Reviewerly/exHarmony huggingface dataset:
Application of the corporate cataloguing technology for the retroconversion of card catalogues

The corporate cataloguing technology has been widely used in Russian libraries to make bibliographic records (BR) of the electronic catalogue (EC). When selecting the proper sources of BR borrowings, many libraries have managed to reduce time and labor expenditures involved in making EC records. The paper provides examples of the most successful Russian corporate library information systems of the federal and regional level. At the same time, most libraries have not solved the problem of reflecting information about the entire library stock in EC, which negatively affects the quality of EC services for users. The experience of federal libraries in solving it is considered. The main described methods of retrospective conversion of card catalogues are: manual keyboard entry, scanning of catalogue cards, recognition of scanned cards, BR making, and records borrowing from authoritative sources. The purpose of this article is to study the effectiveness of using corporate cataloguing technology to perform retrospective conversion of card catalogues. To achieve this goal, the search for bibliographic records of documents published before 1991, and reflected in ECs of the Russian State Library (RSL) and the Russian National Library (RNL), in the LIBNET Corporate Cataloguing System (CCS), in the ARBICON resources and in the IRBIS-corporation was conducted in SPSTL SB RAS EC. Based on the search result, it has been determined that the most acceptable sources of records borrowing are the electronic catalogues of RSL, RSL and the LIBNET CCS. The advantages and disadvantages of using ready-to-use BRs from the aforementioned sources have been defined. It has been fixed that when choosing the source for borrowing while conducting catalogues retro-conversion, it is necessary to take into account the collection specifics, records compliance with the requirements of a concrete library, compatibility of automated library-information systems, readiness to pay for borrowing services, EC availability, and convenience of search.
An Experiential Competency Application Framework

Implementing competency-based training, education, and talent management requires models that compute and apply competencies to answer three questions: Does a person or team possess a given competency, how likely are they to successfully apply or demonstrate it, and what is the best way to acquire it? This paper argues that the underlying mathematical models should be treated as separate, although interrelated, and presents a framework for creating and computing such models that includes notions such as the level of a competency and conditions under which it is performed and that takes experience, practice, knowledge and skill decay, and the spacing effect into account. The source data for the computations outlined in this paper, which are described in the most detail for the model that ascribes possession of a competency, consists of assertions that in practice are derived from results reported by training systems. These computations reflect current practice in competency modeling and include “rollup rules” that respect relations among competencies. This paper includes an analysis of how these models appear and are used in intelligent tutoring systems.
On Some Results of the Work of the Information System for Monitoring Research Activities of a University

The results of using the information system for monitoring research work at the Oryol State University named after I.S. Turgenev are considered. An analysis of the data obtained over 6 years of operation of the system was carried out, regularities were revealed and recommendations were given on the use of the system.
Investigating Adaptive Activity Effectiveness Across Domains: Insights into Design Best Practices

Courseware as an adaptive instructional system is a complex environment to develop. The student will encounter lessons of content with integrated formative practice, adaptive activities, and assessments in their learning path. The alignment of all course features, including the scaffolding structure of the adaptive activities, may vary between courses and the teams who created them. In a previous analysis of adaptive activities [1], these activities had net positive effects on student learning estimates and summative assessment scores. In this paper, we will analyze three additional non-STEM courses that had less effective adaptive activities using the same methods as the original study, and further investigate course features that could be influencing their effectiveness, such as alignment, difficulty, and amount of practice. The results of this analysis can provide guidance on how to best create content for adaptive courseware and provide an example of the critical role data analysis has in the evaluation and iterative improvement of student learning environments.
Collective thought-action: On lecture performances, transmedia knowledge and designing possible worlds

The election of Donald Trump has exposed a politics of resentment dividing rural and urban populations, as well as communities and colleges. This division stretches back to Plato's Academy. When Plato threw the poets out of the Republic, he banished practices such as poetry, music, and dance from the realm of true, epistemic knowledge, which he opposed to doxa or common knowledge. Centuries later, this opposition would shape European colonialism's approach to indigenous life worlds, whose "primitive" rituals, myths, and fetishes would confront the "civilized" methods, histories, and objects of Western knowledge. These same oppositions structure ideological critiques of popular culture. However, the emergence of lecture performances, theory rap, and info comics within twenty-first century research universities suggests that traditional knowledge production is under stress inside and outside the academy. Emerging is a transmedia knowledge that engages different audiences by mixing episteme and doxa. At stake here: the role of aesthetics in post-disciplinary societies of control and in resistant modes of collective thought-action. Across both the arts and sciences, scholars worldwide are turning to transmedia knowledge not simply for communication but also for co-creation of research. Here transmedia knowledge can function as civic discourse and as a conduit of a generalized aesthetics.
Professional Information Needs of Library Specialists in Russia

The article is devoted to the comprehensive study of the professional information needs (IN) of specialists of the central libraries of the subjects of the Russian Federation. The rapid development of high technologies in the field of accumulation, transmission and processing of information, the creation of modern telecommunications systems have led to the emergence of fundamentally new opportunities for organizing the information process. This, in turn, led to the qualitative growth of IN specialists, including those employed in the field of library activities. The specific features of IN library specialists are determined by their place and role in the modern process of cultural activity, industry orientation, nature of work and specialization. During 2018—2019, the Centre for Research of Problems of the Development of Libraries in the Information Society of the Russian State Library carried out research aimed at comprehensive study of the information needs of cultural workers employed in the library sphere. At the first stage of the research, there was comprehended the existing experience of studying professional information needs in the national library science. At the second stage, using the method of questionnaire survey, information needs of library specialists were studied in order to identify the most rational forms and methods of providing them. The analysis of the survey results made it possible to identify the sources of professional information, to reveal information resources that have the greatest importance and characterize specific features of librarians’ information needs. The author concludes on the need to expand the access to full-text databases and electronic versions of periodicals for library staff. The creation of integrated information centre could help providing library professionals with professional information. Currently, the function of such a centre is performed by the National Electronic Library, which includes the professional section for library specialists. The results of the study form theoretical and methodological basis for the rational use of resources and potential of libraries in providing information to professional information needs of library specialists and determine the prospects for further research related to improving the forms and methods of information service for this category of users.
Novel hybrid geometric data perturbation technique by means of sampling data intervals

Privacy preserving data mining has much importance in the data analysis because it maintains confidential or highly sensitive data in the purpose of data analysis. Many perturbation techniques are developed, but do not perform well. There are used for different purposes in various techniques. Though, the reconstruction of original data can be done from the samples of perturbed data. It does not always preserve the distance that exists in the original data samples. During reconstruction of the original data, some of the information is loosed which is again a complex and time consuming task for traditional data mining algorithm. Therefore, a novel hybrid data perturbation technique is introduced that overcomes the above addressed issues. The proposed paper includes a novel data perturbation technique to hybridize the traditional Geometric Data Perturbation Technique along with the Euclidean distance method to preserve privacy. It obtains the distorted or perturbed data which is sampled at different intervals depending on the levels of privacy. The proposed approach attempts to reduce the tradeoffs that persists between privacy levels, accuracy and information loss. The experimental results for this approach is implemented using real world bench mark datasets downloaded from UCI Repositories using three different data mining clustering algorithms. It is shown that clustering accuracy is similar and fairly high for the perturbed data obtained from the proposed approach when matched with the original data set clustering algorithms. The analysis were shown for different privacy levels on different data samples.
The State of Health and Dynamics of the Attitude of Students to Its Strengthening by Means of Physical Culture

The health of students is of great social and state importance. The article presents the results of many years of research on the dynamics of students attitudes to certain aspects that make up health; optimal motor activity, the consequences of violations of the principles of nutrition and its impact on self-esteem. The formation of value orientations, norms, attitudes, patterns of behavior develops more intensively during the student period. The state of health of students, together with the quality of professionalism, is one of the indicators of evaluating the activities of the university training of highly qualified specialists. This affects the economic and social well-being of the country. The analysis of scientific literature and research devoted to this problem is becoming more and more relevant. The article presents the results of many years of research on the dynamics of students attitudes to certain aspects that make up health; optimal motor activity, the consequences of violations of the principles of nutrition and its impact on self-esteem.
BPSO Algorithm with Opposition-Based Learning Method for Association Rule Mining

Traditional association rule mining algorithm does not operate efficiently when processing large and high-dimensional data. With an opposition-based learning method, a binary particle swarm optimization algorithm is proposed to improve the association rule mining problem. The proposed method uses a binary particle swarm algorithm to search for association rules and does not need to manually specify support and confidence thresholds. In addition, opposition-based learning is introduced, and the primary and the secondary opposition-based learning methods are used to reduce the probability of the algorithm falling into local extreme and improve the convergence accuracy of the algorithm. The experimental results show that the improved algorithm converges faster than existing algorithms and balances multiple indexes of reliability, correlation, and comprehensibility, thus mining more effective association rules.
LGCBPNN: Determine the Multilayer Neural Network Using Local Gravitational Clustering

The selection of an effective number of hidden neurons and the number of hidden layers is the most significant matter. Because there is a problem with one input and one output that needs a greater number of hidden neurons and vice versa. Clustering is also an efficient mechanism that is used to select a number of hidden layers and hidden neuron of such hidden layers as well. Here, a local gravitational clustering algorithm is used to determine the number of clusters that is responsible to decide the number of hidden layers. After that, a number of hidden neurons have calculated by various formulas. Various researchers have worked on this problem (i.e. hidden layer) but it depends upon the data samples that need to be trained. Backpropagation neural network is used here to train the network. Hidden layers play an essential role in the case of problems of reasonable behaviors connected to arbitrary decision-making or arbitrary accuracy to enhance the performance of Backpropagation neural network. This neural network used the TrainR activation function and TanSig and LogSig is used as a transfer function. Six types of datasets have been used to perform the simulation and each dataset has its own accuracy and error rate. The best accuracy results are 98.49, 88.94, 71.9, 64.82, 72.79 and 70.13% achieved for Landsat Satellite, Sonar, ECG, P wave, QRS and T wave dataset, respectively. Also, the least minimized error obtained by the Landsat Satellite dataset is 0.014737.
Study and Comparison of Initial Populations on the Performance of Modified Differential Evolution Algorithm

In nature, based on the survival of the fittest principle, the individual with better fitness has more chance to survive and produce a competitive offspring with inherited traits of the parent. Similarly, in evolutionary algorithms, individuals or solutions with better fitness can generate better offspring with higher survival rate. In evolutionary algorithms, the offspring are usually produced by variation operations (mutation and crossover). The generated offspring are compared with parents and better one either from the offspring or parent is selected in the next generation. The quality of initial population (group of individuals or solutions) can affect the performance of evolutionary algorithm. The present work aims to study the effect of initial population on the performance of modified Differential Evolution (DE) algorithm. Four types of initial population, generated by different techniques, are studied and compared for some standard benchmark functions. Four population-generation techniques, namely, random population, random population using the concept of design of experiments, population using symmetric Latin hypercube design and opposition-based learning population, are considered. The results are compared based on the performance measure such as success rate, feasibility rate, success performance, average number of evaluations to a solution, quality measure, enes and population measure.
Question Answering Chatbot Using Memory Networks

Manipulation of a large amount of data becomes a very tedious task. Hence, the authors took the approach of memory networks for the implementation of the chatbot. Traditionally, the LSTM model was used to implement chatbots and QA systems. But the LSTM failed to store relevant information when given a longer information set. On the contrary, the memory networks have an additional memory component with it. This can help in storing long information for further use which is greatly advantageous for the QA and chatbot systems as compared to LSTM. The authors trained and tested their model over Facebook's bAbi dataset which consists of several tasks and has questions regarding each task to retrieve the accuracy of the model. On the pedestal of that dataset, they have presented the accuracy for every task in their study with memory networks.
The landmark book on Nicholas Rubakin

Review of the book: Stolyarov Yu. Rubakin revisited / Yury N. Stolyarov: Russian School Library Association ; Librarianship Department of the International Informatization Academy ; Russian State Library ; Research Center of Book Culture Studies of the Russian Academy of Sciences ; National Library of the Republic of Sakha (Yakutia). – Moscow : RUSLA, 2019. – 416 p., ill. The reviewer emphasizes the importance of the reviewed book, as Nicholas Rubakin’s work of researcher and educator, has not been studied comprehensively yet. The publication coincides with Rubakin’s 160-th anniversary. The reviewer characterizes the book structure and the contents of its five chapters: “The Life”, “The Foreign Native”, “The Focus of N. A. Rubakin’s Creative Interests”, “Bibliopsychological Portraits”, and “The Author of Integral Philosophy”. Yu. N. Stolyarov characterizes Nicholas Rubakin as a science communicator, expert of self-education and (kids) reading guidance and, most of all, as a founder of bibliopsychological theory. The reviewer examines the key works by Rubakin for each of the above-mentioned areas, e.g. “The secret of library work success”, “What the bibliological psychology is”, The Psychology of readers and books”, “Importance of the books”, “On the methodology of the book business”, “The book market in Soviet Russia and its psychology”. The reviewer makes a focus on Rubakin’s masterwork –“Among books” and on Stolyarov’s footnotes that expand the book text and reader knowledge, and points to the book design.
Exploring the Integration of Blockchain Technology and IoT in a Smart University Application Architecture

The ecosystem inherent within currently deployed Internet of Things (IoT) systems is that of low-powered devices equipped with sensors that consume data. The data these devices collect is then stored in use-case specific applications, which are connected through application layer gateways that allow these devices to connect to third party cloud storage platforms for further processing. This stratified architecture has created data silos that introduce complexities such as limited user control and lack of solicitation regarding the usage of user data. The constant proliferation of IoT devices deployed in smart cities which include smart university campus (SUC) has resulted in the need for the development of IoT architecture models which are data-centric. In this paper a blockchain- based architecture model, and specifically, the distributed ledger inherent within the Ethereum blockchain, combined with the Proof Of Authority (POA) consensus mechanism, are proposed as a potential solution to developing a proof of concept architecture model that is data-centric. The proposed architecture model will be tested against with application specific use-cases in a simulated environment within the context of a SUC which is subsumed by a smart city.
Sensitive Chaotic Circuits with Coupled Inductances

Sensors with coupled inductive coils are used in many applications: for metal detection, for linear and angular displacements measurements, for magnetometry, etc. One of the promising ways to increase technical characteristics of these sensors, such as sensitivity, is the use of a chaotic oscillator to excite the transmitting coil and constructing the path from the receiving coil back to the oscillator. The paper proposes several topologies of sensor circuits with coupled inductances, a technique for including inductive coils in chaotic circuits with their minimal modification, and discusses methods for detecting the measurement signal. As a result, we designed a proximity sensor and shown its high performance in a numerical experiment.
HNS Advanced Encryption Standard: An Enhanced Security Approach for IoT Communication

Abstract AES is considered as synchronize encryption whose keys are the same in the sender and the receiver. All the researches work to enhance the security in this type of encryption by making the keys nonlinear. If attackers know any one of the sub-keys, this will lead to knowing other keys in a not difficult way. To make it more nonlinear and more difficult to be attacked, it should be more balanced between some ones and zeros in the ciphertext. We make some changes in the key expansion function to get results more balanced than the original design AES.
Development of Real Environment Datasets Creation Method for Deep Learning to Improve Quality of Depth Image

High quality depth images are required for stable and accurate camera tracking and 3D modeling. The method to improve the depth image quality using deep learning requires large and accurate datasets in advance. Datasets created by Middlebury Datasets which are typical depth image datasets do not always represent the feature of noise caused by depth cameras, and the number of datasets is not enough. In this study, the method for creating datasets for deep learning was developed and evaluated. The proposed method can improve the accuracy of the distance for each pixel by aligning the positions of pixels that capture the same part of the real world with multiple frames. In addition to super-resolution and denoising, the images are preprocessed such as patch division and data augmentation to eliminate the holes in correct depth images. By using this method, large number of real environment datasets can be automatically created. Two neural networks using Middlebury datasets and the datasets generated by the proposed method were trained respectively, and produced high quality depth images with them. In order to compare the Middlebury Datasets and the proposed method, we visually evaluated the hole filling and the smoothness of edges and surfaces of objects from the results. The result showed the network using the datasets created by the proposed method can remove noise rather than that using Middlebury Datasets since they include noise features caused by the performance limits of depth cameras.
Adaptive Augmentative and Alternative Communication Systems for People with Neuromuscular Pathologies

Augmentative and alternative communication systems are devices that seek to solve communication problems in people who require it. People with neuromuscular diseases benefit from these systems, however, there is a growing need to develop systems that adapt to people with these types of diseases due to their degenerative nature. In this paper, a development method based on fuzzy logic is presented to identify the stage of the disease based on heuristic rules whose purpose is to provide a unique and usable system throughout the disease until the death of the person.