Reviewerly/exHarmony huggingface dataset:
Gray Level Homogeneity Analysis: A Novel Approach

In this article, we propose a method that helps us to analyze the homogeneity of gray levels locally by calculating a coefficient for each pixel based on the nature of neighboring pixels. This principle of encoding pixels according to their adjacent neighbors is described the nature of the distribution of gray levels within the image and measures their degree of homogeneity locally. This allows us to detect the different regions of the image and their contours based on the coefficient of homogeneity of the gray levels. In addition, this allows us to exploit these homogeneity coefficients to restructure regions of the image, extract and enhance the image contours while reducing the noise present in the image. This homogeneity study principle has several functions in the study and analysis of image texture, as do other methods of homogeneity assessment, such as the local contrast descriptor (LCD) and the co-occurrence matrix.
Artificial intelligence and customers’ intention to use robo-advisory in banking services

Research background: Robo-advisory is a modern and rapidly developing area of implementing artificial intelligence to support customer decision-making. The current significance of robo-advisory to the financial sector is minor or marginal, and boils down to formulating recommendations and implementing investment strategies. However, the ongoing digital transformation of the economy leads us to believe that in the near future this technology will also be much more widely used with banking products. This makes it necessary for banks and other financial institutions to be prepared to offer this service to their customers. Purpose of the article: The aim of this paper is to identify factors significantly influencing bank customers’ intention to use robo-advisory. Identification of robo-advisory acceptance factors may increase the effectiveness of banks' promotional activities regarding such a service. Methods: Empirical data was obtained through a survey conducted on a representative sample of 911 Polish respondents aged 18–65. Using a multilevel ordered logit model and methods based on machine learning algorithms, the authors identified variables relating to the demographic and socio-economic characteristics, behaviors, and attitudes of consumers that primarily determine respondents’ adoption of robo-advisory. Findings &amp; value added: The results of the study indicate that the variables regarding the respondents' attitude towards the use of artificial intelligence in banking services turned out to be the most important from the point of view of acceptance of robo-advisory. Next in terms of importance were the variables presenting respondents' assessments of the ethics of financial services. An important finding is that experience in using basic financial services is not a significant factor when accepting robo-advisory. From the practical perspective, the article provides recommendations on the use of artificial intelligence technology in finance and ethical aspects of the provision of such services by banks.
Copyright Protection for Works Created by AI Technology under the EU Law and Vietnamese Law

The legislation of the European Union increasingly focuses on expanding the scope of works protected by intellectual property rights, including literary works, music, films, and phonograms. The breakthrough in artificial intelligence (AI) has contributed significantly to creating works of art with little or no human intervention. The article examines the current situation of EU copyright law and Vietnamese law regarding AI-generated works. The article concludes that EU law governs copyright for these works based on the extent of human contribution to the creation of the work. Meanwhile, Vietnamese law still needs to resolve the issue of intellectual property rights for works created by AI.
Noise Estimation and Removal in Natural Language Processing

In the field of natural language processing (NLP), noise estimation and removal play a critical role in refining and purifying textual data. Noise, which has the potential to obscure the true meaning of text, poses challenges for NLP algorithms. The procedure of NLP pipeline begins by identifying various types of noise in NLP applications. Tokenization, a fundamental step in preprocessing, involves breaking down text into tokens. Additionally, text preprocessing encompasses techniques such as part-of-speech tagging, parsing, stemming, stop word removal, and lemmatization. Modern NLP models necessitate text data to be presented in a vector format, a transformation achieved through feature extraction. Artificial intelligence (AI) driven NLP models harness the power of AI to autonomously remove noise within text, making them indispensable for enhancing quality of text in NLP applications.
The Genesis and Spatial Distribution of Ore Mineral Species of the Tschudi Copper Deposit, Namibia; Insights from Sequential Essay Modeling and Petrographic Studies

Tschudi copper mine is situated within a platform sequence of the Neoproterozoic Damara Belt in northern Namibia. The Tschudi orebody is stratiform, dipping ~ 35° north, with a strike length of ~ 2.5 km, extending below 300 m. The deposit is hydrometallurgically complicated due to the presence of a variety of sulfide and oxide copper ores. The sulfide ores include bornite, chalcocite, covellite, and chalcopyrite, while the oxide ores consist of malachite, azurite, and cuprite. The copper is therefore extracted from the ores through selective leaching. Thus, understanding the spatial distribution of different mineral species and genesis is crucial for the economic exploitation of the Tschudi deposit. For this study, sequential assay data obtained from the mine were grouped in different oxidation percentages; < 5% is chalcopyrite, 5 to < 10% is bornite-covellite, 10 to < 50% is chalcocite, and > 50% are oxides. Individual mineral wireframes were created using SURPAC software and polished section petrographic studies, which enabled to model the distribution of ore minerals. The models were coupled with geological observations to discuss the genesis of the deposit. The mineralization is disseminated through sandstone and the intermittent basal conglomerate unit of the Tschudi Formation. The Tschudi Formation lies unconformably on the dolomitic carbonate sediments of the Huttenberg Formation. The model shows that the more acid-soluble copper oxides and chalcocite are proximal to the dolomite footwall. The less acid-soluble bornite, covellite, and chalcopyrite are closer to the hanging wall. The study shows that the Tschudi deposit is a sediment-hosted copper deposit that has undergone in situ supergene copper sulfide oxidation with limited copper supergene enrichment.
LINGUOCOGNITIVE APPROACH TO EXTRACTING TERMS FROM A CORPUS OF VETERINARY TEXTS

This research delves into the intricate landscape of computational linguistics with a focused exploration of term identification challenges within the domain of veterinary medicine. A comprehensive analysis was conducted, balancing the difficulties associated with the automated extraction of single-word terms and the structured patterns observed in two-word terms within veterinary dictionaries and scientific literature. The study commenced with a meticulous manual identification of 462 single-word terms, emphasizing the inherent challenges in automating the extraction of terms characterized by linguistic diversity and potential ambiguity. Simultaneously, the investigation of two-word terms unveiled structured patterns, particularly in dictionaries, offering contrasting simplicity for identification through conventional frequency-based methods. The chosen text type, a veterinary dictionary, revealed its own intricacies with a standardized template governing entry construction. The revelation that only 59% of terms find placement in the title section underscored the need for adaptive extraction methods attuned to the varied distribution of terms within dictionary structures. Scientific texts further complicated the term identification landscape by showcasing varying term frequencies, prompting a critical evaluation of standard lexeme selection methods. Building on these insights, the research proposes strategies for refining automated term identification processes. This includes leveraging advanced natural language processing techniques for single-word terms and advocating for adaptive extraction methods for dictionaries, while also proposing a hybrid approach for scientific texts. The interdisciplinary nature of the research is underscored by the recognition of collaboration between linguists, computational scientists, and domain experts as crucial for developing sophisticated models and ontologies that accurately capture the unique linguistic nuances of veterinary medicine. As the digital landscape evolves, this research not only contributes to the advancement of computational linguistic methodologies but also envisions the creation of terminological resources reflecting the dynamic nature of language within the veterinary domain. Through a comprehensive exploration of challenges and opportunities, this research aspires to pave the way for more accurate and adaptable automated systems, offering implications for the broader field of computational linguistics.
Navigating the digital age: Exploring effective teaching and learning approaches for university students

The paper explores the effectiveness of traditional versus digital teaching and learning methods as perceived by university students. As digital technologies are fundamentally changing the ways of teaching, learning and teacher-student interaction, the impact of digitalization on students’ learning styles remains underresearched. The aim of the study is to determine to what extent teaching and learning methods need to be adapted to reflect the current needs of students in the digital era. An online questionnaire about evaluation of teaching and learning methods was used to collect data amongst students of Faculty of Electrical Engineering and Information Technology of the Slovak University of Technology in Bratislava. The conducted quantitative survey research and subsequent comparative analysis showed students’ preference for high hybridization of teaching and learning methods, i.e., blending of traditional and digital methods. In particular, the students require input that is primarily visual. The research also shows that the role of the teacher’s explanation continues to play a significant role in higher education.
Current stage of the particular segment of augumented reality mobile applications

Immersive AR technology is recently causing revolution in content consumption. Therefore, it is necessary for AR developers who represent the offer of AR technology, companies who represent media space demand in AR content and individuals who represent final consumers to know current evolution stage and possibilities of AR content. The article focuses on AR technology in particular segment of gaming. The main aim of the article is to identify and define the current stage of gaming AR applications. The study identified and examined the most popular gaming AR apps available in iOS store. Information such as initial fee for app download, charges while using the app, amount of ratings, ratings, age, category, number of languages, primary language and data requirements was examined. The research unveiled that the current stage of gaming AR apps is pro-user, leaves payments to the choice of the user, allows downloads to a sufficiently broad target group, and requires acceptable data space on the device into which it is downloaded. Further improvements may focus on data size, which can potentially increase the number of downloads.
Social activism of brands in the age of artificial intelligence (AI)

Individual perception is decisive in relation to the response to socio-political issues because perception influences attitudes and behavior, it leads to decision-making, affects its quality, and at the same time constructs our perception of reality as such. Two phenomena – the social activism of brands and artificial intelligence – have also become the subject of interest in numerous scientific studies in academic research for this reason. However, their mutual influence and effects on the consumer are still considered underresearched. This paper presents the essence of brand activism as a marketing strategy with the potential to influence consumer perceptions, attitudes and subsequent behavior. Using the examples from a study, it points out that perception and attitudes are also influenced by AI tools and social media, which we conceptually define as the intervening factors with an effect of the consequences of activism. Using specific examples, the paper points to the contemporary use of AI tools in the communication of social activism of brands and to the factors affecting the consequences of brand activism.
Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data

Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation.
Human-machine Translation Model Evaluation Based on Artificial Intelligence Translation

As artificial intelligence (AI) translation technology advances, big data, cloud computing, and emerging technologies have enhanced the progress of the data industry over the past several decades. Human-machine translation becomes a new interactive mode between humans and machines and plays an essential role in transmitting information. Nevertheless, several translation models have their drawbacks and limitations, such as error rates and inaccuracy, and they are not able to adapt to the various demands of different groups. Taking the AI-based translation model as the research object, this study conducted an analysis of attention mechanisms and relevant technical means, examined the setbacks of conventional translation models, and proposed an AI-based translation model that produced a clear and high quality translation and presented a reference to further perfect AI-based translation models. The values of the manual and automated evaluation have demonstrated that the human-machine translation model improved the mismatchings between texts and contexts and enhanced the accurate and efficient intelligent recognition and expressions. It is set to a score of 1-10 for evaluation comparison with 30 language users as participants, and the achieved 6 points or above is considered effective. The research results suggested that the language fluency score rose from 4.9667 for conventional Statistical Machine Translation to 6.6333 for the AI-based translation model. As a result, the human-machine translation model improved the efficiency, speed, precision, and accuracy of language input to a certain degree, strengthened the correlation between semantic characteristics and intelligent recognition, and pushed the advancement of intelligent recognition. It can provide accurate and high-quality translation for language users and achieve an understanding of natural language input and output and automatic processing.
PROBLEMS AND MECHANISMS OF FORMING THE VALUES OF CIVIL &#x0D; CONSCIOUSNESS AND PATRIOTISM IN YOUTH IN THE KHABAROVSK REGION

The mechanism of state policy for the formation of values of citizenship and patriotism among young people in the Khabarovsk Territory is presented: goals, objectives, subjects, and means of implementation. It has been established that in the Khabarovsk Territory the number of patriotic associations, the number of participants in patriotic associations, and the number of events aimed at the patriotic education of youth are increasing. Problems have been identified, the solution of which will improve the effectiveness of patriotic education of youth in the Khabarovsk Territory.
ON THE PROBLEMS OF WRITING TERM PAPERS AT THE UNIVERSITY: STUDENTS’ PERSPECTIVE

The article presents reflections on the current (including not openly discussed) problems that students face when writing term papers at the university. It describes the results of a sociological survey of students on the issues under discussion.
Information and Communication Technologies (ICTs) For E-Learning in Tertiary Education in Sri Lanka: A Study of Undergraduates from State Universities in Colombo District

E- learning was introduced to tertiary education during the Covid 19 Pandemic. Therefore, the study aimed to investigate the opportunities and challenges faced by undergraduates in state universities in Colombo District in relation to e-learning. Technology Acceptance Model (TAM) was used as the main theoretical framework and the qualitative approach was applied through interviews and Focus Group Discussions (FGDs). Data were collected from all state universities in Colombo district using purposive sampling. The results showed that the majority of undergraduates have a positive attitude towards e- learning. Because e-learning is timesaving, convenient and cost-saving during the pandemic and socio-economic issues in the country such as fuel issues, power cuts etc. On the other hand, the undergraduates faced some challenges in e- learning such as a lack of ICT tools and equipment, connectivity issues, the cost of data, background noises, a lack of interaction with peers and teachers, health issues such as eye issues, anxiety etc. The results showed that the challenges of e-learning mainly occur due to the issues connected to ICTs. Therefore, the responsible authorities should take proper measures to provide adequate ICT infrastructure and resources for students to have a convenient and flexible e- learning system. In this context, it is also important that the state universities make the arrangements to have flexible learning for undergraduates. Therefore, hybrid learning allows undergraduates to attend traditional face to face sessions while also attending online classes, reducing transport issues, expenses, optimizing ICT infrastructure and resource utilization.
TEACHER’S DIGITAL COMPETENCE IN THE EUROPEAN EDUCATIONAL DISCOURSE

This article highlights the key principles of forming a teacher’s digital competence. The purpose of the article is to highlight the peculiarities of forming a teacher's digital competence, taking into account European approaches to this process. During the problem formulation process, a combination of interdisciplinary methods is employed: theoretical methods such as analysis and synthesis to identify the main directions of research into the digital transformation of teacher education; empirical methods, including the study of scientific works and legislative framework related to the digitization of teacher education, and so on. The documents that cover the issues of forming digital competence in students, in particular future teachers, are considered. It has been found that in the European context, teacher training in digital skills should be based on the principles of digital citizenship. This means a critical approach to information that allows educators to navigate the digital world and develop an understanding of the core values of democracy. Studies have shown that the development of teachers' digital skills can be enhanced by gaining the ability to use digital technologies to improve the quality of teaching, educational development, and personal and continuous professional development. It has been established that modern teachers can use digital technologies to effectively managе and organize the educational process, which significantly expands the capabilities of students, taking into account their abilities, needs and interests. Therefore, mastery of digital technologies will ensure an effective, inclusive and innovative learning environment for students.
Designing Interventions for Sustainability: A Conceptual Framework for Information Scoping in the Design Research Phase

Acknowledging the inherent complexity of many sustainability challenges, and the shortcomings of traditional linear approaches in tackling them, we have seen a rise in design-driven approaches, which are deemed better suited in handling socio-cultural, economic and environmental implications. Existing literature shows that products and services guided by EcoDesign concepts have the potential to promote users’ behaviour towards a sustainable direction. However, due to the primary focus on the material aspect of design (e.g. environmental impact, reusability and recyclability), there is a lack of guidance for incorporating the behaviour perspective of users into the design research phase. This research aims to develop a framework to help designers determine factors that can possibly impact the behaviour of the target user. The study draws on theoretical insights obtained from a non-exhaustive literature review (observed to be driven by Global North perspectives) and a meta-synthesis of five case studies, primarily situated in the Global South. The findings showed that to influence users’ behaviour through design interventions, the designer would need to account for various determinants. We summarized these determinants into three lenses—context, culture and intent and then developed a conceptual framework based on these lenses. We believe this framework can guide designers in selecting relevant user behavioural determinants in their design research phase, thus supporting them in identifying what factors should be considered for introducing design interventions to promote sustainable behaviour.
AI in public relations: Challenges and opportunities

While the adoption of AI in marketing and advertisement is being discussed in different ways, the area of PR is still uncovered, or rather the question of using AI is not as obvious. It seems to be narrowed particularly to the media monitoring and summarization of data and to the question of topic creation and to what extent text-based AI systems are capable of writing a press release or official statement. However, it should be pointed out that even to write a press release, or any output for that matter, AI needs to be provided with the right prompts, and here the position of the PR person is indispensable. Only specialist in PR can provide the AI system with all the necessary data, figures and sensitivity levels to be present in the final output. So it may be clear that AI is an exceptional asset in marketing and communications, there may be areas that are still under the question of what AI can or cannot provide.