Reviewerly/exHarmony huggingface dataset:
Ukrainian education for peace and security 2023: Technological convergence, artificial intelligence

Authors:
raw_author_name: Nataliia Bakhmat
raw_affiliation_string: Kamianets-Podilskyi Ivan Ohiienko National University, Kamianets-Podіlskyi, Ukraine
raw_author_name: Iryna Romanova
raw_affiliation_string: Kharkiv National Medical University, Kharkiv, Ukraine
raw_author_name: Larysa Oronovska
raw_affiliation_string: Ternopil Volodymyr Hnatiuk National Pedagogical University, Ternopil, Ukraine

Abstract: 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

Authors:
raw_author_name: Wen Wen
raw_affiliation_string: School of Computer Science, Guangdong University of Technology, Guangzhou, China
raw_author_name: Ruichu Cai
raw_affiliation_string: School of Computer Science, Guangdong University of Technology, Guangzhou, China
raw_author_name: Zhifeng Hao
raw_affiliation_string: School of Computer Science, Guangdong University of Technology, Guangzhou, China

Abstract: 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

Authors:
raw_author_name: Alona Prokopenko
raw_affiliation_string: Associate Professor of Informatics, Municipal Establishment «Kharkiv Humanitarian-Pedagogical Academy» of Kharkiv Regional Council, Kharkiv, Ukraine
raw_author_name: Oksana Kuznetsova
raw_affiliation_string: Professor of the Department Department of Pedagogy, Psychology, Primary Education and Educational Management, Municipal Establishment «Kharkiv Humanitarian-Pedagogical Academy» of Kharkiv Regional Council, Kharkіv, Ukraine
raw_author_name: Olena Kapustina
raw_affiliation_string: Department of theory and methods of preschool education, Municipal Establishment «Kharkiv Humanitarian-Pedagogical Academy» of Kharkiv Regional Council, Kharkіv, Ukraine
raw_author_name: Alina Drokina
raw_affiliation_string: Department of Pedagogy, Psychology, Primary Education and Educational Management, Municipal Establishment «Kharkiv Humanitarian-Pedagogical Academy» of Kharkiv Regional Council, Kharkiv, Ukraine

Abstract: 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

Authors:
raw_author_name: Serena C. Y. Wang
raw_affiliation_string: Harvard Medical School, Boston, MA, USA
raw_author_name: Kaushik P. Venkatesh
raw_affiliation_string: Harvard Medical School, Boston, MA, USA
raw_author_name: Joseph C. Kvedar
raw_affiliation_string: Harvard Medical School, Boston, MA, USA

Abstract: 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

Authors:
raw_author_name: Nguyen Thi Huyen
raw_affiliation_string: VNU University of Economics and Business, Vietnam National University, Hanoi, Vietnam.
raw_author_name: Pham Minh Tuan
raw_affiliation_string: VNU University of Economics and Business, Vietnam National University, Hanoi, Vietnam.

Abstract: 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.
Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

Authors:
raw_author_name: Benjamin Kommey
raw_affiliation_string: Department of Computer Engineering, Kwame Nkrumah University of Science and Technology
raw_author_name: Elvis Tamakloe
raw_affiliation_string: Department of Computer Engineering, Kwame Nkrumah University of Science and Technology
raw_author_name: Daniel Opoku4
raw_affiliation_string: Department of Computer Engineering, Kwame Nkrumah University of Science and Technology

Abstract: 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

Authors:
raw_author_name: Kamaljit Kaur
raw_affiliation_string: Department of Computer Science, Guru Nanak Dev University, Amritsar, India
raw_author_name: Parminder Kaur
raw_affiliation_string: Department of Computer Science, Guru Nanak Dev University, Amritsar, India

Abstract: 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

Authors:
raw_author_name: Radha Krishna Jana
raw_author_name: Dharmpal Singh
raw_author_name: Saikat Maity
raw_author_name: Hrithik Paul

Abstract: 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
Running-Time Analysis of Brain Storm Optimization Based on Average Gain Model

Authors:
raw_author_name: Guizhen Mai
raw_affiliation_string: School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521041, China
raw_author_name: Yinghan Hong
raw_affiliation_string: School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521041, China
raw_author_name: Junpeng Su
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou 510006, China
raw_author_name: Xiaowei Yang
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou 510006, China
raw_author_name: Han Huang
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou 510006, China

Abstract: 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.
Autoencoder based image quality metric for modelling semantic noise in semantic communications

Authors:
raw_author_name: Thanuj Fernando
raw_affiliation_string: Department of Computer and Information Sciences, University of Strath-clyde, Glasgow, UK
raw_author_name: Vishnu Gowrisetty
raw_affiliation_string: Department of Computer and Information Sciences, University of Strath-clyde, Glasgow, UK
raw_author_name: Thisarani Atulugama
raw_affiliation_string: Department of Computer and Information Sciences, University of Strath-clyde, Glasgow, UK
raw_author_name: Anil Fernando
raw_affiliation_string: Department of Computer and Information Sciences, University of Strath-clyde, Glasgow, UK

Abstract: Abstract Semantic communication has attracted significant attention as a key technology for emerging 6G communications. This paper proposes an autoencoder based image quality metric to quantify the semantic noise. An autoencoder is initially trained with the reference image to generate the encoder‐decoder model and calculate its Latent Vector Space (LVS) and then a semantically generated/received image is inserted into the same autoencoder to create the corresponding LVS. Finally, both LVS are used to define the Euclidean space to calculate the mean square error between two LVS. Results indicate that the proposed model has a high correlation coefficient of 88% with subjective quality assessment and commonly used conventional metrics completely failed in semantic noise modelling.
Pelaksanaan Kegiatan Ekstrakurikuler Band di MAN 2 Kota Padang dengan Pendekatan Literasi Plus Media

Authors:
raw_author_name: Wimbrayardi Wimbrayardi
raw_affiliation_string: Universitas Negeri Padang

Abstract: This study aims to describe the implementation of band extracurricular activities at MAN 2 Padang City. This type of research is qualitative research with descriptive methods. Data collection techniques were carried out by observation, interviews, literature study, and documentation. The data in this study used secondary data. The steps of analyzing data are collecting, describing and concluding data. Band extracurricular activities at MAN 2 Padang City with a literacy plus media approach are carried out with 3 stages, namely planning, implementation, and evaluation. band extracurricular activities at MAN 2 Padang City have become one of the extracurricular activities that can develop students' musical abilities. The planning process, practice, and strong support from coaches and coaches have contributed to the success of this extracurricular band. For the students involved, this extracurricular band has provided a lot of knowledge and experience in developing musical abilities and art activities and has provided inspiration to develop their musical abilities further.
Cyborgs for strategic communication on social media

Authors:
raw_author_name: Lynnette Hui Xian Ng
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, PA, USA
raw_author_name: Dawn C Robertson
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, PA, USA
raw_author_name: Kathleen M Carley
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, PA, USA

Abstract: Social media platforms are a key ground of information consumption and dissemination. Key figures like politicians, celebrities, and activists have leveraged on its wide user base for strategic communication. Strategic communications, or StratCom, is the deliberate act of information creation and distribution. Its techniques are used by key figures for establishing brand and amplifying messages. Automated scripts are used on top of personal touches to effectively perform these tasks. The combination of automation and manual online posting creates a Cyborg social media profile, which is a hybrid between bot and human. In this study, we establish a quantitative definition for a Cyborg account, an account that is detected as bot in one time window, and identified as human in another. This definition makes use of frequent changes in bot classification labels and large differences in bot likelihood scores to identify Cyborgs. We perform a large-scale analysis across over 3.1 million users from Twitter collected from two key events, the 2020 Coronavirus pandemic and the 2020 US Elections. We extract Cyborgs from two datasets and employ tools from network science, natural language processing, and manual annotation to characterize Cyborg accounts. Our analyses identify Cyborg accounts are constructed for strategic communication uses, have a strong duality in their bot/human classification and are tactically positioned in the social media network, aiding these accounts to promote their desired content. Cyborgs are also discovered to have long online lives, indicating their ability to evade bot detectors, or the graciousness of platforms to allow their operations.
Emojis And Miscommunication in Text-Based Interactions Among Nigerian Youths

Authors:
raw_author_name: Uduak Udoudom
raw_affiliation_string: Heritage Polytechnic, Nigeria

Abstract: This paper explores the dynamic role of emojis in text-based communication among Nigerian youths and the potential implications for miscommunication. Emojis have become integral to contemporary digital conversations, offering users a visual means of expressing emotions, tone, and context within the constraints of text-based interactions. In the context of Nigeria, a country with a diverse linguistic landscape and a youthful population heavily engaged in online communication, understanding the impact of emojis on interpersonal exchanges becomes particularly pertinent. This paper examines the prevalence and patterns of emoji usage among Nigerian youths across various digital platforms. It investigates the cultural nuances and interpretations associated with emojis within the Nigerian context, considering factors such as regional differences, linguistic diversity, and socio-cultural influences. Furthermore, the study examines instances where emojis may contribute to miscommunication or misunderstanding, potentially exacerbating conflicts or hindering effective communication. Through a comprehensive review of existing literature, online discourse analysis, and case studies, this research aims to shed light on the ways in which emojis influence the interpretation of textual messages and the potential challenges they pose to clear and accurate communication. The study concludes that as digital communication continues to be a primary mode of interaction, it is essential for users to recognize the potential for misinterpretation, prompting the need for increased emoji literacy and awareness.
A Campus-based Chatbot System using Natural Language Processing and Neural Network

Authors:
raw_author_name: Lee-Ying Chong
raw_affiliation_string: Multimedia University, Malaysia
raw_author_name: Siew-Chin Chong
raw_affiliation_string: Multimedia University, Malaysia

Abstract: A chatbot is designed to simulate human conversation and provide instant responses to users. Chatbots have gained popularity in providing automated customer support and information retrieval among organisations. Besides, it also acts as a virtual assistant to communicate with users by delivering updated answers based on users' input. Most chatbots still use the traditional rule-based chatbot, which can only respond to pre-defined sentences, making the users unlikely to use the chatbot. This paper aims to design and build a campus chatbot for the Faculty of Information Science &amp; Technology (FIST) of Multimedia University that facilitates the study life of FIST students. Before the FIST chatbot can be used, natural language processing techniques such as tokenisation, lemmatisation and bag of word model are used to generate the input that can be used to train the neural network model (multilayer perceptron model). It makes the FIST chatbot comprehends user intent by analysing their questions, enabling it to address a broader range of inquiries and cater to the student's need with accurate answers or information related to the Faculty of Information Science &amp; Technology. Besides, we also developed the backend interface allowing the admin to add and edit the dataset in the proposed chatbot and enable it continuously responds to the student with the latest and updated information.