students. Paul was a passionate mentor with infectious energy and an excitement for geology that naturally inspired his students. Paul taught his students to make astute and detailed field-based observations and apply them to the regional tectonics. He was a tireless advisor, never hesitating to spend days in the field or hours poring over maps and literature with his students, while always keeping things entertaining and fun. Paul and his students, and colleagues covered over 500 km2 of the Lake Mead area with detailed (1:5,000 and 1:10,000 scale) geologic and lithofacies mapping, collected thousands of structural measurements, measured and sampled thousands of meters of Miocene section for tephrochronology, geochronology, and paleomagnetic analysis. These data refine 1) the stratigraphic framework, chronology, and nomenclature of the Miocene Rainbow Gardens Formation and the Horse Spring Formation, 2) the structural model for the accommodation of up to 100° of clockwise vertical axis rotations related to dextral shear on the northwest-striking Las Vegas Valley shear zone, 3) the structural model for strain accommodation at the intersection of the dextral Las Vegas Valley shear zone and the sinistral Lake Mead fault system, and 4) the timing and style of tectonics that accommodated 60 km of west-southwest transport of the Frenchman Mountain block.
General framework and algebraization

AbstractIn this chapter we introduce a general framework for presenting logics and for translating logical questions to algebraic ones..
Performance analysis of a state vector quantum circuit simulation on A64FX processor

Along with the recent development of quantum computers, quantum computer simulators are also exploited to verify and evaluate quantum computers. Since the amount of computation in the quantum circuit simulations increases exponentially with the number of qubits, which indicates the size of the quantum computer, it is important to study the performance of the quantum circuit simulations. In this paper, we analyze the performance of a state vector quantum circuit simulation on A64FX processor. We consider the implementation with the optimization control lines (OCLs), and that written with the Arm C Language Extensions (ACLE) to enhance vectorization, and compare them with the original implementation. Our experiments show that the vectorization improves the performance if the quantum gate simulations involve floating-point arithmetic operations.