Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Factor Assessment of the Mortality in Kyrgyzstan Through Data Analysis
Abstract: The objective is to research the exogenous factors of the crude mortality rate in Kyrgyzstan and to evaluate their immediate influence on the dependent variable using the multiple factor correlation and regression analysis method. According to calculations the average per capita income (X3) and the public expenditures on environment (X9) have the largest contribution in the reduction of mortality in Kyrgyzstan. Practical implications of the calculations include a recommendation for the population development programs in Kyrgyzstan to forecast the reduction of crude mortality by 0.27% (with the average annual per capita income growth of 1%) and by 0.14% (with the growth of public expenditures on environment by 1%). The discussed methodology can be used to determine the adequate contribution of exogenous factors to the dependent variable in a multiple linear regression relationship.
Keywords: demography;exogenous factor;sustainable development triune concept;multicollinearity;Durbin-Watson statistic;temporal factor;F-test
Review score: Moderate
Review comment: The paper titled "Factor Assessment of the Mortality in Kyrgyzstan through Data Analysis" presents an interesting study on the exogenous factors influencing the crude mortality rate in Kyrgyzstan. The authors have used multiple factor correlation and regression analysis methods to evaluate the immediate impact of these factors on the dependent variable. However, the paper's limitations include a lack of discussion of potential confounding variables that may have influenced the study's results. Overall, while the paper presents an interesting analysis of the exogenous factors affecting mortality rates in Kyrgyzstan, it is important to consider the study's limitations when interpreting its results.
Title: 3-Layer Neural Network Classifier With the Heterogeneous Hidden Layers
Abstract: The ways to improve the performance of classifiers based on the bithreshold architecture are considered in the paper. The model of 3-layer neural network binary classifier is proposed whose first hidden layer consists of neural units of 3 kinds: bithreshold neurons, linear threshold units and winner-take-all neurons, and every neuron in the second layer has only 3 inputs with predefined weights. The synthesis algorithm for such networks is designed and estimations of its time complexity and the size of resulting network are presented. The simulation results demonstrate that the application of the new architecture in the classifier design significantly improves its generalization ability.
Keywords: bithreshold neuron;neural network;synthesis algorithm;classification
Review score: Moderate
Review comment: The paper has novelty and has new architecture in this research.
Title: 3-Layer Neural Network Classifier With the Heterogeneous Hidden Layers
Abstract: The ways to improve the performance of classifiers based on the bithreshold architecture are considered in the paper. The model of 3-layer neural network binary classifier is proposed whose first hidden layer consists of neural units of 3 kinds: bithreshold neurons, linear threshold units and winner-take-all neurons, and every neuron in the second layer has only 3 inputs with predefined weights. The synthesis algorithm for such networks is designed and estimations of its time complexity and the size of resulting network are presented. The simulation results demonstrate that the application of the new architecture in the classifier design significantly improves its generalization ability.
Keywords: bithreshold neuron;neural network;synthesis algorithm;classification
Review score: High
Review comment: The research to the scope of the paper is relevance of conference. The paper contains of the ways to improve the performance of classifiers based on the threshold architecture. Accept without improvements.
Title: Defender-Attacker Model Based Secure Software Development Lifecycle for Common Web Vulnerabilities
Abstract: In this paper, we propose a novel secure software development life cycle (SSDLC) algorithm for web applications by observing and demonstrating the 3 most popular web vulnerabilities from both the attacker's and defender's roles. This algorithm is based on the defender-attacker model and aims to provide confidentiality in data.
Keywords: Web security;Software Development Life Cycle;Defender-Attacker Model
Review score: High
Review comment: The reviewed article presents an interesting proposal for the development cycle of secure web-applications. The results stress the importance of including security provisions on all stages of application development. The article presents a novel approach and could be recommended for publication.
Title: Defender-Attacker Model Based Secure Software Development Lifecycle for Common Web Vulnerabilities
Abstract: In this paper, we propose a novel secure software development life cycle (SSDLC) algorithm for web applications by observing and demonstrating the 3 most popular web vulnerabilities from both the attacker's and defender's roles. This algorithm is based on the defender-attacker model and aims to provide confidentiality in data.
Keywords: Web security;Software Development Life Cycle;Defender-Attacker Model
Review score: Moderate
Review comment: The subject of the article is relevant and meets all the requirements for the design of the article.
But, there is a small note, you need to add authors ORCID
Title: System for Water and Electricity Consumption Prediction in Smart Cities Using ML
Abstract: The issue of finding new solutions for efficient water and electricity consumption is urgent for Smart Cities. Such solutions include the use of various machine learning models to predict the consumption of these resources. In this research paper, time series models for predicting electricity consumption, Decision tree, and Random Forest models for predicting water consumption were developed. There has also been a growing increase in the use of smart sensors to be able to track and manage water data, which in turn contributes to smart resource management.
Keywords: water consumption;electricity consumption;machine learning;decision tree;time series;prediction
Review score: High
Review comment: The scientific argument is logical and persuasive. 
The topic is scientifically relevant and useful. 
Conclusions must illustrate the research results, findings and recommendations showing what is new.
There is no complete overview of known, prior solutions. 
Poor list of reference
Title: System for Water and Electricity Consumption Prediction in Smart Cities Using ML
Abstract: The issue of finding new solutions for efficient water and electricity consumption is urgent for Smart Cities. Such solutions include the use of various machine learning models to predict the consumption of these resources. In this research paper, time series models for predicting electricity consumption, Decision tree, and Random Forest models for predicting water consumption were developed. There has also been a growing increase in the use of smart sensors to be able to track and manage water data, which in turn contributes to smart resource management.
Keywords: water consumption;electricity consumption;machine learning;decision tree;time series;prediction
Review score: Moderate
Review comment: I recommend the article "System for Water and Electricity Consumption Prediction in Smart Cities by ML: Case Study For Astana City". The author did an excellent job shedding light on the various applications of machine learning in Kazakhstan and how it is changing people's lives.
Title: Using Big Data Processing for Forming the Information Model of the Mining and Geological Area
Abstract: The article presents the results of the study in the field of information and analytical modeling of territorial objects. The raw data used in the study was obtained using unmanned aerial vehicles (UAV). Big Data technologies such as Apache Hadoop and Apache Spark were used for data storage and analysis. The Delaunay triangulation algorithm was used to construct the surface skeleton. Algorithm A* was implemented to find the shortest paths in the resulting graph.
Keywords: modeling;Delaunay triangulation;A* algorithm;Big Data technologies
Review score: Moderate
Review comment: The article lacks an analysis of related works, the list of references is limited to only 10 works. The list of references needs to be seriously revised to show the significance of this work in a number of related studies.
Title: Using Big Data Processing for Forming the Information Model of the Mining and Geological Area
Abstract: The article presents the results of the study in the field of information and analytical modeling of territorial objects. The raw data used in the study was obtained using unmanned aerial vehicles (UAV). Big Data technologies such as Apache Hadoop and Apache Spark were used for data storage and analysis. The Delaunay triangulation algorithm was used to construct the surface skeleton. Algorithm A* was implemented to find the shortest paths in the resulting graph.
Keywords: modeling;Delaunay triangulation;A* algorithm;Big Data technologies
Review score: High
Review comment: Any improvements and optimizations in this area can lead to optimazing evacuation process  from mining quarries and similar areas of possible emergency and represent a breakthrough in this field.  The implementation of modern technologies for working with big  data shows good results in optimization process. This is an actual area of research, and a new proposal is proposed both in form and on a theoretical basis.
As a result of reviewing the article I recommend the article for publication with minor (technical) corrections «Accepted with minor revision».
- select a separate paragraph for the literary review;
- the article must be written from the third person. It is not allowed to use the pronoun "we", etc.
-the conclusion should be rewritten (the relevance have to be described in introduction, and conclusion should contain a description of the results of the research work and provide information about the achievement of the goal). 
- it is necessary to issue an article according to a template:
•	paper title(In the paper title, capitalize the first letter of the first and last word and all the nouns, pronouns, adjectives, verbs, adverbs, and subordinating conjunctions);
•	figures (Place figures and tables after they are first cited in the text. Use the abbreviation “Fig. 1”, even at the beginning of a sentence);
•	references (Capitalize only the first word in a paper title, except for proper nouns and element symbols).
- the article must be written from the third person. It is not allowed to use the pronoun "we", etc.
- references should be no later than 5-7 years from the current year.
Title: An Experimental Assistant Platform for Teaching Programming Languages Using a Step-by-Step Approach
Abstract: This paper provides one of the ways to provide information for educational purposes. The basis of this method is to combine the “touch typing” method and the “step by step” approach, which will not only reduce the time for learning a programming language, but also increase the student's interest in the language. The problem that the author faced is the provision of material based on slides, the author does not consider it irrelevant, but presents a way that would cover the shortcomings. This method could increase the number of interested students, reduce the time spent on training and keep records to identify factors that reduce efficiency. The result of the study is an application that increases the effectiveness of learning in a school environment for students in grades 7-9. The application architecture is based on the REST service and SPA application. This application can be used both offline and online, as well as within clubs and sections of school programming.
Keywords: eLearning;interactive;web;rest-api;vue;flask
Title: Machine Learning Expert System for Recognizing Emotions in text “Umai Cloud Services”
Abstract: In this research, the focus is on recognizing 28 emotions in a text using the Roberta model, which is a state-of-the-art pre-trained language model that has achieved outstanding results in various natural language processing tasks. The study explores the effectiveness of the Roberta model for emotion recognition and compares it with other approaches, such as CNNs and RNNs. In addition, the research investigates the problem of toxicity detection, which involves identifying and flagging potentially harmful or offensive language in a given text. Various techniques for toxicity detection are considered, including supervised learning and deep learning methods. The study also explores the process of extracting key phrases and words from a text using machine learning algorithms. This involves applying NLP techniques such as part-of-speech tagging, named entity recognition, and text summarization. All of these methods are implemented and tested using a cloud service provided by Umai Cloud Services, a Kazakh startup company that offers machine learning and artificial intelligence solutions. The results of the study demonstrate the effectiveness of the Roberta model for emotion recognition and show promising results for toxicity detection and text summarization.
Keywords: artificial intelligence;neural networks;machine learning;expert systems;cloud services;data analysis;emotion analysis;text toxicity analysis;keyword analysis
Review score: Moderate
Review comment: The paper is well-organized and written in an academic manner. There are some novel outputs in the work.
Title: Machine Learning Expert System for Recognizing Emotions in text “Umai Cloud Services”
Abstract: In this research, the focus is on recognizing 28 emotions in a text using the Roberta model, which is a state-of-the-art pre-trained language model that has achieved outstanding results in various natural language processing tasks. The study explores the effectiveness of the Roberta model for emotion recognition and compares it with other approaches, such as CNNs and RNNs. In addition, the research investigates the problem of toxicity detection, which involves identifying and flagging potentially harmful or offensive language in a given text. Various techniques for toxicity detection are considered, including supervised learning and deep learning methods. The study also explores the process of extracting key phrases and words from a text using machine learning algorithms. This involves applying NLP techniques such as part-of-speech tagging, named entity recognition, and text summarization. All of these methods are implemented and tested using a cloud service provided by Umai Cloud Services, a Kazakh startup company that offers machine learning and artificial intelligence solutions. The results of the study demonstrate the effectiveness of the Roberta model for emotion recognition and show promising results for toxicity detection and text summarization.
Keywords: artificial intelligence;neural networks;machine learning;expert systems;cloud services;data analysis;emotion analysis;text toxicity analysis;keyword analysis
Review score: Moderate
Review comment: 1. Abstract is a short and not discover the idea and result of paper.
2. Please provide the size of dataset and description how was it collected.
3. The 10 epochs are too small for training even it is a pre-trained model. Could you please provide the model loss on the chart for 10 epochs. 
4. How you reached 28 classes of emotions if you used RoDERTAs’ basic model with 6 main emotions?
5. There are not the model metrics after training?
Title: On an Integrated Automated Approach to Enterprise Quality Management
Abstract: This article deals with quality, which serves as one of the main strategic mechanisms in business. Of great importance in the enterprise quality management systems is effectiveness assessment, where it is necessary to consider specifics of qualitative criteria, multilevel nature of the developed system and the choice of the required value of the productivity index and system state monitoring, it is associated with obtaining rational solutions for the management in quality assurance systems. The purpose and relevance of the study is related to the need: 1) to address important research and issues on quality management in the digital economy, according to developed State Programs of the Republic of Kazakhstan; 2) ensuring the competitiveness of medium and small businesses that have strict requirements for the quality of goods and services; 3) Increase the performance of automated quality assurance systems; 4) saving resources (manpower and time) when processing data. The article presents the methodology and model of the system of automated quality assurance of the enterprise, integrated with the control subsystems MICS (Management Information and Control System), which allow for the automation of implementation and maintenance of QMS processes, as well as increase the validity, efficiency and effectiveness of decisions by automating a number of functions performed by those directly responsible for management decisions and employees. This project was supported by a grant from the Ministry of Education and Science of the Republic of Kazakhstan (Zhas Galym project No. AR 13268939 Research and development of digital technology to provide consistency in the media of normative documents of the quality management system).
Keywords: automated management system;quality management;performance evaluation
Review score: Moderate
Review comment: Logic between different sections is recommended.
Title: Composite Citizens in the Smart City: Rethinking the Roles
Abstract: Theimplementation of smart cities has undergone several main stages of development, gradually moving to the understanding that human-centricity provides estimated outputs from innovations. The analysis of the perception of changes in the role of citizens by representatives of the city authorities reveals the potential for involving citizens in the decision-making system in different but not mutually exclusive roles (data source, city planner and knowledge source). Based on the example of Kazakhstani cities, the adequacy of the proposed assessment methodology for monitoring progress and comparing the dynamics of the level of involvement of citizens in the development of a smart city is demonstrated. The results show the emergence of new characteristics that reflect the mixing of functional and structural roles of a citizen in a smart city. It is demonstrated that smart city citizens are involved in city management despite the unconscious and undocumented perception of the role of citizens by the city authorities. Thus, this work can be used to justify the need to discuss changes in the interaction of participants to expand the understanding of the use of citizens' potential in the city's interests.
Keywords: Smart City;Kazakhstan;citizen participation;composite citizen;participation scaffold
Review score: Low
Review comment: Based on the used methodology and results it is hard to see the contribution to the field. All experiments must be proven statistically using proper tests. The paper is very general.
Title: Composite Citizens in the Smart City: Rethinking the Roles
Abstract: Theimplementation of smart cities has undergone several main stages of development, gradually moving to the understanding that human-centricity provides estimated outputs from innovations. The analysis of the perception of changes in the role of citizens by representatives of the city authorities reveals the potential for involving citizens in the decision-making system in different but not mutually exclusive roles (data source, city planner and knowledge source). Based on the example of Kazakhstani cities, the adequacy of the proposed assessment methodology for monitoring progress and comparing the dynamics of the level of involvement of citizens in the development of a smart city is demonstrated. The results show the emergence of new characteristics that reflect the mixing of functional and structural roles of a citizen in a smart city. It is demonstrated that smart city citizens are involved in city management despite the unconscious and undocumented perception of the role of citizens by the city authorities. Thus, this work can be used to justify the need to discuss changes in the interaction of participants to expand the understanding of the use of citizens' potential in the city's interests.
Keywords: Smart City;Kazakhstan;citizen participation;composite citizen;participation scaffold
Review score: Moderate
Review comment: If you look at it with an open mind, it's definitely a survey, not a research paper. I do not see any scientific conclusions or suggested methodologies for addressing the issue raised in the paper. Just general words, no specifics, no suggestions: neither technical, nor even managerial or legislative, which one would expect, given the focus of your article. The thesis: "the need for the city authorities to adopt conceptual changes in strategic planning" it's a good conclusion of a city authorities performance audit, but not for a scientific paper. I suggest you revise the content a bit and add at least some either practical proposals, or a model of city-citizen interaction in the context of smart city development, or anything else that can be counted as a scientific or at least just a practical result.
Nevertheless, you have done an excellent job of researching the material, getting enough statistical data, structuring it well, etc. That is, the article has good potential and you have done a good job
Title: A Case Study of Machine Learning Comparisons for Predicting Apartment Prices in Astana
Abstract: Astana, the capital city of Kazakhstan, has undergone rapid urbanization and economic development in recent years. With this growth comes an increased demand for housing, leading to the need for more accurate and efficient methods for predicting house prices. In this paper, we aim to compare the performance of five popular regression algorithms for predicting house prices in Astana: linear regression, decision trees, random forest, XGBoosting, and CatBoosting. We collected a dataset of housing prices in Astana, including information on the location, size, age, and other relevant features of the properties. We then applied each of the six algorithms to the dataset and evaluated their performance using various metrics such as mean squared error (MSE), mean absolute error (MAE), and R2 score. Our results showed that XGBoosting and CatBoosting algorithms outperformed the other methods in terms of prediction score but it overfits a data. The decision trees and random forest algorithms performed well on training set but there did not generalize the data. Linear regression performed the lowest score comparing with other algorithms, but it generalizes the data. This study provides valuable insights for policymakers and real estate developers in Astana, as well as for researchers and practitioners in the field of machine learning. Our findings suggest that advanced machine learning techniques, such as XGBoosting and CatBoosting, can significantly improve the accuracy of house price prediction and aid in informed decision-making.
Keywords: prediction;prices;methods;algorithms