Reviewerly/exHarmony huggingface dataset:
Green Human Resources to Support Climate-Responsible Entrepreneurship in Digital Economy Markets Through the Integration of the Markets of Education and Labor

This chapter considers the features of the formation and influence of green human resources on the state of climate-responsible entrepreneurship, which are implemented through the interaction of education and the labor market in the sphere of the digital economy. The theoretical framework of the issue is outlined, its connection with the concepts of sustainable development, the digital economy, employment, and education for sustainable development is determined, and the mechanism of the influence of climate change on personnel training is presented. The methodology of this chapter includes the concepts of sustainable development, the digital economy, education, and employment, which evolve and supplement each other. The main provisions of the methodology are presented in the context of the green economy, which effect is aimed at the reduction of emissions and support for the sustainability of the natural environment, as well as the transformation of the spheres of employment and education for tackling climate change satisfying the strategic needs of economic subjects. The graphical method is used to present a generalized view of the problem. It is discovered that climate change influences the state and structure of employment. Also, the factor of climate change is considered a starting point for the formation of green human resources, of which three events arise: change in the conditions of doing business due to changes in usual natural conditions; government policy aimed at decarbonization; environmental behavior of individuals or organizations.
Digital Technologies in the Development of Hydrogen Energy

The climate agenda takes on enormous importance in the development of the economy. Hence, Russia intends to reduce greenhouse gas emissions to 70% by 2030 compared to 1990. Hydrogen energy is among the promising directions for the purposes of decarbonization programs and carbon-neutral transition. Digital technologies play a big part in the struggle with climate change at the present stage. Digitalization also contributes to the development of hydrogen energy, helping to design equipment and develop practical solutions for hydrogen technologies more accurately. However, introducing hydrogen energy based on Industry 4.0 carries certain risks. The first risk is engineering and manufacturing hazards because there is no necessary infrastructure. Second, there are financial and economic risks associated with the escalation in the costs of financing digitalization projects. The third group of risks includes organizational and administrative risks because the introduction of digital technologies demands the restructuring of the existing organizational structure of management, as well as planning and control processes. Simultaneously, despite all advantages of hydrogen energy, the prospects for the global market still appear unclear. In this regard, the analysis of digital technologies in the development of hydrogen energy is quite relevant.
Optimal convergence rates of deep neural networks in a classification setting

We establish optimal convergence rates up to a log factor for a class of deep neural networks in a classification setting under a restraint sometimes referred to as the Tsybakov noise condition. We construct classifiers based on empirical risk minimization in a general setting where the boundary of the Bayes rule can be approximated well by neural networks. Corresponding rates of convergence are proven with respect to the misclassification error using an additional condition that acts as a requirement for the “correct noise exponent”. It is then shown that these rates are optimal in the minimax sense. For other estimation procedures, similar convergence rates have been established. Our first main contribution is to prove that the rates are optimal under the additional condition. Secondly, our main theorem establishes almost optimal rates in a generalized setting. We use this to show optimal rates which circumvent the curse of dimensionality.
THERMOBAROGEOCHEMICAL FUNDAMENTALS AND APPLIED POSSIBILITIES OF LOCAL FORECAST, RESEARCH AND ASSESSMENT OF POST-MAGMATIC MINERALIZATION

Important issues of local forecasting, search and assessment of post-magmatic mineralization during various stages of geological exploration are analysed on a thermobarogeochemical (TBGC) basis. The theoretical basis of TBGC-forecasting, exploration and assessment of mineralization is the stability of the regime of physical-chemical conditions for the formation of productive mineral paragenesis. They are formed in a fairly narrow range of TBGC-parameters changes of the specific to chemical composition and aggregate-density state of ore-forming medium at certain (optimal) temperature and pressure values. This is revealed in the phase typomorphism of the respective families of fluid inclusions practically regardless of geotectonic conditions and metallogenic specialization of the ore regions. For example, the molybdenum-tungsten formation of the greisen type is characterized by the following: alkaline-halogen (fluoride-chloride-potassium-sodium) composition of inclusions with a high salt concentration (65-35 wt.% NaCl), and the spread of syngenetic families of inclusions with minerals-"prisoners" (which testify to the boiling processes of solutions) and solutions of inclusions of critical density. On the other hand, in gold ore formations, the productive gold-bearing stages in the corresponding paragenesises contain water-carbon dioxide inclusions, which homogenize at 290-180°C under conditions of intensive heterogenization and degassing (СО2) of solutions with extremely different phase ratios (G-LСО2-Н2О; LСО2-LН2О; G-LСО2; LСО2-L; LСО2-GСО2). Fluid inclusions of two- and one-phase CO2 with wide variations in density (from 1.02 to 0.4-0.1 g/cm3 for various depth formations) and simultaneous homogenization into liquid and gas phases (boiling) are common. So, we determine the distribution of molybdenum-tungsten or gold mineralization by the distribution of the corresponding families of syngenetic inclusions. An equally important prerequisite for the implementation of applied TBGC problems is the possibility of diagnosis and spatial extrapolation of these parameters (TBGC-zonation) with the determination of the spatial position of zones that are physically and chemically favourable for the development of post-magmatic mineralization. Such tools can be the vertical paleotemperature gradient ∆Т/100 m and its inverse functional relationship with the vertical spread of mineralization, the ratio of the amount of reduced gases to the amount of oxidized gases, etc. Specific examples of mineralization forecasting, search, and assessment using the genetic features of the deposit and the complex application of TBGC-criteria for spatial-temporal modelling of mineralization distribution are given.
Information and analytical resources in the systems of technological and vocational education

A series of quarantines caused by the COVID-19 pandemic has led to the massive introduction of distance learning technologies into the vocational education system. The subsequent decline in the level of effective organization of training of future specialists, including ensuring the quality of knowledge transfer to them and creating comfortable conditions for their acquisition of professional competencies, provoked a pedagogical search for appropriate methods and technologies that would allow satisfying the educational needs of the subjects of the educational process. The article raises the issues of improving the quality of vocational education, in the basis of which it is proposed to take into account the components of a continuous technological process of forming the competence of future specialists and skilled workers. In this vein, technological education is an important link in the formation of the vocational education system, especially in the vector of its modern transformations associated with online learning. The authors focus on the positive and negative aspects of the use of digital technologies, which, on the one hand, ensure the possibility of implementing the educational process of educational institutions during quarantine restrictions, and on the other hand, given that the activity-based basis of training a profession is a priority competence component. At the same time, the use of information and communication technologies can significantly improve the quality of the educational process. Despite the impossibility of full-fledged remote training in various professional and technological operations, the use of augmented reality technologies and virtual laboratories for remote training allows to train various types of activities and combine them in different combinations. It also helps to assimilate technological operations and develop one’s abilities, create communicative situations, automate practical actions, as well as provide an opportunity to influence the leading representative system, implement an individual approach and intensify students’ independent work. The article analyzes a number of electronic educational resources, the use of which can provide significant assistance in the preparation and conduct of classes for teachers of vocational institutions and become a significant help for improving the quality of the educational process.
Information and library support for education in the context of its digitalization

In the process of innovative development of education, an important factor in its modernization is information and library support, which we consider as a purposeful activity to create digital information resources and provide access to them based on the widespread use of the latest ICTs to meet the information needs of users – applicants for education, pedagogical, scientific, scientific and pedagogical workers. The article establishes that in the information and library support of education in Ukraine, the leading role belongs to the network of educational libraries (over 15 thousand libraries), headed by the V. Sukhomlynskyi State Scientific Educational Library of Ukraine of National Academy of Educational Sciences of Ukraine (hereinafter – SSPL). Scientific achievements in the creation of an industry information resource are argued; development and implementation of a system of scientific and information support for education and science in Ukraine; scientific, methodological and informational support for the activities of educational libraries for their adaptation to modern requirements, etc. The practical results of information and library support for education are: provision of various information services to remote users, access to domestic and foreign resources through the SSPL web portal and the websites of the network libraries; creation of 26 electronic libraries in leading educational libraries; development of a system of scientific, informational, cultural and educational events in an online format. The most popular digital resources and services of the SSPL are: databases of an electronic catalogue, digital content of a collection of rare publications, a scientific and pedagogical electronic library, a virtual information and bibliographic resource “Outstanding Pedagogues of Ukraine and the World”, online projects, virtual reading rooms, and the like. To improve the system of information and library support for the innovative development of education, it is necessary to focus on solving the following tasks: the introduction of modern information and computer technologies (hereinafter – ICT) into production processes and service of users, primarily remote users; development of information potential of web portals and sites of educational libraries; improvement of integration processes in the formation of an industry information resource; development of international cooperation.
An Integrated Study of Cybersecurity Investments and Cyber Insurance Purchases

The growing concern over cyber risk has become a pivotal issue in the business world. Firms can mitigate this risk through two primary strategies: investing in cybersecurity practices and purchasing cyber insurance. Cybersecurity investments reduce the compromise probability, while cyber insurance transfers potential losses to insurers. This study employs a network model for the spread of infection among interconnected firms, where each firm’s decisions impact one another. We investigate a noncooperative game in which each firm aims to optimize its own objective function via choices of cybersecurity level and insurance coverage ratio. We define and detail the pure-strategy Nash equilibrium in this context, and derive sufficient conditions to ensure its existence and uniqueness. We also find that cybersecurity investment and insurance purchase are strategic complements. These theoretical results provide the foundation for our numerical studies. We compute the equilibrium decisions on cybersecurity investments and insurance purchases across various network structures. The numerical results illustrate the impact of network structure on equilibrium decisions and how varying insurance premiums influence firms’ cybersecurity investments.
Dynamic Debiasing Network for Visual Commomsense Genenration

The task of Visual Commonsense Generation (VCG) delves into the deeper narratives behind a static image. It seeks to comprehend not just its immediate content, but also the surrounding context. Accordingly, it produces three types of captions for each image: (1) the event that preceded, (2) the character’s current intent, and (3) the anticipated subsequent event. However, a prevailing challenge in VCG research is the under-addressed issue of dataset bias. Such biases result in spurious correlations during model training, causing the model to reflect co-occurrences present in the dataset rather than accurate, contextually appropriate interpretations. This concern is magnified in multimodal tasks, where different data modalities, such as text and images, can each introduce unique biases. Addressing this, we introduce the Dynamic Debiasing Network (DDNet) for Visual Commonsense Generation. DDNet identifies and counteracts modality-specific biases dynamically for each data sample. Our method is twofold: first, we detect the predominant modality-specific bias, and then we dynamically mitigate the detected bias. Our key contributions include identifying and analyzing biases in the VCG dataset, proposing a tailored debiasing framework for multimodal tasks, and introducing DDNet, a novel model-agnostic solution that marks significant progress toward achieving unbiased commonsense inferences. Experimental results show that our proposed network paves the way for more impartial commonsense inferences, emphasizing the crucial need for debiasing in multimodal tasks and bolstering the trustworthiness of machine-generated commonsense narratives.
High-Quality Face Caricature via Style Translation

Caricature is an exaggerated form of artistic portraiture that accentuates unique yet subtle characteristics of human faces. Recently, advancements in deep end-to-end techniques have yielded encouraging outcomes in capturing both style and elevated exaggerations in creating face caricatures. The majority of these approaches tend to produce cartoon-like results that are impractical for real-world applications. In this study, we proposed a high-quality, unpaired face caricature method that is appropriate for use in the real world and uses computer vision techniques and GAN models. We attain the exaggeration of facial features and the stylization of appearance through a two-step process: Face caricature generation and face caricature projection. The face caricature generation step creates new caricature face datasets from real images and trains a generative model using the real and newly created caricature datasets. The Face caricature projection employs an encoder, which is trained with real and caricature faces with the pretrained generator to project real and caricature faces. Using the encoder and generator’s latent space, we perform an iterative facial exaggeration from the real image to the caricature faces. Our projection preserves the facial identity, attributes, and expressions from the input image. Also, it accounts for facial occlusions, such as reading glasses or sunglasses, to enhance the robustness of our model. Furthermore, we conducted a comprehensive comparison of our approach with various state-of-the-art face caricature methods, which highlighted the distinctiveness and exceptional realism of our process.
Model-driven Dependability and Power Consumption Quantification of Kubernetes based Cloud-Fog Continuum

System dependability is pivotal for the reliable execution of designated computing functions. With the emergence of cloud-fog computing and microservices architectures, new challenges and opportunities arise in evaluating system dependability. Enhancing dependability in microservices often involves component replication, potentially increasing energy costs. Thus, discerning optimal redundancy strategies and understanding their energy implications is crucial for both cost efficiency and ecological sustainability. This paper presents a model-driven approach to evaluate the dependability and energy consumption of cloud-fog systems, utilizing Kubernetes, a container application orchestration platform. The developed model considers various determinants affecting system dependability, including hardware and software reliability, resource accessibility, and support personnel availability. Empirical studies validate the model’s effectiveness, demonstrating a 22.33% increase in system availability with only a 1.33% rise in energy consumption. Moreover, this methodology provides a structured framework for understanding cloud-fog system dependability, serves as a reference for comparing dependability across different systems, and aids in resource allocation optimization. This research significantly contributes to the efforts to enhance cloud-fog system dependability.
Junior Software Engineers’ International Communication and Collaboration Competences

Present-day Software Engineering working environment is highly international. Teams are commonly formed of people from different nationalities and cultural backgrounds. The teams’ productivity and efficiency depend significantly on its members’ international communication and collaboration competences. Therefore, understanding the required competences is essential. The software organizations hiring new software engineers need to be able to define and describe the competences, and the junior software engineers applying their first jobs should know what competences they need to possess and demonstrate. In this study, to increase understanding of necessary communication and collaboration competences in the international Software Engineering working environment, competences were, first, analyzed from the job ads applicable to junior software engineers and, second, identified by a literature review. The results were compared to identify what competences junior software engineers should learn and demonstrate, to be competent in the international software engineer job markets. The job ad findings show that the international operational environment expects extensive competence in collaboration, high competence in English, and considerable competence in local language and communication. Intercultural competence and other languages are hardly expected. The literature review emphasizes inter-related communication, collaboration, intercultural, and language competences at different levels. Eventually, junior software engineers should demonstrate a tolerant and adaptable attitude, cooperativeness, independence, openness, courage to influence, oral and written social interaction skills, fluent professional English and local language communication skills as well as field-specific and general collaboration methods. The findings benefit juniors, Software Engineering education, and thus employers, by providing the international working environment with more competent jobseekers.
Improving Domain Generalization in Appearance-based Gaze Estimation with Consistency Regularization

Gaze estimation, a method for understanding human behavior by analyzing where a person is looking, has significant applications in various fields including advertising, driving assistance, medical diagnostics, and human-computer interaction. Although appearance-based methods have shown promising performance in uncontrolled environments, they often perform poorly when applied to similar but different domains due to variances in image quality, gaze distribution, and illumination. To overcome this limitation, this study aims to improve the domain generalization of appearance-based gaze estimation models using deep learning techniques. We propose an end-to-end deep learning approach that facilitates domain-agnostic feature learning and introduce a novel loss function, spherical gaze distance (SGD), and a regularization method, gaze consistency regularization (GCR). Our experiments, conducted using three commonly used datasets for appearance-based gaze estimation: ETH-XGaze, MPIIGaze, and GazeCapture, demonstrate the effectiveness of SGD and GCR. The results show that the proposed approach outperforms all the state-of-the-art methods on the domain generalization task and significantly improves performance when SGD and GCR are combined. These findings have important implications for the field of gaze estimation, suggesting that the proposed method could enhance the robustness and generalizability of gaze estimation models.
LongT5-Mulla: LongT5 with Multi-level Local Attention for a Longer Sequence

Transformer models with full attention cannot efficiently handle long texts due to their quadratic complexity in relation to the input length. To tackle this problem, current efficient Transformer models typically employ local and global attention methods, or utilize hierarchical or recurrent architectures, to process long text inputs in natural language processing tasks. However, these models face challenges in terms of sacrificing either efficiency, accuracy, or compatibility to develop their application in longer sequences. To maintain both the accuracy of global attention and the efficiency of local attention while keeping a good compatibility to be easily applied to an existing pre-trained model, in this paper, we propose LongT5-Mulla sequence-to-sequence model with our newly designed sparse attention method called multi-level local attention (Mulla attention), which is a hierarchical local attention that acts on both the input sequence and multiple pooling sequences of different granularity simultaneously, thus capturing long-range information while maintaining linear or log-linear complexity. We apply Mulla attention to LongT5 by replacing the original attention module in Encoders with Mulla attention to implement our LongT5-Mulla model, without introducing new parameters except positional embeddings. Experiments show that our model can surpass all baseline models including two original variants of LongT5 in the 8~16k-input long text summarization task on the Multi-News, arXiv and WCEP-10 datasets, with improvements of at least +0.22, +0.01, +0.52 percentage point (pp) averaged Rouge scores respectively, while at the meantime being able to effectively process longer sequences that have 16~48k tokens with at least 52.6% lower memory consumption than LongT5-tglobal, and +0.56~1.62 pp averaged Rouge scores higher than LongT5-local. These results demonstrate that our proposed LongT5-Mulla model can effectively process long sequence and extend the maximum input length for long text tasks from 16k to 48k while maintaining accuracy and efficiency.