Reviewerly/exHarmony huggingface dataset:
Interdisciplinary Discourse and Improvement of Scientometric Indicators as Trends in the Development of the “Bibliotekovedenie” Journal

The article presents an overview of the meeting of the Editorial Council and the Editorial Board of the “Bibliotekovedenie” journal, held on May 17, 2023 at the Russian State Library. Changes in the place and status of a scientific specialty “5.10.4. Library science, bibliography, bibliology” in the nomenclature of the Higher Attestation Commission (HAC) led to the need to revise the content of the policy of forming the scientific content of the journal in the direction of strengthening the interdisciplinary synthesis of the specialty with the humanities and social sciences. At the same time, the development of a domestic system of scientometric evaluation of journals, adequate to modern needs and based on the realities and traditions of Russian education and science, as well as organizational innovations of the HAC concerning the activities of expert and dissertation councils, require rethinking the ways and formats of content submission, approaches to the selection of authors of the publication and their qualifications. The interdependence of these trends necessitates the analysis of their components and discussion by experts of the strategy and tactics of the editorial policy of the “Bibliotekovedenie” journal for the period up to 2030. The article identifies the main scientific directions, interdisciplinary synthesis with which will not only significantly enrich library science, but also help to reveal its trends in accordance with the request of the current stage of society development. The expansion of the research field determined by the decision of the HAC actualized the need for a deep cultural understanding of library science, bibliography and book studies, as well as the development of appropriate methodology. The realities of modern life urgently require the inclusion of a geopolitical aspect in the research space of library science, which contributes to the substantiation of the importance of the library as the main institution of cultural identity and the preservation of the written heritage of the peoples of Russia and the world. The role of the journal in the development of scientific communications, strengthening regional ties and supporting library education in Russia was also noted. The results of the discussion will form the basis of the editorial policy of the journal and will be used in the activities of the editorial board.
Publication of Linked Open Data – A Systematic Literature Review for Identifying Problems and Technical Tools Supporting the Process

On the Internet, we find a large amount of information from government institutions that has been published in open format. However, only a part of these data is available in standard formats such as Resource Description Framework (RDF), and to a lesser extent, is published as Linked Open Data (LOD). The main objective of the research presented in this paper is to identify problems and tools used in the process of publishing LOD with the purpose of establishing a basis for the construction of a future framework that will help public institutions to facilitate such processes. To fulfill the objective, we conducted a systematic literature review in order to assess the state-of-the-art in this matter. The contribution of this work is to identify the frequent problems that arise in the LOD publishing process. It also provides a detail of the frameworks proposed in scientific papers grouping the technical tools by phases that correspond to the LOD publication life cycle. In addition, it compiles the characteristics of the ETL (Extract-Transform-Load) tools that predominate in this review, such as Pentaho Data Integration (Kettle) and OpenRefine.
A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm

Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan.
EXTERNAL VALIDATION OF AI PRIMARY GRAFT DYSFUNCTION PREDICTION CALCULATOR

Primary graft dysfunction (PGD) is the leading cause of early mortality after a heart transplantation (HT). Our current incidence rate of severe PGD is 15% per year, which is twice the international average. The International Consortium on PGD was established to identify contemporary risk factors for PGD and as a part of this multicenter collaboration, we trained a multicenter machine learning (ML) algorithm to predict the risk of severe PGD. In this study, we aimed to validate the AI-PGD calculator in our local contemporary population and evaluate its potential as a tool for improving outcomes following HT.
Hell and Heaven: Film Interpretations of Images in the Context of Mental Representations

The idea of applying the experience of cognitive analysis to visual works of art served as a prerequisite for studying the mental representations of hell and heaven on the basis of the most vivid cinematic images. Two films — “What Dreams May Come” (V. Ward, USA, 1998) and “Heavenly Judgement” (A. Zvantsova, Russia, 2011—2014) — were chosen as the research material. The center of scientific interest is the cognition of the “inner man” through art. The article outlines the trajectory of the development of individual and collective artistic interpretations of the afterlife within Christian culture. It is argued that the concept of hell has spread directly through objects of verbal and non-verbal creativity. This has significantly distorted the original doctrine of life as a path of spiritual transformation. By the will of man, the “infernal picture of the world” embraced the sphere of the Earth and was embodied in reality. At the turn of the 20—21 centuries, in the conditions of rapid technical evolution, there is a reassessment of metaphysical experience. A cognitive analysis of two directorial concepts — heaven and hell as a state of consciousness (V. Ward) and as a fictitious form of life (A. Zvantsova) — reveals an important qualitative change. It consists in the transformation of the concepts of the inner world in relation to the traditional vertical structure “heaven — earth — hell”. In the modern cognitive model, “hell” is located between “heaven” and “earth”. In the boundaries of the inner world (soul) hell directly intersects with or absorbs paradise, and heavenly paradise is practically absent. As a result of comprehending the screen images of heaven and hell in the context of the established mental representations, an updated picture of ideas about postmortem existence and the nature of the soul is formed.
Civil Liability Applicable to Artificial Intelligence: A Preliminary Critique of the European Parliament Resolution of 2020

The European Parliament's 2020 Resolution highlighted the importance of defining a clear and harmonised civil liability regime in Europe for the development of AI technologies so as to provide due legal certainty for producers, operators, affected persons and other third parties. The aims of this chapter are to provide a critical assessment of how far this option corresponds to the assumptions on which the Resolution is based, also taking a brief look at the concept of wrongdoing proposed and the scope of compensable harm. Further, it aims to assess the adequacy of the distinction indicated in light of the choices made in the European Parliament's earlier 2017 Resolution, above all, in light of the challenges that the development of AI raises for the traditional foundations on which compensation regimes have been based up to the present.
IMPLICATIONS OF THE APPLICATION OF BLOCKCHAIN TECHNOLOGY IN ACCOUNTING AND PUBLIC FINANCE

This paper aims to analyze the implications of blockchain technology in the accounting profession and the government sector. Blockchain technology, unknown until just a decade ago, emerged from the shadow of bitcoin and is now at the center of the debate about the future of the digital economy. Blockchain is a decentralized database secured by cryptographic protection, which is estimated to store about 10% of global gross domestic product (GDP) on its platform by 2027. Double-entry bookkeeping, which has been used for more than 500 years, in today's era of technological change and applied blockchain technology, could develop a new system, the triple-entry bookkeeping. Dominant market leaders in this field PwC, Deloitte, EY and KPMG have already begun research to introduce this technology into their business practices. The application of blockchain technology, in addition to the private sector, could also be used in the government sector, especially in the field of taxes. Blockchain would eliminate retroactive tax recording and provide an automated and impenetrable platform for many types of tax fraud. However, the adoption of blockchain technology, like any new technology, depends on its ability to overcome regulatory, technical and societal challenges.
Biosemiotics as a Problem Solution of Socio-Techno-Anthroposphere: Discipline Convergence

Biological knowledge of the digital age is in a state of revolutionary transformations. It is becoming multidisciplinary, technoscientific, synergistically included in a global process of convergence of scientific knowledge and technologies: nano, bio, cogno, info. Advances in life sciences transform economies and societies, help to tackle global challenges from climate change to pandemics. At the same time, none of the biotechnologies would be possible without information technology. New tech­nological possibilities have led mankind to a post-digital neuromorphic science, where biology as digital information and digitalization as biology are now synergisti­cally interconnected. At the same time, bio-digitalization is closely related to bio-in­formatics. Both disciplines involve recursive-communicative interaction between the information approach and biology, which, in turn, launches the process of biodig­ital convergence, generating a net-like knowledge ecosystem that allows solving those complex problems that cannot be solved by separate disciplines. This process generates new, interconnected, ecological niches – umwelts of knowledge. So that, biosemiotics is a “bridge” between techno-scientific and socio-humanitarian know­ledge. This is a special lens which helps us to observe living things contacting ro­botics, and the environment. Plantoids are given as an example.
An overview of algorithms for discovering new categories of problems based on unlabeled data

In traditional machine learning, supervised and semi-supervised learning is designed to be used in a closed-world setting where the training data is fixed and does not change over time. Unfortunately, these methods still require a large number of labels for the categories to be categorized, which is expensive and impractical. A new category discovery algorithm is designed so that it can discover new categories while classifying and recognizing labeled images. In this case, the machine can automatically identify new categories without manual marking of image feature categories, which can greatly reduce the cost of image classification. Kai Han et al. named this problem a new category discovery problem and proposed that deep clustering can be used to solve it well. This paper focuses on the comparison of two commonly used robust baselines in the new category discovery and proposes that adding a post-processing model can better improve the accuracy of the model result. This paper applied the relaxed contrast learning method to the Ranking Statistics, and the accuracy of CIFAR-100 is improved by 6%.
A Text Mining based Entity Extraction Model for Power Plant Fault

Entity extraction is widely used in power fault diagnosis to extract valuable knowledge from a large amount of power fault data, supporting fault diagnosis and prevention. Traditional entity extraction methods mainly rely on fixed rules and procedures, which cannot be dynamically learned or changed with new data or knowledge. When dealing with complex and variable power plant fault data, traditional methods have limitations. This paper proposes a text mining-based entity extraction method that updates the model by learning fault data characteristics. This makes the model more adaptive and flexible to cope with complex and variable data. We compared and analyzed experimental results based on real fault data. The results show that our method effectively extracts fault entities from power plants and has a high accuracy and recall rate. This has a significant reference value for improving the efficiency and accuracy of power plant fault processing.
Sensor Fault Detection for UAVs Based on MIC-LSTM With Attention Mechanism

The unmanned aerial vehicle (UAV) sensors are indispensable parts of UAVs, and detecting their faults is of great significance for the safe flight of the entire UAV. The existing fault detection methods have limitations in selecting input variables related to flight data, and do not fully consider the contribution of relevant data in the methods. In order to solve these problems, this paper proposes a sensor fault detection method (MICA-LSTM) based on maximum information coefficient (MIC) and long short-term memory network (LSTM) with attention mechanism. Firstly, MIC is used to select input variables related to the flight data to be detected, reducing interference from irrelevant data. Subsequently, an LSTM model with attention mechanism is used to train the selected input time series data and construct a UAV fault detection model. This method can extract relevant features from large flight data and assign different weights to these features based on different time series, thus achieving accurate fault detection. The proposed method is compared with methods lacking MIC and attention mechanism through experimental validation using simulated data from the University of Minnesota UAV model. The results indicate that the proposed method exhibits better performance and accuracy in UAV sensor fault detection.
Disturbance observer-based fault-tolerant control of UAV with obstacle avoidance

This paper studies the fault-tolerant control (FTC) of fixed-wing unmanned aerial vehicle (UAV) with obstacle avoidance. Firstly, the path planning of the UAV is realized by improving the traditional artificial potential field (APF) method to achieve obstacle avoidance. The improved APF method solves the unreachable target and local minimum problems existing in the traditional APF. Secondly, the lumped uncertainty composed of the thrust fault, external disturbances and parameter inaccuracies is estimated by the nonlinear disturbance observer. In addition, an FTC scheme based on nonsingular fast terminal sliding mode (NFTSM) is proposed for UAV under thrust fault. Finally, simulation is used to confirm that the design plan is proper.
Provably Secure Robust Image Steganography

The maturity of generative models and the popularity of generated data have brought new technical means and camouflage environments to steganography. Numerous generative image steganography methods have emerged, but achieving provable security, robustness, and relatively high capacity simultaneously remains challenging. This paper proposes a provably secure robust image steganography method via the generative adversarial network (GAN), named PARIS. The sender maps the secret message, following a uniform distribution, to latent vectors conforming to a standard Gaussian distribution using inverse transform sampling. Subsequently, the latent vector is fed into the generator, producing the stego image. In this way, the stego image cannot be distinguished from the normally generated image. The receiver extracts the secret message from the recovered latent vector via gradient descent optimization. To enhance the robustness, a noise layer is introduced while recovering the latent vector to simulate potential lossy operations in real scenarios. The security of the proposed method is theoretically proven. Extensive experiments have also verified the proposed method's robustness, security, and relatively high capacity in terms of different GAN architectures, noises, and datasets.
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization

Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking various face accessories into consideration, like glasses, hats, and masks. In the COVID-19 pandemic era, wearing face masks is one of the most effective ways to defend against the novel coronavirus. However, DNNs are known to be vulnerable to adversarial examples with a small but elaborated perturbation. Thus, a facial mask with adversarial perturbations may pose a great threat to the widely used deep learning-based FR models. In this paper, we consider a challenging adversarial setting: targeted attack against FR models. We propose a new stealthy physical masked FR attack via adversarial style optimization. Specifically, we train an adversarial style mask generator that hides adversarial perturbations inside style masks. Moreover, to ameliorate the phenomenon of sub-optimization with one fixed style, we propose to discover the optimal style given a target through style optimization in a continuous relaxation manner. We simultaneously optimize the generator and the style selection for generating strong and stealthy adversarial style masks. We evaluated the effectiveness and transferability of our proposed method via extensive white-box and black-box digital experiments. Furthermore, we also conducted physical attack experiments against local FR models and online platforms.
EMPHASISCHECKER: A Tool for Guiding Chart and Caption Emphasis

Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present EMPHASISCHECKER, an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.
MRCNet: Multi-Resolution LiDAR-camera Calibration using Optical Center Distance Loss Network

The integration of cameras and LiDAR sensors has emerged as a promising approach to enhance environmental perception and 3D reconstruction capabilities in autonomous vehicles and robotic systems. Precise extrinsic calibration is of paramount importance to achieve effective multi-sensor fusion applications. Traditional calibration methods often rely on manual procedures and specific calibration targets, which can be time-consuming and prone to errors. In contrast, Convolutional Neural Networks (CNNs) have shown potential in devising end-to-end calibration systems, leveraging their ability to extract robust features automatically. In this paper, MRCNet, an online end-to-end LiDAR-camera calibration network, which overcomes the limitations of traditional methods and previous CNN-based approaches, is proposed. This article introduces a multi-resolution feature extraction module, enabling the extraction of comprehensive and informative features from RGB images and depth images derived from point clouds. Additionally, the optical center distance loss, a novel concept that accounts for the camera’s optical imaging characteristics, facilitating more effective feature extraction is incorporated. MRCNet is the first online calibration network that considers the influence of camera imaging properties. This paper employs an iterative refinement process to progressively estimate the calibration error, allowing online extrinsic estimation. Evaluation tests on the KITTI Odometry dataset demonstrate the superior performance of MRCNet compared to existing learning-based methods, achieving a mean absolute calibration error of 0.350cm in translation and 0.033° in rotation. Furthermore, ablation studies validate the effectiveness of the modules of MRCNet. The code for MRCNet will be made publicly available at: https://github.com/AlexWang0214/MRCNet.
Using Process Data of Task Performance in Creative Thinking Assessment

&lt;p&gt;Creative thinking is an important skill of the modern world, and its assessment with the help of modern digital tools is becoming an increasingly complex methodological task. The inclusion of process data of task performance in the assessment model of creative thinking is a promising direction that becomes possible in computer testing. The use of such data allows us to consider the processes of creative thinking in dynamics, which makes the assessment of the level of creativity of students more accurate and multifaceted. The purpose of the study was to determine the possibility of using process data of task performance as part of evaluating creative thinking using a tool in a digital environment. The paper presents an analysis of the work of 823 4th grade students who, during the assignment, created images in a closed simulation environment to assess creative and critical thinking. The analysis of process data of completing tasks performance took place using N-grams of various lengths. As a result, the sequences of actions of students with different levels of creative thinking were compared, and various strategies of behavior of the test takers were identified in task for creative thinking compared with a task for critical thinking. Together with information about the level of creativity based on the analysis of the created product, process data of task performance improves the understanding of the functioning of tasks through the prism of the task execution process by the test takers. It also makes a step forward in detailing the feedback that can be obtained as part of testing.&lt;/p&gt;