Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: University Distributed Computer Network Vulnerability Assessment
Abstract: A methodology for evaluating information security (IS) for a distributed computer network (DCN) of a university (hereinafter referred to as UDCN) has been proposed. A mathematical model for calculating the UDCN vulnerability factor has been presented. The UDCN vulnerability coefficient makes it possible to assess its IS level at the UDCN design stage. The results of simulation modeling and virtualization of IS UDCN components have also been presented. In the course of simulation modeling, a model of a protected UDCN was designed and tested. In the experimental UDCN model, the network devices selected in the course of modeling the vulnerability coefficient were emulated on virtual machines (VMs). UDCN resources were reproduced using the Proxmox VE virtualization system. On the UDCN hosts running PVE, the Suricata Intrusion Detection System (IPS) was used. Splunk was used as SIEM.
Keywords: information security;distributed computing network;university;mathematical model;vulnerability coefficient;virtualization;IDS;SIEM
Review score: Moderate
Review comment: In the paper a method for evaluating information security for a distributed computer network of a university and a model for calculating it's vulnerability were proposed. The proposed method can be useful for assessment of the level of vulnerability of distributed computer networks.
Text should be revised, e.g. in the section II. ANALYSIS OF PREVIOUS STUDIES an RVSU abbreviation was used , but it is unclear what it means. Also the sentence in the section III. GOAL was not finished.
Title: Statistical Analysis of the Effect of Extended-Release Epidural Morphine Versus Morphine Sulfate on Postoperative Medical Outcomes
Abstract: Morphine reduces postoperative pain but may cause adverse events. Statistical analysis is often used to evaluate treatments. In this paper, we compare the effect of extended-release epidural morphine 10 mg dose (EREM 10 mg) and morphine sulfate 4–5 mg dose (MS 4–5 mg) on postoperative medical outcomes. We conduct statistical analysis of research articles published in English before November of 2022. The analysis was done by extracting statistical data, calculating mean differences (MD) between EREM 10 mg and MS 4–5 mg groups for continuous data and risk ratios (RR) for dichotomous data, and analyzing these statistics for important medical outcomes. The results of our analysis favor the EREM 10 mg group over the MS 4–5 mg group in postoperative opioid consumption in morphine equivalents (mg); MD with 95% CI is −13.25 [–26.40, −0.09], $\mathrm{p}-\text{value}= 0.05$. However, in postoperative adverse events (no.), the overall effect of the model favors the MS 4–5 mg group over the EREM 10 mg group; RR with 95% CI is 1.29 [1.10, 1.51], $p-\text{value}=0.002$. In a subgroup analysis, the model favors the MS 4–5 mg group over the EREM 10 mg group only in pruritus. These results are statistically significant. There were no significant differences between the groups in the incidence of nausea, vomiting, urinary retention, somnolence, and hypoxia. Our analysis shows interesting results: although patients in the EREM 10 mg group consumed 13.25 mg less morphine (on average), they had a higher risk ratio for adverse events compared to patients in the MS 4–5 mg group. This indicates that more randomized controlled trials should be conducted in the future to determine the advantages of each method.
Keywords: extended-release epidural morphine;morphine sulfate;morphine consumption;adverse events
Review score: Low
Review comment: needs substantial improvement in terms of presentation.
figures with codes should be elimintated from the main part.
better resolution of code should be added as appendix
Title: Statistical Analysis of the Effect of Extended-Release Epidural Morphine Versus Morphine Sulfate on Postoperative Medical Outcomes
Abstract: Morphine reduces postoperative pain but may cause adverse events. Statistical analysis is often used to evaluate treatments. In this paper, we compare the effect of extended-release epidural morphine 10 mg dose (EREM 10 mg) and morphine sulfate 4–5 mg dose (MS 4–5 mg) on postoperative medical outcomes. We conduct statistical analysis of research articles published in English before November of 2022. The analysis was done by extracting statistical data, calculating mean differences (MD) between EREM 10 mg and MS 4–5 mg groups for continuous data and risk ratios (RR) for dichotomous data, and analyzing these statistics for important medical outcomes. The results of our analysis favor the EREM 10 mg group over the MS 4–5 mg group in postoperative opioid consumption in morphine equivalents (mg); MD with 95% CI is −13.25 [–26.40, −0.09], $\mathrm{p}-\text{value}= 0.05$. However, in postoperative adverse events (no.), the overall effect of the model favors the MS 4–5 mg group over the EREM 10 mg group; RR with 95% CI is 1.29 [1.10, 1.51], $p-\text{value}=0.002$. In a subgroup analysis, the model favors the MS 4–5 mg group over the EREM 10 mg group only in pruritus. These results are statistically significant. There were no significant differences between the groups in the incidence of nausea, vomiting, urinary retention, somnolence, and hypoxia. Our analysis shows interesting results: although patients in the EREM 10 mg group consumed 13.25 mg less morphine (on average), they had a higher risk ratio for adverse events compared to patients in the MS 4–5 mg group. This indicates that more randomized controlled trials should be conducted in the future to determine the advantages of each method.
Keywords: extended-release epidural morphine;morphine sulfate;morphine consumption;adverse events
Review score: Moderate
Review comment: 1) Fill the gap in page 3, try to cut Table 1.
2) I will suggest to change "we" in the section 'Protocol'.
3) The first 4 references are used only in the first sentence, I will suggest to change the number of references or add another information in the article.
Title: Innovative Communication Technologies as a Factor in the Development of the Post-War Economy of Ukraine
Abstract: The main part of the study outlines the key strategic goals, main priorities, defines directions, mechanisms and possible options for solving the problem, contains the formulation of key measures for the practical implementation of information and communication technology ideas in the post-war reconstruction of Ukraine. An analysis of the theoretical foundations and modern applied aspects of the use of innovative communication technologies in practical activities has been carried out. A description of the main challenges and threats facing Ukraine, adequate answers to which are possible under the conditions of strengthening the country's innovative development.
Keywords: state;economy;innovative communication technologies;digitalization;efficiency;differential equations;level of quality;numerical modeling
Review score: Moderate
Review comment: Reviewed paper presents a model and general recommendations about further digitalisation of post-war Ukraine. It has some elements of novelty. and may be recommended for conference publication.
Title: Innovative Communication Technologies as a Factor in the Development of the Post-War Economy of Ukraine
Abstract: The main part of the study outlines the key strategic goals, main priorities, defines directions, mechanisms and possible options for solving the problem, contains the formulation of key measures for the practical implementation of information and communication technology ideas in the post-war reconstruction of Ukraine. An analysis of the theoretical foundations and modern applied aspects of the use of innovative communication technologies in practical activities has been carried out. A description of the main challenges and threats facing Ukraine, adequate answers to which are possible under the conditions of strengthening the country's innovative development.
Keywords: state;economy;innovative communication technologies;digitalization;efficiency;differential equations;level of quality;numerical modeling
Review score: Moderate
Review comment: The proposal is relevant and the problem is important and recognized and is presented in a sound way in the proposal. Solving problems like the ones described is a trend in the country but also outside of it. The reviewer is not seen the article as groundbreaking, but is a topic that, if is executed in a sound way, could lead to contributions to the literature. 
The article states some facts without proper substantiation of this statement. Such as, when claiming that Ukraine is a European leader in innovation and new technologies, there is no reference to the research and etc. In chapter 3  would like more information about the proposal of the authors in this problem. In clave 4, do not disclose in more detail about digitalization at the state level from the technical side. Reviewer recommend to detalize this aspects in presentation. 
According to the reviewer, literary references are not enough and it is necessary to analyze the world experience in the digitalization of the economy and add sources of publications from journals included in the Scopus or Web of science database.
The article has potential, it presents a reasonable motivation and there is a need. For all these reasons, the proposal is interesting, the biggest problem being the lack of definition at the methodological level. This reduces the level of feasibility of the proposal.
As a result of reviewing the article I recommend the article for publication with minor (technical) corrections «Accepted with minor revision».
- add literature review by analyzing the articles published in Q1-Q3 according to Clarivate Analytics (Web of Science) Journal Citation Report or Q1-Q3 according to Elsevier (Scopus) SJR or published in International Conference Proceedings ( Conference Laboratoires) indexed in Web of Science or Scopus;
- adjust the design of the figures according to the template (Figure Labels: Use 8 point), keywords (separate by semicolon, delite double point);
- some extra words in the Equation dscrp.
Title: Designing of Inventory Control Processes Based on the Strategy of Risk Pooling In Supply Chains
Abstract: This paper focuses on the design of supply chain inventory control processes based on a risk pooling strategy. The processes of monitoring and controlling project resource inventories in supply chains are the subject of research. Project and supply chain managers are faced with the challenge of determining the optimal level of inventory for project execution. There is a growing popularity of developing and implementing a supply chain risk pooling strategy, the essence of which is to reduce overall inventory and operating costs but maintain or improve the appropriate level of customer and stakeholder service throughout the supply chain.The paper considers the modeling of inventory management in centralised and decentralised supply chain services. To design the control and monitoring processes, the indicators of the standard deviation of demand and the duration of the supply cycle, the values of the cyclical and safety stocks were used. In the case of centralised maintenance of project resources, the impact of demand dependence on the level of safety stock is studied, taking into account the correlation coefficient. In order to increase the efficiency of the supply chain, models for determining the level of safety stock and the duration of the project resource supply cycle under uncertainty of demand are recommended.The study used project management methodology and supply chain management theory, which made it possible to take into account the variability of product demand and supply duration in the project. By designing control processes based on a risk pooling strategy, it is possible to improve project work in terms of resource provision, taking into account the variability of demand, the impact of internal or external risks, possible cost reductions, and the amount of buffer stock. The risk pooling strategy involves the process of aggregating supply chain objects into a group. In other words, a comprehensive strategy for designing supply chain inventory control processes is required.
Keywords: supply chain management;inventory management;project management;control processes;strategy;risk pooling
Review score: High
Review comment: Accepted.
Title: Designing of Inventory Control Processes Based on the Strategy of Risk Pooling In Supply Chains
Abstract: This paper focuses on the design of supply chain inventory control processes based on a risk pooling strategy. The processes of monitoring and controlling project resource inventories in supply chains are the subject of research. Project and supply chain managers are faced with the challenge of determining the optimal level of inventory for project execution. There is a growing popularity of developing and implementing a supply chain risk pooling strategy, the essence of which is to reduce overall inventory and operating costs but maintain or improve the appropriate level of customer and stakeholder service throughout the supply chain.The paper considers the modeling of inventory management in centralised and decentralised supply chain services. To design the control and monitoring processes, the indicators of the standard deviation of demand and the duration of the supply cycle, the values of the cyclical and safety stocks were used. In the case of centralised maintenance of project resources, the impact of demand dependence on the level of safety stock is studied, taking into account the correlation coefficient. In order to increase the efficiency of the supply chain, models for determining the level of safety stock and the duration of the project resource supply cycle under uncertainty of demand are recommended.The study used project management methodology and supply chain management theory, which made it possible to take into account the variability of product demand and supply duration in the project. By designing control processes based on a risk pooling strategy, it is possible to improve project work in terms of resource provision, taking into account the variability of demand, the impact of internal or external risks, possible cost reductions, and the amount of buffer stock. The risk pooling strategy involves the process of aggregating supply chain objects into a group. In other words, a comprehensive strategy for designing supply chain inventory control processes is required.
Keywords: supply chain management;inventory management;project management;control processes;strategy;risk pooling
Review score: High
Review comment: Today, the strategy of risk pooling involves the process of combining the objects of the supply chain into a group, thus is relevant in the design of supply chains. This reduces the risk level of objects in the group compared to the risk of individual objects, which is the novelty of this study. Strategies of supply chain risk aggregation were studied and a model was developed to determine the optimal level of reserve stock for decentralized and centralized variants of inventory flow in supply chains, taking into account possible fluctuations in demand for project resources and uncertainty of the supply cycle.
Title: Detection of Dangerous Situations by Sounds in Real-Time Using Deep Learning
Abstract: This research paper presents a comprehensive exploration of the utilization of advanced machine learning techniques, specifically the Bidirectional Long Short-Term Memory (BiLSTM) neural network model, for the classification of dangerous urban sounds. In the context of increasingly complex urban environments, where a multitude of acoustic signals coexist, accurate identification of hazardous auditory cues is imperative for bolstering urban safety and security. The BiLSTM model emerges as a pivotal tool, adept at capturing intricate temporal relationships and contextual nuances, thereby facilitating the discrimination between benign and perilous urban sounds. Through rigorous experimentation, our model achieves a commendable 90% accuracy rate, signifying its potential for practical deployment in urban settings. Beyond model development, this research extends its impact by introducing a web application that enables real-time sound analysis and user engagement, bridging the gap between sophisticated machine learning capabilities and practical urban safety applications. As urban centers continue to evolve and confront the challenges posed by increasing population density and complexity, the fusion of advanced machine learning with the urban soundscape holds immense promise in the creation of safer and smarter urban ecosystems, ultimately enhancing the well-being and security of urban dwellers worldwide.
Keywords: CNN;RNN;LSTM;urban sounds;impulsive sounds
Review score: High
Review comment: The importance of the problem covered by the paper in the realities of today corresponds to the theme of the conference and is relevant and promising. The materials are practically useful.
The conclusions confirm the practical significance of the research results. Therefore, the work ended logically, where the authors demonstrated their competence in the research.
Title: Detection of Dangerous Situations by Sounds in Real-Time Using Deep Learning
Abstract: This research paper presents a comprehensive exploration of the utilization of advanced machine learning techniques, specifically the Bidirectional Long Short-Term Memory (BiLSTM) neural network model, for the classification of dangerous urban sounds. In the context of increasingly complex urban environments, where a multitude of acoustic signals coexist, accurate identification of hazardous auditory cues is imperative for bolstering urban safety and security. The BiLSTM model emerges as a pivotal tool, adept at capturing intricate temporal relationships and contextual nuances, thereby facilitating the discrimination between benign and perilous urban sounds. Through rigorous experimentation, our model achieves a commendable 90% accuracy rate, signifying its potential for practical deployment in urban settings. Beyond model development, this research extends its impact by introducing a web application that enables real-time sound analysis and user engagement, bridging the gap between sophisticated machine learning capabilities and practical urban safety applications. As urban centers continue to evolve and confront the challenges posed by increasing population density and complexity, the fusion of advanced machine learning with the urban soundscape holds immense promise in the creation of safer and smarter urban ecosystems, ultimately enhancing the well-being and security of urban dwellers worldwide.
Keywords: CNN;RNN;LSTM;urban sounds;impulsive sounds
Review score: High
Review comment: -
Title: CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban Area
Abstract: In the realm of audio signal processing, detecting impulsive urban sounds within continuous audio streams remains a complex endeavor. This paper presents an innovative solution via a hybrid Convolutional Neural Network - Recurrent Neural Network (CNN-RNN) framework. The CNN segments of the model target the extraction of spatial hierarchies from the audio, while the RNN components leverage their temporal memory capabilities to recognize and sequence sporadic urban auditory events. Drawing data from a rich collection of urban audio signals sourced from multiple global metropolises, the hybrid model was rigorously trained and evaluated. Findings underscore its superior detection accuracy in comparison to conventional methods, with a marked decrease in false positives. Moreover, the adaptability of the CNN-RNN architecture was evidenced by its consistent performance across diverse acoustic environments. This advancement in impulsive urban sound detection not only accentuates the efficacy of combined CNN-RNN systems but also has broader implications for urban planning, environmental assessments, and public safety initiatives.
Keywords: CNN;RNN;LSTM;urban sounds;impulsive sounds
Review score: High
Review comment: The research is of scientific interest and meets the requirements of the conference. The article requires some changes.  Please pay attention to the design of the formulas, the quality of the drawings and the bold font.
Title: CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban Area
Abstract: In the realm of audio signal processing, detecting impulsive urban sounds within continuous audio streams remains a complex endeavor. This paper presents an innovative solution via a hybrid Convolutional Neural Network - Recurrent Neural Network (CNN-RNN) framework. The CNN segments of the model target the extraction of spatial hierarchies from the audio, while the RNN components leverage their temporal memory capabilities to recognize and sequence sporadic urban auditory events. Drawing data from a rich collection of urban audio signals sourced from multiple global metropolises, the hybrid model was rigorously trained and evaluated. Findings underscore its superior detection accuracy in comparison to conventional methods, with a marked decrease in false positives. Moreover, the adaptability of the CNN-RNN architecture was evidenced by its consistent performance across diverse acoustic environments. This advancement in impulsive urban sound detection not only accentuates the efficacy of combined CNN-RNN systems but also has broader implications for urban planning, environmental assessments, and public safety initiatives.
Keywords: CNN;RNN;LSTM;urban sounds;impulsive sounds
Review score: Moderate
Review comment: Overall Recommendation: Revise for Formatting and Clarity

While the content of your paper seems promising, there are some formatting issues that need to be addressed before publication. Here's a breakdown of the key areas for revision:

Figure Formatting and Referencing:

Figure 3:
Ensure Figure 3 is properly formatted according to the conference guidelines. This might involve aspects like font size, line thickness, and legend clarity.
Make sure Figure 3 is mentioned and discussed in the text before Figures 4 and 5. The order of figures in the text should correspond to their numerical order.
Figure 4: Similar to Figure 3, ensure Figure 4 is formatted according to the conference guidelines.
Here are some additional suggestions for strengthening your paper (depending on its content):

Highlight the Novelty of Your Work: Clearly explain the unique contribution of your research. What new insights or solutions does your work offer compared to existing approaches?
Strengthen Methodology: If applicable, consider providing more details about your research methodology. This could involve elaborating on data collection procedures, analysis techniques, or experimental design.
Refine Discussion: Expand on the implications of your findings. How do your results contribute to the broader field of research?