Reviewerly/exHarmony huggingface dataset:
Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM

Summary Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM (DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM) is proposed in this manuscript. In OFDM, there is a non‐stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA‐COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO‐OFDM system using deep neural network and MN‐based improved AMO model (DNN‐IAMO‐MIMO‐OFDM), MIMO‐OFDM systems using the deep learning and optimization (RBFNN‐PSO‐MIMO‐OFDM), and MIMO‐OFDM systems using hybrid neural network (HNN‐CSI‐MIMO‐OFDM) respectively.
Federated two-stage decoupling with adaptive personalization layers

Abstract Federated learning has gained significant attention due to its groundbreaking ability to enable distributed learning while maintaining privacy constraints. However, as a consequence of data heterogeneity among decentralized devices, it inherently experiences significant learning degradation and slow convergence speed. Therefore, it is natural to employ the concept of clustering homogeneous clients into the same group, allowing only the model weights within each group to be aggregated. While most existing clustered federated learning methods employ either model gradients or inference outputs as metrics for client partitioning to group similar devices together, heterogeneity may still exist within each cluster. Moreover, there is a scarcity of research exploring the underlying reasons for determining the appropriate timing for clustering, resulting in the common practice of assigning each client to its own individual cluster, particularly in the context of highly non-independent and identically distributed (Non-IID) data. In this paper, we introduce a two-stage decoupling federated learning algorithm with adaptive personalization layers named FedTSDP, where client clustering is performed twice according to inference outputs and model weights, respectively. Hopkins amended sampling is adopted to determine the appropriate timing for clustering and the sampling weight of public unlabeled data. In addition, a simple yet effective approach is developed to adaptively adjust the personalization layers based on varying degrees of data skew. Experimental results show that our proposed method has reliable performance on both IID and non-IID scenarios.
The role of innovative educational technologies in the training of specialists in the field of culture and arts: European experience

The purpose of the article is to determine the role of innovative educational technologies in the training of specialists in the field of culture and arts based on the study of the European experience. To achieve this goal, the methods of analysis, synthesis, deduction, induction, specification, and comparison were used. The results consider the theoretical foundations of the use of innovative technologies in modern education. In the realm of culture and arts, it holds a pivotal role in training specialists. By drawing insights from the European experience, it becomes evident that cutting-edge learning approaches are founded on embracing novel ideas and a continual reevaluation of values. Also, it is crucial to consider its broad accessibility and ongoing nature. It is shown that education in European art universities and academies actively employs collective, modular, integrative, and developmental methods. In the modern practice of art education, the application of new and effective methods is of utmost importance to foster the creative and personal growth of aspiring professionals in the cultural and artistic fields. Among these methods, particular attention is given to students' project activities, which synergize successfully with digital technologies and distance learning. The novelty of the work is the author's own structure of innovative education. The conclusions determine that the main function of modern education is human development, so students in modern circumstances should focus on the possibilities of self-learning and the use of digital technologies (current trends in the field of culture and arts).
Methodological approaches as a scientific basis for creating a pedagogical concept of distance learning organization in higher education institutions

The article examines e-learning as a special form of education and a pedagogical system based on the use of digital technologies. The content of the structural components of e-learning at universities is a pedagogical system that includes goals, means of communication, content of educational information, and subjects of the educational process. with guaranteed achievement of results as a system-forming factor. The following features of the means of communication between the subjects of the educational process are taken into account: students, teachers, tutors, specialist experts, resource developers (designers of pedagogical situations) and their functions. Scientific, methodological and technological support for achieving the desired result of e-learning at universities has been built, taking into account the individual needs of students, expanding the functions of teachers and implementing educational information through digitalization. A diagnostic toolkit has been developed for conducting a predictive pedagogical examination to develop, clarify and adjust students’ individual educational trajectories, taking into account expert opinions.
The paradigm of pedagogical innovation as a factor in the development of high-quality education

The article examined the logistics of pedagogical innovations, representing a mechanism for managing an educational holding that is relevant in the conditions of a postindustrial society in the totality of the conceptual provisions and empirical data that substantiate it. A theoretical justification for the cluster approach in education is given. The concepts of “educational clusters” and levels of clustering have been clarified and are meaningfully determined by educational competencies, educational programs, educational organization and territorial educational clusters. Pedagogical innovations have been substantiated as an essential characteristic of educational clusters, and their typology has been developed based on the cluster approach in education.
Art education as a means of forming cultural identity and civic consciousness

The purpose of the article is to study art education as a means of forming cultural identity and civic consciousness. To achieve this goal, theoretical methods of cognition were used, in particular, the most important methods of analysis, synthesis, and content analysis. The results show that modern technologies are widely used in the field of higher art education. This includes not only technical aspects, but also theoretical and pedagogical approaches to teaching. In modern methods of higher education, the emphasis is on the development of students' personalities, broadening their outlook, skills and abilities. Traditional models of learning that existed before the introduction of distance learning are undergoing significant changes, leading to experimental approaches to teaching in art universities. Particular emphasis is placed on the democratisation of educational processes, digitalisation of art education and the use of computer technology. The desire to ensure unity in diversity, which is a necessary component of modern European integration aimed at overcoming the negative effects of globalisation, is also important. One of the key components of the development of modern higher art education is the use of digital technologies. This allows students to create a significant amount of creative work while studying and include it in their professional portfolio. This approach is particularly relevant in the European labour market. Ukraine's experience in applying modern teaching methods in the field of music is in line with European development trends. At the same time, due to Russian military aggression and obsessive propaganda, there is a need to update research on Ukraine-centric music topics. It is important to emphasise the development of Ukrainian art, the study of the life and legacy of prominent teachers, performers and artists, and the integration of the acquired knowledge into the educational process. The conclusions emphasise that the combination of Ukrainian specificity and modern digitalisation trends creates objective conditions for the development of musicology in Ukraine and its presentation at the international level.
Ukrainian education for peace and security 2023: Technological convergence, artificial intelligence

The article highlights the impact of the integration of artificial intelligence (AI) into the educational environment, focusing on the possibilities of technological convergence. Based on the recognition of the changing world and the need to adapt educational approaches to the challenges of today, the study offers a deeper understanding of the role of innovative technologies. The study reveals the potential of AI to create personalised, adaptive learning paths for students of different educational and qualification levels. The use of AI algorithms to customise the learning process to individual needs promotes critical thinking and collaboration, which are important for building a safe social environment. The study highlights the possibility of using AI to reduce social inequality in areas affected by military aggression. The introduction of AI in the educational process can ensure equal access to quality education for all regions. The authors of the article emphasise the need for a multidisciplinary approach to the use of AI in education, where the interaction of educators and civil society is crucial for the implementation of technologies with ethics and efficiency. An innovative approach to education supported by AI can foster cooperation, understanding, and cohesion, contributing to a stable and harmonious society.
Cross-KG Link Prediction by Learning Substructural Semantics

Abstract Link prediction across different knowledge graphs (i.e. Cross-KG link prediction) plays an important role in discovering new triples and fusing multi-source knowledge. Existing cross-KG link prediction methods mainly rely on entity and relation alignment, and are challenged by the problems of KG incompleteness, semantic implicitness and ambiguosness. To deal with these challenges, we propose a learning framework that incorporates both node-level and substructure-level context for cross-KG link prediction. The proposed method mainly consists of a neural-based tensor-completion module and a graph-convolutional-network module, which respectively captures the node-level and substructure-level semantics to enhance the performance of cross-KG link prediction. Extensive experiments are conducted on three benchmark datasets. The results show that our method significantly outperforms the state-of-the-art baselines and some interesting analysis on real cases are also provided in this paper.
Pedagogical conditions for ensuring the effectiveness of international cooperation among subjects of scientific and educational activity

This article examines the problems of developing the internationalization of education at research universities. The content of the internationalization goal, the sociocultural approach in education, which sets the contextuality of the content and results of education, and the competency-based approach in education, which determines the results of education in terms of the quality of personal new formations and abilities manifested in the process activities of educational programs at a research university. The pedagogical conditions for the internationalization of educational programs at a research university. The ways of organizing interaction between subjects of the international scientific and educational sphere for the internationalization of educational programs are substantiated at a research university.
AI-based diabetes care: risk prediction models and implementation concerns

The utilization of artificial intelligence (AI) in diabetes care has focused on early intervention and treatment management. Notably, usage has expanded to predict an individual’s risk for developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al. shows that while most studies used unimodal AI models, multimodal approaches were superior because they integrate multiple types of data. However, creating multimodal models and determining model performance are challenging tasks given the multi-factored nature of diabetes. For both unimodal and multimodal models, there are also concerns of bias with the lack of external validations and representation of race, age, and gender in training data. The barriers in data quality and evaluation standardization are ripe areas for developing new technologies, especially for entrepreneurs and innovators. Collaboration amongst providers, entrepreneurs, and researchers must be prioritized to ensure that AI in diabetes care is providing quality and equitable patient care.
Prioritizing components of university brands by using the integrated Delphi and AHP methods

Branding is a crucial strategy for universities to gain an advantage over their competitors in the face of increased competition in higher education, especially in admission activities. This study aimed to identify and prioritize the university brand components that influence the attraction of prospective students. In this study, the Delphi method is combined with the analytic hierarchy process (AHP) to identify eight factors, comprising 31 subfactors that constitute the university's brand. These factors and subfactors are then prioritized in attracting prospective students. The data were gathered through four rounds of interviews with experts who were leaders or individuals with extensive experience in communication or enrollment at member universities of Vietnam National University, Hanoi. The research findings indicate that the five factors considered to be the highest priority in attracting potential learners are the university's training program, reputation, human resources, financial policies, and communication activities. Additionally, this study presents the priority rankings of 31 subfactors.
Integrity management systems

Recent years have seen various attempts at conceptualising integrity management within organisations. This chapter focuses on those integrity management models that take a systems approach. This emphasises interconnectedness and requisite variety, as well as the processual and iterative nature of integrity management and the importance of grassroots participation. The chapter discusses five examples of integrity management models that, at least to some extent, exhibit these four characteristics: Organisational Integrity System, Integrity Management Framework, Integrity Infrastructure, Pluralistic Ethics Management Framework, and the Ethics Programme model. The chapter concludes that these models provide a useful basis for practice and research, but it also argues for more in-depth qualitative and quantitative research. Such research would be the basis for more context-sensitive and parsimonious models, which would also take the timing of the introduction of interventions into consideration and would pay more attention to the unintended consequences of ethics management.
Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

Reinforcement Learning (RL) is fast gaining traction as a major branch of machine learning, its applications have expanded well beyond its typical usage in games. Several subfields of reinforcement learning like deep reinforcement learning and multi-agent reinforcement learning are also expanding rapidly. This paper provides an extensive review on the field from the point of view of Machine Learning (ML). It begins by providing a historical perspective on the field then proceeds to lay a theoretical background on the field. It further discusses core reinforcement learning problems and approaches taken by different subfields before discussing the state of the art in the field. An inexhaustive list of applications of reinforcement learning is provided and their practicability and scalability assessed. The paper concludes by highlighting some open areas or issues in the field
The application of AI techniques in requirements classification: a systematic mapping

Abstract Requirement Analysis is the essential sub-field of requirements engineering (RE). From the last decade, numerous automatic techniques are widely exploited in requirements analysis. In this context, requirements identification and classification is challenging for RE community, especially in context of large corpus and app review. As a consequence, several Artificial Intelligence (AI) techniques such as Machine learning (ML), Deep learning (DL) and transfer learning (TL)) have been proposed to reduce the manual efforts of requirement engineer. Although, these approaches reported promising results than traditional automated techniques, but the knowledge of their applicability in real-life and actual use of these approaches is yet incomplete. The main objective of this paper is to systematically investigate and better understand the role of Artificial Intelligence (AI) techniques in identification and classification of software requirements. This study conducted a systematic literature review (SLR) and collect the primary studies on the use of AI techniques in requirements classification. (1) this study found that 60 studies are published that adopted automated techniques in requirements classification. The reported results indicate that transfer learning based approaches extensively used in classification and yielding most accurate results and outperforms the other ML and DL techniques. (2) The data extraction process of SLR indicates that Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are widely used in selected studies. (3) Precision and Recall are the commonly used metrics for evaluating the performance of automated techniques. This paper revealed that while these AI approaches reported promising results in classification. The applicability of these existing techniques in complex and real-world settings has not been reported yet. This SLR calls for the urge for the close alliance between RE and AI techniques to handle the open issues confronted in the development of some real-world automated system.
A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets

Objectives: The objective of this study is to introduce a hybrid model for analyzing the people sentiment on covid-19 tweets. Methods: We used a total no. of 27,500 datasets, 70% of the data sets for training and reserved the other 30% for testing. Due to this separation 19,250 samples are used for training, the remaining 8,250 were used to evaluate the accuracy of the test. This paper proposes a technique for sentiment analysis that integrates deep learning, genetic algorithms (GA), and social media sentiment. For more accuracy and performance, we here suggested a hybrid genetic algorithm-based model. A hybrid model is created by assembling the LSTM model and providing it to the genetic algorithm architecture. Findings: LSTM with a genetic model better than LSTM without genetic model. The accuracy of our suggested model is 96.40%. Novelty : The accuracy of the LSTM model for sentiment analysis is 91%. The accuracy of the proposed model is 96.40%. The proposed model is more accurate for sentiment prediction. Keywords: Social network perception, Crossover, Mutation, LSTM, NLP, GA
Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Purpose Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China. Design/methodology/approach After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis. Findings The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies. Practical implications The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types. Originality/value This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.
Running-Time Analysis of Brain Storm Optimization Based on Average Gain Model

The brain storm optimization (BSO) algorithm has received increased attention in the field of evolutionary computation. While BSO has been applied in numerous industrial scenarios due to its effectiveness and accessibility, there are few theoretical analysis results about its running time. Running-time analysis can be conducted through the estimation of the upper bounds of the expected first hitting time to evaluate the efficiency of BSO. This study estimates the upper bounds of the expected first hitting time on six single individual BSO variants (BSOs with one individual) based on the average gain model. The theoretical analysis indicates the following results. (1) The time complexity of the six BSO variants is O(n) in equal coefficient linear functions regardless of the presence or absence of the disrupting operator, where n is the number of the dimensions. Moreover, the coefficient of the upper bounds on the expected first hitting time shows that the single individual BSOs with the disrupting operator require fewer iterations to obtain the target solution than the single individual BSOs without the disrupting operator. (2) The upper bounds on the expected first hitting time of single individual BSOs with the standard normally distributed mutation operator are lower than those of BSOs with the uniformly distributed mutation operator. (3) The upper bounds on the expected first hitting time of single individual BSOs with the U-12,12 mutation operator are approximately twice those of BSOs with the U(-1,1) mutation operator. The corresponding numerical results are also consistent with the theoretical analysis results.