Reviewerly/exHarmony huggingface dataset:
Current Subjects in Russian Historical Research: Reflection in the LBC

The first issue of the modernized Medium Tables of the Library Bibliographic Classification (LBC) – the national classification system of the Russian Federation – was published in 2001. They were to systematize literature on the social and human sciences, including history. Their analysis was made. The revision and clarification of the conceptual apparatus were performed, the historical periodization was clarified, the ideological division was eliminated. In 2022 the first stage of the LBC Medium Tables modernization was over, but the Library Bibliographic Classification Research and Development Center in the Russian State Library again faces with the task their updating again, which is again to begin with the sociohumanitarian sections. The historical science has continued its development over the past years: the methodology has been improved, and new lines of investigation have emerged. All this needs to be reflected in the LBC schedules. The aim of this article is to analyze the scientific works on the history of Russia, published from 2018 to 2023 and make proposals for adjusting the LBC Medium Tables. The interdisciplinary nature of modern historical research is emphasized, the most popular historical disciplines are mentioned, which include new social history, regional history, the history of everyday life, gender history, etc. The author proposes to make some changes to the historical sciences section of the LBC Medium Tables. They are connected with new methodical instructions in subclasses on the theoretical basis and methodology of historical science, the history of historical science in Russia since the 1990s, changes in formulations and methodical instructions of special subdivisions in section “History”. These changes will make it possible to reflect the current subjects of the Russian historical research in the second edition of the medium LBC Tables.
Composition of Book Collections in Estates of the Smolensk Province (The Panin-Meshchersky Library)

The purpose of the article is to introduce into scientific circulation new facts about the history of the formation of estate libraries in the Smolensk province using the example of the Panin-Meshchersky library in the Dugino estate in Sychevsky district. The work provides brief information about the owners of the library, for the first time presents the description of the composition of its collection, examines the further destiny of the collection and provides examples of books preserved in the collections of the Smolensk Regional Universal Scientific Library named after A. T. Tvardovsky, the library of Smolensk State University, etc. The source base for the study are published and unpublished materials: documents of the State Historical Museum, the Russian State Archive of Ancient Acts, rare books and scientific publication. The author comes to the conclusion that the library in Dugino has begonnen to be collected by its first owner Nikita Ivanovich Panin, further participation in the replenishment of this voluminous collection was taken by representatives of the Panin family Nikita Petrovich and Alexander Nikitich, and a large role in preserving and increasing this collection belongs to Nikolai Petrovich Meshchersky and his son Alexander Nikolaevich. It is shown that this library preserves the memory of not only its owners, but also their preferences, characters, and worldview. Using the example of the library of the Dugino estate, the prestige of family reading among the nobility, the purpose of which was self-development and self-education, becomes clearer. The nobles valued libraries, and their loss during the years of nationalization became a great family tragedy.
Russian Personal Libraries of the Turkestan Region in the Collection of the National Library of Uzbekistan Named after A. Navoi

The article deals with littlestudied issues related to the signs of ownership on the books from Russian personal libraries in the Turkestan region (1967–1917) that were found in the National Library of Uzbekistan named after A. Navoi. The purpose of the article: to determine the character of Russian personal libraries of the Turkestan region in the collection of the National library of Uzbekistan named after A. Navoi and the signs of ownership on their books. The relevance of the research is due to the need to fill in the historiographical gap in the study of Russian personal libraries in Turkestan region. The first results of the work related to identification and attribution of the book signs on some books from the personal libraries of N. V. Dmitrovskiy, N. F. Petrovsky, A. K. Gaines, D. I. Mushketov, P. I. Khomutov, N. A. Burov, K. A. Timayev. The paper presents information about the owners of personal libraries. The author analyzes the owner’s inscriptions, which show that these book collections were formed not only for practical purposes, but also for leisure. The article draws the attention of researchers to the study of privately owned signs as another page in the history of replenishment of the book collections of the National Library of Uzbekistan named after. A. Navoi. Identification and attribution of book signs allowed us to understand better the process related to the movement of the books from the personal libraries of Russian aristocrats, officials and scientists to the Turkestan Public Library. The exemplary analysis of the books allowed us to conclude that bibliophiles of the Turkestan region preferred to use stamps more often, among which there are also “non-special” ones, or to put handwritten owner’s inscriptions on copies.
Literary Reputation of S. Dovlatov and Ven. Erofeev as the Result of Publishing Practices (Based on the Material of the Russian Publishing Market 1990–2010)

The relevance of the article is associated with the increased interest of modern humanitarian thought in the problems of the sociology of literature, literary reputation and the need to identify possible forms and methods of forming the symbolic authority of writers in the postclassical era. The aim of the article is consider the principles of formation of the posthumous literary reputation of S. Dovlatov (1941–1990) and Ven. Erofeev (1938–1990) on the material of domestic publishing practices 1990s–2010s and memoirs about them. The institute of publishers is presented as an agent of influence on the posthumous literary reputation of writers who have been returned to the Russian cultural space in the first post-soviet decade. The article considers literary reputation in a historical, dynamic and procedural way. The analysis of the material (year and type of editions, circulations, reputations of publishers and compilers) makes it possible to identify both the semantic dynamics of the reputation fields around the figures of the writers in question in the chronological framework indicated and the principles of domestic publishing practices that influence the writers’ reputation. The results of the study suggest that on the background of general dynamics of transformation of the domestic publishing market in the 1990–2000s changes in publishing strategies are observed in the publication of the literary heritage of returned writers’names and memoirs and biographical literature about them.
Decryption speed up of RSA by pre-calculation

It is easy to increase the encryption speed by using small public exponent in RSA. In this case, private exponent is full sized (on the order of modulus) and so, most of calculation costs are allocated to decryption. Meanwhile, it is not easy to speed up decryption by reducing private parameters for the security problem. From this, many researches have been done to increase the decryption speed without compromising the security. This paper presents two new modified schemes for implementing the RSA public-key cryptosystem, with the goal of reducing or controlling the time for encryption and decryption, while attempting to maintain security. In other words, we proposed two variants of RSA designed to speed up decryption. Our first variant allows the cost of encryption and decryption to be balanced without modifying the prime generation. Second variant has the faster decryption than the other RSA variants (e.g., rebalanced RSA) with two balanced primes.
Safety assessment method for power operation environment based on multi-source data fusion

Traditional safety evaluation methods often grapple with limitations such as narrow index selection coverage, suboptimal evaluation accuracy, and questionable reliability. To address these challenges, this study introduces a novel safety assessment methodology for power operation environments, leveraging the potential of multi-source data fusion. This approach encompasses multi-modal and multi-source data mining, in conjunction with fusion processing of the gathered data specific to power operation environments. A comprehensive analysis of safety risk factors within power operations facilitates the construction of an intricate fault tree, which is employed to compute security risk values and complete the safety assessment of the power operation environment. Experimental outcomes indicate that the proposed method boasts an average accuracy of 93.2%, demonstrating robust evaluation stability and high efficiency. This efficacy underscores its potential as a valuable tool in enhancing the safety of power operations.
Emotion Classification on Social Media Comments Using Categorical Feature Extraction Along With the Bidirectional Encoder-based Recurrent Neural Network Classification

All across the world, social media is one of the most widely used platforms for information exchange. Comments on relevant information might be made in response to a video or any other piece of information. A remark may include an emotion that may be recognized by an automated recognition system. On Facebook, Twitter, and YouTube comments, we performed studies to determine their emotional categorization. A set of comments is gathered and manually classified using six fundamental emotion labels (happy, sad, angry, surprised, disgust, and fear) and one neutral label, with each emotion label representing a different emotion category. A prominent approach in natural language processing (NLP), deep learning has been used in a wide range of categorization applications. This procedure begins by preprocessing the input data with normalization, followed by categorizing characteristics in feature extraction utilizing the Linguistic and word count analysis (LIWC). Finally, for the categorization stage, the classify features might be supplied. Finally, for categorizing emotions, the Bidirectional Encoder based recurrent neural network classification approach is used. The studies have been carried out with the use of typical social media data that has been acquired from the kaggle data repository. The findings show that the suggested model outperforms all other existing mechanisms in terms of overall performance.
VIRTUAL EDUCATIONAL SPACE AND THE PROBLEM OF MORAL AND AESTHETIC DEVELOPMENT OF PERSONALITY

Moral and aesthetic education is the basis for the formation of humanistic consciousness of a person, which determines the nature of the direction of his actions and deeds in relation to himself, his family and society. Purpose of the article: to consider in the history of development the problem of organization of interaction of subjects of the educational process in the educational space and to substantiate the relevance of application in modern realities of the methodological approach of integration of science and art in the process of pedagogical design of virtual educational space. The methodological approach of science and art integration contributes to the processes of spiritual, moral and aesthetic formation of the student's personality. In order to comprehensively analyze the problem posed, the theoretical and methodological foun-dations for the implementation of the approach of integration of science and art in the conditions of modern pedagogical reality were presented. Special attention is paid to the integration of the latest information and communication technologies: virtual reality (VR), augmented reality (AR), artificial intelligence (AI), the Internet of Things (IoT). The development of quality educational content will be the prospect of future research.
Կրիպտոարժույթներ. ռի՞սկ, թե՞ հնարավորություն / CRYPTOCURRENCIES: RISK OR OPPORTUNITY?

Cryptocurrencies represent a relatively new type of currency in the global monetary and financial system. Since the introduction of the first cryptocurrency in 2009, there are more than 10,000 types of cryptocurrencies, but not all of them are effectively used, mainly due to the oversaturation of the market, greater price volatility and lack of widespread awareness. This disruptive form of currency, known as “Cryptocurrencies”, completely transformed the global payment and transaction system, the field of investment, and economic reality in less than a decade. Being an innovation in the monetary and financial system, cryptocurrencies are repeatedly subjected to sharp criticism, but it is difficult to overlook the positive attitude of experts towards this new phenomenon. In this paper, we aim to present the possible reasons for the success of cryptocurrencies and the risks and opportunities associated with the their application. Additionally, we delve into the regulationary approaches to cryptocurrencies in different countries.
Optimizing Expert Rankings with Multiple Regression Analysis

The primary goal of this research is to establish a methodology that gives significant weight to an expert's qualifications and experience in the expertise ranking process. This methodology aims to enhance the effectiveness of expert identification by taking into account an expert's background and credentials, thus yielding more realistic expert rankings. To achieve this, we incorporate the details of an expert's qualifications and experience into the evaluation process by assigning assumed values, which are then integrated with their expertise level. These combined factors are subsequently utilized as inputs for a multiple regression analysis to generate an optimal ranking of experts. By emphasizing the significance of experience and qualifications in the ranking process, we can significantly improve the precision of our expert ranking mechanism. Our approach employs multiple regression analysis to identify the most suitable subject expert for user query transformation.
Renewable energy sources: is it a panacea for global electricity generation or not?

Renewable energy sources can cover mankind’s entire electricity demand under certain conditions but such carbon-free global power engineering, being heavily dependent on natural factors (wind, insolation, rainfall), will not meet the reliability and fail-safe operation requirements. Nuclear power claims to be the only comparable stabilizing carbon-free energy source to overcome this limitation.
A Robust Deep Learning-Based Approach for Detection of Breast Cancer from Histopathological Images

Breast cancer is a frequently encountered and potentially lethal illness that can affect not only women but also men. It is the most common disease affecting women globally, and is the main cause of morbidity and death. Early and accurate detection of this risky disease is crucial. A timely and precise identification of breast cancer can decrease death rate and can also protect people from additional damage. The traditional methods used for breast cancer detection are very expensive in term of time and cost. The goal of this study is to develop a system which can detect the breast cancer accurately and at an early stage. The primary objective of this research study is to make use of histopathological images to identify breast cancer correctly and faster. In the proposed research work, we have developed a model with the name Breast Cancer Detection Network (BCDecNet), which comprises eleven learnable layers, i.e., eight convolution layers and three fully connected (FC) layers. The architecture has a total of twenty-nine layers, including one input layer, seven leaky ReLu (LR) layers, four ReLu layers, five maximum-pooling layers, six batch-normalization (BN) layers, one cross-channel normalization layer and three dropout layers, a softmax layer, and a classification layer. The proposed work uses image-based data taken from the Kaggle online repository. The suggested model achieved 97.33% accuracy, 96% precision, 96.5% recall and a 96.25% F1 score. Furthermore, the result of the proposed model was compared with other hybrid approaches used for diagnosis of breast cancer at early stages. Our model achieved a more satisfactory result than all other approaches used for breast cancer detection. Additionally, the proposed BCDecNet model can be generally applied to other medical-image datasets for diagnosis of various diseases.
Feature Refinement with Masked Cascaded Network for Temporal Action Localization

Despite the great progress in temporal action localization (TAL), most existing methods directly use video encoders trained on trimmed Kinetics400 dataset to obtain clip-level visual features, ignoring the cross-dataset bias between Kinetics400 and TAL benchmarks. Such a dataset bias leads to poor visual representation, potentially hindering performance in both temporal detection and action recognition for TAL. In this paper, we propose a novel TAL method, termed feature refinement with masked cascaded network (FR-MCN), to tackle the above problem. Specifically, FR-MCN presents a new feature refinement strategy by developing clip-level feature classification task for both action and background clips to improve temporal sensitivity and enhance action semantics of visual features. Moreover, FR-MCN employs a masked cascaded paradigm for refinement to learn semantic disparities between action and background clips near boundary, enabling the starting and ending instants to be detected accurately for TAL. Extensive experimental results on THUMOS14 and ActivityNetv1.3 demonstrate that our FR-MCN, can significantly improve the action localization performance.
Subspace Learning Machine with Soft Partitioning (SLM/SP): Methodology and Performance Benchmarking

Subspace partitioning in a high-dimensional feature space plays a fundamental role in the design of effective classifiers. A novel subspace learning machine (SLM) that projects high-dimensional feature vectors into a 1D feature subspace and partitions it into two disjoint sets was recently proposed. As an extension, SLM with soft partitioning, denoted by SLM/SP, is proposed in this work. SLM/SP adopts the soft decision tree (SDT) data structure for decision learning. It starts by learning an adaptive tree structure by using local greedy subspace partitioning. Once the stopping criteria are met for all child nodes and the tree structure is determined, all projection vectors are updated globally. This methodology enables efficient training, high classification accuracy, and a small model size. It is shown by experimental results that an SLM/SP tree offers a lightweight and high performance classification method.
Detecting the foggy regions and measuring the fog degrees on the foggy regions

This paper develops a single image-based foggy region detection and fog degree estimation method that performs accurately regardless of image scenes. We analyzed the foggy image pixel distributions in the RGB color space to understand how humans perceive the fog degree and to extract fog degree features from the fog attention map generated by a vision transformer. The patterns mapping the fog feature vectors to the fog degrees were approximated using a generalized regression neural network learned from 10,000 synthetic and natural fog images. The proposed method was evaluated for actual indoor smog images. The proposed method estimated the fog degrees sufficiently accurately, regardless of image scenes, to be used for practical systems.
Rate Adaptation for Learned Two-layer B-frame Coding without Signaling Motion Information

This paper explores the potential of a learned two-layer B-frame codec, known as TLZMC. TLZMC is one of the few early attempts that deviate from the hybrid-based coding architecture by skipping motion coding. With TLZMC, a low-resolution base layer is utilized to encode temporally unpredictable information. We address the question of whether adapting the base-layer bitrate can achieve better rate-distortion performance. We apply the feature map modulation technique to enable per-frame bitrate adaptation of the base layer. We then propose and compare three online search strategies for determining the base-layer rate parameter: per-level brute-force search, per-level greedy search, and per-frame greedy search. Experimental results show that our top-performing search strategy achieves 0.6%-15.8% Bjøntegaard-Delta rate savings over TLZMC.
Context-Aware Seam Restoration for Image Extension

A seam is a set of pixels with minimum energy forming a continuous line in an image. By eliminating or duplicating seams iteratively, an input image can be retargeted. However, this process often results in blurring, stretching, or distortion problems around the seams, especially when extending a target image. We propose a novel approach for image extension using content-aware seam restoration to solve this problem. First, we design CSR-Net, which employs features from the horizontal region of target pixels to restore the seams. Second, we develop an image extension scenario based on the seam restoration and the training methodology of CSR-Net. Experimental results demonstrate that the proposed algorithm provides more accurate expanded results at seam pixels the seams than conventional algorithms.
A Deep Learning-based 6DoF Video Synthesizing Method Using Instant-NGPs

This paper introduces a new method for synthesizing six degree of freedom (6DoF) videos using neural radiance fields, which allows training from plain 2D images to render 3D scene at arbitrary viewports. Neural network model representing a previous timepoint is fine-tuned to train models for subsequent timepoints. Additionally, instant neural graphics primitives (Instant-NGP) is applied for speed improvement. The proposed method achieved both improved objective quality for same number of training iterations and enhanced consistency between frames. Furthermore, it shows superiority over other methods for generating 6DoF videos, in terms of quality and time efficiency.
Feature Reinforced and Adaptive Attention Guided Network for Multi-oriented Scene Text Detection

This paper proposes a text detection method for multi-oriented scene text detection based on feature reinforcement and adaptive text attention. Firstly, the method constructs different feature reinforcement modules for different levels of features. The high-level feature reinforcement module (HFRM) acquires multi-scale semantic information through the deformed Inception structure to enhance the expression ability of features; the low-level feature reinforcement module (LFRM) captures richer semantic information by constructing a residual-like connection structure to alleviate the semantic gap problem between different levels of features. Besides, the method also designs an adaptive text attention module (ATAM), which can adaptively adjust the attention weights according to the different input features, thus enhancing the fitting ability of the network. We carry out a series of experiments on ICDAR2015, TD500, and ICDAR2017-MLT to confirm the effectiveness of the proposed method.