Reviewerly/exHarmony huggingface dataset:
INFORMATION TECHNOLOGIES IN THE EDUCATIONAL PROCESS: FEATURES AND ADVANTAGES

The article comprehensively analyzes the features and benefits of introduction of information technologies into the educational process.It is noted that information technologies are becoming an indispensable component of modern education at present, contributing to the improvement of the high-quality and efficiency of education. The interactivity and involvement of students, as well as the individualization of learning, allow students to develop their skills and knowledge at the pace that is optimal for them. The article found out that the individualization of education is implemented with the help of information technologies through the creation of personalized educational programs. Each student has the opportunity to study according to their individual needs and pace. Adaptive learning platforms take into account the level of knowledge and capabilities of each student, providing individual tasks and recommendations. All these significantly improve the level of understanding and assimilationof the educational material.Furthermore, information technologies open up new opportunities for research and study of complex processes and phenomena. Global exchange of knowledge thanks to the Internet allows students and teachers to exchange knowledge and experience with people from all over the world. This broadens horizons and allows you to get a variety of views on subjects of study. It was noted that thanks to analytics for improving learning, information technologies allow collecting and analyzing data on student success and the effectiveness of educational programs. This allows to identify problematic points and improve training based on data.We have found that information technologies have made a significant contribution to the modern educational process, providing interactivity, individualization, accessibility and expanded opportunities for research and global knowledge exchange. This article highlights the key benefits and features of this development in education, which contributes to improving learning and preparing the younger generation for the challenges of the modern world.
DISTANCE FUTURE OF EDUCATIONAL SPACE IN THE CONTEXT OF BLENDED LEARNING

Education should take into account the changes taking place in the structure of educational knowledge of modern society, which determine the relevance of the development and implementation of new educational services based on information and communication and distance technologies. Today, online learning is gaining special importance in the field of education of young people of different ages, cultural and social levels, and educational motivation. The article analyzes the structure of online and offline forms of education, theory and methodology, as well as trends in the implementation of blended learning in the context of the distant future educational space. Distance education can be considered as a means of modernizing the education system.
An Oversampling Technique for Handling Imbalanced Data in Patients with Metabolic Syndrome and Periodontitis

Objectives: Periodontitis has been suggested to be associated with several systemic diseases and conditions including obesity, metabolic syndrome, diabetes, chronic renal disease, respiratory disorders, and cardiovascular diseases. Metabolic syndrome (MetS) is a collection of impairment and is a risk factor for type 2 diabetes and cardiovascular disease. Our study is aimed to handle MetS unbalanced data using the synthetic minority over-sampling technique (SMOTE) to increase accuracy and reliability.&#x0D; Materials and Methods: Six metabolic syndrome patients and 26 systemically healthy subjects with periodontitis were recruited in this study. Clinical parameters (Plaque index (PI), gingival index (GI), probing pocket depth (PPD), clinical attachment loss (CAL), and bleeding on probing (BOP)) were obtained, smoking status and body-mass index (BMI), systemic diseases, fasting glucose levels, hemoglobin A1c (HbA1c) levels and serum advanced glycation end-products (AGE) levels were recorded by one examiner. First, the data was pre-processed by removing missing values, outliers and normalizing the data. Then, SMOTE technique was used to oversample the minority class. SMOTE works by creating synthetic data points that are similar to the existing minority class instances. The experimental dataset included numerous machine learning algorithms and assessed accuracy using both pre- and post-oversampling methods.&#x0D; Results: Our findings suggest that by increasing the sample size of a study, researchers can gain more accurate and reliable results. This is especially important when studying a population with a lower sample size, as the results may be skewed.&#x0D; Conclusion: SMOTE may result in over fitting on numerous copies of minority class samples.
QESAR: Query Effective Decision-Based Attack on Skeletal Action Recognition

Generating high-quality adversarial examples in a black-box setting often requires larger query volumes and adds more noticeable perturbations in skeleton action recognition tasks. We propose a query effective decision-based attack on skeletal action recognition (QESAR) to address this. We use the current gradient direction and successful historical information to reduce query volumes to update the sampling distribution, enabling a quick estimation of the next gradient direction. To ensure the imperceptibility of adversarial examples, we propose a hierarchical joint perturbation method to estimate the direction of perturbation accurately. Additionally, we design an objective function that satisfies joint angle and bone length constraints to minimize the magnitude of perturbation, ensuring that the generated adversarial examples do not exhibit noticeable distortions. Finally, in experiments, we find that QESAR can generate adversarial examples that satisfy skeletal action constraints with lower query volume. On the HDM05 and NTU datasets targeted at the ST-GCN and SGN models, QESAR achieves a 100% attack success rate while reducing the query volume by hundreds to thousands. Specifically, in the untargeted attack scenario, the QESAR scheme outperforms in terms of four metrics on the HDM05 dataset against the SGN model: average joint position deviation, average joint position acceleration deviation, average joint angle deviation, reducing them by 0.0078, 0.0679, and 6.9311, respectively.
Learning Degradation for Real-World Face Super-Resolution

Acquiring degraded faces with corresponding high-resolution (HR) faces is critical for real-world face super-resolution (SR) applications. To generate low-resolution (LR) faces with degradation similar to those in real-world scenarios, most approaches learn a deterministic mapping from HR faces to LR faces. However, these deterministic models fail to model the various degradation of real-world LR faces, which limits the performance of the following face SR models. In this work, we learn a degradation model based on conditional generative adversarial networks (cGANs). Specifically, we propose a simple and effective weight-aware content loss that adaptively assigns different content losses to LR faces generated from the same HR face under different noise vector inputs. It significantly improves the diversity of the generated LR faces while having similar degradation to real-world LR faces. Compared with previous degradation models, the proposed degradation model can generate HR-LR pairs, which can better cover various degradation cases of real-world LR faces and further improve the performance of face SR models in real-world applications. Experiments on four datasets demonstrate that the proposed degradation model can help the face SR model achieve better performance in both quantitative and qualitative results.
Belarusian scientific journals as a source of professional communication

Publishing the findings of scientific research in reputable scientific publications affects the rate of knowledge dissemination. At the same time, most Belarusian scientific journals has been revising their publishing concept to meet the criteria set by citation indexes. The paper aim is to determine the status of the Belarusian scientific periodicals in the conditions of the developing model of electronic scientific communication in the post-Soviet states. At the first stage, the stream of publications by Belarusian researchers was identified, and the positive dynamics in 2011 to 2021 was recorded. Documents were analyzed by type and thematic categories. At the second stage, the list of Belarusian scientific journals was analyzed for their indexation in the science citation databases, the availability of the periodical website, the positive dynamics of the titles on the List of scientific publications for publishing the findings of dissertations recommended by the Higher Attestation Commission of Republic of Belarus (List of publications of the Higher Attestation Commission), etc. Based on the analysis, it was revealed that Belarusian scientific journals are widely represented in the Russian Science Citation Index (RSCI); at the same time, there is a certain “stagnation” in their entering the international citation indices – Scopus or Web of Science. The authors emphasize the challenge of Belarusian scientific periodicals insufficient presence in the Internet, and of the absent websites of some periodicals.
Revitalizing Talent Development in Jiangsu Province's Representative Universities under 'The Belt and Road' Initiative in the Post-Pandemic Landscape: A Data-driven Analysis

Examining four universities in Jiangsu Province's "Belt and Road" international talent programs, this study finds disparities in planning, faculty diversity, and student exchanges. Despite Jiangsu's reputation for excellence in higher education, these institutions perform in a range of ways, most notably in cooperation and planning within the initiative. Although some universities are excellent at internationalizing their faculty, it is still difficult to recruit from "Belt and Road" nations. The pandemic affected participation rates by interfering with exchange and student enrollment programs. Evaluating and correcting shortcomings in Jiangsu universities' "Belt and Road" talent programs was the main goal. With an emphasis on student exchanges, faculty compositions, and educational planning, the study sought to close gaps that prevent successful internationalization. The research sought to reconcile current practices with the urgent need for strong international talent development through careful analysis. Using a thorough methodology, the study examined student exchange data, faculty compositions, and educational plans, identifying notable differences. These results highlight the need for better planning, diverse faculty hiring, and equitable student exchanges. In summary, this study offers crucial recommendations to higher education institutions and decision-makers for improving the success of "Belt and Road" projects in the aftermath of the pandemic, stressing the importance of better international talent development and more robust global alliances.
Implementing the subsystem of research findings control in RNPLS&amp;T’s Single Open Information Archive (SOIA)

The RNPLS&amp;T accomplishes research aimed at improving and developing bibliographic standards and formats, classification and indexing system, frameworks and solutions for library informatization. The materials, publications and reports on the studies make the sources for building the content of RNPLS&amp;T’s Single Open Information Archive (SOIA) [1]. The information model of the subsystem of preservation and management of the data on the research by RNPLS&amp;T professionals was designed on the basis of the experience of using CRIS-systems in Russian academic and research organizations. The authors attempt the analytical review of CRIS-systems implementation in Russian research and analytical organizations, and describe the RNPLS&amp;T’s CRIS-system as a part of SOIA system. They describe in detail the metadata schemes used for presenting data on research findings in RNPLS&amp;T’s CRIS-system. The CERIF standard is based on the data model comprising the entities of “project”, “organization” and “person”, their connections and their attributes. The extended metadata schemes in XML format used for sharing information on research exchange are oai_cerif_openaire and oai_openaire, compatible with CERIF model and supporting usability of OpenAIRE profile. The SOIA metadata exchange subsystem and OAI-PMH data provider generate metadata in oai_cerif_openaire format on the basis bibliographic records in e-catalog, data on authors, organizations and projects as presented in appropriate authority files. Actualization of data on author IDs in the international and national databases (ORCID, RSCI, WoS, Publons, Scopus, ResearchGate) in the authority files is essential. The paper is prepared within the framework of the Government Order No.of August 17, 2023, 075-01235-23-01 of August 17, 2023, theme No. 1021062311369-1-1.2.1;5.8.2;5.8.3 “Information support of research by scientists and specialists on the basis of RNPLS&amp;T’s Open Archive as the system of scientific knowledge aggregation (FNEG-2022-0003)” for 2020–2024.
Audio-Driven Lips and Expression on 3D Human Face

Extensive research has delved into audio-driven 3D facial animation with numerous attempts to achieve human-like performance. However, creating truly realistic and expressive 3D facial animations remains a challenging task, as existing methods often struggle to capture the subtle nuances of anthropomorphic expressions. We propose the Audio-Driven Lips and Expression (ADLE) method, specifically designed to generate highly expressive and lifelike conversations between individuals, complete with essential social signals like laughter and excitement, solely based on audio cues. The foundation of our approach lies in the revolutionary audio-expression-consistency strategy, which effectively disentangles person-specific lip movements from dependent facial expressions. As a result, our ADLE robustly learns lip movements and generic expression parameters on a 3D human face from an audio sequence, which represents a powerful multimodal fusion approach capable of generating accurate lip movements paired with vivid facial expressions on a 3D face, all in real-time. Experiments validates that our ADLE outperforms other state-of-the-art works in this field, making it a highly promising approach for a wide range of applications.
Fire Safety in Smart Cities in Hungary With Regard to Urban Planning

Today, smart city principles are emerging as a highly relevant method in contemporary urban planning and development. In line with international trends, nations almost without exception have smart city strategies. The assessment system supporting smart development strategies for EU municipalities defines complex indicators in six thematic sub-systems: smart mobility, smart environment, smart people, smart living conditions, smart governance, smart economy. One monitoring aspect of the smart living subsystem is safety. The smart cities of the future will be made up of a network of smart buildings, which will fundamentally determine fire safety based on prevention. In this monitoring theme, the researchers will show how the future of fire safety - a cornerstone of safety- will evolve in smart buildings of smart cities. The researchers will describe the potential of building a smart fire safety network using data generated by building information modelling. They will show how BIM-based complex digital fire safety can be integrated into the fire safety network based on the new digital “one urban one plan” regulation in Hungary. The authors show that their method is suitable for the expansion of a smart cities database based on a digital urban plan.
An Adaptive-Guidance GAN for Accurate Face Reenactment

Face reenactment has been widely used in face editing, augmentation and animation. However, it is still challenging to generate photo-realistic target face with accurate pose or expression as reference face, meanwhile retain the identity as the source face. To achieve this goal, we propose an Adaptive-Guidance Generative Adversarial Network (AD-GAN) for accurate face reenactment. Unlike previous methods that control GANs by either directly employing a simple set of vectors or sparse representations (e.g., facial landmarks or boundaries), which ignore the correspondence between reference and source faces, thus leading to inaccurate reenactment or artifacts on target faces. We devise a Correlation Module (CM) that can adaptively establish dense correspondence between a 3D face model as the conditions and the latent features from sources to formulate an indicator map for implementing explicit control of target faces. Besides, the Texture Module (TM) and Guiding Blocks (GB) in generator can restore the facial appearance distorted by expression or pose changes, and progressively guide the generation process. Extensive experiments demonstrate the superiority of our AD-GAN in generating photo-realistic and accurately controllable images.
Reference-Based Line Drawing Colorization Through Diffusion Model

Line drawing colorization is an indispensable stage in the image painting process, however, traditional manual coloring requires a lot of time and energy from professional artists. With the development of deep learning techniques, attempts have been made to colorize line drawings by means of user prompts, text, etc, but these methods also seem to require some manual involvement. In this paper, we propose a reference-based colorization method for cartoon line drawings, which uses a more stable diffusion model to automatically colorize line drawings to improve the quality of the generated images. In addition, to further learn the color of the reference image and improve the quality of the colorized image, we also design a two-stage training strategy. To ensure the generality of the model, in addition to the 17,769 benchmark datasets shared on the Kaggle, we used the cartoon dataset provided by the competition in the fine-tuning stage and created a small garment dataset. Finally, we illustrate the effectiveness of the model in reference-based automatic coloring through a large number of qualitative and quantitative experiments.
Exploration of Artificial Intelligence (AI) in Banking Sector: A Bibliometric Analysis

Shagun Sharma, Research Scholar, Sharda School of Business Studies, Sharda University, Greater Noida, Uttar Pradesh, India
Research of Virtual Try-On Technology Based on Two-Dimensional Image

Virtual try-on based on two-dimensional image refers to trying on the target clothing on a human image to generate the final fitting image. In order to solve the problems of blurred human images, body parts missing and clothing cannot be correctly warped according to the posture of human images after fitting, this paper improves the Flow-Style-VTON network structure and proposes a virtual try-on model A-VITON. This paper adds residual blocks and CBAM attention mechanism to the U-Net network of the try-on module to improve the network’s feature extraction ability for target objects, making the generated try-on images more realistic. Secondly, this paper also proposes a layered virtual try-on method to provide consumers with more diverse fitting services. Finally, in order to reduce the interference of complex background on the try-on results, this paper proposes a virtual try-on method with background for the first time, which can generate high-quality try-on images while preserving the original image background. The try-on results on the VITON dataset show that the proposed method has great advantages in generating high-quality try-on images.
NeuralMAE: Data-Efficient Neural Architecture Predictor with Masked Autoencoder

Predictor-based Neural Architecture Search (NAS) offers a promising solution for enhancing the efficiency of traditional NAS methods. However, it is non-trivial to train the predictor with limited architecture evaluations for efficient NAS. While current approaches typically focus on better utilizing the labeled architectures, the valuable knowledge contained in unlabeled data remains unexplored. In this paper, we propose a self-supervised transformer-based model that effectively leverages unlabeled data to learn meaningful representations of neural architectures, reducing the reliance on labeled data to train a high-performance predictor. Specifically, the predictor is pre-trained with a masking strategy to reconstruct input features in both latent and raw data spaces. To further enhance its representative capability, we introduce a multi-head attention-masking mechanism that guides the model to attend to different representation subspaces from both explicit and implicit perspectives. Extensive experimental results on NAS-Bench-101, NAS-Bench-201 and NAS-Bench-301 demonstrate that our predictor requires less labeled data and achieves superior performance compared to existing predictors. Furthermore, when combined with search strategies, our predictor exhibits promising capability in discovering high-quality architectures.
CaSE-NeRF: Camera Settings Editing of Neural Radiance Fields

Neural Radiance Fields (NeRF) have shown excellent quality in three-dimensional (3D) reconstruction by synthesizing novel views from multi-view images. However, previous NeRF-based methods do not allow users to perform user-controlled camera setting editing in the scene. While existing works have proposed methods to modify the radiance field, these modifications are limited to camera settings within the training set. Hence, we present Camera Settings Editing of Neural Radiance Fields (CaSE-NeRF) to recover a radiance field from a set of views with different camera settings. In our approach, we allow users to perform controlled camera settings editing on the scene and synthesize the novel view images of the edited scene without re-training the network. The key to our method lies in modeling each camera parameter separately and rendering various 3D defocus effects based on thin lens imaging principles. By following the image processing of real cameras, we implicitly model it and learn gains that are continuous in the latent space and independent of the image. The control of color temperature and exposure is plug-and-play, and can be easily integrated into NeRF-based frameworks. As a result, our method allows for manual and free post-capture control of the viewpoint and camera settings of 3D scenes. Through our extensive experiments on two real-scene datasets, we have demonstrated the success of our approach in reconstructing a normal NeRF with consistent 3D geometry and appearance. Our related code and data is available at https://github.com/CPREgroup/CaSE-NeRF .