Reviewerly/exHarmony huggingface dataset:
THE FIGURE OF MARIA MONTESSORI (1870–1952) IN OPEN REFERENCE ELECTRONIC EDITIONS

The article analyzes the reflection in open reference electronic editions of the figure of Maria Montessori - an outstanding researcher in the field of psychology and philosophy, pedagogy and psychiatry, a pedagogue who developed a training program based on her own scientific research. In particular, the content of publications about M. Montessori in electronic encyclopedias in the Ukrainian information space and foreign encyclopedic editions to which there is free access was considered. For this, 7 encyclopedias of a universal nature were studied («Great Modern Encyclopedia», «Great Ukrainian Encyclopedia», «Trekkani Encyclopedia», «Britannica», «Brockhaus Encyclopedia», «Pedagogical Encyclopedia», «Great Danish Encyclopedia»), 4 dictionaries dedicated to history, culture, notable figures («Biographical Dictionary of Italians», «Dictionary of British Teachers», «Biographical Dictionary of American Teachers», «DBE: Biographical Dictionary of Educators 1800‒2000», and the online portal of Danish directories. The creation and functioning of electronic biographical resources in the information space is analyzed. It has been studied that most often electronic encyclopedias and dictionaries, which are digital analogues of printed publications, combine information from different sources. On the basis of the research, the menu of sites was considered, the content of reference publications was characterized: the works of M. Montessori, hyperlinks to them, visual materials, works of researchers of her heritage. Attention is focused on the online portal of Danish reference books, on the pages of which are collected original full-text articles by Danish professionals and scientists about Montessori's pedagogical development. It was determined that the considered reference electronic publications (encyclopedias, dictionaries) are an important component of the general represented information in open access, reflect social events and phenomena, record historical periods and in a concise form highlight information about Maria Montessori, and are also a powerful basis for preserving cultural heritage of a certain nation, in particular, the pedagogical heritage of Italian pedagogy M.&nbsp;Montessori.
Structure-Sensitive Graph Dictionary Embedding for Graph Classification

Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to transform input graphs into the embedding space of a graph dictionary for the graph classification task. Instead of a plain use of a base graph dictionary, we propose the variational graph dictionary adaptation (VGDA) to generate a personalized dictionary (named adapted graph dictionary) for catering to each input graph. In particular, for the adaptation, the Bernoulli sampling is introduced to adjust substructures of base graph keys according to each input, which increases the expression capacity of the base dictionary tremendously. To make cross-graph measurement sensitive as well as stable, multi-sensitivity Wasserstein encoding is proposed to produce the embeddings by designing multi-scale attention on optimal transport. To optimize the framework, we introduce mutual information as the objective, which further deduces variational inference of the adapted graph dictionary. We perform our SS-GDE on multiple datasets of graph classification, and the experimental results demonstrate the effectiveness and superiority over the state-of-the-art methods. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GuangbuLiu/SS-GDE</uri> .
Community-Based Dandelion Algorithm-Enabled Feature Selection and Broad Learning System for Traffic Flow Prediction

In an intelligent transportation system, accurate traffic flow prediction can provide significant help for travel planning. Even though some methods are proposed to do so, they focus on either algorithm or data level studies. This work focuses on both by proposing a Community-based dandelion algorithm-enabled Feature selection and Broad learning system (CFB). Specifically, a feature selection method is adopted to choose suitable features aiming to avoid redundant ones affecting prediction accuracy, and a neural network-based learning algorithm, namely a Broad Learning System (BLS), is used to predict traffic flow. In order to further boost its prediction performance, a Community-based Dandelion Algorithm (CDA) is proposed by considering an individual and its multiple offspring as a community and adopting a learning strategy for different communities. The proposed CDA is used to a) choose the suitable features as a feature selection method; and b) optimize the parameters and network structure of BLS. CDA’s superiority over its competitive peers is first verified on CEC2013’s benchmark functions, and then the proposed CFB is applied to handle the traffic flow prediction problems. The results indicate that it can improve the prediction accuracy by 5%-16% compared to the updated traffic flow prediction methods.
Rules for Expectation: Learning to Generate Rules via Social Environment Modelling

The evolution of natural life is guided by a perpetually adaptive set of rules, encompassing natural laws, human policies, and game mechanics. Automated game design, through the creation of simulated environments populated by AI agents, embodies these rules, aligning with the objectives of artificial life research that seeks to replicate the dynamics of biological life through computational models. This paper presents a comprehensive framework, the Rule Generation Networks (RGN), devised for automated rule design, evaluation, and evolution in line with controllable expectations. We refine and formalize three cardinal elements - rules, strategies, and evaluation - to elucidate the intricate relationships inherent in rule generation tasks. The RGN integrates generative neural networks for rule design and a suite of reinforcement learning models for rule evaluation. To exemplify rule evolution and adaptation across varying environments, we introduce a controllability metric to gauge game dynamics and evolve the rule designer accordingly. Furthermore, we develop two game environments, Maze Run and Trust Evolution, modelling human exploration and societal trade dynamics, to gamify and evaluate the generated rules.
Predictive Control of Cooperative Robots Sharing Common Workspace

We present a model predictive control (MPC) algorithm for online time-optimal trajectory planning of cooperative robotic manipulators. Robotic arms sharing a common confined operational space are exposed to high interrobot collision risks. For collision avoidance, a smooth robot geometry approximation by Bézier curves is applied, utilizing velocity constraints and tangent separating planes, enabling an efficient generation of robot trajectories in real-time. The proposed optimization algorithm is validated on an experimental setup consisting of two collaborative robotic arms performing synchronous pick-and-place tasks.
Traffic Scenario Understanding and Video Captioning via Guidance Attention Captioning Network

Describing a traffic scenario from the driver’s perspective is a challenging process for Advanced Driving Assistance System (ADAS), involving different sub-tasks of detection, tracking, segmentation, etc. Previous methods mainly focus on independent sub-tasks and have difficulties to comprehensively describe the incidents. In this study, this problem is novelly treated as a video captioning task, and a Guidance Attention Captioning Network (GAC-Network) structure is proposed for describing the incidents in a concise single sentence. In GAC-Network, an Attention based Encoder-Decoder Net (AED-Net) is built as the main network; with the temporal spatial attention mechanisms, the AED-Net make it possible to effectively reject the unimportant traffic behaviors and redundant backgrounds. Considering various driving scenarios, the Spatio-Temporal Layer Normalization is used to improve the generalization ability. To generate captions for incidents in driving, the novel Guidance Module is proposed to boost the encoder-decoder model to generate words in a caption, which have better relationship to the past and future words. Because there is no public dataset for captioning of driving scenarios, the Traffic Video Captioning (TVC) dataset is released for the video captioning task in driving scenarios. Experimental results show that the proposed methods can fulfill the captioning task for complex driving scenarios, and achieve higher performance than the methods for comparison, including at least 2.5%, 1.8%, 3.6%, and 13.1% better results on BLEU_1, METEOR, ROUGE_L and CIDEr, respectively.
Neural-Symbolic Coherent Reasoning for Evidence-based Medicine

Deep learning and machine learning technologies are being used to solve specific problems by deriving models that approximate real phenomena based on a limited amount of data. However, these technologies have a simple structure than humans that utilize inductive, deductive, and semantic reasoning methods. In particular, the current deep artificial neural network technology simulates the activity of neurons and synapses, which is suitable for simple classification and generation problems, but not suitable for structural and logical reasoning. In order to solve more complex problems, the artificial intelligence algorithms simulate human reasoning and cognitive mechanisms to enable multilayered inferencing. It is necessary to systematically develop an artificial intelligence module that performs human-like logical reasoning by synthesize the results of neural inferencing and symbolic reasoning.
The color demosaicing and image scaling based on improve Hamilton-Adams

The demosaicing process is the most critical step in image processing, this study presents a Hamilton-Adams-based algorithm for mosaic removal and image scaling. The algorithm consists of three steps: image pre-processing, image scaling, and improved Hamilton-Adams mosaic removal. The image pre-processing stage employs a mean filter to reduce high-frequency signals at the image edges, effectively mitigating the zipper effect caused by image scaling and improving image quality. The improved Hamilton-Adams method incorporates high-quality linear interpolation to achieve more accurate edge computations. In terms of image quality assessment, this paper increased 1.13 dB of PSNR, 1.19 dB of CPSNR, and 0.03 of SSIM values compared to high-quality linear interpolation with bicubic interpolation.
An Improvement of Whale Optimization Algorithm with Rao Algorithm for Optimization Problems

This paper proposes an improvement of whale optimization algorithm for optimization problems. In this study, the Rao algorithm was improved by means of procedures of spiral updating position. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), intersection mutation differential evolution (IMDE) algorithm, and whale optimization algorithm (WOA). The computation results illustrated that the proposed algorithm can produce optimal solutions for three out of six functions. Comparing to the other three algorithms, the proposed algorithm has provided the best results. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function, and the discontinuous step function.
Effect of Machine Translation on Authorship Attribution

In this investigation, the effect of automatic machine translation on stylometry is investigated. For that purpose, an Arabic corpus called Hundred of Arabic Travelers (HAT), containing 100 authors, is used. The idea is to translate all the texts of this corpus, which are written in Arabic, to the French language by using Microsoft office translate. An authorship attribution system is applied on both datasets in order to attribute the author identity for each text before and after translation. Thus, a comparative evaluation based on the author attribution score is made between the two datasets. Several types of features are tested, namely: rare words, words, word bi-gram, word tri-gram, character bi-gram, character tri-gram, and character tetra-gram. Those features are used with a centroid nearest neighbor distance for classification. The experimental results have shown that the effect of machine translation reduces the stylometric identification performances, but preserves some characteristics of the author, which makes the identification still possible even after translation. The accuracy of authorship attribution, with 100 authors, on the translated documents is about 80% of correct attribution, while the best accuracy obtained on the original documents is 97%.
A Survey: Classifying and Predicting Features Based on Facial Analysis

The facial features of a human being are to deliver their opinions and intentions. The human face consists of unique features (like eyes, nose, cheeks, eyebrows, and the rest) that make a person vary from another individual for identification. This paper correlates and analyzes various facial features like gender, age, and emotion. Human emotion analysis considered for estimation till now are fear, happiness, surprise, sadness, neutrality, anger, and contempt. The analysis of gender detection, age estimation, and facial expression recognition obtain via an image, video clip, or real-time detection like a webcam. The facial analysis consists of numerous authentic applications such as human–computer interaction, biometrics, electronics, surveillance, personality development, and cosmetology. This paper allocates several procedures of the facial estimation analysis process and trends.
Performance Comparison of On-Chain and Off-Chain Data Storage Model Using Blockchain Technology

In recent years, the blockchain technology has attracted a lot of attention. Blockchains’ rapid ascent to prominence is largely due to the openness and efficiency they provide. Businesses have begun blockchain technology. As the need for blockchain is growing day by day, it is necessary to evaluate the storage capacity of blockchains. There are two solutions to store data in native to blockchain namely on-chain or off-chain storage. The choice of the storage is based on the scenario or application. Hence in this paper, the data storage in on-chain model and off-chain model is implemented for a real-time scenario “blood donation and transfusion system” using Hyperledger Fabric tool and the performance of the system for both the model is been analyzed. From the results it is found that, off-chain data storage model is faster (5.39% faster) to store data than the on-chain data storage model. Also, the off-chain data storage model is slower (90.425% slower) than on-chain model to retrieve data from blockchain. By interpreting the results it is concluded that on-chain data storage is better than off-chain data storage model for the scenario “blood donation and transfusion system” that we have taken for comparison. On-chain data storage model is needed for applications with high trust on data as it is stored in blockchain. Off-chain data storage is not needed for text-based data.
Metaverse as dispute: challenges for a state action agenda

Introduction: “From 'virtual reality' to the real world: “what challenges and regulatory issues does the 'Metaverse' bring to the State?” based on that, this theoretical essay seeks to articulate authors who base the Metaverse phenomenon with authors who elucidate aspects of technological and informational disputes. So, in order to reflect challenges for the State to deal with issues related to emerging and potentially disruptive technologies for digital governance, this essay directs its reflection to the Metaverse phenomenon. Discussion: Six provocations are elaborated among sections in the form of questions that help to mobilize reflections around critical authors from the perspective of studies on technological and informational disputes. A Metaverse Regulatory Framework is proposed. Conclusion: It points to the technologies of Metaverse and their forms of use built from the disputes between different interests and the strategies of the most powerful actors. In addition, it points to challenges that the public sector will need to observe. Such challenges appear to be markedly regulatory, potentially involving: privacy protection, data security, and protection against monopolies, tax aspects, provision and control of infrastructure and critical services, manipulation of public opinion and others.
Service Mark in Library Publishing Activities

Nowadays the problem of competent use of service mark in library publishing activity is becoming relevant. This marketing tool has not yet received proper coverage in the publications of specialists from the fi eld of book studies, library science and bibliography. For the successful realization of their own intellectual (including publishing) products of libraries is very important its active promotion to the consumer, so it is necessary to develop an individual brand. One of the ways to solve this problem is the purposeful use of a service mark, created, approved and duly officially registered (according to the norms of the current legislation of the Russian Federation). The article also touches upon some issues related to the legal aspects of individualization of library publishing products.
Bibliographic Annals of Russian Bibliology — 2009

A critical review of the next bibliographic index “Bibliology: Index of Literature for 2009” published in 2023 by “Pashkov House” is presented. This book was prepared by the employee of the Russian State Library, authoritative bibliographer L.I. Fursenko. This issue covers the publications on bibliology and book culture (from the general problems to the bibliophily and book art) that published in the Russian Federation during 2009. The reviewer underlines the need to continue one of the most important bibliographic project in the sphere of the contemporary book science and culture. The traditional merits of this voluminous index and its enduring relevance not only for specialists of book business but another ones representing wider spheres of science, culture and education are specially noted.
Formation of Digital Resources On Military History

The article deals with the formation of digital resources on military topics by various institutions and organizations, including libraries of various departmental subordination. The results of the research work (RW) “Representation of military history in the modern information environment” of the Information Historical and Scientific Center — the Military Historical Library of the General Staff of the Armed Forces of the Russian Federation (Military Historical Library), conducted in the first half of 2023, are presented. The article presents an overview of modern military-historical resources in the network space. The general characteristics of thematic digital collections are specified, and the peculiarities of military-historical digital collections are identified and the principles of their creation are formulated. The authors emphasize that it is necessary to take into account the pedagogical component, which should be an integral part of Russian educational projects of patriotic nature. Also, an important feature of the newly created resources should be the pedagogical orientation, providing opportunities to support educational projects of a patriotic nature.
ASKI “Best Books of the Year” Competition as a Navigator for Libraries

The traditional all-Russian contest “Best Books of the Year”, which has been organized by the Association of Book Publishers of Russia (ASKI) since 1991 and in which at least 140—150 publishing houses from different regions participate on average, annually determines the most interesting books in 12 nominations. The competition has a serious informational and professional potential, as the results of 2022 have shown. It was stressed that the main significance of the competition is the public presentation of the best book novelties of the most successful publishing houses, including in the regional centers of the Russian Federation. Currently, many publishing projects important for the national culture have small print runs, but participation in the contest and being among its nominees give them an opportunity to gain public recognition and ensure promotion in the book market. Based on the results of the contest, the top-50 best regional publications are compiled.
The Use of Artificial Intelligence Technology in The Organization's E-Services and The Impact on Customer Satisfaction

Customers have quickly become receptive to digital services that use artificial intelligence technologies, and these services are expected to expand further in the coming years. While artificial intelligence (AI) technologies provide improvements in production and service processes for organizations, they provide many benefits to customers. The use of AI technologies in production process systems and digital services in organizations contributes more effectively to the process of automating complex tasks and improving the customer service experience. Organizations have begun to integrate AI technology into their operations with the expectation of achieving quality productivity and services to raise the level of customer satisfaction, which contributes to gaining loyalty. The research study was based on reviewing several research articles that contributed to achieving the answer to the study's objectives. Which aimed to 1) introduce the importance of AI technology and the need to benefit from it in the organization’s operations as much as possible. 2) Ensure that there is a correlation between AI technology and customer satisfaction. 3) Identify the benefits that can be obtained when increasing customer satisfaction. 4) Identify the pros and cons when applying AI technology to customer satisfaction. Based on the literature reviews, the research objectives were achieved, as detailed in the research study sections.
MCNN-DIC: A mechanical constraints-based digital image correlation by a neural network approach

Digital image correlation (DIC) is a widely used photomechanical method for measuring surface deformation of materials. Practical engineering applications of DIC often encounter challenges such as discontinuous deformation fields, noise interference, and difficulties in measuring boundary deformations. To address these challenges, a new, to the best of our knowledge, DIC method called MCNN-DIC is proposed in this study by incorporating mechanical constraints using neural network technology. The proposed method applied compatibility equation constraints to the measured deformation field through a semi-supervised learning approach, thus making it more physical. The effectiveness of the proposed MCNN-DIC method was demonstrated through simulated experiments and real deformation fields of nuclear graphite material. The results show that the MCNN-DIC method achieves higher accuracy in measuring non-uniform deformation fields than a traditional mechanical constraints-based DIC and can rapidly measure deformation fields without requiring extensive pre-training of the neural network.