Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Developing a Kazakh question-answering model: standing on the shoulders of multilingual giants
Abstract: At present, there is a proliferation of AI-powered systems being developed to enable natural language interactions with humans. However, these systems are primarily designed for a limited set of approximately 40 dominant languages, while the majority of the world’s over 7,000 living languages, including Kazakh, are categorized as under-resourced languages that lack cutting-edge AI technologies like Siri, GPT-3, etc. The solution is to leverage state-of-the-art multilingual models, which have recently emerged as vital resources for lowresource languages, facilitating the rapid development of native language models with acceptable quality. This study focuses on the comprehensive evaluation of various multilingual models, including mT5, XLM-RoBERTa, and mDeBERTa with the aim to determine their suitability as donor models for Kazakh question answering. Furthermore, the study addresses the research question of whether large multilingual models genuinely outperform native language models in the task of question answering. Experimental results confirm this superiority and lead to the conclusion that there is no need to train native models from scratch, considering the associated economic and environmental costs.
Keywords: large language models;multilingual models;question-answering;low resource languages;SQuAD
Review score: High
Review comment: This paper addresses an important issue concerning the under-representation of low-resource languages in the development of AI-powered systems. The approach of leveraging state-of-the-art multilingual models for the development of native language models is innovative. By evaluating various models such as mT5, XLM-RoBERTa, and mDeBERTa, authors provide valuable insights into their suitability as donor models for low-resource languages like Kazakh. The experimental results indicating the superiority of large multilingual models over monolingual ones provide practical implications for future research and development efforts. Drawing parallels with similar research conducted for other languages, such as Scandinavian languages, adds depth to the study.
Title: Developing a Kazakh question-answering model: standing on the shoulders of multilingual giants
Abstract: At present, there is a proliferation of AI-powered systems being developed to enable natural language interactions with humans. However, these systems are primarily designed for a limited set of approximately 40 dominant languages, while the majority of the world’s over 7,000 living languages, including Kazakh, are categorized as under-resourced languages that lack cutting-edge AI technologies like Siri, GPT-3, etc. The solution is to leverage state-of-the-art multilingual models, which have recently emerged as vital resources for lowresource languages, facilitating the rapid development of native language models with acceptable quality. This study focuses on the comprehensive evaluation of various multilingual models, including mT5, XLM-RoBERTa, and mDeBERTa with the aim to determine their suitability as donor models for Kazakh question answering. Furthermore, the study addresses the research question of whether large multilingual models genuinely outperform native language models in the task of question answering. Experimental results confirm this superiority and lead to the conclusion that there is no need to train native models from scratch, considering the associated economic and environmental costs.
Keywords: large language models;multilingual models;question-answering;low resource languages;SQuAD
Review score: High
Review comment: The research presented by Aliya Nugumanova and co-authors offers a significant contribution to addressing the linguistic inclusivity in AI-powered systems through the development of a Kazakh question-answering model leveraging state-of-the-art multilingual giants. This approach is particularly relevant as it seeks to bridge the technology gap for under-resourced languages, exemplified by Kazakh. The comprehensive evaluation of multilingual models (mT5, XLM-RoBERTa, and mDeBERTa) showcases a methodical approach towards selecting the most suitable model as a donor for Kazakh, with experimental results reinforcing the superiority of these models over native ones for the question-answering task. This study not only brings forth a scalable solution for low-resource languages but also emphasizes the economic and environmental considerations of AI model training. Your research marks a notable step towards democratizing AI technologies, ensuring broader accessibility and representation. Future work could potentially explore the integration of dialectal variations within Kazakh and extend the methodology to other under-represented languages, further enriching the AI landscape.
Title: Real Estate Price Management Through Fuzzified Time Impact Factors Using MATLAB: Development Companies’ Approach
Abstract: The profitability of a real estate development company depends on the effective formulation of a marketing strategy that integrates dynamic factors along with static ones in real estate price management. This article explores the fuzzy set theory application using MATLAB for managing the challenges posed by time impact factors, which demonstrate fluctuation during the implementation of various real estate development projects. Evolving consumer preferences, local real estate market conditions, and the construction processes complexness influence these factors. The analysis of Group Impact Factors, influenced by time parameters, discloses trends in real estate prices across different combinations of sale offer exposure periods and construction stages.This article suggests integrating results of MATLAB utilization, specifically Group Impact Factors pseudo-color plots and Time Impact Factors membership function plots, into the real-time formation of development companies' marketing strategies. These tools facilitate a dynamic factors assessment, taking into account not only the expected development project profitability but also the real-time demand for real estate within local markets and individual development projects. Using Time Impact Factors for real estate price formation enhances flexibility, providing not only desired price values but also minimum permissible ones, thereby optimizing price offers and stimulating demand.In conclusion, the authors underline the suitability of fuzzified Time Impact Factors for economic forecasting, risk management, and investment flow optimization in real estate development projects. Proposed MATLAB-based tools and approaches are declared beneficial for development companies in both strategic and tactical planning.
Keywords: real estate development;profit maximization;marketing strategy;fuzzy set theory;digitalization;real estate market;real estate price formation;real estate demand;economic forecasting;construction;consumer preferences
Review score: Moderate
Review comment: This article delves into the application of fuzzy set theory using MATLAB for managing time impact factors in real estate development projects, suggesting integration of MATLAB results into dynamic marketing strategy formation, and it is recommended for publication due to its valuable insights and practical recommendations for development companies seeking to optimize profitability and mitigate risks in real estate ventures.
Title: Real Estate Price Management Through Fuzzified Time Impact Factors Using MATLAB: Development Companies’ Approach
Abstract: The profitability of a real estate development company depends on the effective formulation of a marketing strategy that integrates dynamic factors along with static ones in real estate price management. This article explores the fuzzy set theory application using MATLAB for managing the challenges posed by time impact factors, which demonstrate fluctuation during the implementation of various real estate development projects. Evolving consumer preferences, local real estate market conditions, and the construction processes complexness influence these factors. The analysis of Group Impact Factors, influenced by time parameters, discloses trends in real estate prices across different combinations of sale offer exposure periods and construction stages.This article suggests integrating results of MATLAB utilization, specifically Group Impact Factors pseudo-color plots and Time Impact Factors membership function plots, into the real-time formation of development companies' marketing strategies. These tools facilitate a dynamic factors assessment, taking into account not only the expected development project profitability but also the real-time demand for real estate within local markets and individual development projects. Using Time Impact Factors for real estate price formation enhances flexibility, providing not only desired price values but also minimum permissible ones, thereby optimizing price offers and stimulating demand.In conclusion, the authors underline the suitability of fuzzified Time Impact Factors for economic forecasting, risk management, and investment flow optimization in real estate development projects. Proposed MATLAB-based tools and approaches are declared beneficial for development companies in both strategic and tactical planning.
Keywords: real estate development;profit maximization;marketing strategy;fuzzy set theory;digitalization;real estate market;real estate price formation;real estate demand;economic forecasting;construction;consumer preferences
Review score: Low
Review comment: It is not clear in the paper what data was utilized. The authors did not provide comprehensive details regarding the sources of data, including any relevant datasets, variables, and their significance in the context of the study. This transparency enhances the reproducibility and credibility of the research findings.
Title: Institutional measurement of structural characteristics of residential real estate markets using the method of cluster analysis
Abstract: The article examines the use of IT technologies and cluster analysis tools to solve the problem of classifying regions of Ukraine according to the level of their institutional development. Recognizing the importance of effective institutional frameworks for economic growth, social stability and governance, the research focuses on using data-driven approaches to assess and classify regional institutional dynamics. Through a combination of data collection, statistical analysis, and machine learning methods, the study aims to provide an understanding of the spatial distribution and characteristics of institutional development in the regions of Ukraine. Using cluster analysis methodologies, such as K-means clustering, the study identifies distinct groups or clusters of regions with similar institutional profiles. In addition, the paper explores the role of IT technologies, including geographic information systems (GIS), data visualization tools, and database management systems, in facilitating the analysis, interpretation, and dissemination of results. In addition, the study examines the potential implications of regional classification for policy formulation, resource allocation, and development planning at the national and local levels. Through its innovative approach, the study contributes to the dissemination of knowledge about regional differences, institutional dynamics and development trajectories in Ukraine, as well as providing practical information for policymakers, researchers and practitioners who seek to promote inclusive and sustainable development across the country.
Keywords: IT technologies;cluster analysis;institutional development;regional classification;Ukraine;data-driven approaches;Geoinformation systems;machine learning;policy formulation;development planning;real estate market;construction
Review score: Moderate
Review comment: An article has been submitted for review that addresses issues of the institutional aspect of the structural features of residential real estate markets using cluster analysis. However, I note that the introduction, like the presented literature review, is more focused on the issues and objectives of the institutional development of residential real estate, and insufficient attention has been paid to solving the problem using cluster analysis. Considering the focus of the conference, it is necessary to make additions and changes to the presented work, namely, to focus on the use of IT technologies to solve the problem.
The results of the study are not entirely clear.
Title: Image-Based Dietary Assessment: A Healthy Eating Plate Estimation System
Abstract: The nutritional quality of diets has significantly deteriorated over the past two to three decades, a decline often underestimated by the people. This deterioration, coupled with a hectic lifestyle, has contributed to escalating health concerns. Recognizing this issue, researchers at Harvard have advocated for a balanced nutritional plate model to promote health. Inspired by this research, our paper introduces an innovative Image-Based Dietary Assessment system aimed at evaluating the healthiness of meals through image analysis. Our system employs advanced image segmentation and classification techniques to analyze food items on a plate, assess their proportions, and calculate the meal’s adherence to Harvard’s healthy eating recommendations. This approach leverages machine learning and nutritional science to empower individuals with actionable insights for healthier eating choices. Our four-step framework involves segmenting the image, classifying the items, conducting a nutritional assessment based on the Harvard Healthy Eating Plate research, and offering tailored recommendations. The prototype system has shown promising results in promoting healthier eating habits by providing an accessible, evidence-based tool for dietary assessment.
Review score: High
Review comment: The very interesting and relevant paper, it should be accepted without any corrections.
Title: Image-Based Dietary Assessment: A Healthy Eating Plate Estimation System
Abstract: The nutritional quality of diets has significantly deteriorated over the past two to three decades, a decline often underestimated by the people. This deterioration, coupled with a hectic lifestyle, has contributed to escalating health concerns. Recognizing this issue, researchers at Harvard have advocated for a balanced nutritional plate model to promote health. Inspired by this research, our paper introduces an innovative Image-Based Dietary Assessment system aimed at evaluating the healthiness of meals through image analysis. Our system employs advanced image segmentation and classification techniques to analyze food items on a plate, assess their proportions, and calculate the meal’s adherence to Harvard’s healthy eating recommendations. This approach leverages machine learning and nutritional science to empower individuals with actionable insights for healthier eating choices. Our four-step framework involves segmenting the image, classifying the items, conducting a nutritional assessment based on the Harvard Healthy Eating Plate research, and offering tailored recommendations. The prototype system has shown promising results in promoting healthier eating habits by providing an accessible, evidence-based tool for dietary assessment.
Review score: High
Review comment: The primary advantage and novelty of this paper lie in the concept of estimating the contents of a meal through the simple act of capturing an image and subjecting it to analysis via a trained AI system. The authors weren't restricted in their statement by a particular type of food, considering plates composed of various food items. The authors successfully achieved their objectives under some natural constraints within the experimental plate settings.
Title: Use the neural networks in prediction of environmental processes
Abstract: Forecasting environmental phenomena using neural networks has become increasingly popular due to their ability to analyze complex datasets and provide accurate predictions. In this paper, we investigate the application of neural networks in predicting environmental processes, focusing specifically on their use in forecasting greenhouse gas emissions. We examine several types of neural networks, including recurrent neural networks (RNNs) such as Simple RNN, LSTM-RNN, and stacked LTSM-RNN, to assess their effectiveness in handling raw data and projecting emissions over different spatial and temporal scales. The study demonstrates that the Simple RNN model is highly accurate in outlier prediction. Moreover, the consistency of results obtained through decision tree construction with those from component and cluster analyses is highlighted. The scientific significance of this study lies in its comprehensive exploration of neural networks' ability to forecast environmental processes, with a specific emphasis on greenhouse gas emissions prediction. The findings suggest that neural networks, particularly recurrent architectures, show promise as powerful tools for monitoring and forecasting environmental changes, offering valuable insights for governmental agencies and environmental initiatives.
Keywords: environmental assessment;neural networks;predictive models;CO2 emissions;SimpleRNN;LSTM-RNN;stacked LTSM-RNN
Review score: High
Review comment: This article examines the application of neural networks for forecasting atmospheric air pollution. The authors considered different types of neural networks (recurrent neural networks (RNN), such as SimpleRNN, LSTM-RNN, and stacked LTSM-RNN) for data processing and forecasting greenhouse gas emissions. The research has scientific novelty and corresponds to the theme of the conference. The authors used modern technologies to create new models.
Title: Use the neural networks in prediction of environmental processes
Abstract: Forecasting environmental phenomena using neural networks has become increasingly popular due to their ability to analyze complex datasets and provide accurate predictions. In this paper, we investigate the application of neural networks in predicting environmental processes, focusing specifically on their use in forecasting greenhouse gas emissions. We examine several types of neural networks, including recurrent neural networks (RNNs) such as Simple RNN, LSTM-RNN, and stacked LTSM-RNN, to assess their effectiveness in handling raw data and projecting emissions over different spatial and temporal scales. The study demonstrates that the Simple RNN model is highly accurate in outlier prediction. Moreover, the consistency of results obtained through decision tree construction with those from component and cluster analyses is highlighted. The scientific significance of this study lies in its comprehensive exploration of neural networks' ability to forecast environmental processes, with a specific emphasis on greenhouse gas emissions prediction. The findings suggest that neural networks, particularly recurrent architectures, show promise as powerful tools for monitoring and forecasting environmental changes, offering valuable insights for governmental agencies and environmental initiatives.
Keywords: environmental assessment;neural networks;predictive models;CO2 emissions;SimpleRNN;LSTM-RNN;stacked LTSM-RNN
Review score: Moderate
Review comment: The article is interesting and relevant. The work is of practical importance.
Figure 7 should be added to the article.
To improve the work, it is necessary to single out scientific novelty.
Title: STEM education in Poland and Ukraine: exploring the contextual data of the GGG Index
Abstract: The assessment and monitoring of educational attainment in Poland and Ukraine in terms of ensuring gender parity in science, technology, engineering, and mathematics (STEM) were explored. It is noted that the new educational visions should be based on the acquisition of knowledge in STEM fields and fluency with digital tools. STEM careers are expected to be among the most in-demand in the context of Industry 4.0. The Global Gender Gap Index (GGGI) used to achieve a comprehensive gender analysis. The category subindexes “Educational Attainment” determines the scale of the imbalance between women and men. The retrospective analysis of the transformation of the indicator system for assessing the educational attainment performed. The visualizations of contextual indicators for the educational attainment in Poland and Ukraine (2006–2023) were created, and indicators related to the STEM education and STEM skills were identified. The results of the analysis showed that Poland and Ukraine have high achievements in ensuring equal access for women and men to primary, secondary, and higher education. In 2023, the gender gap in STEM specialties in Poland was 0.40, and in Ukraine – 0.34. The results of this analysis can be used as “input data” to develop models for monitoring and evaluating a university’s (or faculty’s) gender maturity, considering new assessment indicators for STEM education and STEM competencies.
Keywords: STEM education;STEM skills;STEM studies;gender gap;educational attainment;indicators of STEM education
Review score: Low
Review comment: The article is interesting and relevant. The article needs to be refined in the context of IT. Highlight of the conference: "...the article should have an engineering direction. Work can be in related fields, but with the use of IT technologies in different fields..."
I believe that the article can be accepted after revision
Title: STEM education in Poland and Ukraine: exploring the contextual data of the GGG Index
Abstract: The assessment and monitoring of educational attainment in Poland and Ukraine in terms of ensuring gender parity in science, technology, engineering, and mathematics (STEM) were explored. It is noted that the new educational visions should be based on the acquisition of knowledge in STEM fields and fluency with digital tools. STEM careers are expected to be among the most in-demand in the context of Industry 4.0. The Global Gender Gap Index (GGGI) used to achieve a comprehensive gender analysis. The category subindexes “Educational Attainment” determines the scale of the imbalance between women and men. The retrospective analysis of the transformation of the indicator system for assessing the educational attainment performed. The visualizations of contextual indicators for the educational attainment in Poland and Ukraine (2006–2023) were created, and indicators related to the STEM education and STEM skills were identified. The results of the analysis showed that Poland and Ukraine have high achievements in ensuring equal access for women and men to primary, secondary, and higher education. In 2023, the gender gap in STEM specialties in Poland was 0.40, and in Ukraine – 0.34. The results of this analysis can be used as “input data” to develop models for monitoring and evaluating a university’s (or faculty’s) gender maturity, considering new assessment indicators for STEM education and STEM competencies.
Keywords: STEM education;STEM skills;STEM studies;gender gap;educational attainment;indicators of STEM education
Review score: Moderate
Review comment: The work submitted for consideration examines gender assessment and monitoring of the Polish and Ukrainian education systems from the point of view of ensuring equal access to education for girls/women and boys/men. CONTEXTUAL INDICATORS OF EDUCATION AND TECHNOLOGY, SCIENCE, TECHNOLOGY AND RESEARCH, etc.: POLAND AND UKRAINE by year. The work does not present a solution to the problem and task using IT technologies, as well as the scientific novelty of the work.
Requires revision taking into account comments.